Quantum private comparison (QPC) allows at least two users to compare the equality of their secret information, for which the security is based on the properties of quantum mechanics. To improve the use of quantum resources and the efficiency of private comparison, a new QPC protocol based on GHZ-like states is proposed. The protocol adopts unitary operations to encode the secret information instead of performing quantum key distribution (QKD), which can reduce the amount of computation required to perform QKD and improve the utilization of quantum resources. The decoy photon technique used to detect channel eavesdropping ensures that the protocol is resistant to external attacks. The quantum efficiency of the protocol reaches 66%. Compared with many previous QPC schemes, the proposed protocol does not need to share a key and has advantages in quantum efficiency and quantum resources. In the traditional field of information security, encryption technology has been the key to protecting sensitive information. However, with the rapid development of computing power, especially the development of quantum computing, securing traditional encryption methods has been substantially challenging. In this context, research on quantum cryptography, including quantum key distribution (QKD), quantum digital signature , quantumQKD technology primarily relies on the fundamental principles of quantum mechanics to ensure that users generate secure and dependable keys during the communication progress ,4. MoreoFor a QPC protocol, the privacy security issue is an unavoidable research focus. A qualified QPC protocol should meet the following two conditions: (1) the security of the private information and (2) the fairness of the comparison results. Both parties need to know the final result of the comparison and ensure that the result is the same as that of the actual calculation. Therefore, it is imperative to process or encrypt the secret information, guaranteeing that the parties involved in the comparison do not have direct access to each other’s secret data and eliminating the possibility of inferring secret information from measurement results. Furthermore, the involvement of a third party in the protocol can facilitate secure and equitable private comparison by assisting the two parties in comparing their secret data and publicly publishing the comparison result. However, the integrity and trustworthiness of the third party are crucial considerations. Ensuring the credibility of the third party and the smooth progress of the protocol necessitates the implementation of necessary measures to safeguard and regulate the behavior of the third party.Based on the above analysis, we propose a new quantum privacy comparison protocol without a shared key. With the assistance of a semi-honest third party, this protocol ensures fairness in comparing users’ secret information without the necessity for key sharing or direct user-to-user communication. This results in heightened privacy protection capabilities. Furthermore, the protocol incorporates decoy photon technology and hash functions to defend against internal and external attacks, effectively securing the performance of this protocol. Notably, this protocol boasts a higher quantum efficiency compared to the majority of previous QPC protocols.The structure of this paper is as follows: the related work and the knowledge preparation are introduced in The earliest QPC protocol was inspired by quantum secret sharing protocols, and the privacy comparison problem originating from Yao’s millionaires problem. In this problem, two millionaires sought to ascertain their relative wealth without revealing specific assets. The first QPC protocol was introduced by Yang and Wen in 2009 . This prIn 2020, Lang  introducGHZ-like states are a class of quantum states that have certain similarities to GHZ states, such as multi-partite entanglement, where the entanglement between these qubits of GHZ-like states is multipartite rather than just bipartite. Moreover, the GHZ-like state is not limited to specific forms of GHZ states.The three-particle GHZ-like states used in the proposed QPC protocol are transformed from ordinary GHZ states. An n-particle GHZ state is a kind of quantum entanglement, which can be described asnum(1) is an even value, the number of 1 in each vector num(1) is an odd value, the number of 1 in each vector According to Equation (1), an n-particle GHZ-like state  transforAccording to Equation (1), an n-particle GHZ state can exit According to Equations (1) and (2), the GHZ-like state The According to Equation (7), when the state of the first particle of The unitary operations used in the proposed protocol are Pauli gates. Pauli gates  are three quantum gates, which operate on a single qubit. The Pauli-X gate flips the state of a qubit from The protocol participants are introduced as follows:TP: TP is a semi-honesty third party that can help compare the equality of the secret information. TP needs to honestly execute the steps specified in the protocol but allows it to attempt to obtain secret information through some illegal means.Alice (Bob): Alice (Bob) is a quantum user with complete quantum capabilities who can achieve the preparation and complete measurement of quantum states.L. The secret information of Alice can be expressed as i represents the i-th particle of the particle sequence X or Y. Moreover, the protocol is described in detail as follows .L denotes the initial length of the binary sequence and K denotes the length of the binary sequence after performing the shared hash function H.K gets the value of 2n in the protocol.n groups, respectively.Thereafter, Alice and Bob divide Step 6: Alice and Bob perform a corresponding operation, which is shown in m decoy photons each, insert them into Step 7: After receiving Step 8: TP combines both sequences to form the i-th particle of i-th particle of Step 9: TP judges the equality of the secret information of Alice and Bob based on the measurement results in Step 8. If the measurement results of Bell basis and H (Shown as Equation (8)). The purpose of performing the hash operation is to convert the comparative secret information to a specific length and to perform the first encryption processing. For convenience of presentation, we assume that Suppose the quantum users Alice and Bob want to compare the secret information TP prepares four three-particle GHZ-like states and divides them into three sequences TP performs eavesdropping detection and carries out the Bell basis and Assume the external attacker is Eve. The attack methods that Eve can use to steal the secret information of Alice or Bob or both Alice and Bob are intercept-resend attack, measure-resend attack, and entanglement attack. The following is a detailed analysis of these three attacks.The external attacker Eve may carry out an intercept-resend attack by first intercepting the particle sequences sent by TP to Alice and Bob and storing them. Then, Eve prepares the same and specified amount of single particles and sends them to Alice and Bob. After Alice and Bob perform their operations, Eve again intercepts the quantum particle sequences sent by Alice and Bob to TP and makes measurements to obtain secret information about Alice and Bob, while returning the previously stored quantum particle sequences to TP.n is the number of decoy photons measured in the eavesdropping detection. When the value of n is large enough, the probability of Eve being discovered is infinitely close to 1. Therefore, the intercept-resend attack is invalid for the method of the present invention.However, Eve’s attack will inevitably introduce errors because Alice, Bob, and TP will perform eavesdropping detection as soon as they receive the particle sequence. The receivers will require the sender to disclose the location and measurement basis of the bait particles, and Eve cannot know the specific state of these particles. When the receiver selects the measurement basis announced by the sender to measure the single particle sent by Eve, there is a 50% probability of obtaining an incorrect result. Eve does not prepare a single particle with the same state as the particle sequences, for example, the original decoy photon has a state of The external attacker Eve can execute the measure-resend attack by first intercepting the particle sequences sent by TP to Alice and Bob and performing Nonetheless, Eve’s attack will unavoidably result in errors because the decoy photons prepared by Alice, Bob, and TP have four states, shown in . TherefoThe external attacker Eve can perform the entanglement attack by first entangling her auxiliary quantum particles When Eve uses the unitary operation The decoy particles When Eve uses the auxiliary particle In the above equation, some conditions must be satisfied to avoid Eve introducing errors when the users perform eavesdropping detection.In the proposed protocol, the entanglement attack of Eve mainly acts on the second and third particles of the GHZ-like states used for transmitting information. The GHZ-like state The second and third particles of the GHZ-like state can be considered as a Bell state together. Without losing generality, the following Bell state expressions are used for analysis.After Eve performs the unitary operation According to Equation (14), the tensor product of the attacker Eve’s auxiliary particle and its target particle can be expressed as a simple product of the two, so Eve’s auxiliary particle and target particle are independent of each other. Overall, if Eve does not want to introduce errors in eavesdropping detection in the present invention, Eve’s auxiliary particles and target particles are independent of each other, which means that there is no entanglement. Eve is unable to obtain information about the target particle by measuring her auxiliary particles. Hence, the entanglement attacks are ineffective against our protocol.Above all, this protocol is impervious to external attacks. The protocol uses the decoy photon technique, which also has been proven to be unconditional security ,22, to eApart from external attacks, a QPC protocol may also be attacked by internal participants. In the following, two attacks from the participants are analyzed in detail.Without loss of generality, assume that Bob is a malicious user aiming to acquire Alice’s sensitive information. As mentioned in our protocol, The impact of a semi-honest third-party TP on the QPC protocol cannot be disregarded, as he/she communicates directly with the participants. In our protocol, TP participated in the preparation of GHZ-like state particles and the measurement of encoded quantum particles. TP may infer the secret information being compared based on the final measurement results, but this approach is not feasible. On the one hand, the user’s secret information is encoded on GHZ-like particles through unitary operations, and TP cannot know the specific unitary operations selected by the users. On the other hand, the two binary bits corresponding to each unitary operation are not actual secret information but ciphertext formed after performing a hash function. As a participant in the protocol, TP may also engage in side-channel attacks, which analyze and obtain relevant information of secret information from the physical characteristics of the system, such as the power consumption and processing time during the preparation or encoding of quantum particles. However, the application of decoy photon technology in this protocol can effectively interfere with the execution of side-channel attacks, while hash operations also avoid the leakage of true secret information. In addition, TP may adopt attack methods similar to Eve, but such attacks can be effectively resisted by our protocol. Therefore, the proposed protocol can resist an attack from TP.In this section, the quantum efficiency of our protocol is analyzed in detail. The quantum efficiency of a QPC protocol can be evaluated by comparing the number of classical bits to the number of quantum particles used in the comparison. It is well known that the efficiency of a QPC protocol can be expressed with the following equation :(24)ηe=ηn three-particle GHZ-like states to compare L classical bits of secret data. After performing the hash operation, the length of classical bits transforms into K, which is given the value of 2n. The total number of quantum particles is 3n. Therefore, the quantum efficiency is In the proposed protocol, we generated A secure and efficient QPC protocol using GHZ-like states is proposed in this paper. Two quantum users, Alice and Bob, can compare the equality of their secret information with the help of a third-party TP. Compared with most previous QPC protocols, the proposed protocol is more efficient. And in the proposed protocol, the GHZ-like state is used to disseminate quantum information. The unitary operations are used to encode the GHZ-like state particles according to secret information encrypted with a shared hash function. The main feature of the protocol is that users can complete the comparison without sharing a quantum key and communicating, which makes the protocol greatly improved in terms of efficiency while ensuring security, and the protocol makes good use of quantum resources.


The GENESIS mission prepared for launch in 2027 integrates the four space-geodetic techniques on a single spaceborne platform in medium Earth orbit. With its unique observations and alternative tie concepts, the mission aims to contribute to an improved accuracy and homogeneity of future terrestrial reference system realizations. To assess the expected contribution of Global Navigation Satellite System (GNSS) tracking, a comprehensive GNSS coverage analysis is performed based on detailed link-budget simulations, taking into account the best available gain patterns and signal-specific transmit power estimates derived for this work from measurements of a high-gain dish antenna. The benefit of different receiver antenna concepts for the GENESIS spacecraft is assessed, and it is demonstrated that a single-antenna system with either a nadir-looking or side-looking boresight is a viable alternative to the dual-antenna configuration considered in initial mission studies. Compared to terrestrial users and missions in low Earth orbit, GENESIS will collect GNSS signals transmitted at up to two times larger off-boresight angles. Only limited information on the actual transmit antenna phase patterns is presently available in this region, which hampers a quantitative assessment of the expected measurement and orbit determination accuracy. As such, a comprehensive release of manufacturer calibrations is encouraged for all blocks of GPS and Galileo satellites. In parallel, a need for in-flight characterization and calibration of the GNSS transmit antennas for off-boresight angles of up to  GENESIS  satellites, but also contains a small number of In-Orbit Validation (IOV) satellites that have been built by a different manufacturer and make use of a different transmit antenna design. So far, no gain pattern calibrations have been released for either IOV or FOC satellites by the Galileo project. In view of this limitation, we refer to partial information made available by the antenna manufacturers in the early development stage. While this information might not be fully representative of the final antenna design, it represents the best available public information and is expected to provide a suitable basis for the purpose of Galileo link-budget computations in GNSS visibility studies. Specifically, we made use of gain patterns for the lower and upper L-band presented in Monjas et al.  and VallThe block-specific patterns for the upper and lower L-band are illustrated in Fig. Next to the gain patterns of the GNSS satellites, the modeling of received signal powers depends on knowledge of the corresponding transmit powers. In principle, transmit power values for individual signals can be inferred from specifications of the minimum received signal power made by the GNSS providers as part of their performance commitments. However, it is difficult to use this information in practice to derive the actual transmit powers in view of conservative link budgets and notable design margins of the manufacturers Betz . SatelliWe therefore made use of block-average transmit power measurements obtained for representative satellites of the current GPS and Galileo constellation with the 30 m high-gain dish antenna of DLR’s signal-monitoring facility in Weilheim, Germany  and the LNA noise figure. Assuming values of 1 dB and 1.5 dB, respectively, an equivalent system temperatures at the level of The above values outline the expected range of noise values in general GNSS receiver systems but can only provide a coarse reference for the specific case of GENESIS. Given the restricted availability of public information on the noise characteristics of actual space receiver systems, we therefore performed an empirical calibration of the combined noise temperature and losses for the Sentinel-6A GNSS subsystem as a basis for the present study. Based on the comparison of observed While the individual contributions are not known with certainty, this noise factor can be understood by an equivalent system temperature of roughly Even though all effort has been made to come up with realistic assumptions for the GENESIS link-budget computations, it is obvious that actual receiver and antenna characteristics are unknown in the present project stage. Also, conservative pre-mission performance assumptions were exceeded in various high-altitude missions such as MMS  will inevitably vary throughout the mission Vallado . Withoutz of the receiving antenna is obtained due to the correlation of distance and receive/transmit antenna off-boresight angles for the specific antenna orientations  mostly offer an increased model EC , observaFocusing on the upper L-band, Galileo FOC satellites can be tracked in our simulations up to off-boresight angles of about The overall statistics for the frequency of tracked satellites obtained with the two antennas for the individual GNSS constellations and signals are collated in Table n et al.  in a purConsidering the increased hardware complexity of a dual-antenna GNSS system and prevailing limitations in the total number of tracking channels available in current space receivers, alternative boresight orientations may be considered. By way of example, a single antenna oriented “sideways” along the spacecraft In addition to the mean number of tracked satellites for the nominal simulation conditions, Table Orbit determination of the GENESIS spacecraft is expected to be performed in a dynamic or reduced-dynamic  and the standard deviation of the measurement errors Fang . While oIn the case of a dual-constellation, dual-antenna receiver as considered for the GENESIS mission a total of four distinct receiver clock offset parameters k et al. , as wellBased on the previously described simulations, DOP statistics for the GENESIS mission have been determined considering various GNSSs and antenna configurations Table . For GPSIn accord with the earlier link-budget and visibility analysis, tracking with only the zenith antenna would not enable an adequate positioning performance and deliver unacceptable DOP values for more than 50% of the time, even if considering combined GPS+Galileo tracking. Combined use of the zenith and nadir antenna offers good DOP values for the dual-constellation case and even reasonable performance for single-GNSS processing. However, it may be noted that very similar DOP values are achieved with only the nadir looking antenna. This may appear surprising in view of the fairly restricted off-boresight angle range covered by that antenna, but is partly explained by the fact that our DOP analysis assumes the estimation of independent, epoch-wise clock offset parameters for each antenna. As such, the joint availability of observations from both below and beneath the GENESIS spacecraft does not facilitate a better separation of the vertical position component and the clock offset parameter in the positioning compared to a single-antenna configuration.Obviously, the DOP analysis does not account for the potentially different measurement and model errors of observations collected with the two antennas, but suggests that the proposed concept of a dual-antenna system requires further consideration. Based on Table In this context, it is also instructive to consider alternative antenna placements with different viewing directions. In analogy with the earlier visibility analysis, Table Given prevailing uncertainties in the link-budget assumptions, we also investigated the impact of a 3 dB Finally, we compare the GENESIS DOP simulations with actual DOP results for the current Sentinel-6A mission, which uses a single, zenith-pointing antenna and operates in a low Earth orbit well within the Terrestrial Service Volume. Along with this, we also consider a simulated receiver in the same orbit, but offering more tracking channels and able to track all satellites in the antenna field of view down to the antenna horizon. While the latter configuration clearly exceeds the performance of all other test cases, the GENESIS mission can be expected to offer DOP values close to the actual Sentinel-6A mission under the nominal simulation assumptions. This confirms the overall feasibility of GNSS use in the adopted medium Earth orbit, but likewise suggests that only limited benefits of GENESIS for the GNSS-specific TRF realization can be expected compared to that of existing LEO missions.The DOP analysis presented above describes the impact of the geometric distribution of tracked satellites and specifies the resulting positioning uncertainty for a unit ranging error. Statistical values of the overall position errors are then obtained by multiplication of the DOP with the expected standard deviation of the aggregate measurement and modeling errors. While it is difficult to fully specify the carrier phase error budget for the GENESIS mission at this stage due to major uncertainties in various system elements, we aim to provide at least a general discussion of the key contributions and expected range of magnitudes.Except for the zenith antenna tracking, which benefits from the reduced GNSS satellite distance, GENESIS operates at systematically reduced /N0 Betz  and woulBased on the theory of code and phase tracking errors due to thermal noise as a function of /N0 Ward  and receOn the other hand, various sources of systematic errors may be envisaged that impact the overall error budget. Irrespective of the high altitude of GENESIS, observations collected with the nadir and side-looking antenna may still pass the Earth’s atmosphere, which needs to be taken into account in the measurement modeling. While Earth-grazing signals passing through the neutral atmosphere can safely be discarded without relevant impact on the number of available observations, unmodeled higher-order ionospheric (HOI) effects may affect observations with tangent point altitudes of up to about 1000 km, or, equivalently, off-nadir-angle between Another key source of systematic errors is given by unmodeled phase variations that remain after consideration of a priori values for the phase centers and patterns of both, receiver and transmit antennas. While pre-flight calibrations of GNSS receiver antennas for geodetic space missions are common practice by now, experience from numerous missions demonstrates notable deviations between the phase pattern of a stand-alone antenna calibration and the pattern observed after integration into the LEO satellite  with GNSS, DORIS, and SLR co-location (Schreiner et al. Concerning the GNSS contribution of GENESIS, the assessment of the expected tracking coverage and the kinematic orbit determination performance presently suffers from an incomplete characterization of the GNSS transmit antennas. Despite a partial release of metadata for the GPS and Galileo satellites, major gaps remain in the understanding of gain and phase patterns beyond the Earth coverage region for various satellite and antenna types. However, sidelobe characteristics are known to have vital impact for the availability of GNSS tracking at high altitude and proper understanding is likewise desired to ensure optimum performance of GNSS tracking in GENESIS. As such, a comprehensive public release of pre-flight calibrations by GNSS providers or satellite manufacturers is strongly desired and encouraged.


As important geospatial data, point cloud collected from an aerial laser scanner (ALS) provides three-dimensional (3D) information for the study of the distribution of typical urban land cover, which is critical in the construction of a “digital city”. However, existing point cloud classification methods usually use a single machine learning classifier that experiences uncertainty in making decisions for fuzzy samples in confusing areas. This limits the improvement of classification accuracy. To take full advantage of different classifiers and reduce uncertainty, we propose a classification method based on possibility theory and multi-classifier fusion. Firstly, the feature importance measure was performed by the XGBoost algorithm to construct a feature space, and two commonly used support vector machines (SVMs) were the chosen base classifiers. Then, classification results from the two base classifiers were quantitatively evaluated to define the confusing areas in classification. Finally, the confidence degree of each classifier for different categories was calculated by the confusion matrix and normalized to obtain the weights. Then, we synthesize different classifiers based on possibility theory to achieve more accurate classification in the confusion areas. DALES datasets were utilized to assess the proposed method. The results reveal that the proposed method can significantly improve classification accuracy in confusing areas. Land cover classification is a focus of research among photogrammetry and remote sensing communities . With thMachine learning algorithms were widely used in automated land cover classifications over the past decade ,10, withIn multi-classifier fusion, base classifiers refer to individual classifiers used in classification. The problem that a single base classifier cannot achieve can be effectively solved by fusion of these base classifiers in a MCS ,31. UsinA method for defining confusion areas is proposed to classify the pre-classification results into uncertainty regions. These regions will be used as an important basis for processing uncertainty information in later studies.In the optimization process of the confusing area, the possibility theory fully considers the classification advantages of the base classifiers for different classes and the correlation between the two base classifiers. It can effectively reduce the influence of conflicts on the fusion results and achieve more accurate results.In this paper, we present an effective approach for urban land cover classification from LiDAR point clouds and co-registered visible images based on SVM and possibility theory. It employs SVM classifiers in the initial classification and then adapts the possibility theory for the post-processing. Firstly, we define confusing areas through the classification uncertainty quantitatively, then, an optimized possibility theory is applied for multi-classifier fusion to improve the classification accuracy. However, algorithmic complexity and computational power must be considered when performing large-scale fusion computations. Therefore, by synthesizing samples with high uncertainty regions, we reduce the number of computations in this paper. Meanwhile, we choose simple weighted synthesis operators to reduce the complexity. The main contributions of our work can be summarized as follows:The rest of the paper is organized as follows: In The literature review covers both traditional and deep learning-based point cloud classification methods.Traditional point cloud classification: Traditional point cloud classification methods are mainly based on manually extracting feature descriptors from the point cloud as the input to classifiers ,39,40. TX × Y × Z and convolve it with 3D kernels. However, these methods suffer from high memory consumption due to density and complexity of the original point cloud, which generates a large number of sparse voxels. Multi-view methods project point cloud data from different directions onto a two-dimensional plane and use these 2D views as input to a 2D CNN model. This approach provides an effective way to leverage the strengths of 2D CNNs and extend them to handle three-dimensional point clouds , , , and  for comparison experiments. NP in From The classification results of dataset 1 are shown in To verify the robustness of our method, we conducted the same experiment on another area in DALES. The classification results are shown in To evaluate the classification performance quantitatively, the overall accuracy and the Kappa coefficient are used. The results of datasets 1 and 2 are shown in To validate the advantages of our method in dealing with the classification of the confusing areas, highlighted in The three confusing areas reflect three possible scenarios in the testing area. Region A shows buildings misclassified as vegetation. This could be due to the presence of tall vegetation shading low buildings. In region B, vegetation points are largely classified as buildings. This is due to the similarity of the points of the two types of features generated from point clouds. In region C, the main problem is at the junctions between buildings, which tend to be more complex in terms of building types and tend to create areas of confusion. By fusing the classification results of different classifiers and reassigning the classes of the point cloud, the classification uncertainty is reduced. The classification accuracy is demonstrated in Table 6The classification accuracy of the confusing areas is significantly lower than the overall classification accuracy shown in For dataset 2, the same experiment for confusing areas is conducted. With a 0.96% improvement in ground classifications over SVM-RBF and a 11.74% improvement in vegetation over SVM-Linear. The average accuracy also improves by 2.77% over the highest of the other two classifiers. The classification accuracy of the confusing areas is as shown in As can be seen from In Overall, with the above error analysis and taking into account some of the limitations of the methodology in this paper, our next steps will be to continuously explore how to deal with other misclassified points in confusing areas in terms of the following measures: Firstly, the use of more effective features can be further explored, such as different point cloud feature descriptors, such as point feature histograms (PFH), fast point feature histograms (FPFH), color features, and attribute features. By introducing more features, more information can be provided to distinguish different feature classes and reduce the confusion problem. Then, the confusing area can be defined directly from the correspondence between features and classes. By analyzing the similarities and differences of features between different land cover classes, the range of feature values in the confusing area is determined. It allows features to be mapped into intervals and these intervals can be processed using uncertain information processing methods; finally, use more advanced machine learning or deep learning techniques for efficient land cover classification.In conclusion, through the steps of exploring more effective features, identifying confusing areas, and constructing efficient classifiers, the classification model for LiDAR data can be further improved to solve the confusing problem and enhance classification accuracy.In this study, we proposed a novel method for the land cover classification of LiDAR point clouds based on possibility theory and muti-classifier fusion. By optimizing the fuzzy uncertainty information in the classification process, the method integrates the advantages of different SVM classifiers and overcomes the limitations. The proposed method includes three strategies to effectively improve the classification accuracy: (1) feature space construction using the XGBoost algorithm to measure feature importance; (2) definition of the confusing area and classifier confidence based on the base classifier results; (3) weighted possibility distribution synthesis to avoid the misclassification of categories boundaries. The quantitative analysis results show that the overall accuracy of this method can reach 94.14%, the Kappa coefficient can reach 88.45%, and in confusing areas, classification accuracies of the ground, vegetation, and buildings can reach 88.20%, 73.09%, and 70.61%, respectively. Therefore, the method in this paper can improve the classification accuracy of land cover classification and can be effective in confusing areas.However, there are still some shortcomings in this study, such as the correlation and complementarity between features, the selection of classifiers and synthesis rules, etc. As the effectiveness of deep learning models depends on the quantity and quality of data used for training, their performance is not always superior to traditional statistical methods. Therefore, we will continue to explore the application of machine learning classifiers to land cover classification in the future. The classification model of LiDAR data will be further improved by exploring more effective features, identifying the confusing area from feature analysis, and constructing an efficient classifier to increase the classification accuracy so that our method can better serve remote sensing application scenarios.


The mental health problem of college students has gradually become the focus of people’s attention. The music appreciation course in university is a very effective approach of psychological counseling, and it is urgent to explore the role of music appreciation in psychological adjustment. Therefore, we propose an emotion classification model based on particle swarm optimization (PSO) to study the effect of inter active music appreciation teaching on the mental health of college students. We first extract musical features as input. Then, the extracted music appreciation features generate subtitles of music information. Finally, we weight the above features, input them into the network, modify the network through particle swarm optimization, and output the emotional class of music. The experimental results show that the music emotion classification model has a high classification accuracy of 82.6%, and can obtain the emotional categories included in interactive music appreciation, which is helpful to guide the mental health of college students in music appreciation teaching. With the emergence of more and more psychological problems in college students, society urgently needs proper psychological adjustment for students. Interactive music appreciation teaching is very influential among many adjusting methods. Based on the content of traditional narration education, multimedia adds new elements to make music education intuitive and vivid, significantly different from the conventional single narration way. Through multimedia, various music education situations are constructed, conducive to the interaction between teachers and students and enabling students to participate in music learning actively. At the same time, multimedia can centrally acquire and save information, integrate all kinds of resources, and present it in the form of data, which strengthens students’ impression of knowledge points. Through multimedia, music education content and materials can be rearranged, avoiding the repetition and monotonicity of traditional education mode.As a medium of emotion expression and emotion regulation, music can affect people’s emotional state and mental health. Interactive music appreciation teaching can provide a richer and more personalized music experience, help college students better understand and express their emotions, and thus enhance their mental health. Proper music appreciation and participation in interactive music instruction can be effective ways to relieve stress and anxiety. The pleasure and relaxation effect of music can help to reduce the psychological burden and improve the happiness of students. Music has a positive effect on learning and memory. Through interactive music appreciation teaching, students can learn in a more fun and enjoyable atmosphere, increasing engagement and motivation in learning, and thereby enhancing learning outcomes. By studying the influence of interactive music appreciation teaching on the mental health of college students, we can better understand the role of music in education, and provide useful enlightenment and guidance for educational institutions and educators. Meanwhile, the teachers should concentrate on cultivating students’ music participation consciousness, thinking ability, and language expression ability to improve students’ music appreciation ability constantly. However, we need to choose the right music. Many kinds of music for college students trap into different psychological problems. Therefore, we must propose an emotion classification model in interactive music appreciation teaching.In recent years, convolutional neural network (CNN) has become a hot basic structure with strong learning ability and has proven effective in many classification tasks. With the complete knowledge of CNN, etc. Subsequently, Furthermore, they apply the migration study to extend the model and make the sample layer CNN visible by learning each layer filter, which can identify the hierarchical learning features. Music therapy is a method based on the theory of psychological diagnosis and treatment . Throughvia the extracted features of music appreciation. Finally, we identify the emotion types in music appreciation. In this way, it is helpful to analyze the mental health problems of college students and put forward a plan to alleviate the psychological issues. Main contributions are as follows:Therefore, we should re-structure the deep network and propose a model optimized by adopting a particle swarm to improve the emotion classification deep network for music appreciation, designed to speed up the optimization ability of neural network and increase the accuracy of classifying emotion in music appreciation. We first extract the music features as the input to construct the emotion classification model for music appreciation based on particle swarm optimization (PSO). Then, we generate captions for music information  1.We extract short-term energy features, time-domain variance features, and other music appreciation features to construct the music appreciation classification model. 2.We apply the particle swarm optimization to boost the neural network, which can help us achieve the optimal results.Music therapy is widely accepted as an effective tool to relieve negative emotions. But traditional music therapy has the following problems: first, it is not clear the patient’s emotional orientation; Second, different people have different feelings about the same concert. For students, it is difficult for music teachers to grasp the emotional feedback of each person accurately. Third, due to the numerous and miscellaneous music, it is difficult to simplify and improve the effect in a limited time. via LSTM network. via traditional machine learning algorithms, improving music classification  Extract different categories of features from interactive music.2) We apply the grey relational analysis method to calculate the contribution of different interactive music features. Then, we use the assistance to represent the importance of interactive music features for emotion classification.3) The PSO improves the deep network to obtain the best neural network. The interactive music emotion classification results are obtained through the web.The framework of the interactive music sentiment classification model is shown in As shown in We must extract short-term energy features, time-domain variance features, and other music appreciation features to construct the music appreciation classification model for the diversity of music therapy features . The spem represents the energy of the music appreciation signal 1) Short-term energy features of appreciation music. There is a big difference between the energy of ordinary sound and appreciation music. Compared with ordinary sound, appreciation music has a higher energy value. Therefore, we can extract the energy features of short-term from each frame in appreciated music. Assume that Em can be presented as follows:where 2) The variance features Time-Domain in appreciated music. The following formula can express any frame of appreciated music:where Combined with formula 3) The variance features of Frequency-Domain in appreciated music. Any frame We adopt the wavelet transform smoothing operation We associate 1) We assume 2) We apply the preprocessing 3) We apply 4) We calculate the grey correlation coefficient of music appreciation features. The formula is:α represents the resolution coefficient,where We calculate the grey correlation degree of music appreciation features. The formula is as follows: i represents the weight. According to where wThrough the above methods, we can directly obtain the short-time energy feature, time domain variance feature and frequency domain variance, and realize the fusion of their features. Using the fusion feature, we adopt ResNet as the backbone of our method, with the help of ResNet’s residual structure, to achieve the classification of musical emotions. However, considering the characteristics of feature fusion, we need to optimize the neural network structure.We optimize the deep network by the PSO to obtain the optimal neural network structure. Subsequently, we can achieve the classification results through the optimal neural network structure. Before we utilize the PSO to optimize neural networks, we should first analyze how the principle of the PSO algorithm improves the system of deep networks . The folIn interactive music appreciation, emotion classification is an important task, which aims to understand and capture the emotions generated by listeners when they enjoy music. In order to effectively classify emotions, we modify the neural network to improve the classification performance. Considering that music feature is a multi-modal fusion feature, and PSO can quickly find the global optimal solution in the search space, the global search strategy helps to avoid falling into the local optimal solution, thus improving the generalization performance of the network. In addition, the music feature is a high-dimensional feature, and PSO performs well in high-dimensional Spaces because it does not involve gradient information, but optimizes the objective function through a simple and easy-to-understand particle update mechanismThe process of the PSO algorithm is as follows: we assume that the search space is L dimensions, in which the groups occupy the K particles. The positions of the j-th particle are expressed as 1 and z2 denotes the acceleration factor and the acceleration factor is an average number. In addition, rd indicates a random value between where g represents the inertia factor, zave and smax represent the average adaptation value of particles in each generation and the maximum adaptation value in the particle swarm, respectively.where s represents particle adaptation value, sAccording to the principle of the particle swarm optimization algorithm analyzed above, we can achieve the optimal thresholds and weightings by training the weightings and thresholds of the deep network with the PSO. Therefore, we can construct the network by these optimal weights and thresholds. The optimization process can be as follows:I. Besides, we take many appreciative music classes LB to be the output nodes SI of network. The geometric average of input and output layer nodes is the number of the hidden node. The individual particles in the population are represented as real number vectors obtained by encoding all the thresholds and connection weights between neurons.1) First, we normalize the training and testing samples and set up a three-layer neural network topology structure. Then, we regard dimension M of the weighted three vectors obtained in section 1.2 as the input nodes number S2) Design the fitness function. The fitness function is the metric of evaluating the neural network to solve problems. We adopt the mean square error of the output value of our model to obtain the objective function. Furthermore, we treat the inverse of the objective function as the fitness function.3) Initialize and set parameters. The parameters include the number of particles, initial position, and velocity. We control the initial position and velocity between  to train our model faster. We also set the acceleration factor and the maximum number of iterations simultaneously.4) According to the input and output samples, we calculate the fitness function to obtain the value of individual particles using 5) We follow 6) We calculate the fitness function to obtain a value of the updated particles and change the inertia weight according to the fitness value. Then, we search for the best position of the particle and check whether it satisfies the end judgment.However, the performance of PSO is affected by the selection of the initial particle swarm, and local optimization is still a potential influence for complex non-convex problems. It is difficult to adjust parameters, such as inertia weight, individual learning factor and group learning factor, and the setting of these parameters may affect the performance of the algorithm. Meanwhile, compared with our proposed model, user survey and psychological test can directly obtain students’ subjective feedback and psychological feelings, which involves higher labor and time costs. Our model can quickly process large amounts of data and automate sentiment classification, saving labor and time costs.To experiment with classifying emotion in music therapy, we collect the different types of music, such as ensemble music, orchestral music, silk and bamboo music, piano music, pop music, and so on., to construct our dataset. The training set contains 50,000 samples, and the test set includes 5,000 pieces.Each track in the dataset was sourced from publicly available music databases and labeled as belonging to a different emotional category. The characteristics of music emotion classification tasks usually involve the processing of audio signals and the extraction of music metadata. Accuracy is an important metric in the music emotion classification task because we want the model to accurately predict the listener’s emotional response to the music. The confusion matrix provides more comprehensive performance information, helping us understand how the classifier performs on different emotional categories, as well as the types of classification errors that may exist.We conduct our experiments with the i7-12900k Cpu, 3080ti Gpu, and the PyTorch deep learning framework. The training settings of the classification model of emotion in interactive music appreciation teaching are presented in We apply the accuracy as the evaluating metric, which is presented as follows:c refers to the accounts of correct classification samples and Itotal relates to the counts for the whole samples.where IBefore conducting experiments to compare with other methods, we should divide all music samples into different types to prepare for the further analysis of emotion, as shown in In addition, we analyze the distribution and statistics of the entire dataset; the results are presented in To evaluate the classification results of our model in this paper, we adopt the interactive music appreciation pitch and length to represent the frequency features of music appreciation. At the same time, we apply the reverberation time and tone to describe the time characteristics of music appreciation. Besides, the mean square of short-term energy in music represents the energy features of music appreciation. We use the music appreciation emotion classification model to output the 10 emotion classes of music appreciation. The performance of our model is presented in As shown in Music appreciation teaching can provide students with rich and colorful music experience and help them better understand the language and expression of music. By learning about different types of music and emotional categories, students can develop an appreciation for music and an aesthetic awareness. Music appreciation teaching can also enhance students’ emotional expression ability, improve emotional cognition and emotional management ability, and play a positive role in relieving stress and emotional regulation.The significance of accurate predicting emotional categories for teaching strategies and mental health. Accurately predicting students’ emotional categories can help teachers better understand students’ emotional states and emotional needs. According to the predicted results of different emotional categories, teachers can formulate corresponding teaching strategies to promote students’ positive mental health. For students to show positive emotions, teachers can use more active and pleasant music to teach, stimulate students’ learning interest and participation. For students who show negative emotions, teachers can adopt a more caring, supportive and understanding attitude, provide positive music to relieve them, encourage students to talk about their emotions and seek professional help.In addition, the accurate prediction of emotional categories can also be used to evaluate teaching effectiveness and mental health status, providing schools with important student emotional data and mental health feedback. By obtaining the information of students’ emotional state in time, schools can take more effective mental health intervention measures, provide necessary psychological counseling and support services, and promote students’ all-round development and mental health.To study how to regulate the psychology of college students through interactive music appreciation teaching, we propose a method to classify the emotions of interactive music to achieve the influence of music on people’s emotions. First, we extract different categories of speech information from the music and de-noise it. Then, we perform frame splitting and endpoint detection on the music we enjoy. Next, time domain features, frequency domain variance features and short-term energy features are extracted to improve performance. Finally, the contribution of features is calculated by using grey relational analysis and the features are weighted. Subsequently, we feed these features into a deep network optimized by the PSO. The experimental results show that the model has a good effect of music therapy emotion classification, the accuracy rate can reach 82.6%, and further regulate the psychology of college students.10.7717/peerj-cs.1589/supp-1Supplemental Information 1Click here for additional data file.


This paper analyzes the electrical test items of the EOL testing line in automotive manufacturers. On this basis, this paper proposes and designs an integrated and automated testing strategy to deal with the problems of slow testing speed, high dependence on manual labor and low efficiency. This article mainly analyzes the various tests of the two main tests in battery EOL testing: Battery Management System (BMS) testing and electrical testing. We propose an innovative integrated solution based on various testing items, including the reception, transmission, and self-analysis of different UDS protocol messages, a unique automated electrical performance measurement scheme, and a requirement and logic design of an integrated software end based on Python. The experimental results of actual testing show that the implementation of the integrated strategy greatly reduces the complexity of the testing steps, improves the testing efficiency, and reduces errors caused by human operation. The environmental impact brought by global warming in recent years has gradually aroused great attention around the world, and vigorously developing new energy vehicles and controlling greenhouse gas emissions have become the widespread consensus among various countries and regions around the world. The EU launched the “European Green Deal” in 2019 to solemnly commit to the world to reduce emissions targets: that is, to reduce greenhouse gas emissions by 50% by 2030 and strive to reach 55% (based on 1990) to ensure that carbon neutrality is achieved by 2050. Last year, each of the EU countries reached a new energy agreement again: to promote the development of new energy sources and to ban the production of new fuel vehicles from 2035 onwards. The United States, for its part, expects to reach a goal of 50% industry penetration of new energy vehicles by 2030. China has also released the New Energy Vehicle Industry Development Plan (2021–2035), aiming for new energy vehicle sales to reach about 20% of total new vehicle sales by 2025 [Pure electric vehicles are the mainstream models of new energy vehicles, with good market prospects and strong market competitiveness. As batteries are the energy source of new energy vehicles, how to predict the battery’s state accurately under complex working conditions has also become a global challenge that needs to be solved urgently ,3. At prThe earliest EOL testing relied entirely on manual labor. With the popularization of general-purpose measuring instruments, EOL testing gradually began to change from relying on manual experience to standard instrumentation, which has greatly improved a range of aspects, from testing efficiency to testing accuracy. However, as the automotive industry evolves, vehicle electronics are becoming increasingly complex. To address this, the International Standard Organization (ISO) has developed corresponding power battery diagnostic standards, including ISO 14229 . With thIntelligent testing of new energy vehicle batteries is one of the most important steps to ensure the safety of the entire vehicle. In order to conveniently, quickly and efficiently test the performance of the battery pack, various performance indicators are dynamically displayed in real-time. The entire system includes four main modules: the BMS communication module, the integrated control module in EOL test cabinet, the high voltage charge and discharge control module, and the human–computer interaction module. A schematic diagram of the EOL system testing structure is shown in (1)By conducting a detailed investigation of the EOL test, the typical test content of EOL is divided into two categories. The content and qualification standards of the EOL test are sorted out, and the integration strategy of the EOL test is separately studied.(2)A detailed interpretation of various message mechanisms of the Unified Diagnostic Services (UDS) protocol in BMS testing, including single-frame message, multi-frame message, NRC and DTC, and the possibility of automatic analysis and integration are studied.(3)The concept of including the electrical performance detection in the integration research is proposed. The working principle and control method of the power relay in the power battery are studied. The specific content and method of electrical performance detection are fully displayed, including some program control methods for the electrical performance detection equipment.(4)The applicability of LabVIEW and Python in this article is compared and the possibility of using Python language and its rich library resources as an integrated strategy development tool is analyzed.In summary, the following contributions have been made by this article:The EOL testing for a single battery generally includes SOC (State of Charge), SOH  and safety performance. The accurate state parameter, including the core factors such as SOC, SOH, state of power (SOP), and RUL, is the basis for ensuring safety and effective control . HoweverThe BMS reads the sensor parameters integrated inside the power battery to monitor the core parameters inside the automotive power battery in real time, providing information on remaining power, battery status, current, etc. This can prevent over-charging, over-discharging, over-voltage, over-current and an overly high temperature of the battery. The merits and drawbacks of the BMS directly affect the service life of the power battery pack ; a suitaThe electrical performance of the power battery is also the focus of EOL testing, and the requirements and testing methods for the electrical performance of automotive power batteries are presented in GB 18384-2020  and GB 3In summary, the typical test contents and passing criteria of power battery EOL testing are shown in The main function of the BMS is to protect the battery  and prevISO 14229 proposes a unified solution UDS protocol (Unified Diagnostic Services) for multiple control units inside the power battery, and ISO 15765 defines how the TP network transport layer handles single and multiple frames for different CAN message formats in the diagnostic model. CAN-based UDS is the preferred solution for BMS testing because the test requires only one test tool compatible with ISO 14229 and ISO 15765 . TherefoThe UDS protocol specifies the specific way for external devices to communicate with the car battery BMS and uses one byte to represent the diagnostic service, which is called Service ID, or SID for short. The response format is “SID + 40 + specific data”; the negative response format is a fixed format “7F + SID + one byte of NRC in the request message”. When the test does not involve special functions, simply read the corresponding test SID in the SID list to send and receive data according to the fixed target address, and identify the response status of the test by judging whether the SID bits in the received data are equal to the “SID + 40” of the sent data. If the response is positive, the test results of the test item can be obtained by processing the data content of the other data bits in a specified format, as shown in The NRC (Negative Response Code) is specified in Schedule A of ISO 15765, so when the diagnostic equipment receives the NRC, it is necessary to parse the NRC feedback from the BMS to record the cause and corresponding SID of the generated NRC. However, not all requests will receive the NRC; as a diagnostic device, it can communicate with all BMSs together or specify a BMS alone. Therefore, both Functionally Addressed and Physically Addressed are specified in the UDS. Functional Addressed broadcasts a diagnostic request and waits for the BMS on the bus to respond. Physical Addressed sends a specific diagnostic request and waits for a response from the specified BMS. In the case of physical addressing, the negative response should be sent in the specified format. In the case of functional addressing, when the NRC is 0 × 11 (service not supported), 0 × 12 (subfunction not supported) and 0 × 31 (request out of range), the functional addressing will not send the response. The BMS response rules are shown in Trouble diagnosis is one of the important functions of the BMS. The DTC (Diagnostic Trouble Code) is used in the BMS to record the detailed information of vehicle failure, and will alert the user and help prevent accidents. The composition of a trouble code is illustrated in In the previous subsection, we showed how to read single frame (SF) data from the BMS, but for reading multi-frame data like the DTC list, detailed provisions are also made in the UDS protocol. The multi-frame message generally starts by sending the first frame (FF) to the Server side; after the Server side confirms that the first frame is sent successfully, it sends the flow control frame (FC) to maintain the transmission state. After the Server side receives the flow control frame, it will set the timer to maintain the state according to the STmin  in the flow control frame and wait for the request of the Client side to send consecutive frames (CF); when the Server side receives the consecutive frames within the time specified in STmin, it will write them to the buffer and read them. GB 18384-2020 specifies the voltage range of Class B power batteries as 60–1500 V, but in practice, as the power source of pure electric vehicles, the voltage of power batteries is commonly above 300 V . The higAs demonstrated in the analysis of the electrical test items of the power battery in There are generally five relays in the pure electric vehicle power battery: main positive relay (MainPosRelay), main negative relay (MainNegRelay), pre-charge relay (PreChrgRelay), fast charge relay (DCChrgRelay) and slow charge relay (ACChrgRelay). The electrical relationship of the power relays in the power battery is shown in After understanding the electrical relationships of the five relays mentioned above, it can be found that the PreChrgRelay is a relatively special relay in terms of function. Its equivalent circuit with the MainPosRelay is shown in Relays for power batteries are high-voltage relays, for which there are often more stringent control policies to ensure the security of their use. The higher security control privileges required by the 27 service are provided in the UDS protocol, and the different security levels of the service can be defined by each vehicle manufacturer. It stipulates that when certain operations with higher security levels need to be implemented, further security access levels need to be obtained before they can be performed. The acquisition of the security access service seed for external diagnostic equipment and the resolution of the corresponding key are the focus and difficulty of the service, and the specific program control implementation process is shown in The electrical functions of the power battery are realized by different combinations of battery high-voltage relays, which mainly include four common electrical functions: power-on mode, power-off mode, slow-charge mode and fast-charge mode. The relay function detection of the power battery can be completed by monitoring the relay timing of the power battery in different modes and measuring the battery output voltage in different modes through the BMS. iU at the input in the voltage divider, iU can be calculated by simply measuring the voltage oU at the output according to the formula shown in Equation (1).The voltage of the power battery is a high voltage, although the voltage level of the power battery is not high in the voltage level of the power grid; therefore, many manufacturers for power battery voltage measurement often directly use a desktop digital voltmeter to measure. However, this measurement method often has a high risk and low efficiency, because the high voltage from the terminal is released to the human body; a safe voltage level takes a certain amount of time, for the integration of a challenge, and often one has to set up multiple levels of protection because of the presence of high voltage, which will greatly increase the cost of this type of integrated measurement equipment. There are three DC high-voltage measurement methods commonly used in the power grid: the voltage division method, the high-voltage electrostatic voltmeter, and the ball gap method ,20. TablR1 and C1 can be calculated from the above figure:The equivalent resistance of R2 and C2 is:The equivalent resistance of Using the voltage division formula for series resistors:The Substituting Equations (1) and (2) into Equation (3) yields:R1 of this type of equipment not only requires a larger resistance value but also needs a higher voltage withstand level, which leads to the larger size of the voltage divider. However, the power battery voltage measured in this system usually only has about 400 V, so the voltage division resistor R1 with a lower voltage withstand level and resistance value can be used, greatly reducing the volume of the voltage divider. In addition, by encapsulating the actual circuit and using high-voltage sealing glue to make a high-voltage package, the high-voltage creeping distance can be further reduced, the volume of the voltage divider can be reduced, and the cost of the integrated device can be reduced. By using this method, only a current protection is needed at the front of the circuit, and the high voltage relay used to measure different high voltages inside the integrated device can be replaced by a low voltage relay, further reducing the cost of the integrated device and ensuring safety. The high-voltage measurement equipment based on the voltage division method often has a larger size. This is because the measurement upper limit of this type of equipment is usually between several tens to several hundreds of kilovolts. Therefore, the voltage division resistor Any electrical products must undergo mandatory electrical safety testing, and this is also true for automotive power batteries. In 2019, for new energy vehicle battery safety, China formulated and published “GB 18384-2020 electric vehicle safety requirements”, “GB 38031-2020 electric vehicle with power battery safety requirements” and other mandatory national standards ; at the Three general electrical safety tests are listed in The withstand voltage test is mainly used to apply a voltage higher than the normal working voltage between the output end of the tested power battery and the battery case for a specified period of time; if the leakage current value is less than the standard leakage current threshold of 1 mA, it can be judged to be qualified. The test voltage must gradually rise from zero to the required voltage value within a certain period of time. The purpose of this test is to prove that the material can work safely at the rated voltage or due to switching, arcing and other similar phenomena caused by the instantaneous overvoltage.When there is no insulation fault, the high voltage wire harness of the power battery is isolated by an insulating layer, and the insulation internal resistance value is in the order of megabytes. If the insulation fault occurs, the insulation performance will be reduced, resulting in safety hazards. Therefore, it is necessary to carry out insulation fault detection. The occurrence of insulation fault means the decrease of insulation internal resistance, and how to obtain the accurate insulation resistance is the key point of insulation fault diagnosis . As per Requirements for potential equalization testing include:(a) The connection impedance between the exposed conductive part and the electric platform should be no more than 0.1 Ω;(b) For a potential equalization pathway, the distance is not more than 2.5 m between the two conductive parts of the resistance and should not be greater than 0.2 Ω.The significance of the above requirements is that when there is a base insulation failure and the human body is touching any exposed conductive parts, given that the potential balance is small enough, the potential difference can be negligible and the human body will not be electrocuted.The following X represents the average value of all test data, and d represents the absolute deviation of the test result, with d equal to i-Xx, where ix is the result at the i time trial. In this equation, The development of power electronics has promoted the popularity of universal measurement instruments, which has improved many test operations with high risk factors in the test field. During the rapid rise of the pure electric vehicle field, some general-purpose measuring instruments were gradually involved in the production testing of pure electric vehicles, and the application of these general-purpose measuring instruments in the EOL testing of vehicles is shown in In the above, EOL testing items were used with several general measurement instruments, such as the digital multimeter, insulation withstand voltage tester and equipotential tester; in the actual system we choose, the measurement accuracy is higher than the measurement standard measuring instruments to ensure the accuracy of the test. Their remote control used SCPI (Standard Commands for Programmable Instruments) commands. SCPI are a kind of existing standards based on IEEE488.1 and IEEE488.2, and follow the IEEE754 standard in the floating point rules, ISO646 information exchange 7-bit encoding symbols  and other standards of standardized instrument programming language . The EOLBy using the innovative communication method described above, each general measurement device connected to the computer can be quickly identified during software testing, and its corresponding script can be called to control and read measurement data. This eliminates the need for a customized embedded system. To address the issue of multiple devices alternatingly connecting to the battery output port, simply add a communication-enabled embedded relay system.The testing software is the most important part of the whole EOL integrated system, and its functional requirements are shown in Currently, most of the production of collaborative software in the industry that involves an image manipulation interface is developed using LabVIEW language, a graphical programming language built specifically for engineering applications in test, measurement or control. However, with the rapid development of Python in the field of automated testing, many complex development processes can be simplified by Python’s rich library resources. Python’s rich resources and excellent performance in data processing and database system are also suitable for the collection, processing and storage of a large amount of test data during EOL testing, so we innovatively use Python and its rich extension resources to develop an EOL testing software.In the actual research and development process, the PyVisa library in Python can be used to control SCPI devices more conveniently, while the PyQT5 library in Python can make it easier to design a fully functional graphical user interface. This is because PyQt5 successfully combines the Python language and the Qt library, making it compatible with all major operating systems. Due to the special nature of EOL testing, the display interface must be able to show the measurement results of each item in real time. Based on this limitation, a multi-threaded development mode was used in the software design stage; i.e., the main thread only receives and displays the test results, while the child threads make multi-device script calls and process the measurement data, which improves CPU utilization and program execution efficiency. The execution flow of the EOL testing software we wrote is illustrated in Considering the measurement pressure of the testers, we simplified the factors expressed in the test process, and the main interface of the software selected the test items, test content, three tests and test average value, test results and measurement equipment to display the test items. In this paper, we analyze the test requirements of the current EOL testing process, and reduce the professionalism of the testers in BMS testing by writing directed UDS automation analysis scripts for the test items; through the actual use of each measurement item and each measurement device in electrical testing, we use universal instrument control scripts and unique identification methods to connect all measurement devices to the same online PC. By using a Python solution, which is different from the traditional software solution, the measurement system can be built quickly by making full use of existing resources, and the scripted programming method is conducive to the expansion of measurement items afterwards. In summary, this paper presents an integrated strategy for power battery EOL testing that differs from existing manual measurement approaches. Through research on actual EOL test items, the strategy innovatively achieves automated data analysis of BMS, automated measurement of electrical performance, and an integrated software end based on Python, thereby increasing the safety and automation level of EOL testing while ensuring measurement accuracy.