Power compensation control method and device, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a power compensation control method and device, electronic equipment, a storage medium and a program product. Relates to the technical field of computers. The method comprises the following steps: receiving vehicle data reported by a target vehicle terminal; wherein the vehicle data comprises vehicle condition data and position information of the target vehicle terminal. And determining an actual temperature value of the region where the target vehicle terminal is located according to the position information. According to a preset power supply prediction model, performing power supply prediction processing on the vehicle condition data and the actual temperature value to generate prediction result information; wherein the prediction result information represents whether the target vehicle terminal meets the preset charging rule information or not. If it is determined that the prediction result information represents that the power supply rule information is satisfied, generating a power supply request, and issuing the power supply request to the target vehicle terminal; wherein the charging request is used for indicating the charging operation. The method is used for achieving the effect of improving the accuracy of the starting charging time of the vehicle terminal.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a power replenishment control method, device, electronic device, storage medium and program product. Background Art

[0002] At present, with the improvement of people's quality of life, more and more vehicles are purchased. For vehicles that need to be charged, users are required to charge them in time to ensure the health and service life of the battery.

[0003] In the prior art, the current charging method is based on the voltage threshold of the battery, and charging is performed when the voltage is lower than the threshold.

[0004] However, in the prior art, the existing charging method is based on a single judgment strategy for charging, which can solve the problem of low battery power in new energy vehicles to a certain extent, but there are many problems. For example, based on various external factors, it will affect the battery charging judgment, which will lead to inaccurate starting charging time of the vehicle terminal, which may cause battery damage, etc. Summary of the invention

[0005] The embodiments of the present application provide a charging control method, device, electronic device, storage medium and program product to achieve the effect of improving the accuracy of the starting charging time of a vehicle terminal.

[0006] In a first aspect, an embodiment of the present application provides a power replenishment control method, including:

[0007] Receiving vehicle data reported by a target vehicle terminal; wherein the vehicle data includes vehicle condition data and location information of the target vehicle terminal;

[0008] Determining the actual temperature value of the area where the target vehicle terminal is located according to the location information;

[0009] According to a preset power replenishment prediction model, the vehicle condition data and the actual temperature value are processed for power replenishment prediction to generate prediction result information; wherein the prediction result information indicates whether the target vehicle terminal meets the preset power replenishment rule information;

[0010] If it is determined that the prediction result information indicates that the charging rule information is satisfied, a charging request is generated and sent to the target vehicle terminal; wherein the charging request is used to indicate a charging operation.

[0011] In a possible implementation manner, the vehicle condition data includes fault information, battery information, power battery information, and driving data.

[0012] In a possible implementation, performing power replenishment prediction processing on the vehicle condition data and the actual temperature value according to a preset power replenishment prediction model to generate prediction result information includes:

[0013] According to the vehicle condition data and the actual temperature value, a preset power replenishment prediction model is updated; wherein the power replenishment prediction model is trained based on the basic data of the target vehicle terminal and the vehicle condition data generated from the first startup to the current startup;

[0014] According to the updated supplementary power prediction model, supplementary power prediction processing is performed on the vehicle condition data and the actual temperature value to generate prediction result information.

[0015] In a possible implementation manner, the power replenishment prediction model includes a starting voltage prediction model and a power battery health prediction model; the vehicle condition data includes fault information, battery information, power battery information, and basic data;

[0016] According to the updated power replenishment prediction model, the vehicle condition data and the actual temperature value are processed for power replenishment prediction to generate prediction result information, including:

[0017] According to the updated starting voltage prediction model, the battery information and the actual temperature value are subjected to a power replenishment prediction process to generate voltage prediction result information in the prediction result information; wherein the voltage prediction result information indicates whether the actual voltage of the target vehicle terminal is greater than a preset starting voltage threshold value;

[0018] According to the updated power battery health prediction model, the power battery information is processed for power replenishment prediction to generate battery health prediction result information in the prediction result information; wherein, the battery health prediction result information represents whether the health value of the power battery of the target vehicle terminal is greater than a preset battery health threshold.

[0019] In a possible implementation manner, if it is determined that the prediction result information represents that the power replenishment rule information is satisfied, generating a power replenishment request includes:

[0020] If it is determined that the voltage prediction result information, the battery health prediction result information, and the fault information all represent a normal state, a power replenishment request is generated; wherein the prediction result information includes the voltage prediction result information and the battery health prediction result information.

[0021] In a possible implementation, the method further includes:

[0022] If it is determined that any one of the voltage prediction result information, the battery health prediction result information, and the fault information represents a dangerous state, a prompt message is generated and sent to the target vehicle terminal.

[0023] In a second aspect, an embodiment of the present application provides a power replenishment control device, including:

[0024] A receiving module, used to receive vehicle data reported by a target vehicle terminal; wherein the vehicle data includes vehicle condition data and location information of the target vehicle terminal;

[0025] A determination module, used to determine the actual temperature value of the area where the target vehicle terminal is located according to the location information;

[0026] A prediction module, configured to perform a power replenishment prediction process on the vehicle condition data and the actual temperature value according to a preset power replenishment prediction model, and generate prediction result information; wherein the prediction result information indicates whether the target vehicle terminal satisfies the preset power replenishment rule information;

[0027] A generating module, configured to generate a power replenishment request if it is determined that the prediction result information represents that the power replenishment rule information is satisfied;

[0028] A sending module is used to send the power replenishment request to the target vehicle terminal; wherein the power replenishment request is used to indicate a charging operation.

[0029] In a possible implementation manner, the vehicle condition data includes fault information, battery information, power battery information, and driving data.

[0030] In a possible implementation, the prediction module includes:

[0031] An updating unit, configured to update a preset power replenishment prediction model according to the vehicle condition data and the actual temperature value; wherein the power replenishment prediction model is obtained by training based on basic data of the target vehicle terminal and vehicle condition data generated from the first startup to the current startup;

[0032] The prediction unit is used to perform a power replenishment prediction process on the vehicle condition data and the actual temperature value according to the updated power replenishment prediction model to generate prediction result information.

[0033] In a possible implementation manner, the power replenishment prediction model includes a starting voltage prediction model and a power battery health prediction model; the vehicle condition data includes fault information, battery information, power battery information, and basic data;

[0034] The prediction unit is specifically used for:

[0035] According to the updated starting voltage prediction model, the battery information and the actual temperature value are subjected to a power replenishment prediction process to generate voltage prediction result information in the prediction result information; wherein the voltage prediction result information indicates whether the actual voltage of the target vehicle terminal is greater than a preset starting voltage threshold value;

[0036] According to the updated power battery health prediction model, the power battery information is processed for power replenishment prediction to generate battery health prediction result information in the prediction result information; wherein, the battery health prediction result information represents whether the health value of the power battery of the target vehicle terminal is greater than a preset battery health threshold.

[0037] In a possible implementation manner, the generating module is specifically used to:

[0038] If it is determined that the voltage prediction result information, the battery health prediction result information, and the fault information all represent a normal state, a power replenishment request is generated; wherein the prediction result information includes the voltage prediction result information and the battery health prediction result information.

[0039] In a possible implementation manner, the device is further specifically used for:

[0040] If it is determined that any one of the voltage prediction result information, the battery health prediction result information, and the fault information represents a dangerous state, a prompt message is generated and sent to the target vehicle terminal.

[0041] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0042] The memory stores computer-executable instructions;

[0043] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0044] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0045] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0046] The charging control method, device, electronic device, storage medium and program product provided in the embodiments of the present application receive vehicle data reported by the target vehicle terminal; wherein the vehicle data includes the vehicle condition data and location information of the target vehicle terminal. According to the location information, the actual temperature value of the area where the target vehicle terminal is located is determined. According to the preset charging prediction model, the vehicle condition data and the actual temperature value are processed for charging prediction to generate prediction result information; wherein the prediction result information represents whether the target vehicle terminal satisfies the preset charging rule information. If it is determined that the prediction result information represents that the charging rule information is satisfied, a charging request is generated and sent to the target vehicle terminal; wherein the charging request is used to indicate the charging operation. In this scheme, parameters such as location information, local weather conditions at the time, and vehicle condition data are obtained as input to establish a big data analysis model, namely a charging prediction model, so as to more accurately output whether the battery needs to be charged and how long the charging should be done, so as to maximize the benefits. Therefore, taking into account the impact of multiple factors such as the vehicle's geographical location (temperature factors) on battery charging, the charging prediction model is based on big data statistics, and is obtained through multi-factor fusion analysis and training in the input data. It has strong computing power, is easy to implement, and is more accurate and instructive. As the input data continues to accumulate, the estimation results will become more and more accurate, thereby achieving the effect of improving the accuracy of the vehicle terminal's start charging time. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0048] Figure 1 A schematic diagram of a power replenishment control method provided in an embodiment of the present application Figure 1 ;

[0049] Figure 2 A system architecture diagram of a power replenishment control method provided in an embodiment of the present application;

[0050] Figure 3 A schematic diagram of another power replenishment control method provided in an embodiment of the present application Figure 2 ;

[0051] Figure 4 A schematic diagram of the structure of a power replenishment control device provided in an embodiment of the present application;

[0052] Figure 5 A schematic diagram of the structure of another power replenishment control device provided in an embodiment of the present application;

[0053] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0054] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0055] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0056] At present, with the improvement of people's quality of life, more and more vehicles are purchased. For vehicles that need to be charged, users are required to charge them in time to ensure the health and service life of the battery.

[0057] In one example, the current charging method is based on the voltage threshold of the battery, and charging is performed when the voltage is below the threshold. However, in the prior art, the existing charging method is based on a single judgment strategy to perform charging, which can solve the problem of low battery power in new energy vehicles to a certain extent, but there are many problems. For example, various external factors will affect the judgment of battery charging, which will lead to inaccurate start time of charging at the vehicle terminal, which may cause battery damage, etc.

[0058] In combination with the above scenario, it can be seen that in the prior art, there is a technical problem that the starting time of charging the vehicle terminal is inaccurate.

[0059] The charging control method provided in the present application obtains parameters such as location information, local weather conditions at that time, and vehicle condition data as input, and establishes a big data analysis model, namely, a charging prediction model, and then more accurately outputs whether the battery needs to be charged and how long the charging should take, thereby solving the technical problem of low accuracy of the starting charging time of the vehicle terminal.

[0060] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0061] Figure 1 A schematic diagram of a power supply control method provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0062] S101. Receive vehicle data reported by a target vehicle terminal; wherein the vehicle data includes vehicle condition data and location information of the target vehicle terminal.

[0063] For example, the execution subject of this embodiment may be an electronic device, or a terminal device, or a power supply control device or device, or other devices or equipment that can execute this embodiment, and there is no limitation on this. In this embodiment, the execution subject is introduced as an electronic device.

[0064] First, the electronic device interacts with the vehicle terminal. The Internet of Vehicles platform is deployed in the electronic device. The electronic device can be a vehicle controller or a vehicle control system, etc. There are no restrictions on this. There are two types of charging methods set by the vehicle terminal, including timed charging and intelligent charging. Figure 2 A system architecture diagram of a power replenishment control method provided in an embodiment of the present application, such as Figure 2 As shown, it includes: Internet of Vehicles platform (referred to as platform), vehicle-mounted intelligent terminal T-Box, vehicle controller VCU, battery management system BMS, and other circuit components. T-Box is responsible for real-time data collection of vehicles and the initiator of charging events. VCU is responsible for judging whether the current vehicle meets the charging conditions of the scheduled charging mode and the execution of charging. The charging conditions are the preset charging cycle T; the platform is used for data storage and data model training. The scheduled charging mode is awakened by the fixed cycle T of T-Box; the intelligent charging mode is that T-Box collects vehicle location information, driving data, battery information, power battery information and other vehicle condition information in real time, and periodically uploads it to the Internet of Vehicles platform, and the Internet of Vehicles platform determines whether charging is needed.

[0065] In this step, the electronic device receives the vehicle data reported by the target vehicle terminal through the Internet of Vehicles data platform. The vehicle data includes the vehicle condition data and location information of the target vehicle terminal; the vehicle condition data includes fault information, battery information, power battery information, driving data, and longitude and latitude data; the driving data includes whether to drive, speed, controller status, etc.; the battery information of the vehicle battery SC includes the cumulative number of charging times C sc 、Discharge times D sc , Use time T sc , battery voltage, etc.; the power battery information of the vehicle power battery PB includes the cumulative number of charging times C pb 、Discharge times D pb , Use time T pb .

[0066] S102. Determine the actual temperature value of the area where the target vehicle terminal is located according to the location information.

[0067] For example, the actual temperature value t of the area where the target vehicle terminal is located is determined according to the location information. Alternatively, the temperature data is collected by the onboard sensor of the target vehicle terminal, and the collected temperature data is uploaded to the Internet of Vehicles platform, so that the data accuracy and real-time performance are better.

[0068] S103. Perform power replenishment prediction processing on the vehicle condition data and the actual temperature value according to the preset power replenishment prediction model to generate prediction result information; wherein the prediction result information indicates whether the target vehicle terminal satisfies the preset power replenishment rule information.

[0069] Exemplarily, the preset power replenishment prediction model includes a starting voltage prediction model and a power battery health prediction model. According to the starting voltage prediction model, the battery information and the actual temperature value are processed for power replenishment prediction, and the voltage prediction result information in the prediction result information is generated. The voltage prediction result information indicates whether the actual voltage of the target vehicle terminal is greater than the preset starting voltage threshold. And according to the power battery health prediction model, the power battery information is processed for power replenishment prediction, and the battery health prediction result information in the prediction result information is generated. The battery health prediction result information indicates whether the health value of the power battery of the target vehicle terminal is greater than the preset battery health threshold. Furthermore, the voltage prediction result information may include the power replenishment duration, etc.

[0070] S104: If it is determined that the prediction result information indicates that the charging rule information is satisfied, a charging request is generated and sent to the target vehicle terminal; wherein the charging request is used to indicate a charging operation.

[0071] Exemplarily, the prediction result information indicates whether the target vehicle terminal meets the preset power replenishment rule information. If it is determined that the voltage prediction result information, the battery health prediction result information, and the fault information all represent a normal state, it means that the target vehicle terminal meets the preset power replenishment rule information, a power replenishment request is generated, and the power replenishment request is sent to the target vehicle terminal. When the target vehicle terminal receives the power replenishment request, the charging operation can be performed. Alternatively, if it is determined that any one of the voltage prediction result information, the battery health prediction result information, and the fault information represents a dangerous state, it means that the target vehicle terminal does not meet the preset power replenishment rule information and cannot be charged. A prompt message is generated and sent to the target vehicle terminal. When the target vehicle terminal receives the prompt message, maintenance and other operations can be performed.

[0072] Therefore, this application recharges the vehicle by utilizing the Internet of Vehicles to obtain the local temperature of the vehicle at that time and combining it with timed wake-up, real-time monitoring and other methods. It continuously monitors the battery voltage when the vehicle is in an abnormal wake-up condition, and recharges the battery when the voltage is lower than the threshold to avoid battery depletion and damage.

[0073] The power replenishment control method provided in the embodiment of the present application receives vehicle data reported by the target vehicle terminal; wherein the vehicle data includes the vehicle condition data and location information of the target vehicle terminal. According to the location information, the actual temperature value of the area where the target vehicle terminal is located is determined. According to the preset power replenishment prediction model, the vehicle condition data and the actual temperature value are processed for power replenishment prediction to generate prediction result information; wherein the prediction result information represents whether the target vehicle terminal satisfies the preset power replenishment rule information. If it is determined that the prediction result information represents that the power replenishment rule information is satisfied, a power replenishment request is generated and sent to the target vehicle terminal; wherein the power replenishment request is used to indicate the charging operation. In this scheme, parameters such as location information, local weather conditions at the time, and vehicle condition data are obtained as input to establish a big data analysis model, namely a power replenishment prediction model, so as to more accurately output whether the battery needs to be recharged and how long the recharge should be done, so as to maximize benefits. Therefore, considering the impact of multiple factors such as the geographical location of the vehicle (temperature factors) on battery charging, the charging prediction model is based on big data statistics, and is obtained through fusion analysis and training of multiple factors in the input data. It has strong computing power, is easy to implement, and is more accurate and instructive. As the input data continues to accumulate, the estimation results will become more and more accurate, so as to achieve the effect of improving the accuracy of the start charging time of the vehicle terminal. Taking into account the impact of multiple factors such as the geographical location of the vehicle terminal (temperature factors), the life of the vehicle or battery (number of times it has been charged and discharged, and health status) on battery charging.

[0074] Figure 3 A schematic diagram of a power supply control method provided in this application Figure 2 ,like Figure 3 As shown, in this embodiment Figure 1 Based on the embodiment, the power supplement control method is described in detail, and the method includes:

[0075] S201. Receive vehicle data reported by a target vehicle terminal; wherein the vehicle data includes vehicle condition data and location information of the target vehicle terminal.

[0076] In one example, the vehicle condition data includes fault information, battery information, power battery information, and driving data.

[0077] For example, this step can be referred to Figure 1 Step 101 in the above is not described in detail.

[0078] S202: Determine the actual temperature value of the area where the target vehicle terminal is located according to the location information.

[0079] Exemplarily, the electronic device may determine the actual temperature value t of the area where the target vehicle terminal is located based on the location information.

[0080] S203. Update a preset power replenishment prediction model according to the vehicle condition data and the actual temperature value; wherein the power replenishment prediction model is trained based on basic data of the target vehicle terminal and vehicle condition data generated from the first startup to the current startup.

[0081] Exemplarily, static basic data is established for each vehicle when it leaves the factory. After the vehicle terminal is started for the first time, the electronic device will associate the vehicle terminal according to the basic data, and conduct modeling training in combination with the dynamic vehicle condition data generated by the vehicle terminal from the first start-up to obtain a power replenishment prediction model. And at each subsequent start-up, the generated dynamic vehicle condition data is obtained for modeling training to obtain an updated power replenishment prediction model. Among them, the basic data includes national standard data and enterprise standard data, for example, the basic data includes the model of the vehicle terminal, etc., which is not limited to this.

[0082] In this step, the preset power replenishment prediction model is updated again according to the currently acquired vehicle condition data and actual temperature value to obtain an updated power replenishment prediction model.

[0083] S204: Perform power replenishment prediction processing on the vehicle condition data and the actual temperature value according to the updated power replenishment prediction model to generate prediction result information.

[0084] In one example, the power replenishment prediction model includes a starting voltage prediction model and a power battery health prediction model; the vehicle condition data includes fault information, battery information, power battery information, and basic data; S204 includes: according to the updated starting voltage prediction model, the battery information and the actual temperature value are processed for power replenishment prediction, and the voltage prediction result information in the prediction result information is generated; wherein, the voltage prediction result information represents whether the actual voltage of the target vehicle terminal is greater than the preset starting voltage threshold; according to the updated power battery health prediction model, the power battery information is processed for power replenishment prediction, and the battery health prediction result information in the prediction result information is generated; wherein, the battery health prediction result information represents whether the health value of the power battery of the target vehicle terminal is greater than the preset battery health threshold.

[0085] Exemplarily, the power replenishment prediction model includes a starting voltage prediction model and a power battery health prediction model. In the intelligent power replenishment mode, based on the vehicle condition data reported in real time each time, first determine whether the battery voltage is greater than or equal to the preset Vstart based on the starting voltage prediction model. Then, determine whether the vehicle terminal has fault information related to battery replenishment within the preset time period. For example, the preset time period is 24 hours, and there is no limit on this. The vehicle fault is divided into 3 levels. The fault code information (spn+fmi) in the reported fault information can be used to distinguish which controller has a fault. Finally, based on the power battery health prediction model, determine whether the power battery is healthy, and combine the three to determine whether to issue a power replenishment request.

[0086] Specifically, the starting voltage prediction model is obtained by combining the starting voltage prediction model 1 and the starting voltage prediction model 2. The starting voltage prediction model 1 is the relationship between the minimum starting voltage Vstart of the battery (the vehicle cannot start normally if the voltage is lower than this value) and the ambient temperature, that is, Vstart = f(t). The starting voltage prediction model 2 is the relationship between the minimum starting voltage Vstart of the battery and the use time T sc , cumulative charging times C sc 、Discharge times D sc The relationship is Vstart = F(T sc , C sc , D sc ). Combining the above two models, the startup voltage prediction model is Vstart=F(T sc , C sc , D sc ))*f(t).

[0087] The electronic device can use the updated startup voltage prediction model, that is, Vstart = F(T sc , C sc , D sc ))*f(t), perform power replenishment prediction processing on the battery information and the actual temperature value, and generate voltage prediction result information in the prediction result information. The voltage prediction result information indicates whether the actual voltage Vstart of the target vehicle terminal is greater than or equal to the preset starting voltage threshold; if the actual voltage Vstart is greater than or equal to the preset starting voltage threshold, it means that the vehicle terminal can be started; if the actual voltage Vstart is less than the preset starting voltage threshold, it means that the vehicle terminal cannot be started.

[0088] The power battery health prediction model is a combination of the power battery health status and usage time T pb , cumulative charging times C pb 、Discharge times D pb The relationship is Vpb = G(T pb , C pb , D pb ). According to the updated power battery health prediction model, Vpb = G(T pb , C pb , D pb ), performs replenishment prediction processing on the power battery information, and generates battery health prediction result information in the prediction result information. The battery health prediction result information indicates whether the health value of the power battery of the target vehicle terminal is greater than or equal to the preset battery health threshold; if the health value is greater than or equal to the preset battery health threshold, it means that the power battery is in a healthy state; if the health value is less than the preset battery health threshold, it means that the power battery is in a dangerous state.

[0089] Furthermore, the actual execution of each charging is reported to the platform again, and the big data model (i.e., the starting voltage prediction model and the power battery health prediction model) is corrected. A complete intelligent charging record should have two records, one for the start of charging and the other for the end of charging. Charging start record: The record type is 0, including the start time of charging, the start soc of charging, and the start of charging battery voltage information. The end of charging information defaults to zero value, and the server does not parse it. Directly discard the end of charging record: The record type is 1, including the start time of charging, the start soc of charging, the start of charging battery voltage, the end time of charging, the end of charging soc, the end of charging battery voltage, and the charging duration information, as shown in Table 1 below:

[0090] Table 1

[0091]

[0092] It should be noted that the power battery health assessment method is as follows:

[0093] 1) Battery capacity decay rate evaluation method: The health status of the battery is evaluated by measuring the difference between the actual capacity and the designed capacity of the battery. The higher the capacity decay rate, the worse the health status of the battery. 2) Battery internal resistance evaluation method: The increase in battery internal resistance will lead to increased energy loss, which will affect the battery's range and power output capacity. The larger the internal resistance, the worse the health status of the battery. 3) Battery open circuit voltage evaluation method: The open circuit voltage is related to the remaining capacity and internal resistance of the battery. The health status of the battery can be evaluated by measuring the change in open circuit voltage. 4) Battery temperature evaluation method: If the battery operates at too high or too low a temperature, it will lead to performance degradation and shortened life. The health status of the battery is evaluated by monitoring the temperature change of the battery. 5) Cycle life evaluation method: The life and health status of the battery are evaluated through charge and discharge cycle tests. The longer the cycle life, the better the health status of the battery.

[0094] Factors that affect the health of power batteries include:

[0095] 1) Temperature: The ideal operating temperature range of power batteries is between 0℃ and 40℃. Too low or too high temperature will affect the performance and life of the battery. 2) Internal resistance: Chemical changes inside the battery will cause the internal resistance to gradually increase, consume more electricity, and thus affect the health of the battery. 3) Service life: As the use time increases, the active substances inside the battery will be depleted, resulting in a decrease in capacity, which will affect the health of the battery.

[0096] S205: If it is determined that the prediction result information indicates that the power replenishment rule information is satisfied, a power replenishment request is generated.

[0097] In one example, S205 includes: if it is determined that the voltage prediction result information, the battery health prediction result information, and the fault information all represent a normal state, then generating a power replenishment request; wherein the prediction result information includes the voltage prediction result information and the battery health prediction result information.

[0098] Exemplarily, if the electronic device determines that the voltage prediction result information, the battery health prediction result information, and the fault information all represent a normal state, it means that the target vehicle terminal meets the preset power replenishment rule information, generates a power replenishment request, and sends the power replenishment request to the target vehicle terminal.

[0099] S206: Send a charging request to the target vehicle terminal; wherein the charging request is used to indicate a charging operation.

[0100] Exemplarily, the electronic device may send a power replenishment request to the target vehicle terminal, and the target vehicle terminal may perform a charging operation upon receiving the power replenishment request.

[0101] S207: If it is determined that any one of the voltage prediction result information, the battery health prediction result information, and the fault information represents a dangerous state, a prompt message is generated and sent to the target vehicle terminal.

[0102] Exemplarily, if the electronic device determines that any one of the voltage prediction result information, the battery health prediction result information, and the fault information represents a dangerous state, it means that the target vehicle terminal does not meet the preset power replenishment rule information and cannot be charged. A prompt message is generated and sent to the target vehicle terminal. When the target vehicle terminal receives the prompt message, it can perform maintenance and other operations.

[0103] The power replenishment control method provided in the embodiment of the present application receives vehicle data reported by the target vehicle terminal; wherein the vehicle data includes the vehicle condition data and location information of the target vehicle terminal. According to the location information, the actual temperature value of the area where the target vehicle terminal is located is determined. According to the vehicle condition data and the actual temperature value, the preset power replenishment prediction model is updated; wherein the power replenishment prediction model is trained based on the basic data of the target vehicle terminal and the vehicle condition data generated from the first to the current startup. According to the updated power replenishment prediction model, the vehicle condition data and the actual temperature value are processed for power replenishment prediction to generate prediction result information. If it is determined that the prediction result information represents that the power replenishment rule information is satisfied, a power replenishment request is generated. The power replenishment request is sent to the target vehicle terminal; wherein the power replenishment request is used to indicate the charging operation. If it is determined that any one of the voltage prediction result information, the battery health prediction result information, and the fault information represents a dangerous state, a prompt message is generated and the prompt message is sent to the target vehicle terminal. Therefore, taking into account the impact of multiple factors on battery charging, such as the geographical location of the vehicle terminal (temperature factors), the life of the vehicle or battery (number of charge and discharge times, health status), the charging prediction model is based on big data statistics, and is obtained through multi-factor fusion analysis and training in the input data. It has strong computing power, is easy to implement, and is more accurate and instructive. As the input data continues to accumulate, the estimation results will become more and more accurate, thereby achieving the effect of improving the accuracy of the start time of charging for the vehicle terminal.

[0104] Figure 4 A schematic diagram of the structure of a power supply control device provided in this application is shown in FIG. Figure 4 As shown, the power supplement control device 30 provided in this embodiment includes:

[0105] The receiving module 31 is used to receive the vehicle data reported by the target vehicle terminal; wherein the vehicle data includes the vehicle condition data and location information of the target vehicle terminal;

[0106] A determination module 32, for determining an actual temperature value of the area where the target vehicle terminal is located according to the location information;

[0107] The prediction module 33 is used to perform a power replenishment prediction process on the vehicle condition data and the actual temperature value according to a preset power replenishment prediction model, and generate prediction result information; wherein the prediction result information indicates whether the target vehicle terminal meets the preset power replenishment rule information;

[0108] A generating module 34 is configured to generate a power replenishment request if it is determined that the prediction result information indicates that the power replenishment rule information is satisfied;

[0109] The sending module 35 is used to send the charging request to the target vehicle terminal; wherein the charging request is used to indicate the charging operation.

[0110] Figure 5 A schematic diagram of the structure of another power supply control device provided in an embodiment of the present application, Figure 4 Based on the embodiment shown, Figure 5 As shown, the vehicle condition data includes fault information, battery information, power battery information, and driving data.

[0111] In a possible implementation, the prediction module 33 includes:

[0112] The updating unit 331 is used to update the preset power replenishment prediction model according to the vehicle condition data and the actual temperature value; wherein the power replenishment prediction model is trained based on the basic data of the target vehicle terminal and the vehicle condition data generated from the first start to the current start;

[0113] The prediction unit 332 is used to perform a power replenishment prediction process on the vehicle condition data and the actual temperature value according to the updated power replenishment prediction model to generate prediction result information.

[0114] In a possible implementation, the power replenishment prediction model includes a starting voltage prediction model and a power battery health prediction model; the vehicle condition data includes fault information, battery information, power battery information, and basic data;

[0115] The prediction unit 332 is specifically configured to:

[0116] According to the updated starting voltage prediction model, the battery information and the actual temperature value are processed for power replenishment prediction, and the voltage prediction result information in the prediction result information is generated; wherein the voltage prediction result information indicates whether the actual voltage of the target vehicle terminal is greater than the preset starting voltage threshold value;

[0117] According to the updated power battery health prediction model, the power battery information is processed for charging prediction to generate battery health prediction result information in the prediction result information; wherein, the battery health prediction result information represents whether the health value of the power battery of the target vehicle terminal is greater than a preset battery health threshold.

[0118] In a possible implementation, the generating module 34 is specifically configured to:

[0119] If it is determined that the voltage prediction result information, the battery health prediction result information, and the fault information all represent a normal state, a power replenishment request is generated; wherein the prediction result information includes the voltage prediction result information and the battery health prediction result information.

[0120] In a possible implementation, the device is further specifically used for:

[0121] If it is determined that any one of the voltage prediction result information, the battery health prediction result information, and the fault information represents a dangerous state, a prompt message is generated and sent to the target vehicle terminal.

[0122] The device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0123] Figure 6 This is a schematic diagram of the structure of the electronic device provided in this application. Figure 6 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0124] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0125] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0126] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0127] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0128] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0129] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0130] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0131] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0132] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0133] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0136] If the function 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 present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0137] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0138] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A power supply control method, characterized in that: include: Receiving vehicle data reported by a target vehicle terminal; wherein the vehicle data includes vehicle condition data and location information of the target vehicle terminal; Determining the actual temperature value of the area where the target vehicle terminal is located according to the location information; According to a preset power replenishment prediction model, the vehicle condition data and the actual temperature value are processed for power replenishment prediction to generate prediction result information; wherein the prediction result information indicates whether the target vehicle terminal meets the preset power replenishment rule information; If it is determined that the prediction result information indicates that the charging rule information is satisfied, a charging request is generated and sent to the target vehicle terminal; wherein the charging request is used to indicate a charging operation.

2. The method according to claim 1, characterized in that The vehicle condition data includes fault information, battery information, power battery information, and driving data.

3. The method according to claim 1, characterized in that The method of performing power replenishment prediction processing on the vehicle condition data and the actual temperature value according to the preset power replenishment prediction model to generate prediction result information includes: According to the vehicle condition data and the actual temperature value, a preset power replenishment prediction model is updated; wherein the power replenishment prediction model is trained based on the basic data of the target vehicle terminal and the vehicle condition data generated from the first startup to the current startup; According to the updated supplementary power prediction model, supplementary power prediction processing is performed on the vehicle condition data and the actual temperature value to generate prediction result information.

4. The method according to claim 3, characterized in that The power replenishment prediction model includes a starting voltage prediction model and a power battery health prediction model; the vehicle condition data includes fault information, battery information, power battery information, and basic data; According to the updated power replenishment prediction model, the vehicle condition data and the actual temperature value are processed for power replenishment prediction to generate prediction result information, including: According to the updated starting voltage prediction model, the battery information and the actual temperature value are subjected to a power replenishment prediction process to generate voltage prediction result information in the prediction result information; wherein the voltage prediction result information indicates whether the actual voltage of the target vehicle terminal is greater than a preset starting voltage threshold value; According to the updated power battery health prediction model, the power battery information is processed for power replenishment prediction to generate battery health prediction result information in the prediction result information; wherein, the battery health prediction result information represents whether the health value of the power battery of the target vehicle terminal is greater than a preset battery health threshold.

5. The method according to any one of claims 1 to 4, characterized in that: If it is determined that the prediction result information indicates that the power replenishment rule information is satisfied, generating a power replenishment request includes: If it is determined that the voltage prediction result information, the battery health prediction result information, and the fault information all represent a normal state, a power replenishment request is generated; wherein the prediction result information includes the voltage prediction result information and the battery health prediction result information.

6. The method according to claim 5, characterized in that The method further comprises: If it is determined that any one of the voltage prediction result information, the battery health prediction result information, and the fault information represents a dangerous state, a prompt message is generated and sent to the target vehicle terminal.

7. A power supply control device, characterized in that: include: A receiving module, used to receive vehicle data reported by a target vehicle terminal; wherein the vehicle data includes vehicle condition data and location information of the target vehicle terminal; A determination module, used to determine the actual temperature value of the area where the target vehicle terminal is located according to the location information; A prediction module, configured to perform a power replenishment prediction process on the vehicle condition data and the actual temperature value according to a preset power replenishment prediction model, and generate prediction result information; wherein the prediction result information indicates whether the target vehicle terminal satisfies the preset power replenishment rule information; A generating module, configured to generate a power replenishment request if it is determined that the prediction result information represents that the power replenishment rule information is satisfied; A sending module is used to send the power replenishment request to the target vehicle terminal; wherein the power replenishment request is used to indicate a charging operation.

8. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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