A method, device and medium for detecting abnormal charging of new energy equipment

Abstract

The present application discloses a method, device and medium for detecting charging anomalies of new energy equipment, wherein the method includes pre-establishing a charging anomaly database, obtaining the identity data of the new energy equipment to be charged when receiving a charging request from the new energy equipment to be charged, and determining that the new energy equipment to be charged is a high-risk device when the identity data matches the charging anomaly database, wherein the charging anomaly database contains reference identity data corresponding to the high-risk new energy equipment. Applied to this solution, when receiving a charging request from the new energy equipment to be charged, it is possible to detect whether the device is charging abnormally by obtaining the identity data once, thereby reducing the workload of detecting charging anomalies of the new energy equipment. At the same time, there is no need to detect abnormal devices after waiting for the device to be charged, thereby improving the efficiency of detecting charging anomalies of the energy equipment.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a method, device and medium for detecting charging anomalies of new energy equipment. Background Art

[0002] With the development of new energy technologies and the charging industry, more and more new energy devices (such as electric vehicles) are entering the market. Due to the surge in sales and production of electric vehicles, a large number of electric vehicles with quality defects (such as those with faulty or problematic batteries) have entered the market, causing the rate of quality problems occurring during the trial period to rise sharply.

[0003] To prevent charging accidents caused by factors such as excessive battery temperature during charging, manufacturers typically conduct battery testing experiments to determine safety thresholds for various battery charging variables. During each charging process of a new energy device, the manufacturer continuously compares the battery charging parameters during the charging process with the set safety thresholds. The manufacturer then waits until the new energy device has finished charging to determine whether there are any abnormalities in the battery charging (for example, when judging by the maximum voltage, the maximum voltage during the charging process of the new energy device can only be determined after the new energy device has finished charging). Since the battery charging parameters need to be continuously obtained during each charge, the workload of detecting abnormalities in the charging of new energy devices increases. At the same time, determining abnormal devices after the new energy device has finished charging reduces the efficiency of detecting abnormalities in the charging of new energy devices.

[0004] It can be seen that during the charging process of new energy equipment, how to reduce the workload of detecting abnormal charging of energy equipment and improve the efficiency of detecting abnormal charging of energy equipment are problems that need to be solved urgently by technical personnel in this field. Summary of the Invention

[0005] The purpose of this application is to provide a method for detecting charging anomalies of new energy equipment, which is used to reduce the workload of detecting charging anomalies of new energy equipment and improve the efficiency of detecting charging anomalies of new energy equipment during the charging process of new energy equipment. The purpose of this application is also to provide a device and medium for detecting charging anomalies of new energy equipment.

[0006] To solve the above technical problems, the present application provides a method for detecting abnormal charging of new energy equipment, comprising:

[0007] Pre-establishing a charging anomaly database, wherein the charging anomaly database contains reference identity data corresponding to high-risk new energy equipment;

[0008] Upon receiving a charging request from a new energy device to be charged, obtaining identity data of the new energy device to be charged;

[0009] When it is determined that the identity data matches the charging anomaly database, the new energy device to be charged is determined to be a high-risk device.

[0010] Preferably, the pre-establishing of a charging anomaly database specifically includes:

[0011] Determine the target new energy equipment as high-risk new energy equipment based on the preset safety assessment model;

[0012] Acquire high-risk data, where the high-risk data includes identity data of the target new energy device and corresponding abnormal data;

[0013] The charging abnormality database is established according to the high-risk data.

[0014] Preferably, after establishing the charging anomaly database according to the high-risk data, the method further includes:

[0015] Sending high-risk data to the operation and maintenance platform so that the operation and maintenance platform can obtain feedback data based on the high-risk data, and sending the feedback data to the charging cloud platform, wherein the feedback data contains the identity data of the target new energy device and an identifier indicating whether the target new energy device has been successfully repaired;

[0016] receiving the feedback data;

[0017] The charging abnormality database is updated according to the feedback signal.

[0018] Preferably, the method further includes: updating the charging abnormality database when the target new energy equipment is successfully repaired.

[0019] Preferably, the security evaluation model is specifically a big data security evaluation model;

[0020] Then, the target new energy equipment is determined to be a high-risk new energy equipment according to the big data security evaluation model, specifically:

[0021] Determining the type of the target new energy equipment;

[0022] Select multiple new energy equipment sets under the type as analysis objects;

[0023] Acquire a reference charging process data of the analysis object within a preset time range and matching the analysis object, wherein the reference charging process data is data generated by the analysis object during the charging process;

[0024] Calculating secondary reference charging process data corresponding to each variable and used to characterize a variable change trend based on the primary reference charging process data;

[0025] Calculating secondary actual charging process data corresponding to each variable for characterizing a variable change trend based on the primary actual charging process data of the target new energy device; wherein the primary actual charging process data is data generated during the current charging process of the target new energy device;

[0026] Based on the correspondence between the primary reference charging process data and / or the secondary reference charging process data and time, determining a first safety threshold corresponding to the primary reference charging process data and / or a second safety threshold corresponding to the secondary reference charging process data, using the first safety threshold as a comparison object to compare with the primary actual charging process data of the target new energy device and / or using the second safety threshold as a comparison object to compare with the secondary actual charging process data of the target new energy device, so as to determine that the target new energy device is a high-risk new energy device;

[0027] According to the preset correspondence between the degree of deviation and the health status, the health status corresponding to the degree of deviation between the primary actual charging process data and the primary safety threshold and / or the health status corresponding to the degree of deviation between the secondary actual charging process data and the secondary safety threshold are determined.

[0028] Preferably, after determining that the new energy device to be charged is a high-risk device, the method further includes: sending an alarm signal in a preset alarm manner.

[0029] Preferably, after determining that the new energy device to be charged is a high-risk device, the method further includes:

[0030] Sending a protection signal including abnormal data to a user terminal, wherein the abnormal data represents identity data of the new energy device to be charged and the corresponding abnormal data, so that the user terminal obtains an indication of an interference charging event according to the protection signal;

[0031] Obtaining the high-risk times of the new energy equipment to be charged;

[0032] When it is determined that the high-risk number does not exceed the preset number, a first charging interference instruction is sent to the new energy device to be charged, where the first charging interference instruction is generated according to the interference charging event indication;

[0033] When it is determined that the high-risk number exceeds the preset number, a second charging interference instruction is sent to the new energy device to be charged, and the second charging interference instruction is generated according to a preset charging protection measure.

[0034] Preferably, the sending of the alarm signal by a preset alarm method is specifically:

[0035] Send identification reports via any combination of email, SMS, and application software;

[0036] The identification report includes basic information of the target new energy equipment, the total number of charging orders, the number of abnormal charging orders, identification data in the form of charts, and identification results of the target new energy equipment.

[0037] To solve the above technical problems, the present application also provides a device for detecting abnormal charging of new energy equipment, comprising:

[0038] A first establishing module is used to pre-establish a charging anomaly database, wherein the charging anomaly database contains reference identity data corresponding to high-risk new energy equipment;

[0039] A first acquisition module is configured to acquire identity data of the new energy device to be charged upon receiving a charging request from the new energy device to be charged;

[0040] The first determination module is configured to determine that the new energy device to be charged is a high-risk device when it is determined that the identity data matches the charging anomaly database.

[0041] To solve the above technical problems, the present application also provides a device for detecting abnormal charging of new energy equipment, comprising:

[0042] memory for storing computer programs;

[0043] A processor is used to implement the steps of the above-mentioned method for detecting abnormal charging of new energy equipment when executing the computer program.

[0044] To solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the new energy device charging anomaly detection method as described above are implemented.

[0045] The method for detecting charging anomalies of new energy devices provided by the present application pre-establishes a charging anomaly database. When a charging request is received from a new energy device to be charged, the identity data of the new energy device to be charged is obtained. When the identity data is determined to match the charging anomaly database, the new energy device to be charged is determined to be a high-risk device, wherein the charging anomaly database contains reference identity data corresponding to high-risk new energy devices. Applied to this solution, when a charging request is received from a new energy device to be charged, the device can be detected to have a charging anomaly by obtaining the identity data once, thereby reducing the workload of detecting charging anomalies of new energy devices. At the same time, there is no need to detect abnormal devices after waiting for the device to be charged, thereby improving the efficiency of detecting charging anomalies of new energy devices.

[0046] In addition, the new energy equipment charging anomaly detection device and medium provided in this application correspond to the above-mentioned new energy equipment charging anomaly detection method, and the effects are the same as above. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 A structural diagram of a charging management system for an electric vehicle provided in an embodiment of the present application;

[0049] Figure 2 This is a flow chart of a method for detecting abnormal charging of new energy equipment provided in an embodiment of the present application;

[0050] Figure 3 A flowchart of establishing a charging anomaly database provided in an embodiment of the present application;

[0051] Figure 4 A flowchart of a charging safety protection method provided in an embodiment of the present application;

[0052] Figure 5 A flowchart of a method for determining that a target new energy device is a high-risk new energy device based on a big data security evaluation model provided in an embodiment of the present application;

[0053] Figure 6 A flowchart of another charging safety protection method provided in an embodiment of the present application;

[0054] Figure 7 A schematic diagram of the structure of a device for detecting abnormal charging of new energy equipment provided in an embodiment of the present application;

[0055] Figure 8 This is a structural diagram of another new energy device charging anomaly detection device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0057] The core of this application is to provide a method for detecting charging anomalies in new energy devices, which is used to reduce the workload and improve the efficiency of detecting charging anomalies in new energy devices during the charging process. The core of this application is also to provide a device and medium for detecting charging anomalies in new energy devices. The new energy devices proposed in this application can be electric vehicles or other electric devices. The following description uses electric vehicles as an example. The charging safety protection methods described in the embodiments of this application can be applied to charging cloud platforms or charging devices, or to unmanned vehicle management platforms (applicable to unmanned vehicles). The following description uses the charging safety protection methods applied to charging cloud platforms. The charging cloud platform is connected to the charging devices for unified management of multiple charging devices. Typically, the charging cloud platform consists of multiple computers working together to implement corresponding functions. Charging devices generally have two hardware configurations: one is a single charger and charging terminal, which is larger in size and is commonly used in fast charging scenarios such as highway service areas. The other is a separate charger and charging terminal, where a single charger can communicate with multiple charging terminals for unified management of multiple charging terminals. Since the charger and charging terminal are set separately, the charging terminal is small in size and directly interacts with the electric vehicle for data. Its functions are relatively simple. Usually, the acquired vehicle data is sent to the corresponding charger, which completes the more complex data calculations and then returns the calculation results to the charging terminal. Figure 1 This is a structural diagram of a charging management system for an electric vehicle provided in an embodiment of the present application. Figure 1 As shown, the charging management system includes a charging cloud platform and multiple charging devices that are connected to the charging cloud platform. The charging devices obtain relevant data of the electric vehicle, such as charging start information, and send the charging start information to the charging cloud platform. The charging cloud platform identifies the device model based on the charging start information, and then performs relevant calculations on the charging process data that matches the device signal to obtain the safety threshold. It should be noted that Figure 1 It is just a specific application scenario and does not mean that the charging cloud platform must be able to detect abnormal charging of new energy equipment.

[0058] The above describes the hardware usage scenarios corresponding to the charging safety protection method provided by this application. The following describes an embodiment of the charging safety protection method. In order to enable those skilled in the art to better understand the present application, the technical solutions in this application are clearly and completely described below in conjunction with the accompanying drawings and specific implementation plans. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0059] Figure 2This is a flow chart of a method for detecting abnormal charging of new energy equipment provided in an embodiment of the present application. Figure 2 As shown, the method includes:

[0060] S10: Pre-establish a charging abnormality database.

[0061] In the embodiment of the present application, the charging anomaly database contains reference identity data corresponding to high-risk new energy equipment.

[0062] It is understandable that when the detection result of the new energy equipment charging anomaly detection is only to inform the user whether the new energy equipment is a high-risk equipment or a normal equipment, the charging anomaly database only needs to include the reference identity data. When the detection result of the new energy equipment charging anomaly detection also includes the abnormal data corresponding to the high-risk new energy equipment, the charging anomaly database also includes the high-risk data corresponding to the reference identity data, thereby improving the user experience.

[0063] S11: upon receiving a charging request from a new energy device to be charged, obtaining identity data of the new energy device to be charged.

[0064] It is understandable that the identity data of the new energy equipment to be charged can be obtained from the vehicle through the charging equipment during the charging process (handshake stage, parameter configuration stage, charging stage, and any stage after the end of charging). It can also be obtained by deploying a camera on the charging equipment to take pictures of the vehicle during the charging process, thereby obtaining the vehicle's macro identity data such as vehicle brand, type and license plate number, and determining the user information corresponding to the vehicle (for example: owner's name, phone number, address, etc.) based on the user data stored in the cloud platform system supporting the charging business.

[0065] S12: When it is determined that the identity data matches the charging anomaly database, the new energy device to be charged is determined to be a high-risk device.

[0066] The method for detecting charging anomalies of new energy devices provided in the embodiment of the present application pre-establishes a charging anomaly database. When a charging request is received from a new energy device to be charged, the identity data of the new energy device to be charged is obtained. When it is determined that the identity data matches the charging anomaly database, the new energy device to be charged is determined to be a high-risk device, wherein the charging anomaly database contains reference identity data corresponding to high-risk new energy devices. Applied to this solution, when a charging request is received from a new energy device to be charged, it is possible to detect whether the device is charging abnormally by obtaining the identity data once, thereby reducing the workload of detecting charging anomalies of new energy devices. At the same time, there is no need to detect abnormal devices after waiting for the device to be charged, thereby improving the efficiency of detecting charging anomalies of energy devices.

[0067] Figure 3 This is a flowchart of establishing a charging anomaly database provided by an embodiment of the present application. Figure 3 As shown, based on the above embodiment, S10 specifically includes:

[0068] S20: Determine the target new energy equipment as high-risk new energy equipment based on a preset safety evaluation model.

[0069] In an embodiment of the present application, the preset safety evaluation model can be a safety evaluation model that determines whether there is any abnormality in battery charging by comparing the current battery parameters with the safety threshold value set by the manufacturer during the charging process, or it can be a safety evaluation model constructed through historical big data to determine whether there is any abnormality in battery charging.

[0070] S21: Acquire high-risk data, where the high-risk data includes identity data of the target new energy equipment and corresponding abnormal data.

[0071] It is understandable that, in a specific implementation, high-risk data may only include identity data, but in order to improve the user experience, high-risk data includes both identity data and corresponding abnormal data.

[0072] It should be noted that the abnormal data of the target new energy equipment included in the high-risk data can be obtained through the abnormal data generated by the new energy equipment during the charging process. Such abnormal data can be obtained by: directly obtaining primary data during the charging process (for example, the maximum temperature of the power battery, the minimum temperature of the power battery, the SOC of the power battery, the maximum voltage of the single cell, the minimum voltage of the single cell, etc.), and determining the primary abnormal data based on the corresponding safety threshold; and / or obtaining secondary data used to characterize the trend of variable changes based on the primary data during the charging process (for example, the maximum temperature difference of the power battery, the maximum pressure difference of the power battery, the maximum temperature rise rate of the power battery, the maximum SOC change rate of the power battery, the maximum change rate of the single cell voltage, etc.), and determining the secondary abnormal data based on the corresponding safety threshold. It will be understood that the more variables in the primary data, the more variables in the secondary data, the more comprehensive the tracking data that can be obtained, and the more accurate the detection results of the optimized safety assessment model.

[0073] S22: Establish a charging anomaly database based on high-risk data.

[0074] The new energy equipment charging anomaly detection method provided in the embodiment of the present application establishes a charging anomaly database through a preset safety evaluation model, thereby improving the efficiency of establishing the charging anomaly database. In addition, when the safety evaluation model is continuously optimized, the charging anomaly database can be continuously improved, thereby improving the accuracy of the data in the charging anomaly database.

[0075] Figure 4 This is a flow chart of a charging safety protection method provided in an embodiment of the present application. Figure 4 As shown, based on the above embodiment, after S22, the following steps are specifically included:

[0076] S30: Send high-risk data to the operation and maintenance platform so that the operation and maintenance platform can obtain feedback data based on the high-risk data and send the feedback data to the charging cloud platform.

[0077] Among them, high-risk data represents the identity data of the target new energy equipment and the corresponding abnormal data, the tracking data contains the abnormal cause of the target new energy equipment; the feedback data contains the identity data of the target new energy equipment and an identifier indicating whether the target new energy equipment has been successfully repaired.

[0078] It is understandable that the above-mentioned high-risk data may also include tracking prompts or tracking instructions, so that the operation and maintenance platform can perform targeted tracking according to the tracking prompts or tracking instructions, thereby improving the efficiency of obtaining tracking data.

[0079] In specific implementation, the tracking signal can be a work order or tracking file containing high-risk data. After obtaining the cause of the abnormality of the target new energy equipment, the security specialist of the operation and maintenance platform can complete the work order or tracking file and feedback it to the operation and maintenance platform.

[0080] Specifically, a high-risk new energy equipment tracking application software running on the operation and maintenance platform can be constructed through a computer program. The application software can implement the charging safety protection method provided in this application, that is, after the operation and maintenance platform receives the tracking signal, the high-risk new energy equipment tracking application software can form a corresponding work order or tracking file based on the tracking signal, and send it to each safety specialist, so that the safety specialist can track the high-risk new energy equipment according to the work order or tracking file. After completing the work order or tracking file, the high-risk new energy equipment tracking application software forms a feedback signal, and the operation and maintenance platform sends it to the charging cloud platform.

[0081] It is understandable that, in addition to the data corresponding to the tracking signal, the work order or tracking file may also include the identity information and phone number of the owner of the high-risk new energy equipment, so that the safety specialist can track and supervise.

[0082] S31: receiving feedback data.

[0083] S32: Update the charging abnormality database according to the feedback signal.

[0084] In order to improve the practicality of the charging cloud platform, as a preferred embodiment, an alarm signal is sent when the target new energy device is determined to be a high-risk new energy device. It is understandable that the alarm signal can be sent by the charging cloud platform to the target new energy device, or it can be sent to the target user in the form of text messages, emails, phone calls, etc. based on the identity data of the target user after the target user corresponding to the target new energy device is determined through user data stored in the cloud platform system. It should be noted that the alarm signal can also include primary abnormality data and / or secondary abnormality data, so that the user can fully understand the abnormal cause of the target new energy device and then carry out targeted maintenance on the target new energy device.

[0085] In order to prevent the phenomenon that after the target new energy device is repaired, the charging abnormality database still contains data related to the target new energy device, which leads to the need for corresponding protective measures for the target new energy device, as a preferred embodiment, the following is also included:

[0086] When the target new energy equipment is successfully repaired, the charging anomaly database is updated.

[0087] In a specific implementation, when the target new energy equipment is repaired successfully, the identity data of the target new energy equipment is obtained so as to update the charging anomaly database according to the identity data, for example, relevant data corresponding to the identity data is deleted from the charging anomaly database.

[0088] The method for detecting charging anomalies of new energy equipment provided in the embodiment of the present application sends a tracking signal containing high-risk data to the operation and maintenance platform, so that the operation and maintenance platform obtains the tracking data containing the cause of the anomaly based on the tracking signal, and then sends a feedback signal containing the tracking data to the charging cloud platform, receives the feedback signal and updates the charging anomaly database based on the feedback signal. It can be seen that when applied to this solution, the charging anomaly database can be adjusted and updated through the tracking data contained in the feedback signal, thereby improving the accuracy of the charging anomaly database during actual use, thereby improving the accuracy of identifying charging anomalies. In addition, when applied to this solution, it can also effectively supervise and track the maintenance of high-risk new energy equipment, reducing the risk of using high-risk new energy equipment.

[0089] Based on the above embodiment, the security evaluation model is specifically a big data security evaluation model. Figure 5 This is a flow chart of a method for determining that a target new energy device is a high-risk new energy device based on a big data security evaluation model provided in an embodiment of the present application. Figure 5 As shown in the figure, the target new energy equipment is determined to be high-risk new energy equipment according to the big data security evaluation model, specifically:

[0090] S40: Determine the type of target new energy equipment.

[0091] The target new energy device mentioned in this embodiment is one type of new energy device. The purpose of determining the type of the target new energy device is to select multiple new energy devices of this type as analysis objects.

[0092] S41: Select multiple new energy equipment sets under the type as analysis objects.

[0093] It should be noted that the analysis object must be at least the same type of device as the target new energy device. In this embodiment, the analysis object can be the same type as the target new energy device, or the same type and age as the target new energy device. The purpose of selecting multiple new energy devices of the same type as the analysis object is to ensure that the reference charging process data obtained can accurately reflect the charging status of the target new energy device, thereby making the detection results more accurate. As a preferred embodiment, multiple new energy devices of the same type, in the same region, and / or of the same age are selected as the analysis objects.

[0094] S42: Acquire a reference charging process data of the analysis object within a preset time range that matches the analysis object.

[0095] The charging process data mentioned in this application is the data generated by any new energy device during the charging process. The charging process data comes from the charging cloud platform and the charging equipment, including charging system data and charging data. The charging system data is mainly the charging pile / charging terminal data, user data, and vehicle data stored in the cloud platform system that supports the charging business. The charging data is obtained from the vehicle by the charging equipment during the charging process. The reference charging process data is the data generated by the analysis object during the charging process. The reference charging process data and the actual charging process data mentioned below are both one type of charging process data, that is, the data generated by the new energy device during the charging process. However, in order to distinguish, the data generated by the target new energy device in the current charging process is called the actual charging process data, and the charging process data of the new energy device (analysis object) of the same model as the target new energy device is called the reference charging process data, which is used as reference data.

[0096] Correspondingly, a single reference charging process data set can be charging process data for a new energy device of the same type as the target new energy device, or for a new energy device of the same type and age. Taking electric vehicles as an example, reference charging process data can be data generated during the charging process for the following new energy devices: same model + past time period / current time; same model + same region (such as the same city) + past time period / current time; same model + same age + past time period / current time. For example, if the target new energy device type is Tesla Model 3, selecting multiple new energy devices under that type as analysis targets could be: Obtain Tesla Model 3s in Chengdu, with a vehicle age of three years, from January 1 to January 31, 2021, as analysis targets.

[0097] As a preferred embodiment, a reference charging process data includes the maximum temperature of the power battery, the minimum temperature of the power battery, the SOC of the power battery, the maximum voltage of the single cell, the minimum voltage of the single cell, the number of the single cell maximum voltage, the maximum temperature monitoring point number and the minimum temperature monitoring point number. It should be noted that the SOC of the power battery mentioned in this embodiment includes the SOC during normal charging and the SOC when the imbalance abnormality is terminated. The SOC when the imbalance abnormality is terminated belongs to the charging process data, but it is just that after the charging abnormality occurs, the SOC at the end of charging is analyzed in reverse. The SOC when the imbalance abnormality is terminated is the battery SOC when the power battery is abnormally terminated due to imbalance. The abnormal termination reasons that are more closely related to imbalance are that the single cell voltage of the new energy equipment reaches the target value and the power battery reaches the target SOC. In a specific embodiment, the more variables in the single reference charging process data, the more accurate the charging abnormality detection result. On this basis, the secondary reference charging process data includes the maximum temperature difference of the power battery, the maximum pressure difference of the power battery, the maximum temperature rise rate of the power battery, the maximum SOC change rate of the power battery, the maximum change rate of the single cell voltage, the Shannon entropy value of the highest temperature monitoring point number, the Shannon entropy value of the lowest temperature monitoring point number, and the Shannon entropy value of the single cell with the highest voltage.

[0098] The maximum temperature difference refers to the difference between the maximum and minimum battery temperatures at the same moment in the charging process, obtained from the maximum and minimum temperatures of the power battery. The maximum temperature difference refers to the maximum temperature difference during a single charge. The maximum voltage difference refers to the difference between the maximum and minimum voltages of a single cell at the end of a single charge. The maximum temperature rise rate refers to the change in the maximum battery temperature at a specific frequency (milliseconds, seconds, minutes) during the charging process. The maximum temperature rise rate refers to the maximum temperature rise rate during a single charge. The maximum SOC change rate refers to the change in the SOC transmitted by the BMS at a specific frequency (milliseconds, seconds, minutes) during a single charge. The maximum SOC change rate refers to the maximum SOC change rate during a single charge. The single cell voltage change rate refers to the change in the maximum single cell voltage transmitted by the BMS at a specific frequency (milliseconds, seconds, minutes) during the charging process. The maximum single cell voltage change rate refers to the maximum single cell voltage change rate during a single charge. The specific Shannon entropy value of the highest temperature monitoring point number is: based on the highest temperature detection point number obtained at a specific frequency (milliseconds, seconds, minutes) during a single charging process, combined with the Shannon entropy algorithm to calculate the Shannon entropy value of the highest temperature monitoring point number. The specific Shannon entropy value of the lowest temperature monitoring point number is: based on the lowest temperature detection point number obtained at a specific frequency (milliseconds, seconds, minutes) during a single charging process, combined with the Shannon entropy algorithm to calculate the Shannon entropy value of the lowest temperature monitoring point number. The specific Shannon entropy value of the number where the highest voltage of the single cell is located is: based on the highest voltage detection point number of the single cell obtained at a specific frequency (milliseconds, seconds, minutes) during a single charging process, combined with the Shannon entropy algorithm to calculate the Shannon entropy value of the number where the highest voltage of the single cell is located.

[0099] It can be understood that the Shannon entropy value can show the degree of dispersion of the highest temperature monitoring point number and the highest voltage number of the single cell during the charging process. The lower the degree of dispersion, the greater the possibility of charging abnormality.

[0100] In addition, the single-time reference charging process data obtained in this step can be obtained online after the charging start information of the target new energy source is obtained, or it can be pre-stored in a local database and directly retrieved from the local database after the charging start information of the target new energy source is obtained. It is understood that if the single-time reference charging process data is obtained online after the charging start information of the target new energy source is obtained, the single-time reference charging process data can be historical data or real-time data. If the single-time reference charging process data is directly retrieved from the local database after the charging start information of the target new energy source is obtained, the single-time reference charging process data is historical data.

[0101] S43: Calculating secondary reference charging process data corresponding to each variable and used to characterize a variable change trend based on the primary reference charging process data.

[0102] The secondary reference charging process data is derived from the primary reference charging process data and is used to characterize variable variation trends, such as variable degradation and gradient changes. It is understood that the number of variables included in the primary reference charging process data and the secondary reference charging process data may be the same or different, but the variable types must be different.

[0103] S44: Calculating secondary actual charging process data corresponding to each variable and used to characterize a variable change trend based on the primary actual charging process data of the target new energy device.

[0104] The primary actual charging process data is the data generated during the current charging process of the target new energy device. The secondary actual charging process data is obtained based on the primary actual charging process data and is used to characterize the trend of variable changes, such as variable variation, gradient change, degree of discreteness, etc. It should be noted that the method of obtaining the secondary reference charging process data from the primary reference charging process data is the same as the method of obtaining the secondary actual charging process data from the primary actual charging process data. It is understandable that the number of variables contained in the primary reference charging process data and the number of variables contained in the secondary reference charging process data can be the same or different, but the variable types must be different.

[0105] S45: Determine a first safety threshold corresponding to the primary reference charging process data and / or a second safety threshold corresponding to the secondary reference charging process data based on a correspondence between the primary reference charging process data and / or the secondary reference charging process data and time.

[0106] In this step, the first safety threshold and the second safety threshold are used as comparison objects to be compared with the primary actual charging process data and the secondary actual charging process data of the target new energy device, respectively, to determine whether the target new energy device is charging abnormally. It should be noted that the calculation method of the primary safety threshold and the secondary safety threshold is not limited in this embodiment, and can be determined using statistical analysis methods or cluster analysis methods. The primary safety threshold and the secondary safety threshold in this step and the existing fixed thresholds obtained through experiments are all used to measure whether the charging is abnormal, except that the primary safety threshold and the secondary safety threshold in this step are obtained through real data of the same type of new energy device as the target new energy device during the charging process, so they can truly reflect the charging status of the same type of device.

[0107] The charging process of a target new energy device is divided into four stages: handshake phase, parameter configuration phase, charging phase, and charging completion phase. The actual charging process data can be data from one or all of these four stages. Since the primary and secondary safety thresholds are determined based on the charging process data of new energy devices of the same type as the target new energy device, they can serve as detection criteria for abnormalities in the target new energy device. As long as at least one of the primary or secondary actual charging process data exceeds the corresponding safety threshold, the target new energy device is determined to be charging abnormally.

[0108] In addition, in this step, the first safety threshold and / or the second safety threshold can be determined by a statistical analysis method or a cluster analysis method. As a preferred embodiment, the statistical analysis method includes a normal distribution statistical method, and the cluster analysis method includes a Gaussian mixture clustering method.

[0109] S46: Determine the health status corresponding to the degree of deviation between the primary actual charging process data and the primary safety threshold and / or the health status corresponding to the degree of deviation between the secondary actual charging process data and the secondary safety threshold based on the preset correspondence between the deviation degree and the health status.

[0110] It should be noted that steps S45 and S46 are independent of each other. Even if the target new energy device does not experience charging anomalies, its health status can still be assessed. In this embodiment, the actual health level of the target new energy device is determined by the deviation between the primary actual charging process data and the primary safety threshold, as well as the deviation between the secondary actual charging process data and the secondary safety threshold, allowing users to promptly understand the health status of the device.

[0111] In addition, S46 may include: obtaining multiple historical charging orders within a predetermined time period for the target new energy device, obtaining a historical charging process data from each historical charging order, and calculating an average value corresponding to each variable in the historical charging process data as a primary actual average value, calculating a primary reference average value corresponding to each variable in the reference charging process data within a predetermined time period, determining a primary variable deviation between the primary actual average value corresponding to the same variable and a primary safety threshold, determining a primary actual health level corresponding to the primary variable deviation based on a predetermined correspondence between the variable deviation and the health level, and / or calculating secondary historical charging process data corresponding to each variable for characterizing a variable change trend based on the primary historical charging process data, calculating an average value corresponding to each variable in the secondary historical charging process data as a secondary actual average value, calculating a secondary reference average value corresponding to each variable in the secondary reference charging process data within a predetermined time period, determining a secondary variable deviation between the secondary actual average value corresponding to the same variable and a secondary safety threshold, determining a secondary actual health level corresponding to the secondary variable deviation based on a predetermined correspondence between the variable deviation and the health level, and determining the health status of the target new energy device based on the primary actual health level and / or the secondary actual health level.

[0112] S46 may also include: determining the primary actual score data of each variable in the primary actual charging process data based on a primary scoring model corresponding to each variable set in advance, determining the primary actual health level corresponding to the primary actual score data based on a predetermined correspondence between the score data and the health level, and / or determining the secondary actual score data of each variable in the secondary actual charging process data based on a secondary scoring model corresponding to each variable set in advance, and determining the secondary actual health level corresponding to the secondary actual score data based on a predetermined correspondence between the score data and the health level.

[0113] The primary scoring model can be constructed by dividing the data into multiple intervals based on the mean and variance of each variable in the primary reference charging process data, and establishing a corresponding relationship between the degree of deviation and the score data based on the degree of deviation between the actual value of each variable and the critical value of the corresponding interval. The secondary scoring model can be constructed by dividing the data into multiple intervals based on the mean and variance of each variable in the secondary reference charging process data, and establishing a corresponding relationship between the degree of deviation and the score data based on the degree of deviation between the actual value of each variable and the critical value of the corresponding interval. For example, the variable is maximum temperature, which is divided into three levels: good, medium, and poor. The intervals include: (0, μ), (μ, μ+3σ), (μ+3σ, ∞). Assuming μ+3σ is 60 points, (0, μ) is 100 points (good), 60 points < (μ, μ+3σ) < 100 points (medium), and (μ+3σ, ∞) < 60 points (poor). It should be noted that the score divisions corresponding to good, medium, and poor need to be set according to specific needs and variable type.

[0114] The charging safety protection method provided in the embodiment of the present application can promptly provide the user with a prompt of the health status of the current device, thereby improving the user experience and avoiding the serious consequences caused by charging when the health status is poor. In addition, the primary safety threshold and the secondary safety threshold are obtained through a reference charging process data, and the primary reference charging process data is real data. Therefore, compared with the fixed threshold in the prior art, the primary safety threshold and the secondary safety threshold obtained by this technical solution can improve the accuracy of charging anomaly detection. Finally, the secondary reference charging process data can reflect the dynamic development of variables, so the obtained secondary safety threshold can quantify the dynamic development of variables and can identify charging anomalies in a timely manner.

[0115] On the basis of the above embodiment, after S12, the method further includes: sending an alarm signal in a preset alarm manner.

[0116] Among them, sending an alarm signal through a preset alarm method can be sending an identification report in the form of any combination of email, text message, and application software, wherein the identification report includes basic information of the target new energy equipment, the total number of charging orders, the number of abnormal charging orders, identification data in the form of charts, and identification results of the target new energy equipment.

[0117] To facilitate user review, in a specific implementation, the basic information of the target new energy device may include the device brand, device model, and the number of devices of that brand and model in the region. Furthermore, the identification report may include a chronological comparison chart of the total number of charging orders and the number of abnormal charging orders, with abnormal charging orders marked in red and normal charging orders marked in blue. The identification data in the form of a chart may specifically include: a chronological comparison chart of primary reference charging process data and primary actual charging process data, and / or a chronological comparison chart of secondary reference charging process data and secondary actual charging process data. The primary reference charging process data and the primary actual charging process data may include the maximum temperature, minimum temperature, SOC, maximum cell voltage, and minimum cell voltage of the power battery, and the secondary reference charging process data and the secondary actual charging process data may include the maximum temperature difference, maximum voltage difference, maximum temperature rise rate, maximum SOC change rate, and maximum cell voltage change rate of the power battery. The identification result of the target new energy device may be the cause of the target new energy device abnormality determined by the safety assessment model based on the comparison chart.

[0118] Figure 6 This is a flow chart of another charging safety protection method provided in an embodiment of the present application. Figure 6 As shown, based on the above embodiment, after S12, the following steps are further included:

[0119] S50: Sending a protection signal including abnormal data to the user terminal, so that the user terminal can obtain an interference charging event indication according to the protection signal.

[0120] The abnormal data represents the identity data of the new energy device to be charged and the corresponding abnormal data.

[0121] S51: Obtain the high-risk times of the new energy equipment to be charged.

[0122] High-risk charges specifically refer to the number of high-risk charging events during the high-risk charging process after the target new energy device is identified as such. In practice, the high-risk charge count can be expressed as the number of high-risk charging orders, but this is not the only way to obtain the high-risk charge count.

[0123] S52: Determine whether the number of high-risk times exceeds a preset number. If yes, proceed to S54; if not, proceed to S53.

[0124] S53: Sending a first charging interference instruction generated according to the interference charging event indication to the target new energy equipment.

[0125] S54: Sending a second charging interference instruction generated according to the preset charging protection measure to the target new energy equipment.

[0126] In specific implementations, the preset charging protection measures may include prohibiting charging of a target new energy device if it is determined to be high-risk based on the safety assessment model. It should be noted that high-risk protection measures are set according to the security protection requirements of the charging cloud platform. These protection measures may be applied to all high-risk devices, or different levels of protection may be applied based on the hazard level of the high-risk device.

[0127] As a preferred embodiment, it also includes: determining the target charging place, determining the target hazard level corresponding to the target charging place based on a first correspondence between each charging place and each hazard level, determining the target protection measures corresponding to the target hazard level based on a second correspondence between each hazard level and each protection measure, and sending a third charging interference instruction generated according to the target protection measures to the target new energy equipment.

[0128] In this application, the target charging location is the current charging position of the target new energy device. The target charging location can be determined by the target new energy device being charged, or the charging location of the current target new energy device can be determined based on positioning.

[0129] Among them, the danger level can be divided into high-risk key level, key level and general level. The charging equipment near gas stations, gas stations and crowded places (such as national AAAA-level tourist attractions and residential areas) can be set to high-risk key level, the charging equipment near places with relatively dense crowds (such as national AAA-level and below tourist attractions, parks near residential areas) can be set to key level, and the charging equipment near places with sparse population can be set to general level.

[0130] Different danger levels correspond to different protective measures. For example, the protective measures corresponding to the high-risk key level are prohibiting charging of high-risk new energy equipment, the protective measures corresponding to the key level are reducing the charging current to 50% of the original and / or limiting the SOC value to 50% of the original, and the protective measures corresponding to the general level are reducing the charging current to 80% of the original and / or limiting the SOC value to 80% of the original.

[0131] The method for detecting abnormal charging of new energy devices provided in the embodiment of the present application sends a protection signal containing high-risk data to a user terminal, so that the user terminal can obtain an interference charging event indication based on the protection signal and obtain the high-risk number of the target new energy device. When the high-risk number does not exceed the preset number, a first charging interference instruction containing an interference charging event indication is sent to the target new energy device. When the high-risk number exceeds the preset number, a second charging interference instruction containing a preset charging protection measure is sent to the target new energy device. By applying this technical solution, when the high-risk number does not exceed the preset number, charging interference can be performed on high-risk new energy devices based on the interference charging event indication input by the user. This ensures the charging safety of high-risk new energy devices while improving practicality and user experience.

[0132] In the above embodiments, a method for detecting abnormal charging of new energy equipment is described in detail. This application also provides corresponding embodiments of a device for detecting abnormal charging of new energy equipment. It should be noted that this application describes the embodiments of the device from two perspectives: one is based on the functional module perspective, and the other is based on the hardware perspective.

[0133] Figure 7 This is a structural diagram of a new energy device charging anomaly detection device provided in an embodiment of the present application. Figure 7 As shown, based on the perspective of functional modules, the device includes:

[0134] The first establishing module 10 is used to pre-establish a charging anomaly database, which contains reference identity data corresponding to high-risk new energy equipment.

[0135] The first acquisition module 11 is configured to acquire identity data of the new energy device to be charged upon receiving a charging request from the new energy device to be charged.

[0136] The first determining module 12 is configured to determine that the new energy device to be charged is a high-risk device when the identity data matches the charging anomaly database.

[0137] Since the embodiments of the apparatus part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the apparatus part, and they will not be repeated here.

[0138] As a preferred embodiment, the first establishing module 10 includes:

[0139] The second determination module is used to determine that the target new energy equipment is a high-risk new energy equipment according to a preset safety evaluation model.

[0140] The second acquisition module is used to acquire high-risk data, where the high-risk data includes identity data of the target new energy equipment and corresponding abnormal data.

[0141] The second establishing module is used to establish a charging anomaly database based on high-risk data.

[0142] The second building module also includes:

[0143] The first sending module is used to send high-risk data to the operation and maintenance platform so that the operation and maintenance platform can obtain feedback data based on the high-risk data, and send feedback data to the charging cloud platform. The feedback data contains the identity data of the target new energy equipment and an identifier indicating whether the target new energy equipment has been successfully repaired.

[0144] The receiving module is used to receive feedback data.

[0145] The updating module is used to update the charging abnormality database according to the feedback signal.

[0146] The second determination module specifically includes:

[0147] The third determining module is used to determine the type of the target new energy equipment.

[0148] The selection module is used to select multiple new energy equipment sets under a type as analysis objects.

[0149] The third acquisition module is used to obtain a reference charging process data of the analysis object that matches the analysis object within a preset time range, where the reference charging process data is data generated by the analysis object during the charging process.

[0150] The first calculation module is used to calculate the secondary reference charging process data corresponding to each variable and used to characterize the variable change trend based on the primary reference charging process data.

[0151] The second calculation module is used to calculate the secondary actual charging process data corresponding to each variable for characterizing the variable change trend based on the primary actual charging process data of the target new energy device; wherein the primary actual charging process data is the data generated during the current charging process of the target new energy device.

[0152] The fourth determination module is used to determine the first safety threshold corresponding to the primary reference charging process data and / or the second safety threshold corresponding to the secondary reference charging process data based on the correspondence between the primary reference charging process data and / or the secondary reference charging process data and time. The first safety threshold is used as a comparison object for comparison with the primary actual charging process data of the target new energy device and / or the second safety threshold is used as a comparison object for comparison with the secondary actual charging process data of the target new energy device to determine that the target new energy device is a high-risk new energy device.

[0153] The fifth determination module is used to determine the health status corresponding to the degree of deviation between the actual charging process data of the first instance and the primary safety threshold and / or the health status corresponding to the degree of deviation between the actual charging process data of the second instance and the secondary safety threshold based on the correspondence between the preset deviation degree and the health status.

[0154] Also includes:

[0155] The second sending module is used to send an alarm signal in a preset alarm manner.

[0156] Also includes:

[0157] The third sending module is used to send a protection signal containing abnormal data to the user terminal, where the abnormal data represents the identity data of the new energy device to be charged and the corresponding abnormal data, so that the user terminal can obtain an interference charging event indication according to the protection signal.

[0158] The fourth acquisition module is used to obtain the high-risk number of the new energy equipment to be charged.

[0159] The fourth sending module is used to send a first charging interference instruction to the new energy device to be charged when it is determined that the high-risk number does not exceed the preset number, and the first charging interference instruction is generated according to the interference charging event indication.

[0160] The fifth sending module is used to send a second charging interference instruction to the new energy equipment to be charged when it is determined that the high-risk number exceeds a preset number, and the second charging interference instruction is generated according to the preset charging protection measures.

[0161] The device for detecting anomalies in charging of new energy equipment provided in the embodiment of the present application pre-establishes a charging anomaly database. When a charging request is received from a new energy device to be charged, the device obtains the identity data of the new energy device to be charged, and when it is determined that the identity data matches the charging anomaly database, the device to be charged is determined to be a high-risk device, wherein the charging anomaly database contains reference identity data corresponding to high-risk new energy devices. Applied to this solution, when a charging request is received from a new energy device to be charged, the device can be detected to determine whether the charging is abnormal by obtaining the identity data once, thereby reducing the workload of detecting abnormal charging of new energy devices. At the same time, there is no need to detect abnormal devices after waiting for the device to be charged, thereby improving the efficiency of detecting abnormal charging of energy devices.

[0162] Figure 8 This is a structural diagram of another new energy equipment charging anomaly detection device provided in an embodiment of the present application. Figure 8 As shown, based on the hardware structure, the device includes:

[0163] Memory 20, for storing computer programs;

[0164] The processor 21 is configured to implement the steps of the method for detecting abnormal charging of new energy equipment in the above embodiment when executing a computer program.

[0165] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0166] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory, and non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201, wherein, after the computer program 201 is loaded and executed by the processor 21, it can implement the relevant steps of the new energy device charging anomaly detection method disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include but is not limited to data involved in the new energy device charging anomaly detection method, etc.

[0167] In some embodiments, the new energy equipment charging anomaly detection device may further include a display screen 22 , an input / output interface 23 , a communication interface 24 , a power supply 25 , and a communication bus 26 .

[0168] Those skilled in the art will understand that Figure 8The structure shown in the figure does not constitute a limitation on the device for detecting abnormal charging of new energy equipment, and may include more or fewer components than shown in the figure.

[0169] The embodiment of the present application provides a device for detecting anomalies in charging of new energy equipment, including a memory and a processor. When the processor executes a program stored in the memory, it can implement the following method: pre-establish a charging anomaly database, obtain the identity data of the new energy equipment to be charged when receiving a charging request from the new energy equipment to be charged, and determine that the new energy equipment to be charged is a high-risk device when the identity data matches the charging anomaly database, wherein the charging anomaly database contains reference identity data corresponding to the high-risk new energy equipment. Applied to this solution, when receiving a charging request from the new energy equipment to be charged, it can detect whether the device is charging abnormally by obtaining the identity data once, thereby reducing the workload of detecting abnormal charging of new energy equipment. At the same time, there is no need to detect abnormal equipment after waiting for the device to be charged, thereby improving the efficiency of detecting abnormal charging of energy equipment.

[0170] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiment.

[0171] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium and executes all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0172] The computer-readable storage medium provided in the embodiment of the present application has a computer program stored thereon. When the computer program is executed by a processor, the following method can be implemented: a charging anomaly database is pre-established, and upon receiving a charging request from a new energy device to be charged, the identity data of the new energy device to be charged is obtained, and upon determining that the identity data matches the charging anomaly database, the new energy device to be charged is determined to be a high-risk device, wherein the charging anomaly database contains reference identity data corresponding to the high-risk new energy device. Applied to this solution, upon receiving a charging request from a new energy device to be charged, it is possible to detect whether the device is charging abnormally by obtaining the identity data once, thereby reducing the workload of detecting charging anomalies of new energy devices. At the same time, there is no need to detect abnormal devices after waiting for the device to be charged, thereby improving the efficiency of detecting charging anomalies of energy devices.

[0173] The above is a detailed introduction to a method, device and medium for detecting abnormal charging of new energy equipment provided by the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of this application.

[0174] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A method for detecting abnormal charging of new energy equipment, characterized in that: include: Pre-establishing a charging anomaly database, wherein the charging anomaly database contains reference identity data corresponding to high-risk new energy equipment; Upon receiving a charging request from a new energy device to be charged, obtaining identity data of the new energy device to be charged; When it is determined that the identity data matches the charging anomaly database, determining that the new energy device to be charged is a high-risk device; After determining that the new energy device to be charged is a high-risk device, the method further includes: Sending a protection signal including abnormal data to a user terminal, wherein the abnormal data represents identity data of the new energy device to be charged and the corresponding abnormal data, so that the user terminal obtains an indication of an interference charging event according to the protection signal; Obtaining the high-risk times of the new energy equipment to be charged; When it is determined that the high-risk number does not exceed the preset number, a first charging interference instruction is sent to the new energy device to be charged, where the first charging interference instruction is generated according to the interference charging event indication; When it is determined that the high-risk number exceeds the preset number, a second charging intervention instruction is sent to the new energy device to be charged, where the second charging intervention instruction is generated according to a preset charging protection measure; The high-risk times refers to the number of high-risk charging times of the target new energy equipment during the high-risk charging process after the target new energy equipment is determined to be a high-risk new energy equipment; The preset charging protection measures include protection measures of different levels according to the danger level of high-risk equipment; The process of determining the preset charging protection measures includes: Determine the target charging location; determine the target hazard level corresponding to the target charging location based on a first correspondence between each charging location and each hazard level; and determine the target protective measure corresponding to the target hazard level based on a second correspondence between each hazard level and each protective measure.

2. The method for detecting abnormal charging of new energy equipment according to claim 1, characterized in that: The pre-establishment of the charging anomaly database specifically includes: Determine the target new energy equipment as high-risk new energy equipment based on the preset safety assessment model; Acquire high-risk data, where the high-risk data includes identity data of the target new energy device and corresponding abnormal data; The charging abnormality database is established according to the high-risk data.

3. The method for detecting abnormal charging of new energy equipment according to claim 2, characterized in that: After establishing the charging anomaly database according to the high-risk data, the method further includes: Sending high-risk data to the operation and maintenance platform so that the operation and maintenance platform can obtain feedback data based on the high-risk data, and sending the feedback data to the charging cloud platform, wherein the feedback data contains the identity data of the target new energy device and an identifier indicating whether the target new energy device has been successfully repaired; receiving the feedback data; The charging abnormality database is updated according to the feedback data.

4. The method for detecting abnormal charging of new energy equipment according to claim 3, characterized in that: Also includes: When the target new energy equipment is successfully repaired, the charging abnormality database is updated.

5. The method for detecting abnormal charging of new energy equipment according to claim 2, characterized in that: The security evaluation model is specifically a big data security evaluation model; Then, the target new energy equipment is determined to be a high-risk new energy equipment according to the big data security evaluation model, specifically: Determining the type of the target new energy equipment; Select multiple new energy equipment sets under the type as analysis objects; Acquire a reference charging process data of the analysis object within a preset time range and matching the analysis object, wherein the reference charging process data is data generated by the analysis object during the charging process; Calculating secondary reference charging process data corresponding to each variable and used to characterize a variable change trend based on the primary reference charging process data; Calculating secondary actual charging process data corresponding to each variable for characterizing a variable change trend based on the primary actual charging process data of the target new energy device; wherein the primary actual charging process data is data generated during the current charging process of the target new energy device; Based on the correspondence between the primary reference charging process data and / or the secondary reference charging process data and time, determining a first safety threshold corresponding to the primary reference charging process data and / or a second safety threshold corresponding to the secondary reference charging process data, using the first safety threshold as a comparison object to compare with the primary actual charging process data of the target new energy device and / or using the second safety threshold as a comparison object to compare with the secondary actual charging process data of the target new energy device, so as to determine that the target new energy device is a high-risk new energy device; According to the preset correspondence between the degree of deviation and the health status, the health status corresponding to the degree of deviation between the actual primary charging process data and the first safety threshold and / or the health status corresponding to the degree of deviation between the actual secondary charging process data and the second safety threshold are determined.

6. The method for detecting abnormal charging of new energy equipment according to claim 1, characterized in that: After determining that the new energy device to be charged is a high-risk device, the method further includes: sending an alarm signal in a preset alarm manner.

7. The method for detecting abnormal charging of new energy equipment according to claim 6, characterized in that: The sending of the alarm signal by the preset alarm method is specifically: Send identification reports via any combination of email, SMS, and application software; The identification report includes basic information of the target new energy equipment, the total number of charging orders, the number of abnormal charging orders, identification data in the form of charts, and identification results of the target new energy equipment.

8. A device for detecting abnormal charging of new energy equipment, characterized in that: include: A first establishing module is used to pre-establish a charging anomaly database, wherein the charging anomaly database contains reference identity data corresponding to high-risk new energy equipment; A first acquisition module is configured to acquire identity data of the new energy device to be charged upon receiving a charging request from the new energy device to be charged; a first determining module, configured to determine that the new energy device to be charged is a high-risk device when it is determined that the identity data matches the charging anomaly database; Also includes: a third sending module, configured to, after determining that the new energy device to be charged is a high-risk device, send a protection signal containing abnormal data to a user terminal, wherein the abnormal data represents the identity data of the new energy device to be charged and the corresponding abnormal data, so that the user terminal can obtain an interference charging event indication according to the protection signal; A fourth acquisition module is used to obtain the high-risk number of the new energy equipment to be charged; a fourth sending module, configured to send a first charging interference instruction to the new energy device to be charged when it is determined that the high-risk number does not exceed a preset number, wherein the first charging interference instruction is generated according to the interference charging event indication; a fifth sending module, configured to send a second charging interference instruction to the new energy device to be charged when it is determined that the high-risk number exceeds the preset number, wherein the second charging interference instruction is generated according to a preset charging protection measure; The high-risk times refers to the number of high-risk charging times of the target new energy equipment during the high-risk charging process after the target new energy equipment is determined to be a high-risk new energy equipment; The preset charging protection measures include protection measures of different levels according to the danger level of high-risk equipment; The process of determining the preset charging protection measures includes: Determine the target charging location; determine the target hazard level corresponding to the target charging location based on a first correspondence between each charging location and each hazard level; and determine the target protective measure corresponding to the target hazard level based on a second correspondence between each hazard level and each protective measure.

9. A device for detecting abnormal charging of new energy equipment, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the method for detecting abnormal charging of new energy equipment as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for detecting abnormal charging of new energy equipment according to any one of claims 1 to 7 are implemented.

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