Charging risk assessment method based on charging operation data of new energy commercial vehicle
By extracting the Internet of Vehicles and Charging Operation Data of New Energy Operating Vehicles, entering the risk assessment model, evaluating the potential risks of the vehicle in the future charging process, solving the safety hazards during the charging process of new energy operators, and achieving operational safety Assure.
Patent Information
- Application Number
- CN202411335254.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-24
- Publication Date
- 2025-02-25
AI Technical Summary
There are safety hazards during the charging process of new energy operating vehicles, such as overheating of batteries, short circuits, etc., which may lead to damage to charging facilities or fires, affecting operation stability and pose a risk of personnel safety.
By obtaining the Internet of Vehicles and charging operation data of new energy operating vehicles, the vehicle configuration data, charging process data and charging safety data are extracted, and these data are input into the pre-trained risk assessment model to evaluate the potential risks of the vehicle in the future charging process. .
The detection and evaluation of the charging safety of new energy operating vehicles has been achieved, potential risks are identified, vehicle operation safety is ensured, and the risk of economic and personnel losses has been reduced.
Smart Images
Figure CN119514838A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of data processing technology, and in particular to a charging risk assessment method, device, electronic device and readable storage medium based on charging operation data of new energy commercial vehicles. Background Art
[0002] At present, new energy vehicles are developing rapidly around the world and have become an important trend in the automotive industry. With the continuous advancement of technology, the performance and efficiency of new energy vehicles have been significantly improved, making them increasingly popular in the market. The advancement of battery technology is particularly critical, which not only increases the vehicle's range, but also reduces costs, making new energy vehicles more economical.
[0003] In addition, the environmentally friendly characteristics of new energy vehicles have also attracted widespread attention. They reduce dependence on fossil fuels, help reduce greenhouse gas emissions, and fight global climate change. As consumers pursue a sustainable lifestyle, the popularity of new energy vehicles is expected to continue to grow.
[0004] However, new energy vehicles still have some problems, especially charging safety. There may be safety hazards in the charging process of new energy vehicles, such as battery overheating and short circuit, which may cause damage to charging facilities or even fire. The unstable operation of new energy vehicles caused by charging problems may lead to the burning of goods, resulting in economic losses, and there are also safety hazards that may cause casualties. Summary of the invention
[0005] In view of this, one or more embodiments of the present disclosure provide a charging risk assessment method, device, electronic device and readable storage medium based on the charging operation data of new energy commercial vehicles, which can realize charging safety detection of new energy commercial vehicles, assess the potential risks in the vehicle's future charging process, and ensure the vehicle's operational safety.
[0006] In a first aspect, the present disclosure provides a charging risk assessment method based on charging operation data of a new energy commercial vehicle, the method comprising: obtaining Internet of Vehicles data of the new energy commercial vehicle, and determining vehicle configuration data based on the Internet of Vehicles data; obtaining target charging message data in the charging operation data, and determining charging process data and charging safety data based on the target charging message data; inputting the vehicle configuration data, the charging process data and the charging safety data into a pre-trained risk assessment model; and determining the potential charging risk of the new energy commercial vehicle based on the output result of the risk assessment model.
[0007] In a second aspect, the present disclosure provides a charging risk assessment device based on the charging operation data of a new energy commercial vehicle, the device comprising: a first acquisition module, used to acquire the Internet of Vehicles data of the new energy commercial vehicle, and determine the vehicle configuration data based on the Internet of Vehicles data; a second acquisition module, used to acquire the target charging message data in the charging operation data, and determine the charging process data and the charging safety data based on the target charging message data; a data scheduling module, used to input the vehicle configuration data, the charging process data and the charging safety data into a pre-trained risk assessment model; a risk assessment module, used to determine the potential charging risk of the new energy commercial vehicle according to the output result of the risk assessment model.
[0008] In a third aspect, the present disclosure provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the charging risk assessment method based on the charging operation data of new energy commercial vehicles is implemented.
[0009] In a fourth aspect, the present disclosure provides a computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, the charging risk assessment method based on the charging operation data of new energy commercial vehicles on the above-mentioned landing page is implemented.
[0010] The technical solution provided by one or more embodiments of the present disclosure can determine the vehicle configuration data that characterizes the vehicle characteristics from the Internet of Vehicles data; and can extract two types of key indicator data, charging process data and charging safety data, from the charging operation data of new energy commercial vehicles through big data technology. Subsequently, using the pre-trained risk assessment model, for new energy commercial vehicles with determined characteristics, the data characteristics contained in the vehicle charging indicator data can be accurately identified, and the potential charging risks of the vehicle can be quickly determined.
[0011] The technical solution provided by one or more embodiments of the present disclosure can construct a charging potential risk assessment method based on the charging operation data of new energy commercial vehicles, and realize the charging safety detection of new energy commercial vehicles. On this basis, the potential risks in the future charging process of the vehicle can be assessed to ensure the operational safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The features and advantages of the embodiments of the present disclosure may be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present disclosure in any way. In the accompanying drawings:
[0013] Figure 1 A schematic diagram showing the steps of a charging risk assessment method based on charging operation data of new energy commercial vehicles in one embodiment of the present disclosure is shown;
[0014] Figure 2 A schematic diagram of functional modules of a charging risk assessment device based on charging operation data of new energy commercial vehicles in one embodiment of the present disclosure is shown;
[0015] Figure 3 A schematic structural diagram of an electronic device in one embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0016] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.
[0017] The charging risk assessment method based on the charging operation data of new energy commercial vehicles provided in one embodiment of the present disclosure can be applied to an offline test device. The offline test device can be an electronic device with data processing capabilities. For example, the offline test device can be a personal computer, a workstation, etc. In addition, the method can also be applied to software running in the above-mentioned offline test device.
[0018] See also Figure 1 , a charging risk assessment method based on charging operation data of new energy commercial vehicles provided by an embodiment of the present disclosure may include the following steps.
[0019] S1: Acquire the Internet of Vehicles data of the new energy operating vehicle, and determine the vehicle configuration data based on the Internet of Vehicles data.
[0020] In this embodiment, the vehicle configuration data can reflect the characteristics of the vehicle model and can be some static data. The vehicle configuration data can be extracted from the Internet of Vehicles data. The vehicle configuration data can also be extracted from the local data of the vehicle and then supplemented from the Internet of Vehicles data. After the vehicle configuration data is extracted, it can be stored in a form.
[0021] In some embodiments, referring to Table 1, the vehicle configuration data may include at least one of the following: vehicle identification, vehicle model, vehicle brand, battery material of vehicle battery, nominal endurance of vehicle battery, rated voltage of vehicle battery, and accumulated mileage of vehicle. Optionally, different new energy vehicles may be uniquely identified and distinguished by vehicle identification.
[0022] Table 1 Vehicle configuration information table
[0023] Field Name Vehicle identification Vehicle Model Vehicle Brand Battery Materials Nominal battery life Battery rated voltage Accumulated mileage
[0024] Using a variety of vehicle configuration data to participate in charging risk assessment can make the assessment results more targeted and improve the accuracy of charging risk assessment.
[0025] S2: Obtain target charging message data in the charging operation data, and determine charging process data and charging safety data based on the target charging message data.
[0026] In this embodiment, the charging operation data may be charging operation data of new energy buses collected in the cloud. The charging operation data may be historical data stored for a long time, and the key indicator data for evaluating charging safety, such as charging process data and charging safety data, may be parsed through big data processing technology. After the charging process data and charging safety data are extracted, they may be stored in a form.
[0027] In some embodiments, referring to Table 2, the charging process data may include at least one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charging cell voltage maximum value, charging cell voltage minimum value, charging cell temperature maximum value, charging cell temperature minimum value, single cumulative charge amount, and charging end reason. The charging process data mainly records the time information, charge amount information, voltage extreme value and temperature extreme value of the single battery, and other attribute information related to the charging state during a completed charging process.
[0028] Table 2 Charging process information table
[0029] Field Name Charging start time Charging end time State of charge at the start of charging State of charge at the end of charging Maximum charging cell voltage Minimum charging cell voltage Maximum charging cell temperature Minimum charging cell temperature This cumulative charge amount Charging end reason
[0030] Utilizing a variety of charging process data to participate in charging risk assessment improves the diversity of data, makes data analysis and mining more in-depth, and improves the accuracy of risk assessment results.
[0031] In some embodiments, referring to Table 3, the charging safety data may include at least one of the following: battery system total voltage over-limit value, battery system total current over-limit value, charging interface voltage drop value, battery system charging alarm times, battery system maximum charging level, charging temperature difference, battery system insulation resistance value, battery system voltage difference, battery cell voltage, battery system probe temperature rise rate, battery system battery temperature rise abnormality, bus voltage data fluctuation abnormality times, bus current data fluctuation abnormality times, battery system power factor. The charging safety data mainly records sensitive information related to charging safety, such as the battery system over-limit value, charging warning times, battery temperature rise abnormality, bus voltage data fluctuation abnormality times, bus current data fluctuation abnormality times, etc., during a completed charging process.
[0032] Table 3 Charging safety information table
[0033] Field Name System total voltage exceeds limit The total system current exceeds the limit Charging port voltage drop System charging alarm times System maximum charging level Charging temperature difference System insulation resistance System pressure difference Battery cell voltage Probe temperature rise rate Abnormal battery temperature rise Number of abnormal bus voltage data fluctuations Number of abnormal bus current data fluctuations Power Factor
[0034] It should be noted that the charging safety data can be used to perform charging safety detection operations including but not limited to battery system total voltage over-limit detection, battery system total current over-limit detection, battery system charging interface voltage drop test, determination of battery system charging alarm information, determination of battery system maximum charging level, determination of battery system charging temperature difference, determination of battery system insulation resistance value, battery system voltage difference abnormality detection, battery cell voltage over-limit detection, battery system probe temperature rise rate abnormality detection, determination of battery system probe temperature rise abnormality information, determination of battery system battery temperature rise abnormality information, bus voltage data fluctuation abnormality detection, bus current data fluctuation abnormality detection, power factor abnormality detection and other charging safety detection operations.
[0035] Utilizing a variety of charging safety data to participate in charging risk assessment makes the assessment items more diverse, thus improving the effectiveness and practicality of charging risk assessment.
[0036] S3: Inputting the vehicle configuration data, the charging process data and the charging safety data into a pre-trained risk assessment model.
[0037] S4: Determine the potential risk of charging the new energy operating vehicle based on the output result of the risk assessment model.
[0038] In this embodiment, the pre-trained risk assessment model can be a machine learning model that has been trained by inputting a large amount of data, which can scientifically capture the common features and patterns in the data. By learning a large amount of historical data, the machine learning model can more accurately predict problems that may occur during the charging process. The risk assessment model can automatically complete risk assessment, reduce reliance on manual monitoring, and improve assessment efficiency. In practical applications, the risk assessment model can be updated and optimized through new data to ensure the accuracy and reliability of its assessment results.
[0039] In some embodiments, the pre-training process of the risk assessment model includes: obtaining vehicle configuration information of a sample vehicle; obtaining historical charging process information and historical charging safety information of the sample vehicle before a failure occurs; obtaining calibration information of the sample vehicle, wherein the calibration information is used to characterize whether a failure occurs after the next charge of the sample vehicle; and training the risk assessment model based on the vehicle configuration information, the historical charging process information, the historical charging safety information, and the calibration information.
[0040] In some embodiments, the training of the risk assessment model based on the vehicle configuration information, the historical charging process information, the historical charging safety information and the calibration information includes: randomly dividing the sample vehicles into training sample vehicles and verification sample vehicles according to a preset ratio; inputting the training vehicle configuration information, the training vehicle historical charging process information, the training vehicle historical charging safety information and the training vehicle calibration information corresponding to the training sample vehicles into a predetermined support vector machine model for training to obtain a candidate assessment model; inputting the verification vehicle configuration information, the verification vehicle historical charging process information, the verification vehicle historical charging safety information and the verification vehicle calibration information corresponding to the verification sample vehicles into the candidate assessment model for verification to obtain an accuracy report; based on the accuracy report, optimizing the candidate assessment model to determine the risk assessment model.
[0041] In a practical application example, the positive and negative samples extracted from nearly 600 faulty vehicle samples can be combined to extract the vehicle configuration information, charging process information, charging safety information, and calibration information of whether the vehicle is in a faulty state during the next charge. The relevant information of these faulty vehicle samples can be randomly divided into a training set and a validation set in a ratio of 7:3.
[0042] By inputting the training set into the Support Vector Machine (SVM) model for training, a candidate evaluation model can be obtained. By using the trained candidate evaluation model to fit the features in the validation set, an accuracy report can be obtained. Based on the accuracy report, the candidate evaluation model can be adjusted and optimized until the accuracy converges within the preset range. In this way, it can be ensured that the candidate evaluation model achieves the best performance and can be determined as the final risk assessment model. Using the risk assessment model, it is possible to predict whether the vehicle will have safety issues.
[0043] In some embodiments, determining the potential risk of charging the new energy operating vehicle includes: determining whether there is a charging safety risk for the new energy operating vehicle during a predetermined number of charging times in the future.
[0044] In a practical application example, referring to Table 4, feature extraction of vehicle charging preprocessing data can obtain feature data in three dimensions: vehicle feature information, charging process information, and charging safety information. Inputting the feature data of these three dimensions into the trained risk assessment model can realize charging risk assessment and determine whether the vehicle will have charging safety issues in the future. The evaluation results of the risk assessment model can integrate vehicle configuration information, vehicle charging process information, and vehicle charging safety information to generate an analysis report. The analysis report can be fed back to the new energy vehicle charging operation organization so that the new energy vehicle charging operation organization can identify whether there are potential charging risks for operating vehicles in the future.
[0045] Table 4 Charging risk assessment table
[0046] Field Name Is there a safety risk in the next charging? Is there a safety risk in the future of charging twice? Are there safety risks in the next three charges? In the future... will charging be a safety risk? Are there safety risks in the future n charging times?
[0047] The technical solution provided by one or more embodiments of the present disclosure can determine the vehicle configuration data that characterizes the vehicle characteristics from the Internet of Vehicles data; and can extract two types of key indicator data, charging process data and charging safety data, from the charging operation data of new energy commercial vehicles through big data technology. Subsequently, using the pre-trained risk assessment model, for new energy commercial vehicles with determined characteristics, the data characteristics contained in the vehicle charging indicator data can be accurately identified, and the potential charging risks of the vehicle can be quickly determined.
[0048] The technical solution provided by one or more embodiments of the present disclosure can construct a charging potential risk assessment method based on the charging operation data of new energy commercial vehicles, and realize the charging safety detection of new energy commercial vehicles. On this basis, the potential risks in the future charging process of the vehicle can be assessed to ensure the operational safety of the vehicle.
[0049] See also Figure 2 The present disclosure also provides a charging risk assessment device based on charging operation data of new energy commercial vehicles, the device comprising:
[0050] The first acquisition module 100 is used to acquire the Internet of Vehicles data of the new energy operating vehicle and determine the vehicle configuration data based on the Internet of Vehicles data;
[0051] A second acquisition module 200 is used to acquire target charging message data in the charging operation data, and determine charging process data and charging safety data based on the target charging message data;
[0052] A data scheduling module 300, for inputting the vehicle configuration data, the charging process data and the charging safety data into a pre-trained risk assessment model;
[0053] The risk assessment module 400 is used to determine the potential risk of charging the new energy operating vehicle according to the output result of the risk assessment model.
[0054] In one embodiment, the vehicle configuration data includes at least one of the following: vehicle identification, vehicle model, vehicle brand, battery material of the vehicle battery, nominal endurance of the vehicle battery, rated voltage of the vehicle battery, and cumulative mileage of the vehicle.
[0055] In one embodiment, the charging process data includes at least one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charging cell voltage maximum value, charging cell voltage minimum value, charging cell temperature maximum value, charging cell temperature minimum value, single cumulative charge amount, and charging end reason.
[0056] In one embodiment, the charging safety data includes at least one of the following: a total voltage exceeding the limit value of the battery system, a total current exceeding the limit value of the battery system, a voltage drop value of the charging interface, the number of charging alarms of the battery system, the maximum charging level of the battery system, the charging temperature difference, the insulation resistance value of the battery system, the voltage difference of the battery system, the battery cell voltage, the probe temperature rise rate of the battery system, the battery temperature rise abnormality of the battery system, the number of abnormal bus voltage data fluctuations, the number of abnormal bus current data fluctuations, and the power factor of the battery system.
[0057] In one embodiment, the risk assessment module 400 is specifically used to determine whether there is a charging safety risk for the new energy operating vehicle during a predetermined number of charging processes in the future.
[0058] In one embodiment, the pre-training process of the risk assessment model includes: obtaining vehicle configuration information of a sample vehicle; obtaining historical charging process information and historical charging safety information of the sample vehicle before a failure occurs; obtaining calibration information of the sample vehicle, wherein the calibration information is used to characterize whether a failure occurs after the next charge of the sample vehicle; and training the risk assessment model based on the vehicle configuration information, the historical charging process information, the historical charging safety information, and the calibration information.
[0059] In one embodiment, the training of the risk assessment model based on the vehicle configuration information, the historical charging process information, the historical charging safety information and the calibration information includes: randomly dividing the sample vehicles into training sample vehicles and verification sample vehicles according to a preset ratio; inputting the training vehicle configuration information, the training vehicle historical charging process information, the training vehicle historical charging safety information and the training vehicle calibration information corresponding to the training sample vehicles into a predetermined support vector machine model for training to obtain a candidate assessment model; inputting the verification vehicle configuration information, the verification vehicle historical charging process information, the verification vehicle historical charging safety information and the verification vehicle calibration information corresponding to the verification sample vehicles into the candidate assessment model for verification to obtain an accuracy report; based on the accuracy report, optimizing the candidate assessment model to determine the risk assessment model.
[0060] The modules described in the above embodiments can be implemented by computer chips or products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
[0061] For the convenience of description, the above devices are described in terms of functions and modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.
[0062] See also Figure 3 The present disclosure also provides an electronic device, which includes a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the charging risk assessment method based on the charging operation data of new energy commercial vehicles is implemented.
[0063] The present disclosure also provides a computer-readable storage medium, which is used to store a computer program. When the computer program is executed by a processor, it implements the above-mentioned charging risk assessment method based on the charging operation data of new energy commercial vehicles.
[0064] The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.
[0065] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory, that is, implementing the method in the above method embodiment.
[0066] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0067] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.
[0068] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.
[0069] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
[0070] Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations are all within the scope defined by the appended claims.
Claims
1. A charging risk assessment method based on charging operation data of new energy commercial vehicles, characterized in that: The method comprises: Acquire Internet of Vehicles data of the new energy operating vehicle, and determine vehicle configuration data based on the Internet of Vehicles data; Acquire target charging message data in the charging operation data, and determine charging process data and charging safety data based on the target charging message data; Inputting the vehicle configuration data, the charging process data and the charging safety data into a pre-trained risk assessment model; According to the output result of the risk assessment model, the potential risk of charging the new energy operating vehicle is determined.
2. The method according to claim 1, characterized in that The determining of the potential risk of charging of the new energy operating vehicle includes: Determine whether there is a charging safety risk for the new energy operating vehicle during a predetermined number of charging times in the future.
3. The method according to claim 1 or 2, characterized in that: The pre-training process of the risk assessment model includes: Obtain vehicle configuration information of sample vehicles; Obtaining historical charging process information and historical charging safety information of the sample vehicle before the failure occurs; Acquiring calibration information of the sample vehicle, wherein the calibration information is used to indicate whether a fault occurs after the next charging of the sample vehicle; The risk assessment model is trained based on the vehicle configuration information, the historical charging process information, the historical charging safety information and the calibration information.
4. The method according to claim 3, characterized in that The training of the risk assessment model based on the vehicle configuration information, the historical charging process information, the historical charging safety information, and the calibration information includes: According to a preset ratio, the sample vehicles are randomly divided into training sample vehicles and verification sample vehicles; Inputting the training vehicle configuration information, the training vehicle historical charging process information, the training vehicle historical charging safety information and the training vehicle calibration information corresponding to the training sample vehicle into a predetermined support vector machine model for training to obtain a candidate evaluation model; Inputting the verification vehicle configuration information, verification vehicle historical charging process information, verification vehicle historical charging safety information, and verification vehicle calibration information corresponding to the verification sample vehicle into the candidate evaluation model for verification to obtain an accuracy report; Based on the accuracy report, the candidate assessment model is optimized to determine the risk assessment model.
5. The method according to claim 1, characterized in that The vehicle configuration data includes at least one of the following: Vehicle identification, vehicle model, vehicle brand, battery material of the vehicle battery, nominal range of the vehicle battery, rated voltage of the vehicle battery, and accumulated mileage of the vehicle.
6. The method according to claim 1, characterized in that The charging process data includes at least one of the following: Charging start time, charging end time, charging start state of charge, charging end state of charge, charging cell voltage maximum value, charging cell voltage minimum value, charging cell temperature maximum value, charging cell temperature minimum value, single cumulative charge amount, charging end reason.
7. The method according to claim 1, characterized in that The charging safety data includes at least one of the following: The total voltage of the battery system exceeds the limit, the total current of the battery system exceeds the limit, the voltage drop value of the charging interface, the number of charging alarms of the battery system, the maximum charging level of the battery system, the charging temperature difference, the insulation resistance of the battery system, the voltage difference of the battery system, the battery cell voltage, the probe temperature rise rate of the battery system, the battery temperature rise abnormality of the battery system, the number of abnormal bus voltage data fluctuations, the number of abnormal bus current data fluctuations, and the power factor of the battery system.
8. A charging risk assessment device based on charging operation data of new energy commercial vehicles, characterized in that: The device comprises: A first acquisition module is used to acquire Internet of Vehicles data of the new energy operating vehicle and determine vehicle configuration data based on the Internet of Vehicles data; a second acquisition module, configured to acquire target charging message data in the charging operation data, and determine charging process data and charging safety data based on the target charging message data; A data scheduling module, used to input the vehicle configuration data, the charging process data and the charging safety data into a pre-trained risk assessment model; The risk assessment module is used to determine the potential risk of charging the new energy operating vehicle according to the output result of the risk assessment model.
9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.