A comprehensive performance testing and evaluation method and system for electric flying cars

Abstract

This application belongs to the field of new energy vehicle testing technology, and provides a method and system for comprehensive performance testing and evaluation of electric flying cars. By testing a fleet of vehicles consisting of multiple target vehicles, the method can effectively reduce the impact of single-sample testing bias on the accuracy of comprehensive performance evaluation of the target vehicles. Furthermore, the indicator types of each secondary test data can be determined by the vehicle type and applicable scenario of the target vehicles, and the secondary test data can be standardized by using the indicator types of each secondary test data to obtain a target data matrix. By using the entropy weight method in conjunction with the target data matrix obtained through standardization, the weights of each secondary test data can be calculated, resulting in target weights that are more consistent with actual vehicle usage, thereby obtaining a more accurate comprehensive performance evaluation result for the fleet of vehicles under test.

Description

Technical Field

[0001] This application belongs to the field of new energy vehicle testing technology, and more specifically, relates to a comprehensive performance testing and evaluation method and system for electric flying cars. Background Technology

[0002] Electric flying cars, as a new type of vehicle integrating ground driving and aerial flight functions, differ significantly from traditional ground-based electric vehicles in their technical characteristics and application scenarios. Structurally, electric flying cars are generally equipped with flight drive components such as rotors and thrust vectoring, forming diverse structural types such as stationary rotorcraft, helicopter-type, and multi-rotor types. In terms of application scenarios, they cover a variety of fields including urban air traffic, agricultural operations, logistics transportation, and emergency rescue. Therefore, their core evaluation indicators differ significantly from those of ground-based electric vehicles, making it difficult to comprehensively evaluate their performance using existing comprehensive performance testing methods for electric flying cars.

[0003] Currently, electric flying cars are still in the early stages of technological development, with very limited market circulation and an immature testing and evaluation system. Industry evaluations of their performance are often limited to single-category indicators, such as isolated tests on target battery range, maximum flight speed, or noise levels, lacking a systematic integration of multi-dimensional performance aspects such as range, payload, power, economy, environmental impact, and safety. This fragmented evaluation approach fails to comprehensively reflect the overall performance of electric flying cars, leaving consumers, businesses, and regulators without complete performance references and lacking accurate evaluation methods that cover overall performance. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the existing technology, this application provides a comprehensive performance testing and evaluation method and system for electric flying cars, which aims to accurately evaluate the comprehensive performance of electric flying cars based on various test parameters of electric flying cars in different testing processes.

[0005] Firstly, this application provides a comprehensive performance testing and evaluation method for electric flying cars, including: The target detection process is used to detect the vehicle fleet to be inspected and to obtain the target detection data of the vehicle fleet, which includes multiple target vehicles with the same configuration. Based on the target detection data, the vehicle type and applicable scenario of the target vehicle, obtain the target weight of the secondary detection data of each target vehicle in the target detection data; A target comprehensive scoring function is established, and based on the target comprehensive scoring function, target detection data, and target weights of each secondary detection data, the comprehensive performance evaluation results of the vehicle fleet to be tested are obtained.

[0006] By testing a fleet of vehicles with the same configuration, the interference of test results from vehicles in abnormal condition on the overall performance evaluation of the vehicle can be reduced, thus improving the accuracy of the target test data.

[0007] Furthermore, before inspecting the vehicle fleet based on the target detection process, the following steps are also included: Thermal runaway treatment is performed on the target batteries used in the target vehicles in the convoy to be tested, and battery status data of the target batteries under thermal runaway state is obtained. The battery status data includes the maximum temperature, voltage and proportion of gas components released by the target batteries within the target time period. When the battery status data does not meet the preset battery status standard data, the target detection process of the vehicle fleet to be inspected is terminated.

[0008] In particular, the method involves conducting numerous flight tests on the target vehicles of the test fleet, simulating thermal runaway states of the target batteries used in the target vehicles to obtain battery status data, and determining whether the target batteries meet safety standards based on the battery status data. This can effectively improve the safety of the execution process.

[0009] Furthermore, based on the target detection process, the vehicle fleet to be inspected is inspected, and target detection data of the vehicle fleet to be inspected is obtained, including: The target batteries of each target vehicle are tested based on the battery operation test process to obtain the first test data; The target vehicles are inspected based on the whole vehicle inspection process to obtain second inspection data, and the target inspection data is obtained based on the first inspection data and the second inspection data.

[0010] The operating conditions required for testing the battery used in the target vehicle are different from those required for testing the entire vehicle during operation. Therefore, the acquisition methods for the secondary test data of the first test data and the second test data are different. By adopting the method of separate testing and final integration, the accuracy of each secondary test data of the target test data can be effectively improved.

[0011] Furthermore, based on the battery operation testing process, the target batteries of each target vehicle are tested to obtain the first test data, including: Acquire the first battery detection data of each target vehicle within the target temperature range. The first battery detection data includes the optimal SOC (State of Charge) of the target battery of each target vehicle and the optimal operating temperature corresponding to the optimal SOC. The system acquires second battery detection data after performing a preset number of charge-discharge cycles on each target battery at the optimal operating temperature, and obtains first detection data based on the first battery detection data and the second battery detection data. The second battery detection data includes the power retention rate.

[0012] Furthermore, based on the whole vehicle inspection process, each target vehicle is inspected to obtain second inspection data, including: Altitude detection is performed on each target vehicle in a fully loaded and fully charged state to obtain the first vehicle data during the process of each target vehicle taking off from the ground to the highest position and falling from the highest position to the ground. The first vehicle data includes the minimum take-off time, maximum climb height and minimum safe battery level of the target vehicle when taking off from the ground to the highest position. The system performs full-vehicle cruise testing on each target vehicle in a fully loaded and fully charged state, and obtains second full-vehicle data for each target vehicle during its take-off from the ground to the target cruise altitude and during its acceleration to the maximum flight speed at the target cruise altitude. The second full-vehicle data includes the target vehicle's average flight noise level, maximum cruise range, and electric energy conversion efficiency.

[0013] Furthermore, based on the target detection data, the vehicle type of the target vehicle, and the applicable scenario, the target weights of the secondary detection data for each target vehicle in the target detection data are obtained, including: Based on the vehicle type and applicable scenario of the target vehicle, the index type of the secondary detection data of each target vehicle is determined, and the secondary detection data is standardized based on the index type of each secondary detection data. A target data matrix is ​​constructed based on the standardized secondary detection data, and the target weights of each secondary detection data are obtained by calculating the weights of the target data matrix using the entropy weight method.

[0014] Furthermore, based on the indicator types of each secondary detection data, the secondary detection data are standardized, including: When the indicator type of the secondary detection data is a positive indicator, the secondary detection data is standardized using the first standardization formula; When the index type of the secondary detection data is a non-directional index, the second standardization formula is used to perform adaptive standardization on the secondary detection data. When the indicator type of the secondary detection data is a negative indicator, the secondary detection data is standardized using the third standardization formula.

[0015] In this study, the types of indicators for secondary inspection data change depending on the target vehicle type and the applicable scenarios. For example, in tropical scenarios, lower vehicle temperatures generally indicate better performance, while in cold climates, the opposite is true. Therefore, the secondary inspection data is standardized based on its indicator types to establish a target data matrix. Since some target vehicles in the test fleet may experience significant deviations in their test results due to wear and tear or damage to certain components, this can lead to considerable dispersion in the same category of secondary inspection data. Directly using subjective assignment methods to determine the target weights for each secondary inspection data point introduces significant bias. Therefore, the entropy weight method is employed to determine the target weights based on the dispersion of the secondary inspection data for each target vehicle. This approach allows the evaluation of the target vehicles in the test fleet to more closely reflect actual usage conditions, resulting in a more accurate overall performance evaluation of the test fleet.

[0016] Furthermore, the second standardized formula is:

[0017] in, Indicates the first The minimum value allowed for a secondary detection data point. Indicates the first The maximum value of each secondary detection data point , Represents a constant parameter. Indicates the first The first target vehicle One secondary test data, Represents the standardized version of the first... The first target vehicle Two levels of test data.

[0018] Secondly, this application also provides a comprehensive performance testing and evaluation system for electric flying cars, used to implement any of the methods in the first aspect, including: The data acquisition module is used to acquire target detection data of the vehicle fleet to be inspected; The weight acquisition module is used to acquire the target weights of the secondary detection data of each target vehicle in the target detection data; The comprehensive performance evaluation module is used to obtain the comprehensive performance evaluation results of the vehicle fleet to be tested based on the target comprehensive scoring function, target detection data, and target weights of each secondary detection data. The visualization module is used to display the comprehensive performance evaluation results of the vehicle fleet under test in the form of visual images.

[0019] Thirdly, this application also provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in any possible implementation of the first aspect.

[0020] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art: This application, by testing a fleet of vehicles consisting of multiple target vehicles, can effectively reduce the impact of single-sample detection bias on the accuracy of the comprehensive performance evaluation of the target vehicles. Furthermore, by determining the indicator types of each secondary detection data point based on the vehicle type and applicable scenarios of the target vehicles, and by standardizing each secondary detection data point using its indicator types, a target data matrix can be obtained. The weights of each secondary detection data point are then calculated using the entropy weight method in conjunction with the standardized target data matrix, resulting in target weights that better reflect actual vehicle usage, thus leading to a more accurate comprehensive performance evaluation result for the fleet of vehicles under test. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this application or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a comprehensive performance testing and evaluation method for electric flying cars provided in an embodiment of this application.

[0023] Figure 2 This is a schematic diagram illustrating the execution process of a vehicle inspection procedure provided in an embodiment of this application.

[0024] Figure 3 This is a schematic diagram of the structure of the comprehensive performance testing and evaluation system for electric flying cars provided in the embodiments of this application.

[0025] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0026] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0027] In the following description, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The following description provides multiple embodiments of this application, which can be substituted or combined with each other. Therefore, this application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more other possible combinations of A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0028] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this application. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0029] like Figure 1 As shown in the embodiments of this application, a comprehensive performance testing and evaluation method for electric flying cars includes at least the following steps: S1. Based on the target detection process, detect the vehicle fleet to be detected and obtain the target detection data of the vehicle fleet to be detected. The vehicle fleet to be detected includes multiple target vehicles with the same configuration.

[0030] The execution entity of this application's method can be the central controller of the battery testing platform and the external main controller of the sensors used to measure various secondary testing data of the target vehicle. In this embodiment, the comprehensive performance testing of the target vehicle adopts a multi-sample testing method, that is, testing is performed using a fleet of vehicles of the same type, which can improve the robustness of the method in this embodiment.

[0031] In one possible implementation, before inspecting the vehicle fleet to be inspected based on the target detection process, the process further includes: Thermal runaway treatment is performed on the target batteries used in the target vehicles in the convoy to be tested, and battery status data of the target batteries under thermal runaway state is obtained. The battery status data includes the maximum temperature, voltage and proportion of gas components released by the target batteries within the target time period. When the battery status data does not meet the preset battery status standard data, the target detection process of the vehicle fleet to be inspected is terminated.

[0032] In the embodiments of this application, such as Figure 2As shown, each target vehicle needs to fly for an extended period during the testing process. Since the power consumption of electric flying cars is much higher than that of regular cars, battery thermal management is crucial. Excessive battery temperature can shorten battery life and may cause thermal runaway, fire, or even a crash. Therefore, to ensure flight safety during the target vehicle testing process, it is necessary to simulate thermal runaway using the same type of target battery as the target vehicles. By measuring the battery state data of each target battery under thermal runaway conditions, it is possible to determine whether a fire or explosion will occur after thermal runaway, thus ensuring that the electric flying car can still spin and land even after the target battery stops supplying power.

[0033] The simulation of the target battery's thermal runaway state can be triggered on a laboratory bench by external heating (such as a hot plate), overcharging, or needle penetration; at the same time, battery status data such as temperature changes, voltage drops, and gas release are monitored by temperature, voltage, and gas sensors.

[0034] In one possible implementation, the vehicle fleet to be inspected is inspected based on a target detection process, and target detection data of the vehicle fleet to be inspected is obtained, including: The target batteries of each target vehicle are tested based on the battery operation test process to obtain the first test data; The target vehicles are inspected based on the whole vehicle inspection process to obtain second inspection data, and the target inspection data is obtained based on the first inspection data and the second inspection data.

[0035] In this embodiment, the first detection data and each sub-detection data within the second detection data are all secondary detection data of the target detection data. Since the operating conditions corresponding to the indicators during target battery detection differ significantly from those during whole-vehicle measurement of the target vehicle, measuring the target battery data at different temperatures requires simulating high-altitude, low-temperature flight conditions on a professional test bench. However, due to airflow changes, flying cars cannot quantitatively simulate this by adjusting altitude. Therefore, in this embodiment, the target battery detection and whole-vehicle detection processes are performed separately, thereby obtaining more accurate secondary detection data.

[0036] In one possible implementation, the target batteries of each target vehicle are tested based on a battery operation testing process to obtain first testing data, including: Acquire the first battery test data of each target vehicle within the target temperature range. The first battery test data includes the optimal SOC of the target battery of each target vehicle and the optimal operating temperature corresponding to the optimal SOC. The system acquires second battery detection data after performing a preset number of charge-discharge cycles on each target battery at the optimal operating temperature, and obtains first detection data based on the first battery detection data and the second battery detection data. The second battery detection data includes the power retention rate.

[0037] In this embodiment of the application, the acquisition of the first and second detection data of the target battery is performed on an adjustable temperature workbench, and the preset number of charge-discharge cycles can be set according to international battery life standards.

[0038] In one possible implementation, each target vehicle is inspected based on a whole vehicle inspection process to obtain second inspection data, including: Altitude detection is performed on each target vehicle in a fully loaded and fully charged state to obtain the first vehicle data during the process of each target vehicle taking off from the ground to the highest position and falling from the highest position to the ground. The first vehicle data includes the minimum take-off time, maximum climb height and minimum safe battery level of the target vehicle when taking off from the ground to the highest position. The system performs full-vehicle cruise testing on each target vehicle in a fully loaded and fully charged state, and obtains second full-vehicle data for each target vehicle during its take-off from the ground to the target cruise altitude and during its acceleration to the maximum flight speed at the target cruise altitude. The second full-vehicle data includes the target vehicle's average flight noise level, maximum cruise range, and electric energy conversion efficiency.

[0039] In the embodiments of this application, such as Figure 2 As shown, the vehicle inspection process during flight is relatively simple. This is because flying cars do not have complex cruising environments such as uphill and downhill sections or real-time changes in road conditions. Therefore, only one altitude detection and one vehicle cruise inspection are required for each target vehicle, which can significantly reduce the operational damage to each target vehicle during the execution of the method in this embodiment.

[0040] S2. Based on the target detection data, the vehicle type of the target vehicle, and the applicable scenario, obtain the target weight of the secondary detection data of each target vehicle in the target detection data.

[0041] In one possible implementation, based on target detection data, the vehicle type of the target vehicle, and the applicable scenario, the target weight of the secondary detection data of each target vehicle in the target detection data is obtained, including: Based on the vehicle type and applicable scenario of the target vehicle, the index type of the secondary detection data of each target vehicle is determined, and the secondary detection data is standardized based on the index type of each secondary detection data. A target data matrix is ​​constructed based on the standardized secondary detection data, and the target weights of each secondary detection data are obtained by calculating the weights of the target data matrix using the entropy weight method.

[0042] In this embodiment, the types of indicators for secondary detection data will differ for target vehicles of different types in different applicable scenarios. These indicator types are categorized into positive indicators, negative indicators, and multi-stage non-directional indicators. Positive indicators, such as driving range, electric power efficiency, and maximum climb altitude, represent better performance; negative indicators, such as average flight noise and exhaust emissions, represent worse performance. Other secondary detection data, such as maximum flight drag, exhibit completely opposite indicator types in low-speed, stable cruising scenarios like deserts compared to normal cruising scenarios. Therefore, it is necessary to determine the indicator types based on vehicle type and applicable scenario to obtain more appropriate target weights for each secondary detection data point.

[0043] The expression for the target data matrix is:

[0044] in, Represents the standardized version of the first... The first target vehicle One secondary test data, This represents the target detection data in the target data matrix.

[0045] In this embodiment, the weights of each secondary detection data point are calculated using the target data matrix obtained through entropy weighting combined with standardization processing. As an objective weighting method, the target weights calculated by the entropy weighting method are determined by the dispersion of each secondary detection data. This can effectively solve the bias problem of traditional subjective weighting and obtain target weights that are more in line with actual vehicle usage.

[0046] In one possible implementation, the secondary detection data are standardized based on the indicator type of each secondary detection data, including: When the indicator type of the secondary detection data is a positive indicator, the secondary detection data is standardized using the first standardization formula; When the index type of the secondary detection data is a non-directional index, the second standardization formula is used to perform adaptive standardization on the secondary detection data. When the indicator type of the secondary detection data is a negative indicator, the secondary detection data is standardized using the third standardization formula.

[0047] In this embodiment of the application, the first standardized formula is: ; The third standardization formula is: ; The second standardized formula is:

[0048] in, Indicates the first The minimum value allowed for a secondary detection data point. Indicates the first The maximum value of each secondary detection data point , This represents a constant parameter used to characterize the boundary range of non-directional indicators in the second detection data. Indicates the first The first target vehicle One secondary test data, Represents the standardized version of the first... The first target vehicle Two levels of test data.

[0049] S3. Establish a target comprehensive scoring function, and based on the target comprehensive scoring function, target detection data, and target weights of each secondary detection data, obtain the comprehensive performance evaluation results of the vehicle fleet to be tested.

[0050] In this embodiment, for a fleet of vehicles of the same type and applicable scenario, the target weights of the same type of secondary detection data corresponding to each target vehicle are the same. The expression for the target comprehensive scoring function is:

[0051] in, This represents the overall performance score of the vehicle fleet to be inspected, where m represents the total number of target vehicles and n represents the number of categories of secondary inspection data. Represents the standardized version of the first... The first target vehicle One secondary test data, Indicates the first The target weight of each secondary detection data point.

[0052] Furthermore, after obtaining the comprehensive performance score of the test fleet, the score of the target vehicle of the corresponding vehicle type in the corresponding applicable scenario is obtained. Then, the comprehensive performance evaluation result of the test fleet can be quickly determined according to the established automatic rating method, thereby completing the comprehensive performance test evaluation of the corresponding electric flying car. For example, a score below 60 is considered unqualified, a score between 60 and 75 is considered qualified, a score between 75 and 85 is considered good, and a score of 85 or above is considered excellent.

[0053] Figure 3 This is a schematic diagram of the structure of the comprehensive performance testing and evaluation system for electric flying cars provided in the embodiments of this application, as shown below. Figure 3As shown, the system includes at least: The data acquisition module is used to acquire target detection data of the vehicle fleet to be inspected; The weight acquisition module is used to acquire the target weights of the secondary detection data of each target vehicle in the target detection data; The comprehensive performance evaluation module is used to obtain the comprehensive performance evaluation results of the vehicle fleet to be tested based on the target comprehensive scoring function, target detection data, and target weights of each secondary detection data. The visualization module is used to display the comprehensive performance evaluation results of the vehicle fleet under test in the form of visual images.

[0054] like Figure 4 As shown, Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: a processor 401, a communications interface 402, a memory 403, and a communication bus 404. The processor 401, communications interface 402, and memory 403 communicate with each other via the communication bus 404. The processor 401 can call software instructions in the memory 403 to execute the methods described in the above embodiments.

[0055] Furthermore, the logical instructions in the aforementioned memory 403 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0056] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0057] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0058] It is understood that the processor in the embodiments of this application can be a CPU (Central Processing Unit), or other general-purpose processors, DSPs (Digital Signal Processors), ASICs (Application Specific Integrated Circuits), FPGAs (Field Programmable Gate Arrays), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0059] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, ROM (Read-only Memory), PROM (Programmable ROM), EPROM (Erasable PROM), EEPROM (Electrically Erasable EPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0060] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line DSL) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD (Solid State Disk)).

[0061] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0062] Those skilled in the art will readily understand that the above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for comprehensive performance testing and evaluation of electric flying cars, characterized in that, include: The target detection process is used to detect the vehicle fleet to be detected and to obtain the target detection data of the vehicle fleet to be detected, wherein the vehicle fleet to be detected includes multiple target vehicles with the same configuration; Based on the target detection data, the vehicle type and applicable scenario of the target vehicle, the target weight of the secondary detection data of each target vehicle in the target detection data is obtained; A target comprehensive scoring function is established, and based on the target comprehensive scoring function, the target detection data, and the target weights of each of the secondary detection data, the comprehensive performance evaluation results of the vehicle fleet to be detected are obtained; The step of detecting the vehicle fleet based on the target detection process and obtaining the target detection data of the vehicle fleet includes: Based on the battery operation detection process, the target batteries of each of the target vehicles are detected to obtain the first detection data; The target vehicles are inspected based on the whole vehicle inspection process to obtain second inspection data, and target inspection data is obtained based on the first inspection data and the second inspection data. The step of detecting the target battery of each target vehicle based on the battery operation detection process and obtaining first detection data includes: Acquire first battery detection data for each of the target vehicles within the target temperature range. The first battery detection data includes the optimal SOC of the target battery of each target vehicle and the optimal operating temperature corresponding to the optimal SOC. The second battery detection data is obtained after each of the target batteries has undergone a preset number of charge-discharge cycles at the optimal operating temperature. The first detection data is obtained based on the first battery detection data and the second battery detection data. The second battery detection data includes the power retention rate. The step of inspecting each of the target vehicles based on the whole vehicle inspection process to obtain second inspection data includes: Altitude detection is performed on each of the target vehicles in a fully loaded and fully charged state to obtain the first vehicle data of each target vehicle during the process of taking off from the ground to the highest position and falling from the highest position to the ground. The first vehicle data includes the minimum take-off time, maximum climbing height and minimum safe battery level of the target vehicle when taking off from the ground to the highest position. The target vehicles in a fully loaded and fully charged state are subjected to full-vehicle cruise detection. Second vehicle data is obtained for each target vehicle from the ground to the target cruise altitude and during the acceleration to the maximum flight speed at the target cruise altitude. Second detection data is obtained based on the first vehicle data and the second vehicle data. The second vehicle data includes the average flight noise level, maximum cruise range and electric power conversion efficiency of the target vehicle.

2. The method for comprehensive performance testing and evaluation of electric flying cars according to claim 1, characterized in that, Before the target detection process is performed on the vehicle fleet to be inspected, it also includes: Thermal runaway treatment is performed on the target battery used by the target vehicle in the convoy to be tested, and the battery status data of the target battery under thermal runaway state is obtained. The battery status data includes the maximum temperature, voltage and proportion of gas components released by the target battery within the target time period. When the battery status data does not meet the preset battery status standard data, the target detection process of the vehicle fleet to be detected is terminated.

3. The method for comprehensive performance testing and evaluation of electric flying cars according to claim 1, characterized in that, The step of obtaining the target weight of each secondary detection data of the target vehicle in the target detection data based on the target detection data, the vehicle type of the target vehicle, and the applicable scenario includes: Based on the vehicle type and applicable scenario of the target vehicle, the index type of the secondary detection data of each target vehicle is determined, and the secondary detection data is standardized based on the index type of each secondary detection data. A target data matrix is ​​constructed based on the standardized secondary detection data, and the target weights of each secondary detection data are obtained by calculating the weights of the target data matrix using the entropy weight method.

4. The method for comprehensive performance testing and evaluation of electric flying cars according to claim 3, characterized in that, The standardization process for each of the secondary detection data based on the indicator type includes: When the index type of the secondary detection data is a positive index, the secondary detection data is standardized using the first standardization formula; When the index type of the secondary detection data is a non-directional index, the secondary detection data is adaptively standardized using the second standardization formula. When the index type of the secondary detection data is a negative index, the secondary detection data is standardized using the third standardization formula.

5. The method for comprehensive performance testing and evaluation of electric flying cars according to claim 4, characterized in that, The second standardized formula is: in, Indicates the first The minimum value allowed for a secondary detection data point. Indicates the first The maximum value of each secondary detection data point , Represents a constant parameter. Indicates the first The first target vehicle One secondary test data, Represents the standardized version of the first... The first target vehicle Two levels of test data.

6. A comprehensive performance testing and evaluation system for electric flying cars, used to implement the method described in any one of claims 1-5, characterized in that, include: The data acquisition module is used to acquire target detection data of the vehicle fleet to be inspected; The weight acquisition module is used to acquire the target weights of the secondary detection data of each target vehicle in the target detection data; The comprehensive performance evaluation module is used to obtain the comprehensive performance evaluation result of the vehicle fleet to be tested based on the target comprehensive scoring function, the target detection data, and the target weights of each of the secondary detection data; The visualization module is used to display the comprehensive performance evaluation results of the vehicle fleet under test in the form of visual images.

7. An electronic device, characterized in that, include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method as described in any one of claims 1-5.

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