Detection method and detection device for vehicle operation safety performance

Abstract

The invention provides a vehicle operation safety performance detection method and device, a vehicle comprises an electric control system, the method comprises the steps that interval operation data of the vehicle is acquired, and the interval operation data comprises multiple pieces of electric control real-time data; determining a health index of the electronic control system based on the multiple pieces of electronic control real-time data; determining the health level of the electronic control system based on the health index and a preset health level library; and based on the health level, generating an operation safety performance detection result of the vehicle, so that the health level of the current state of the electric control system can be accurately determined based on the electric control real-time data of the vehicle, and the safety performance of the electric control system can be monitored in real time. The situation that potential safety hazards occur to the vehicle due to the fact that the operation safety performance detection result does not include the safety level of the electric control system is avoided.

Description

Technical Field

[0001] This application relates to the technical field of vehicle performance detection, and particularly to a method and a device for detecting the running safety performance of a vehicle. Background Art

[0002] With the increase in the service life of new energy vehicles, the accumulation of driving mileage, and the frequent use of irregular charging behaviors such as fast charging / slow charging, the vehicle health shows a gradually decreasing trend, and the safety accidents caused thereby have attracted extensive attention from all sectors of society.

[0003] Currently, the safety performance detection of vehicles is basically concentrated on the safety detection of power batteries, and a small part will detect the safety of the drive motors of vehicles, but the detected scope is not comprehensive enough, resulting in inaccurate detection results of the overall safety performance of vehicles. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a method and a device for detecting the running safety performance of a vehicle.

[0005] Based on the above purpose, the first aspect of this application provides a method for detecting the running safety performance of a vehicle, where the vehicle includes an electronic control system, and the method includes:

[0006] Obtain the interval running data of the vehicle, where the interval running data includes a plurality of electronic control real-time data;

[0007] Based on the plurality of electronic control real-time data, determine the health index of the electronic control system;

[0008] Based on the health index and a preset health level library, determine the health level of the electronic control system;

[0009] Based on the health level, generate a detection result of the running safety performance of the vehicle.

[0010] Optionally, the determining the health index of the electronic control system based on the plurality of electronic control real-time data includes:

[0011] Perform deviation operations on each electronic control real-time data among the plurality of electronic control real-time data to obtain a performance degradation index corresponding to each electronic control real-time data;

[0012] Based on a preset weight rule, determine a weight coefficient corresponding to each electronic control real-time data;

[0013] Based on the performance degradation indices and weight coefficients corresponding to all the electronic control real-time data, determine the health index of the electronic control system.

[0014] Optionally, each electronic control real-time data includes a plurality of electronic control sub-data;

[0015] Performing deviation calculation based on each of the multiple real-time electronic control data to obtain a performance degradation index corresponding to each of the real-time electronic control data, including:

[0016] Based on each of the electronic control sub-data in each of the real-time electronic control data and preset normal data, determining a deviation value corresponding to each of the electronic control sub-data in the real-time electronic control data;

[0017] Determining the average value of the deviation values corresponding to all the electronic control sub-data in the real-time electronic control data as the performance degradation index corresponding to the real-time electronic control data.

[0018] Optionally, determining the health index of the electronic control system based on the performance degradation indices corresponding to all the real-time electronic control data and weight coefficients, including:

[0019] Determining the product of the performance degradation index corresponding to each of the real-time electronic control data and the weight coefficient as the first product corresponding to the real-time electronic control data;

[0020] Determining the sum of the first products corresponding to all the real-time electronic control data as the first total;

[0021] Determining the difference between the total weight coefficient and the first total as the health index of the electronic control system;

[0022] Wherein, the total weight coefficient is the sum of the weight coefficients corresponding to all the real-time electronic control data.

[0023] Optionally, the vehicle further includes a power battery; the interval operation data further includes real-time power battery data;

[0024] Generating a running safety performance detection result of the vehicle based on the health level, including:

[0025] Obtaining historical normal operation data of the vehicle, the historical normal operation data including a plurality of historical battery data;

[0026] Based on the plurality of historical battery data and the real-time power battery data, determining the safety level of the power battery;

[0027] Generating a running safety performance detection result of the vehicle based on the health level and the safety level of the power battery.

[0028] Optionally, determining the safety level of the power battery based on the plurality of historical battery data and the real-time power battery data, including:

[0029] Performing entropy value calculation based on each of the plurality of historical battery data to obtain an entropy value range corresponding to each of the historical battery data;

[0030] Determine the safety level of the power battery based on the real-time data of the power battery and the entropy value range.

[0031] Optionally, each of the historical battery data includes a plurality of battery sub-data;

[0032] The entropy value operation is performed on each of the historical battery data among the plurality of historical battery data to obtain the entropy value range corresponding to each of the historical battery data, including:

[0033] Discretize the plurality of battery sub-data in each of the historical battery data to obtain a plurality of battery sub-data arranged in ascending order;

[0034] Divide the plurality of battery sub-data arranged in ascending order into a plurality of intervals according to a preset interval rule, and determine the information entropy corresponding to each interval;

[0035] Determine the entropy value range corresponding to the historical battery data based on the information entropy corresponding to all intervals corresponding to each of the historical battery data.

[0036] Optionally, the determining the information entropy corresponding to each interval includes:

[0037] Determine the data probability corresponding to the interval as the ratio of the number of battery sub-data in each interval to the number of all battery sub-data in the historical battery data corresponding to the interval;

[0038] Determine the probability product corresponding to the interval as the product of the data probability corresponding to each interval and the logarithm of the data probability;

[0039] Determine the sum of probabilities as the sum of the probability products corresponding to each interval and all intervals before the interval;

[0040] Determine the negative value of the sum of probabilities as the information entropy corresponding to the interval.

[0041] Optionally, the vehicle further includes a motor, and the interval operation data further includes motor real-time data;

[0042] The generating the running safety performance detection result of the vehicle based on the health level and the safety level of the power battery includes:

[0043] Determine the performance level of the motor based on the motor real-time data and a preset threshold;

[0044] Generate the running safety performance detection result of the vehicle based on the health level of the electronic control system, the safety level of the power battery, and the performance level of the motor.

[0045] Based on the same inventive concept, the second aspect of the present application provides a detection device for the vehicle operation safety performance, including:

[0046] An acquisition module, configured to acquire the interval operation data of the vehicle, where the interval operation data includes a plurality of electronic control real-time data;

[0047] An operation module, configured to determine the health index of the electronic control system based on the plurality of electronic control real-time data;

[0048] A determination module, configured to determine the health level of the electronic control system based on the health index and a preset health level library;

[0049] A generation module, configured to generate a detection result of the vehicle operation safety performance based on the health level.

[0050] As can be seen from the above, the detection method and detection device for the vehicle operation safety performance provided by the present application perform deviation calculation based on a plurality of electronic control real-time data in the acquired interval operation data to obtain the health index of the electronic control system, and then determine the health level of the electronic control system based on the health index and the preset health level library. In this way, the health level of the current state of the electronic control system can be accurately determined based on the electronic control real-time data of the vehicle to perform real-time monitoring of the safety performance of the electronic control system, and then generate a detection result of the vehicle operation safety performance based on the health level. In this way, the finally obtained detection result of the vehicle operation safety performance at least includes the health level of the electronic control system of the vehicle, which is convenient for the user to timely understand the performance safety of the electronic control system of the vehicle, improves the comprehensiveness and accuracy of the finally generated detection result of the vehicle operation safety performance, and ensures that there will be no situation where the vehicle has safety hazards due to the lack of the safety level of the electronic control system in the detection result of the operation safety performance, providing an important support for the safe operation of new energy vehicles. Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions in the present application or related technologies, the following will briefly introduce the drawings required for use in the embodiments or related technology descriptions. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 The first schematic diagram of the detection method for the vehicle operation safety performance according to the embodiment of the present application;

[0053] Figure 2 The second schematic diagram of the detection method for the vehicle operation safety performance according to the embodiment of the present application;

[0054] Figure 3 A schematic diagram of a vehicle operation safety performance detection device according to an embodiment of the present application;

[0055] Figure 4 A schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0057] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0058] With the increase in the use years of new energy vehicles, the accumulation of mileage, and the frequent use of irregular charging behaviors such as fast charging / slow charging, the health of vehicles shows a gradual downward trend, and the safety accidents caused by this have attracted widespread attention from all walks of life.

[0059] To meet this challenge, relevant inspection procedures have been issued, which have been established as mandatory standards for the annual inspection of new energy vehicles. The procedures innovatively adopt a combination of online and offline inspection modes, in which online inspection focuses on core areas such as power battery safety, drive motor safety and electronic control system safety.

[0060] However, in actual testing, due to the high complexity and significant diversity of new energy vehicle data, how to test the safety of various aspects of the vehicle based on various data of new energy vehicles is a key technical problem that needs to be solved urgently.

[0061] In addition, the existing safety performance detection of vehicles mainly focuses on the safety detection of power batteries. A small part will detect the safety of the drive motors of vehicles, and there are almost no standards and methods for detecting the safety of the electronic control system. Therefore, the existing detection results of the overall safety performance of vehicles hardly include the safety detection results of the electronic control system.

[0062] However, the electronic control system is the "brain" of new energy vehicles, and its health status directly affects the control accuracy and safety of vehicles. Therefore, if the safety performance of the electronic control performance of vehicles is not monitored, it will bring great potential safety hazards to vehicles, and the inaccurate detection results of the overall safety performance of vehicles will be caused due to the lack of safety detection results of the electronic control system in the detection results.

[0063] Therefore, how to accurately detect the performance of the electronic control system of vehicles based on the complex and diverse data of new energy vehicles is an urgent problem to be solved. If a method that can accurately detect the safety status of the electronic control system and / or other important components on vehicles can be provided, it is not only an urgent need to meet the annual inspection standards of new energy vehicles, but also an important technical support for ensuring the safe operation of new energy vehicles.

[0064] Based on this, referring to Figure 1 and Figure 2 , the present application provides a method for detecting the running safety performance of a vehicle. The vehicle includes an electronic control system, and the vehicle may further include a motor and a power battery. The detection method can be executed by an online detection system such as a cloud platform / new energy monitoring platform, or can be executed by the vehicle's vehicle controller, and the specific execution entity is not limited.

[0065] The method specifically includes the following steps:

[0066] Step S100: Obtain the interval running data of the vehicle, where the interval running data includes a plurality of real-time electronic control data;

[0067] Step S200: Determine the health index of the electronic control system based on the plurality of real-time electronic control data;

[0068] Step S300: Determine the health level of the electronic control system based on the health index and a preset health level library;

[0069] Step S400: Generate a detection result of the running safety performance of the vehicle based on the health level.

[0070] Specifically, obtain the interval operation data of the vehicle. The interval operation data is all real-time data of the vehicle within a period of time before the current moment (including the current moment). Exemplarily, the interval operation data may include real-time power battery data related to the vehicle's power battery, may also include multiple real-time electronic control data related to the vehicle's electronic control system, and may further include real-time motor data related to the vehicle's drive motor.

[0071] Then, based on the multiple real-time electronic control data, determine the health index of the electronic control system. Specifically, deviation calculation can be performed on each of the multiple real-time electronic control data to obtain the performance degradation situation of each real-time electronic control data, and then the health index of the electronic control system is calculated based on the performance degradation situations of all real-time electronic control data.

[0072] The health index is a specific value calculated based on multiple real-time electronic control data, and this health index represents the current health status of the electronic control system.

[0073] Then, based on the health index and the preset health level library, determine the health level of the electronic control system. Among them, the preset health level library is in a database, data table or data graph preset based on the health index and health levels.

[0074] After determining the health index of the electronic control system, the health level corresponding to the health index can be determined from the preset health level library, and the determined health level is the health level of the electronic control system.

[0075] Exemplarily, assuming that H is used to represent the health index, then the preset health level library can be:

[0076] When H≥0.9, the health level is the normal level, indicating that the function of the electronic control system is hardly degraded at this time and the function of the electronic control system is completely normal;

[0077] When 0.8≤H<0.9, the health level is the warning level, indicating that the function of the electronic control system is slightly degraded at this time and a warning appears for the function of the electronic control system;

[0078] When H<0.8, the health level is the failure level, indicating that the function of the electronic control system is significantly degraded at this time and the function of the electronic control system may malfunction.

[0079] Then, exemplarily, when the health index of the electronic control system obtained by deviation operation based on the multiple real-time electronic control data is 0.95, the health level corresponding to the health index 0.95 can be determined from the preset health level library as the normal level.

[0080] Finally, based on the health level, generate the detection result of the vehicle's operation safety performance.

[0081] Among them, the vehicle's running safety performance detection result can be generated only based on the health level. In this case, the generated running safety performance detection result only includes the health level of the electronic control system.

[0082] Alternatively, it is also possible to continue to monitor the performance of the vehicle's power battery and / or drive motor to obtain the performance evaluation result of the power battery and / or drive motor. Finally, based on the health level of the electronic control system and the performance evaluation result of the power battery and / or drive motor, the vehicle's running safety performance detection result is generated. In this case, the generated running safety performance detection result not only includes the health level of the electronic control system, but also includes the performance evaluation result of the power battery and / or drive motor.

[0083] In this application, deviation operations are performed on multiple electronic control real-time data in the obtained interval operation data to obtain the health index of the electronic control system. Then, based on the health index and the preset health level library, the health level of the electronic control system is determined. In this way, the health level of the current state of the electronic control system can be accurately determined based on the electronic control real-time data of the vehicle to monitor the safety performance of the electronic control system in real time. Then, based on the health level, the vehicle's running safety performance detection result is generated. In this way, the finally obtained running safety performance detection result at least includes the health level of the vehicle's electronic control system, which is convenient for users to timely understand the performance safety of the vehicle's electronic control system, improves the comprehensiveness and accuracy of the finally generated vehicle's running safety performance detection result, and ensures that there will be no situation where the vehicle has safety hazards due to the lack of the safety level of the electronic control system in the running safety performance detection result, providing important support for the safe operation of new energy vehicles.

[0084] Through the monitoring method described in this application, the health level of the electronic control system can be determined based on the complex and diverse interval operation data of the vehicle, making up for the defect that the performance of the electronic control system cannot be detected or quantified in the prior art, solving the problem that the safety detection result of the electronic control system is not included in the detection result of the overall safety performance of the existing vehicle, and improving the accuracy and comprehensiveness of the finally obtained running safety performance detection result.

[0085] In some embodiments, step S200 of determining the health index of the electronic control system based on the multiple electronic control real-time data includes:

[0086] Step S210: Perform deviation operations on each electronic control real-time data among the multiple electronic control real-time data to obtain the performance degradation index corresponding to each electronic control real-time data;

[0087] Step S220: Determine the weight coefficient corresponding to each electronic control real-time data based on the preset weight rule;

[0088] Step S230: Determine the health index of the electronic control system based on the performance degradation indices and weight coefficients corresponding to all the real-time electronic control data.

[0089] Specifically, each piece of the real-time electronic control data is respectively used to characterize the function of an electronic control system.

[0090] Exemplarily, multiple pieces of real-time electronic control data related to the electronic control system may include the switching frequency of an insulated gate bipolar transistor (IGBT), the pulse width modulation (PWM) duty cycle, the packet loss rate of controller area network (CAN) communication, and the fault code of the electronic control system.

[0091] Among them, the switching frequency of the insulated gate bipolar transistor is used to characterize the switching function of high-power switch control in the electronic control system. The pulse width modulation duty cycle is used to characterize the flexibility and accuracy of the drive of the electronic control system. The packet loss rate of controller area network communication is used to characterize the communication function of the electronic control system. The fault code of the electronic control system is used to characterize whether the electronic control system has a fault.

[0092] When determining the health index of the electronic control system based on the multiple pieces of real-time electronic control data, first, deviation operations need to be performed based on each piece of real-time electronic control data among the multiple pieces of real-time electronic control data to obtain the performance degradation index corresponding to each piece of real-time electronic control data. The performance degradation index is used to characterize the difference between the current function and the normal function of the electronic control system corresponding to this piece of real-time electronic control data.

[0093] Then, after determining the performance degradation index corresponding to each piece of real-time electronic control data, based on a preset weight rule, determine the weight coefficient corresponding to each piece of real-time electronic control data.

[0094] The preset weight rule is the contribution degree of each preset piece of real-time electronic control data to the function degradation of the electronic control system. Among them, the sum of the weight coefficients of all the real-time electronic control data is 1. Exemplarily, the larger the contribution degree of a piece of real-time electronic control data, the larger the corresponding weight coefficient, and the smaller the contribution degree of a piece of real-time electronic control data, the smaller the corresponding weight coefficient.

[0095] Exemplarily, multiple pieces of real-time electronic control data related to the electronic control system may include the switching frequency of an insulated gate bipolar transistor, the pulse width modulation duty cycle, the packet loss rate of controller area network communication, and the fault code of the electronic control system.

[0096] The weight coefficient corresponding to the switching frequency of the insulated gate bipolar transistor determined based on the preset weight rule can be a, the weight coefficient corresponding to the pulse width modulation duty cycle can be b, the weight coefficient corresponding to the controller area network communication packet loss rate can be c, and the weight coefficient corresponding to the electronic control system fault code can be d, and a + b + c + d = 1.

[0097] Finally, based on the performance degradation indices and weight coefficients corresponding to all the electronic control real-time data, the overall health index of the electronic control system is determined. In this way, based on the performance degradation indices and weight coefficients corresponding to all the electronic control real-time data in the electronic control system, the overall health index of the electronic control system can be comprehensively determined, and this health index is used to characterize the health degree or performance degradation degree of the electronic control system.

[0098] In this application, when determining the overall health index of the electronic control system based on the multiple electronic control real-time data, first, the performance degradation index corresponding to each of the electronic control real-time data in the multiple electronic control real-time data is determined separately, and then, based on the performance degradation index corresponding to each electronic control real-time data and its corresponding weight coefficient, the overall health index of the electronic control system is determined. In this way, the accuracy of the determined health index can be improved.

[0099] In some embodiments, each of the electronic control real-time data includes multiple electronic control sub-data; the step S210 performs a deviation operation based on each of the electronic control real-time data in the multiple electronic control real-time data to obtain the performance degradation index corresponding to each of the electronic control real-time data, including:

[0100] Step S211: Based on each of the electronic control sub-data in each of the electronic control real-time data and the preset normal data, determine the deviation value corresponding to each of the electronic control sub-data in this electronic control real-time data;

[0101] Step S212: Determine the average value of the deviation values corresponding to all the electronic control sub-data in this electronic control real-time data as the performance degradation index corresponding to this electronic control real-time data.

[0102] Specifically, since the interval operation data is all the real-time data within a certain period of time before the current moment of the vehicle. Therefore, each of the electronic control real-time data in the interval operation data includes multiple electronic control sub-data, and each electronic control sub-data corresponds to the data of the vehicle at different moments.

[0103] Exemplarily, the obtained interval operation data is all the real-time data within the time period from 30 minutes before the current moment of the vehicle to the current moment. The interval operation data includes four electronic control real-time data: the switching frequency of the insulated gate bipolar transistor, the pulse width modulation duty cycle, the controller area network communication packet loss rate, and the electronic control system fault code.

[0104] Among them, the switching frequency of the electronically controlled real-time data insulated gate bipolar transistor also includes the switching frequencies corresponding to each acquisition moment within the time period from 30 minutes before the current moment of the vehicle to the current moment, that is, it includes multiple electronically controlled sub-data.

[0105] Correspondingly, among the three electronically controlled real-time data of pulse width modulation duty cycle, controller area network communication packet loss rate, and electronically controlled system fault code, there are also multiple related data at different acquisition moments, that is, electronically controlled sub-data. Only the specific contents of the multiple electronically controlled sub-data included in each electronically controlled real-time data are different.

[0106] When performing deviation calculation based on each electronically controlled real-time data among multiple electronically controlled real-time data to obtain the performance degradation index corresponding to each said electronically controlled real-time data, first, based on each said electronically controlled sub-data in each said electronically controlled real-time data and preset normal data, determine the deviation value corresponding to each said electronically controlled sub-data in this electronically controlled real-time data. Among them, the preset normal data is the average value when the preset electronically controlled real-time data is normal.

[0107] For an electronically controlled sub-data, its corresponding deviation value can be determined in the following way: the absolute value of the ratio of the difference between this electronically controlled sub-data and the preset normal data to the preset normal data is determined as the said deviation value.

[0108] Exemplarily, calculate according to the following formula: where, y i is the actual value of the electronically controlled sub-data, y ref is the preset normal data, and D0 is the deviation value.

[0109] After respectively determining the sum of the deviation values corresponding to each electronically controlled sub-data, the average value of the said deviation values corresponding to all the electronically controlled sub-data in this electronically controlled real-time data is determined as the performance degradation index corresponding to this electronically controlled real-time data.

[0110] Exemplarily, determine the performance degradation index corresponding to this electronically controlled real-time data according to the following formula: where, n is the number of electronically controlled sub-data included in this electronically controlled real-time data, and D is the performance degradation index.

[0111] Exemplarily, the four electronically controlled real-time data include insulated gate bipolar transistor switching frequency, pulse width modulation duty cycle, controller area network communication packet loss rate, and electronically controlled system fault code. Then, for each electronically controlled real-time data, respectively determine the performance degradation index corresponding to it according to the execution steps of S211 - S212 above.

[0112] In this application, when determining the performance degradation index corresponding to each piece of real-time electronic control data, the deviation value corresponding to each sub-electronic control data in each piece of real-time electronic control data is first determined separately, and then the average value of all the deviation values is used to determine the performance degradation index, which improves the accuracy of the determined performance degradation index and avoids the influence of individual unreasonable data or individual abnormal data on the finally determined performance degradation index.

[0113] In some embodiments, step S230 determines the health index of the electronic control system based on the performance degradation index and the weight coefficient corresponding to all the real-time electronic control data, including:

[0114] Step S231: Determine the first product corresponding to each piece of real-time electronic control data by multiplying the performance degradation index corresponding to each piece of real-time electronic control data by the weight coefficient.

[0115] Step S232: Determine the first sum as the sum of all the first products corresponding to all the real-time electronic control data.

[0116] Step S233: Determine the difference between the total weight coefficient and the first sum as the health index of the electronic control system.

[0117] Wherein, the total weight coefficient is the sum of the weight coefficients corresponding to all the real-time electronic control data.

[0118] Specifically, after determining the performance degradation index corresponding to each piece of real-time electronic control data, determine the first product corresponding to each piece of real-time electronic control data by multiplying the performance degradation index corresponding to each piece of real-time electronic control data by the weight coefficient.

[0119] Then, determine the first sum as the sum of all the first products corresponding to all the real-time electronic control data. The first sum is the total performance degradation of all the real-time electronic control data. The larger the first sum, the more serious the performance degradation of the electronic control system.

[0120] Determine the difference between the total weight coefficient and the first sum as the health index of the electronic control system, that is, the larger the first sum, the more serious the performance degradation, and then the finally obtained health index is smaller, and the corresponding health level of the health index is worse.

[0121] Exemplarily, the health index of the electronic control system is calculated according to the following formula: Wherein, m is the number of pieces of real-time electronic control data related to the electronic control system, w j is the weight coefficient corresponding to each piece of real-time electronic control data, Dj is the performance degradation index of each piece of real-time electronic control data, 1 is the total weight coefficient, and H is the health index.

[0122] The larger the finally calculated health index is, the smaller the degree of performance degradation of the electronic control system is, the higher the safety performance of the electronic control performance is, and the higher the health level of the electronic control performance is.

[0123] Exemplarily, when H≥0.9, the total number of performance degradations <0.1 (or 10%), indicating that the function of the electronic control system is almost not degraded at this time, and the function of the electronic control system is completely normal. Therefore, the health level of the electronic control system is the normal level at this time.

[0124] When 0.8≤H<0.9, the total number of performance degradations is between 0.1 and 0.2 (or 10% - 20%), indicating that the function of the electronic control system is slightly degraded at this time, and the function of the electronic control system issues a warning. Therefore, the health level of the electronic control system is the warning level at this time.

[0125] When H<0.8, the total number of performance degradations is greater than 0.2 (or 20%), indicating that the function of the electronic control system is significantly degraded at this time, and the function of the electronic control system may malfunction. Therefore, the health level of the electronic control system is the fault level at this time.

[0126] In this application, based on multiple real-time electronic control data in the interval operation data of the vehicle obtained, the health index of the electronic control system of the vehicle can be accurately determined, and the health index can be quantified, so that users can clearly understand the safety performance of the electronic control system.

[0127] In some embodiments, step S400 generates a detection result of the running safety performance of the vehicle based on the health level, including:

[0128] Step S410, obtain the historical normal operation data of the vehicle, and the historical normal operation data includes multiple historical battery data;

[0129] Step S420, determine the safety level of the power battery based on the multiple historical battery data and the real-time power battery data;

[0130] Step S430, generate a detection result of the running safety performance of the vehicle based on the health level and the safety level of the power battery.

[0131] Specifically, as described above, when generating a detection result of the running safety performance of the vehicle based on the health level, the safety performance of the power battery of the vehicle can be monitored, and then a detection result of the running safety performance of the vehicle is generated together based on the health level and the safety performance monitoring result of the power battery.

[0132] Therefore, in the process of generating the operation safety performance detection result of the vehicle based on the health level, historical normal operation data of the vehicle can be obtained. The historical normal operation data is all the normal operation data of the vehicle's full life cycle, that is, all the normal operation data from the vehicle's factory to the current moment.

[0133] The historical normal operation data includes a plurality of historical battery data, and the historical battery data is historical data related to the power battery of the vehicle. The plurality of historical battery data may include data such as battery voltage, battery current, and battery temperature.

[0134] Then, based on the plurality of historical battery data and the real-time power battery data, the safety level of the power battery is determined. Specifically, the normal threshold range of each historical battery data can be determined based on the plurality of historical battery data, and then the deviation between the current real-time power battery data and the normal data is determined by comparing the real-time power battery data with the normal threshold range, so as to determine the safety level of the power battery.

[0135] Then, based on the health level and the safety level of the power battery, the operation safety performance detection result of the vehicle is generated.

[0136] Among them, the operation safety performance detection result of the vehicle can be generated only based on the health level and the safety level of the power battery.

[0137] It is also possible to first monitor the safety performance of the drive motor of the vehicle, and jointly generate the safety performance detection result of the vehicle based on the safety performance monitoring result of the drive motor, the health level, and the safety level of the power battery.

[0138] In this application, not only can the performance of the vehicle's electronic control system be monitored based on the interval operation data, but also the safety performance of the vehicle's power battery can be monitored based on the historical battery data and the real-time power battery data in the interval operation data to obtain the safety level of the power battery. Thus, in the operation safety performance detection result of the vehicle generated based on the health level and the safety level of the power battery, it includes both the safety level of the power battery and the health level of the electronic control system, improving the comprehensiveness and reliability of the operation safety performance detection result.

[0139] In some embodiments, the step S420 of determining the safety level of the power battery based on the plurality of historical battery data and the real-time power battery data includes:

[0140] Step S421: Perform entropy value operation on each historical battery data in the plurality of historical battery data to obtain the entropy value range corresponding to each historical battery data;

[0141] Step S422: Determine the safety level of the power battery based on the real-time data of the power battery and the entropy value range.

[0142] Specifically, perform entropy value operations on each piece of historical battery data among the multiple pieces of historical battery data. The entropy value operations may sequentially include steps such as discretization processing, frequency statistics, normalization processing, and information entropy calculation, and finally obtain the entropy value range corresponding to each piece of historical battery data.

[0143] During the entire entropy value operation process, the complex and chaotic historical battery data can be integrated, and finally the entropy value range reflecting the data distribution uniformity is obtained to eliminate the significant influence of individual data on the entropy value, so that the finally obtained entropy value range can more accurately reflect the distribution of the corresponding historical battery data.

[0144] Then, determine the safety level of the power battery based on the real-time data of the power battery and the entropy value range.

[0145] Specifically, the real-time data of the power battery includes multiple sub-battery data, and the multiple sub-battery data correspond one-to-one with the multiple pieces of historical battery data. That is, exemplarily, the multiple pieces of historical battery data are respectively battery voltage, battery current, and battery temperature. Then, correspondingly, the real-time data of the power battery also includes real-time battery voltage, real-time battery current, and real-time battery temperature.

[0146] Based on each sub-battery data, determine whether the sub-battery data exceeds the entropy value range of its corresponding historical battery data and the degree of exceeding the entropy value range, and then determine the abnormality level of the sub-battery data based on whether it exceeds the entropy value range and the degree of exceeding.

[0147] Finally, determine the safety level of the power battery based on the abnormality levels of all sub-battery data and the preset battery safety level rules. Among them, the preset battery safety level rules are rules preset based on the battery voltage abnormality level, battery current abnormality level, battery temperature abnormality level, and overall battery safety level.

[0148] Based on the battery voltage abnormality level, battery current abnormality level, battery temperature abnormality level, and preset battery safety level rules, the safety level of the power battery can be determined.

[0149] For each sub-battery data, when the sub-battery data is within the corresponding entropy value range, the abnormality level of the sub-battery data is normal; when the sub-battery data exceeds the entropy value range by 5%-15%, the abnormality level of the sub-battery data is a warning; when the sub-battery data exceeds the entropy value range by more than 15%, the abnormality level of the sub-battery data is a fault.

[0150] Exemplarily, the preset battery safety level rule may be: when the abnormality level of each sub-battery data is normal, the safety level of the power battery can be determined as the battery safety level; when the abnormality level of at least one sub-battery data is a warning and the abnormality level of no sub-battery data is a fault, the safety level of the power battery can be determined as the battery warning level; when the abnormality level of at least one sub-battery data is a fault, the safety level of the power battery can be determined as the battery fault level.

[0151] Exemplarily, the real-time power battery data also includes the real-time battery voltage, the real-time battery current, and the real-time battery temperature.

[0152] Assume that the real-time battery voltage is within the entropy value range corresponding to the battery voltage. Then, the abnormality level of the real-time battery voltage is normal at this time; if the real-time battery current exceeds 5% of the entropy value range corresponding to the battery current, then the abnormality level of the real-time battery current is a warning at this time; if the real-time battery temperature exceeds 20% of the entropy value range corresponding to the battery temperature, then the abnormality level of the real-time battery temperature is a fault at this time.

[0153] Then, based on the battery voltage abnormality level, the battery current abnormality level, the battery temperature abnormality level, and the preset battery safety level rule, the safety level of the power battery can be determined as the battery fault level.

[0154] In this application, when determining the safety level of the power battery, first, entropy value operations are performed on each historical battery data in the multiple historical battery data to obtain the entropy value range corresponding to each historical battery data. Then, based on the real-time power battery data and the entropy value range, the safety level of the power battery is determined. In this way, the safety level of the power battery can be accurately quantified based on the historical battery data and the real-time power battery data to precisely monitor the real-time operating state of the power battery.

[0155] In some embodiments, the step S421 of performing entropy value operations on each historical battery data in the multiple historical battery data to obtain the entropy value range corresponding to each historical battery data includes:

[0156] Step S4211: Discretize multiple battery sub-data in each historical battery data to obtain multiple battery sub-data arranged in ascending order;

[0157] Step S4212: Divide the multiple battery sub-data arranged in ascending order into multiple intervals according to the preset interval rule, and determine the information entropy corresponding to each interval;

[0158] Step S4213: Based on the information entropy corresponding to all intervals corresponding to each historical battery data, determine the entropy value range corresponding to this historical battery data.

[0159] Specifically, since the historical battery data is the historical data of the entire life cycle of the power battery, for each of the historical battery data, it includes multiple battery sub-data, and the multiple battery sub-data respectively correspond to the historical data at different times.

[0160] Exemplarily, the multiple historical battery data may include data such as battery voltage, battery current, and battery temperature. Then, for one of the historical battery data, battery voltage, it includes the battery voltage values corresponding to all monitoring times from the start of using the power battery to the current time, and the battery voltage values corresponding to all monitoring times are the multiple battery sub-data corresponding to the historical battery data of battery voltage.

[0161] Therefore, in the process of performing entropy value calculation based on each of the multiple historical battery data to obtain the entropy value range corresponding to each of the historical battery data, first, the multiple battery sub-data in each of the historical battery data are discretized to obtain multiple battery sub-data arranged in ascending order.

[0162] Exemplarily, taking the historical battery data as battery voltage as an example, the historical battery data includes battery sub-data such as 3.5, 3.7, 3.6, 3.8, and 3.5. First, these battery sub-data are discretized to obtain multiple battery sub-data arranged in ascending order, that is, 3.5, 3.5, 3.6, 3.7, 3.8.

[0163] Then, the multiple battery sub-data arranged in ascending order are divided into multiple intervals according to the preset interval rule, and the information entropy corresponding to each interval is determined.

[0164] Among them, the preset interval rule is the rule for presetting the division of independent intervals, and for different historical battery data, there may be different preset interval rules. Exemplarily, when the historical battery data is battery voltage, the preset interval rule may be to divide independent intervals with an interval width of 0.1V; when the historical battery data is battery current, the preset interval rule may be to divide independent intervals with an interval width of 0.15A; when the historical battery data is battery temperature, the preset interval rule may be to divide independent intervals with an interval width of 0.5°C.

[0165] Then, continuing to take the historical battery data as battery voltage as an example, the multiple battery sub-data arranged from small to large, that is, 3.5, 3.5, 3.6, 3.7, 3.8, are divided into multiple intervals according to the preset interval rule (that is, dividing independent intervals with an interval width of 0.1V), that is, the three intervals [3.5 - 3.6), [3.6 - 3.7), and [3.7 - 3.8].

[0166] Then, the information entropy corresponding to each of the intervals is determined respectively. Specifically, the information entropy corresponding to each interval is determined based on the number of the battery sub-data within each interval and the number of all the battery sub-data in the historical battery data corresponding to this interval.

[0167] Finally, based on the information entropy corresponding to all the intervals corresponding to each historical battery data, the entropy value range corresponding to this historical battery data is determined. In this way, the finally determined entropy value range is jointly determined by the information entropy of multiple intervals, which can improve the accuracy of the determined entropy value range.

[0168] Specifically, the information entropy corresponding to all the intervals can be input into a pre-trained learning model to make it output the final entropy value range. It is also possible to calculate the mean value μ and the standard deviation α based on the information entropy of all the intervals, and then determine the entropy value range as [μ - κα, μ + κα] (κ is a user-defined coefficient).

[0169] In this application, when determining the entropy value range corresponding to the historical battery data, first, multiple battery sub-data in the historical battery data are discretized, and then the discretized battery sub-data are divided into multiple intervals. Then, based on the battery sub-data within each interval, the corresponding information entropy is determined. Finally, based on the information entropy corresponding to all the intervals corresponding to the historical battery data, the corresponding entropy value range is determined. In this way, the messy, disordered and complex historical battery data can be effectively integrated, and the entropy value range is determined based on the integrated data, which improves the accuracy of the determined entropy value range; in addition, the data is divided into multiple intervals, and after the information entropy of each interval is determined respectively, the entropy value range is determined based on the information entropy of all the intervals. In this way, a large number of battery sub-data are respectively divided into multiple intervals, reducing the number of battery sub-data within each interval, thereby simplifying the complexity of determining the information entropy of each interval, improving the accuracy of the determined information entropy, and further improving the accuracy of the finally determined entropy value range.

[0170] In some embodiments, determining the information entropy corresponding to each of the intervals in step S4212 includes:

[0171] Step S42121: Determine the ratio of the number of the battery sub-data within each interval to the number of all the battery sub-data in the historical battery data corresponding to this interval as the data probability corresponding to this interval;

[0172] Step S42122: Determine the product of the data probability corresponding to each interval and the logarithm of this data probability as the probability product corresponding to this interval;

[0173] Step S42123: Determine the sum of the probability products corresponding to each interval and all the intervals before this interval as the sum of probabilities;

[0174] Step S42124: Determine the negative of the sum of the probabilities as the information entropy corresponding to this interval.

[0175] Specifically, taking the historical battery data as the battery voltage as an example, the historical battery data includes battery sub-data such as 3.5, 3.7, 3.6, 3.8, and 3.5. First, discretize these battery sub-data to obtain multiple battery sub-data arranged in ascending order, namely 3.5, 3.5, 3.6, 3.7, 3.8.

[0176] Then, divide the multiple battery sub-data arranged in ascending order, namely 3.5, 3.5, 3.6, 3.7, 3.8, into three intervals according to the preset interval rule, which are the first interval [3.5 - 3.6), the second interval [3.6 - 3.7), and the third interval [3.7 - 3.8].

[0177] Then, calculate the data probability corresponding to each interval respectively.

[0178] The data probability P1 corresponding to the first interval [3.5 - 3.6) = 2 / 5 = 0.4, where 2 is the number of battery sub-data in the first interval [3.5 - 3.6) (2, which are 3.5 and 3.5 respectively), and 5 is the number of all battery sub-data in the historical battery data corresponding to this interval (5, which are 3.5, 3.7, 3.6, 3.8, 3.5 respectively).

[0179] The data probability P2 corresponding to the second interval [3.6 - 3.7) = 1 / 5 = 0.2, where 1 is the number of battery sub-data in the second interval [3.6 - 3.7) (1, which is 3.6), and 5 is the number of all battery sub-data in the historical battery data corresponding to this interval (5, which are 3.5, 3.7, 3.6, 3.8, 3.5 respectively).

[0180] The data probability P3 corresponding to the third interval [3.7 - 3.8] = 2 / 5 = 0.4, where 2 is the number of battery sub-data in the third interval [3.7 - 3.8] (2, which are 3.7 and 3.8 respectively), and 5 is the number of all battery sub-data in the historical battery data corresponding to this interval (5, which are 3.5, 3.7, 3.6, 3.8, 3.5 respectively).

[0181] Then, determine the product of the data probability corresponding to each interval and the logarithm of this data probability as the probability product corresponding to this interval. Exemplarily, the probability product corresponding to the third interval [3.7 - 3.8] is 0.4 * log0.4, the probability product corresponding to the second interval [3.6 - 3.7) is 0.2 * log0.2, and the probability product corresponding to the first interval [3.5 - 3.6) is 0.4 * log0.4.

[0182] Then, the sum of the probability products corresponding to each of the intervals and all the intervals before that interval is determined as the sum of probabilities. Exemplarily, for the third interval, its sum of probabilities is the sum of the probability product of the first interval, the probability product of the second interval, and the probability product of the third interval, that is, 0.4 * log0.4 + 0.2 * log0.2 + 0.4 * log0.4.

[0183] Finally, the negative of the sum of probabilities is determined as the information entropy corresponding to this interval, that is, the information entropy is the negative of 0.4 * log0.4 + 0.2 * log0.2 + 0.4 * log0.4.

[0184] Specifically, when the number of intervals corresponding to a piece of historical battery data is n, the information entropy corresponding to each interval can be calculated respectively according to the following formula:

[0185]

[0186] where H(X) is the information entropy, and P(x i ) is the data probability corresponding to this interval.

[0187] In this application, the ratio of the number of battery sub - data within each interval to the number of all battery sub - data in the historical battery data corresponding to this interval is determined as the data probability corresponding to this interval. Then, a series of calculations are performed based on the data probability, and finally the information entropy corresponding to this interval is determined. The information entropy reflects the uniformity of data distribution. Abnormal data (such as sudden voltage drop, sudden temperature rise) will cause the distribution to be concentrated or dispersed, resulting in a significant change in the entropy value. Therefore, by calculating the information entropy, the data chaos degree can be quantified, and then the most accurate entropy value range can be obtained.

[0188] Based on the determined information entropy, the entropy value range is determined. And since the historical data is updated in real - time, the determined information entropy and the entropy value range will also be updated in real - time. The safety level of the battery determined based on the real - time updated entropy value range is more accurate and more in line with the actual situation.

[0189] In addition, in this application, the entropy value range is determined based on the determined information entropy. Therefore, there is no need to set a preset threshold, and the safety level of the power battery finally determined does not depend on a fixed threshold (such as voltage < 3.0V is abnormal), avoiding the limitation of the fixed threshold on the monitoring result and improving the accuracy of the determined safety level of the power battery.

[0190] In some embodiments, step S430 generates a detection result of the running safety performance of the vehicle based on the health level and the safety level of the power battery, including:

[0191] Step S431: Determine the performance level of the motor based on the real-time motor data and the preset threshold values.

[0192] Step S432: Generate a detection result of the vehicle's running safety performance based on the health level of the electric control system, the safety level of the power battery, and the performance level of the motor.

[0193] Specifically, as described above, when generating a detection result of the vehicle's running safety performance based on the health level and the safety level of the power battery, the performance of the drive motor of the vehicle can be detected first to obtain the performance level of the motor, and then a detection result of the vehicle's running safety performance is jointly generated based on the health level of the electric control system, the safety level of the power battery, and the performance level of the motor.

[0194] When detecting the performance of the drive motor, determine the performance level of the motor based on the real-time motor data and the preset threshold values.

[0195] The real-time motor data includes real-time data related to the drive motor, and the real-time motor data may include the winding temperature, bearing temperature, output torque, rotational speed, and motor efficiency of the drive motor, etc.

[0196] The preset threshold values are a threshold range set based on preset rules. The preset threshold values at least include a winding temperature threshold range, a bearing temperature threshold range, and a motor efficiency threshold range.

[0197] When presetting the preset threshold values, set the winding temperature threshold range and the bearing temperature threshold range based on the motor materials and the historical data of the motor, and the temperature threshold range can also be flexibly adjusted in combination with the load conditions (such as high-speed conditions or climbing conditions, etc.).

[0198] When the motor materials are different, the winding temperature threshold range is different. Exemplarily, when the motor winding material uses a specific insulation temperature-resistant material (such as H-class insulation material or F-class temperature-resistant material, etc.), then the maximum value of the winding temperature is obtained by subtracting the safety margin from the maximum temperature resistance value of the insulation temperature-resistant material (such as the H-class insulation material has a temperature resistance of 180°C or the F-class temperature-resistant material has a temperature resistance of 155°C). Exemplarily, for a drive motor using H-class insulation material, the maximum value of the winding temperature is (180°C - 10°C), that is, 170°C.

[0199] The upper limit of the bearing temperature depends on the dropping point of the motor grease (such as lithium-based grease has a temperature resistance of about 120°C) and the thermal deformation temperature of the bearing steel, so it can be set in combination with engineering experience (such as 90°C).

[0200] Statistically analyze the historical temperature data of the motor under normal operating conditions to determine the 95% confidence interval of the temperature distribution (for example, if the average winding temperature during normal operation is 120°C and the standard deviation σ is 10°C, the upper limit can be set at 120°C + 3σ = 150°C).

[0201] Combine the temperature peaks under extreme operating conditions (such as continuous climbing, high-speed driving) to ensure that the threshold covers the entire scenario.

[0202] Therefore, finally, based on the motor materials, historical temperature data under normal operating conditions, and temperature peaks under extreme operating conditions, comprehensively determine the preset threshold.

[0203] When determining the threshold range of the motor efficiency, the threshold range of the motor efficiency can be determined based on the pre-drawn motor efficiency-load curve.

[0204] Then, based on the real-time data of the motor, the preset threshold, and the preset motor rating rules, determine the performance rating of the motor.

[0205] The preset motor rating rules are rules preset based on winding temperature, bearing temperature, motor efficiency, and motor performance rating. Based on the winding temperature, bearing temperature, motor efficiency, and preset motor rating rules, the performance rating of the motor can be determined.

[0206] Exemplarily, when the winding temperature, bearing temperature, and motor efficiency are all within the corresponding threshold ranges, determine that the performance rating of the motor is normal motor performance;

[0207] When the winding temperature / bearing temperature exceeds the corresponding threshold range by more than 0% and less than 5%, and / or the motor efficiency drops by more than 0% and less than 10% compared to the threshold range, determine that the performance rating of the motor is motor performance warning;

[0208] When the winding temperature / bearing temperature exceeds the corresponding threshold range by more than 5%, and / or the motor efficiency drops by more than 10% compared to the threshold range, determine that the performance rating of the motor is motor performance failure.

[0209] After determining the performance rating of the motor, based on the health rating of the electronic control system, the safety rating of the power battery, and the performance rating of the motor, generate the operation safety performance detection result of the vehicle. In this way, in the finally generated operation safety performance detection result, it simultaneously includes the health rating of the electronic control system, the safety rating of the power battery, and the performance rating of the motor, ensuring that the operation safety performance detection result includes the detection results of all core components of the vehicle, improving the accuracy and reliability of the operation safety performance detection result, and providing important support for the safe operation of new energy vehicles.

[0210] In this application, a method for detecting the operating safety performance of new energy vehicles is proposed. This method can analyze and detect all the data of new energy vehicles. Specifically, it can detect the electronic control system, power battery, and drive motor of the vehicle separately, sequentially, or simultaneously to generate the final detection result of the vehicle's operating safety performance.

[0211] In this application, to ensure that new energy vehicles that have been in use for 6 years can successfully pass the online annual inspection, intelligent analysis and diagnosis are carried out based on the multi-dimensional data reported throughout the vehicle's life cycle to comprehensively evaluate the operating states of the power battery, drive motor, and electronic control system, thereby accurately determining the vehicle's safety performance.

[0212] When it is detected that the vehicle's safety status may not meet the online annual inspection standards, a warning notice will be sent to the user in a timely manner through the intelligent information push system, and a nearby 4S store will be recommended for professional maintenance. This method not only realizes the early warning and active intervention of vehicle safety hazards, effectively improves the passing rate of the annual inspection, but more importantly, ensures the safe operating performance of the vehicle, effectively safeguards the legitimate rights and interests of users, and provides a strong guarantee for the safe use of new energy vehicles.

[0213] Specifically, the operating data of the power battery is quantitatively analyzed through the information entropy theory, the motor state is evaluated by combining the dynamic thresholds of temperature and motor efficiency, and the accurate evaluation of the health state of the electronic control system is realized based on the control performance degradation index, thereby constructing a multi-dimensional safety state detection method for the core components of new energy vehicles.

[0214] It should be noted that the method of the embodiment of this application can be executed by a single device, such as a computer or a server, etc. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In this case of a distributed scenario, one of these multiple devices can only execute one or more steps of the method of the embodiment of this application, and these multiple devices will interact with each other to complete the described method.

[0215] It should be noted that some embodiments of this application have been described. In some cases, the actions or steps recorded in the above embodiments can be executed in a different order from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multi-tasking and parallel processing are also possible or may be beneficial.

[0216] Based on the same inventive concept, corresponding to the method of any of the above embodiments, this application also provides a detection device for the operating safety performance of a vehicle, and the vehicle includes an electronic control system.

[0217] Refer toFigure 3 , the detection device for the vehicle operation safety performance includes:

[0218] An acquisition module 100, configured to acquire the interval operation data of the vehicle, where the interval operation data includes a plurality of electronic control real-time data;

[0219] An operation module 200, configured to determine the health index of the electronic control system based on the plurality of electronic control real-time data;

[0220] A determination module 300, configured to determine the health level of the electronic control system based on the health index and a preset health level library;

[0221] A generation module 400, configured to generate a detection result of the vehicle operation safety performance based on the health level.

[0222] In some embodiments, the operation module 200 is further configured to:

[0223] Perform deviation operations on each electronic control real-time data among the plurality of electronic control real-time data to obtain a performance degradation index corresponding to each electronic control real-time data;

[0224] Determine a weight coefficient corresponding to each electronic control real-time data based on a preset weight rule;

[0225] Determine the health index of the electronic control system based on the performance degradation indexes and weight coefficients corresponding to all the electronic control real-time data.

[0226] In some embodiments, each electronic control real-time data includes a plurality of electronic control sub-data.

[0227] In some embodiments, the operation module 200 is further configured to:

[0228] Determine a deviation value corresponding to each electronic control sub-data in each electronic control real-time data based on each electronic control sub-data in each electronic control real-time data and preset normal data;

[0229] Determine the average value of the deviation values corresponding to all the electronic control sub-data in the electronic control real-time data as the performance degradation index corresponding to the electronic control real-time data.

[0230] In some embodiments, the operation module 200 is further configured to:

[0231] Determine the product of the performance degradation index and the weight coefficient corresponding to each electronic control real-time data as the first product corresponding to the electronic control real-time data;

[0232] Determine the sum of the first products corresponding to all the electronic control real-time data as the first total sum;

[0233] Determine the difference between the total weight coefficient and the first sum as the health index of the electronic control system;

[0234] Wherein, the total weight coefficient is the sum of the weight coefficients corresponding to all the electronic control real-time data.

[0235] In some embodiments, the vehicle further includes a power battery; the interval operation data further includes power battery real-time data.

[0236] In some embodiments, the generating module 400 is further configured to:

[0237] Obtain the historical normal operation data of the vehicle, where the historical normal operation data includes a plurality of historical battery data;

[0238] Based on the plurality of historical battery data and the power battery real-time data, determine the safety level of the power battery;

[0239] Based on the health level and the safety level of the power battery, generate a detection result of the running safety performance of the vehicle.

[0240] In some embodiments, the generating module 400 is further configured to:

[0241] Perform entropy value operation on each historical battery data among the plurality of historical battery data to obtain an entropy value range corresponding to each historical battery data;

[0242] Based on the power battery real-time data and the entropy value range, determine the safety level of the power battery.

[0243] In some embodiments, each historical battery data includes a plurality of battery sub-data.

[0244] In some embodiments, the generating module 400 is further configured to:

[0245] Discretize the plurality of battery sub-data in each historical battery data to obtain a plurality of battery sub-data arranged in ascending order;

[0246] Divide the plurality of battery sub-data arranged in ascending order into a plurality of intervals according to a preset interval rule, and determine the information entropy corresponding to each interval;

[0247] Based on the information entropy corresponding to all the intervals corresponding to each historical battery data, determine the entropy value range corresponding to the historical battery data.

[0248] In some embodiments, the generating module 400 is further configured to:

[0249] Determine the ratio of the number of the battery sub - data within each of the intervals to the number of all the battery sub - data in the historical battery data corresponding to that interval as the data probability corresponding to that interval;

[0250] Determine the product of the data probability corresponding to each of the intervals and the logarithm of this data probability as the probability product corresponding to that interval;

[0251] Determine the sum of the probability products corresponding to each of the intervals and all the intervals before that interval as the sum of probabilities;

[0252] Determine the negative of the sum of probabilities as the information entropy corresponding to that interval.

[0253] In some embodiments, the vehicle further includes a motor, and the interval operation data further includes motor real - time data.

[0254] In some embodiments, the generating module 400 is further configured to:

[0255] Determine the performance level of the motor based on the motor real - time data and a preset threshold;

[0256] Generate a detection result of the running safety performance of the vehicle based on the health level of the electronic control system, the safety level of the power battery, and the performance level of the motor.

[0257] For the sake of convenience of description, when describing the above device, it is divided into various modules according to functions for separate description. Of course, when implementing the present application, the functions of each module can be realized in the same or multiple software and / or hardware.

[0258] The device in the above - mentioned embodiments is used to implement the detection method of the corresponding vehicle running safety performance in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0259] Based on the same inventive concept, corresponding to the method in any of the above - mentioned embodiments, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the detection method of the vehicle running safety performance in any of the above - mentioned embodiments.

[0260] Figure 4 Figure 34 shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 achieve communication connection with each other inside the device through the bus 1050.

[0261] The processor 1010 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0262] The memory 1020 can be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0263] The input / output interface 1030 is used to connect to the input / output module to realize information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0264] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to realize the communication interaction between this device and other devices. Among them, the communication module can realize communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0265] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0266] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, this device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0267] The electronic device in the above embodiments is used to implement the detection method for the vehicle running safety performance in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0268] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application further provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the detection method for the vehicle running safety performance in any of the foregoing embodiments.

[0269] The computer-readable medium in this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0270] The computer instructions stored in the storage medium in the above embodiments are used to cause the computer to execute the detection method for the vehicle running safety performance in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0271] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application further provides a computer program product including computer program instructions, which when running on a computer, cause the computer to execute the detection method for the vehicle running safety performance in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0272] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application further provides a vehicle including the detection device, electronic device, computer-readable storage medium, or computer program product in any of the foregoing embodiments.

[0273] The vehicle has the beneficial effects in any of the above embodiments, which will not be elaborated here.

[0274] It is understandable that before using the technical solutions of the various embodiments of the present disclosure, the types, usage scopes, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the authorization of the user will be obtained.

[0275] For example, when responding to receiving an active request from the user, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application program, server, or storage medium that performs the operations of the technical solutions of the present disclosure according to the prompt message.

[0276] As an optional but non-limiting implementation manner, the way of sending a prompt message to the user in response to receiving an active request from the user can be, for example, in the form of a pop-up window, and the prompt message can be presented in text in the pop-up window. In addition, the pop-up window can also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0277] It is understandable that the above process of notifying and obtaining the user's authorization is only illustrative and does not limit the implementation manner of the present disclosure, and other ways that meet relevant laws and regulations can also be applied to the implementation manner of the present disclosure.

[0278] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present application is limited to these examples; under the idea of the present application, the technical features between the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present application as described above, and they are not provided in detail for the sake of brevity.

[0279] In addition, for the sake of simplicity of description and discussion, and in order not to make the embodiments of the present application difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. In addition, the device may be shown in the form of a block diagram in order not to make the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation manner of these block diagram devices are highly dependent on the platform on which the embodiments of the present application will be implemented (that is, these details should be completely within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0280] Although the present application has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0281] Embodiments of the present application are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the present application. Accordingly, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. A method for detecting the running safety performance of a vehicle, the vehicle including an electronic control system, characterized in that, The method includes: Obtaining the interval operation data of the vehicle, where the interval operation data includes a plurality of real-time electronic control data; Determining the health index of the electronic control system based on the plurality of real-time electronic control data; Determining the health level of the electronic control system based on the health index and a preset health level library; Generating a running safety performance detection result of the vehicle based on the health level.

2. The method according to claim 1, wherein The determining the health index of the electronic control system based on the plurality of real-time electronic control data includes: Performing deviation calculation on each real-time electronic control data among the plurality of real-time electronic control data to obtain a performance degradation index corresponding to each real-time electronic control data; Determining a weight coefficient corresponding to each real-time electronic control data based on a preset weight rule; Determining the health index of the electronic control system based on the performance degradation indexes and weight coefficients corresponding to all the real-time electronic control data.

3. The method according to claim 2, wherein Each real-time electronic control data includes a plurality of electronic control sub-data; The performing deviation calculation on each real-time electronic control data among the plurality of real-time electronic control data to obtain a performance degradation index corresponding to each real-time electronic control data includes: Determining a deviation value corresponding to each electronic control sub-data in the real-time electronic control data based on each electronic control sub-data in the real-time electronic control data and preset normal data; Determining the average value of the deviation values corresponding to all the electronic control sub-data in the real-time electronic control data as the performance degradation index corresponding to the real-time electronic control data.

4. The method according to claim 2, wherein The determining the health index of the electronic control system based on the performance degradation indexes and weight coefficients corresponding to all the real-time electronic control data includes: Determining the product of the performance degradation index and the weight coefficient corresponding to each real-time electronic control data as a first product corresponding to the real-time electronic control data; Determining the sum of the first products corresponding to all the real-time electronic control data as a first total sum; Determining the difference between the total weight coefficient and the first total sum as the health index of the electronic control system; Wherein, the total weight coefficient is the sum of the weight coefficients corresponding to all the real-time electronic control data.

5. The method according to claim 1, wherein The vehicle further includes a power battery; the interval operation data further includes real-time power battery data; The generating a running safety performance detection result of the vehicle based on the health level includes: Obtaining the historical normal operation data of the vehicle, where the historical normal operation data includes a plurality of historical battery data; Determining the safety level of the power battery based on the plurality of historical battery data and the real-time power battery data; Generating a running safety performance detection result of the vehicle based on the health level and the safety level of the power battery.

6. The method according to claim 5, characterized in that, The determining the safety level of the power battery based on the plurality of historical battery data and the real-time power battery data includes: Performing entropy value calculation on each historical battery data among the plurality of historical battery data to obtain an entropy value range corresponding to each historical battery data; Determining the safety level of the power battery based on the real-time power battery data and the entropy value range.

7. The method according to claim 6, wherein Each historical battery data includes a plurality of battery sub-data; Performing entropy value operations on each of the multiple historical battery data to obtain the entropy value range corresponding to each of the historical battery data, including: Performing discretization processing on multiple battery sub-data in each of the historical battery data to obtain multiple battery sub-data arranged in ascending order; Dividing the multiple battery sub-data arranged in ascending order into multiple intervals according to a preset interval rule, and determining the information entropy corresponding to each of the intervals; Based on the information entropy corresponding to all the intervals corresponding to each of the historical battery data, determining the entropy value range corresponding to the historical battery data.

8. The method according to claim 7, characterized in that, The determining the information entropy corresponding to each of the intervals includes: Determining the ratio of the number of the battery sub-data in each of the intervals to the number of all the battery sub-data in the historical battery data corresponding to the interval as the data probability corresponding to the interval; Determining the product of the data probability corresponding to each of the intervals and the logarithm of the data probability as the probability product corresponding to the interval; Determining the sum of the probability products corresponding to each of the intervals and all the intervals before the interval as the sum of probabilities; Determining the negative of the sum of probabilities as the information entropy corresponding to the interval.

9. The method according to claim 5, characterized in that, The vehicle further includes a motor, and the interval operation data further includes motor real-time data; Generating a running safety performance detection result of the vehicle based on the health level and the safety level of the power battery, including: Based on the motor real-time data and a preset threshold, determining the performance level of the motor; Generating a running safety performance detection result of the vehicle based on the health level of the electronic control system, the safety level of the power battery, and the performance level of the motor.

10. A detection device for the running safety performance of a vehicle, characterized in that, Including: An acquisition module configured to acquire interval operation data of the vehicle, where the interval operation data includes a plurality of electronic control real-time data; An operation module configured to determine the health index of the electronic control system based on the plurality of electronic control real-time data; A determination module configured to determine the health level of the electronic control system based on the health index and a preset health level library; A generation module configured to generate a running safety performance detection result of the vehicle based on the health level.

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