Power supply control method, device, electronic device, storage medium and program product

Abstract

The embodiments of the present application provide a power replenishment control method, device, electronic device, storage medium and program product. It relates to the field of computer technology. The method includes: receiving 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 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 power replenishment rule information is met, 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. This method is used to achieve the effect of improving the accuracy of the start time of power replenishment 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 existing technology, 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. 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 improve the accuracy of the charging start time of a vehicle terminal.

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

[0007] 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;

[0008] Determining an actual temperature value of the area where the target vehicle terminal is located based on the location information;

[0009] According to a preset charging prediction model, the vehicle condition data and the actual temperature value are subjected to charging prediction processing to generate prediction result information; wherein the prediction result information indicates whether the target vehicle terminal meets the preset charging 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 instruct a charging operation.

[0011] In a possible implementation, 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] updating a preset power replenishment prediction model based on 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;

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

[0015] In one 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;

[0016] According to the updated power replenishment prediction model, power replenishment prediction processing is performed on the vehicle condition data and the actual temperature value to generate prediction result information, including:

[0017] Performing a charge prediction process on the battery information and the actual temperature value according to the updated starting voltage prediction model 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;

[0018] According to the updated power battery health prediction model, the power battery information is subjected to a charging prediction process to generate the battery health prediction result information in the prediction result information; wherein, the battery health prediction result information indicates 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, if it is determined that the prediction result information indicates 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 indicate 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 one 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, comprising:

[0024] A receiving module, configured 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, configured to determine an actual temperature value of the area where the target vehicle terminal is located based on 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 indicates that the power replenishment rule information is satisfied;

[0028] The 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, 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 based on 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;

[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 one 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;

[0034] The prediction unit is specifically used to:

[0035] Performing a charge prediction process on the battery information and the actual temperature value according to the updated starting voltage prediction model 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;

[0036] According to the updated power battery health prediction model, the power battery information is subjected to a charging prediction process to generate the battery health prediction result information in the prediction result information; wherein, the battery health prediction result information indicates 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, the generating module is specifically configured to:

[0038] If it is determined that the voltage prediction result information, the battery health prediction result information, and the fault information all indicate 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, comprising: 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 implementation methods 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 meets the preset charging rule information. If it is determined that the prediction result information represents that the charging rule information is met, 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 solution, 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, and then more accurately output whether the battery needs to be charged and how long the charging should take, thereby maximizing 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 supply 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 structural diagram of a power supply control device provided in an embodiment of the present application;

[0052] Figure 5 A schematic structural diagram 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 illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0055] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain 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, current charging methods rely on a battery voltage threshold, initiating charging only when the battery voltage falls below the threshold. However, existing charging methods rely on a single judgment strategy, which can address the issue of low battery life in new energy vehicles to a certain extent. However, they also present numerous challenges. For example, various external factors can influence the battery charging judgment, leading to inaccurate charging start times on the vehicle terminal, potentially damaging the battery.

[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 this application obtains parameters such as location information, local weather conditions, and vehicle condition data as input, 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, solving the technical problem of low accuracy of the vehicle terminal's starting charging time.

[0060] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. 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 the process 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, a terminal device, a power supply control device or device, or other devices or equipment that can execute this embodiment, without limitation. This embodiment is described with the execution subject being an electronic device.

[0064] First, electronic devices interact with vehicle terminals. The Internet of Vehicles platform is deployed within these electronic devices. These devices can be either onboard controllers or onboard control systems, with no specific restrictions. There are two types of charging methods configured on vehicle terminals: scheduled charging and intelligent charging. Figure 2 This is 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: the Internet of Vehicles platform (hereinafter referred to as the platform), the on-board intelligent terminal T-Box, the vehicle controller VCU, the battery management system BMS, and other circuit components. The T-Box is responsible for real-time vehicle data collection and the initiator of the charging event. The VCU is responsible for determining whether the current vehicle meets the charging conditions for the scheduled charging method and executing the charging. The charging condition is the preset charging period T. The platform is used for data storage and data model training. The scheduled charging method is awakened by the fixed period T of the T-Box. The intelligent charging method uses the T-Box to collect 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. 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 vehicle network 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 latitude and longitude data; the driving data includes whether it is driving, 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 , usage 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 , usage time T pb .

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

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

[0068] S103. Perform 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; wherein the prediction result information indicates whether the target vehicle terminal meets the preset power replenishment rule information.

[0069] Exemplarily, the preset charging prediction model includes a starting voltage prediction model and a power battery health prediction model. Based on the starting voltage prediction model, the battery information and actual temperature are processed for charging prediction, generating voltage prediction information within the prediction result information. This voltage prediction information indicates whether the actual voltage of the target vehicle terminal is greater than a preset starting voltage threshold. Based on the power battery health prediction model, the power battery information is processed for charging prediction, generating battery health prediction information within the prediction result information. This battery health prediction information indicates whether the health value of the power battery of the target vehicle terminal is greater than a preset battery health threshold. Furthermore, the voltage prediction information may include information such as the charging duration.

[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 instruct a charging operation.

[0071] For example, the prediction result information indicates whether the target vehicle terminal meets the preset charging rule information. If the voltage prediction result information, battery health prediction result information, and fault information all indicate a normal state, the target vehicle terminal meets the preset charging rule information, and a charging request is generated and sent to the target vehicle terminal. Upon receiving the charging request, the target vehicle terminal can proceed with charging. Alternatively, if any one of the voltage prediction result information, battery health prediction result information, or fault information indicates a dangerous state, the target vehicle terminal does not meet the preset charging rule information and cannot be charged. A prompt message is generated and sent to the target vehicle terminal. Upon receiving the prompt message, the target vehicle terminal can proceed with maintenance or other operations.

[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 multiple methods such as timed wake-up and real-time monitoring. 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 charging 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 charging prediction model, the vehicle condition data and the actual temperature value are subjected to charging prediction processing to generate prediction result information; wherein the prediction result information represents whether the target vehicle terminal meets the preset charging rule information. If it is determined that the prediction result information represents that the charging rule information is met, 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 solution, 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, and then more accurately output whether the battery needs to be charged and how long the charging should take, so as to maximize benefits. Therefore, the battery recharge prediction model takes into account the impact of multiple factors on battery recharge, such as the vehicle's location (temperature factors). It is based on big data statistics and is derived through multi-factor fusion analysis and training of input data. It has strong computing power, is easy to implement, and is more accurate and instructive. As input data accumulates, the estimated results will become increasingly accurate, thereby improving the accuracy of the vehicle terminal's recharge start time. The model takes into account the impact of multiple factors on battery recharge, such as the vehicle's location (temperature factors), the vehicle or battery life (number of charge and discharge cycles, health status), etc.

[0074] Figure 3 A schematic diagram of the process of a power supply control method provided in this application Figure 2 ,like Figure 3 As shown, this embodiment Figure 1 Based on the embodiment, the power replenishment 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, 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 description will not be repeated.

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

[0079] For example, 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 based on 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] For example, static basic data is established for each vehicle at the factory. After the vehicle terminal is first powered on, the electronic device associates the basic data with the vehicle terminal and uses it to perform model training based on the dynamic vehicle condition data generated from the first power-up to generate a power replenishment prediction model. During each subsequent power-up, the generated dynamic vehicle condition data is then used for model training to generate an updated power replenishment prediction model. The basic data includes national standard data and enterprise standard data, for example, the basic data may include the vehicle terminal model, etc., without limitation.

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

[0083] S204 : Performing 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. Vehicle faults are divided into three levels. The fault code information (spn+fmi) in the reported fault information can be used to distinguish which controller has failed. Finally, determine whether the power battery is healthy based on the power battery health prediction model, and combine the three to determine whether to issue a power replenishment request.

[0086] Specifically, the starting voltage prediction model is obtained by combining starting voltage prediction model 1 and starting voltage prediction model 2. 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). Starting voltage prediction model 2 is the relationship between the minimum starting voltage Vstart of the battery and the usage 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 predict the startup voltage based on the updated model, that is, Vstart = F(T sc , C sc , D sc ))*f(t), performs charging prediction processing on the battery information and actual temperature value to 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 indicates that the vehicle terminal can be started. If the actual voltage Vstart is less than the preset starting voltage threshold, it indicates 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 to generate 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 a preset battery health threshold. If the health value is greater than or equal to the preset battery health threshold, the power battery is in a healthy state; if the health value is less than the preset battery health threshold, 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 revised. A complete intelligent charging record should have two records, one for the charging start record and one for the charging end record. Charging start record: The record type is 0, including the charging start time, charging start soc, and charging start battery voltage information. The charging end information defaults to zero value, and the server does not parse it. Directly discard the charging end record: The record type is 1, including the charging start time, charging start soc, charging start battery voltage, charging end time, charging end soc, charging end battery voltage, and 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 Fade Rate Assessment Method: The battery's health is assessed by measuring the difference between the actual and designed capacity. A higher capacity fade rate indicates worse battery health. 2) Battery Internal Resistance Assessment Method: An increase in battery internal resistance leads to increased energy loss, which affects the battery's range and power output. A higher internal resistance indicates worse battery health. 3) Battery Open Circuit Voltage Assessment Method: The open circuit voltage is related to the battery's remaining capacity and internal resistance. The battery's health can be assessed by measuring changes in the open circuit voltage. 4) Battery Temperature Assessment Method: Operating the battery at excessively high or low temperatures can lead to degraded performance and shortened lifespan. The battery's health is assessed by monitoring changes in temperature. 5) Cycle Life Assessment Method: The battery's lifespan and health are assessed through charge and discharge cycle testing. A longer cycle life indicates better battery health.

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

[0095] 1) Temperature: The ideal operating temperature range for power batteries is between 0°C and 40°C. Temperatures that are too low or too high can affect battery performance and lifespan. 2) Internal resistance: Chemical changes within the battery cause internal resistance to gradually increase, consuming more energy and thus affecting battery health. 3) Service life: Over time, the active materials within the battery degrade, resulting in a decrease in capacity and, consequently, influencing battery health.

[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, generating a power replenishment request; wherein the prediction result information includes the voltage prediction result information and the battery health prediction result information.

[0098] For example, 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 the charging request to the target vehicle terminal; wherein the charging request is used to indicate a charging operation.

[0100] For example, 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] For example, if the electronic device determines that any one of the voltage prediction result information, battery health prediction result information, and fault information represents a dangerous state, it means that the target vehicle terminal does not meet the preset charging 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 startup 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 met, 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 vehicle terminal's start charging time.

[0104] Figure 4 This is a schematic diagram of the structure of a power supply control device provided in this application, such as Figure 4 As shown, the power replenishment control device 30 provided in this embodiment includes:

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

[0106] A determination module 32 is used to determine the actual temperature value of the area where the target vehicle terminal is located based on the location information;

[0107] The prediction module 33 is 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;

[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 a charging operation.

[0110] Figure 5 This is a structural diagram 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] An updating unit 331 is configured to update a preset power replenishment prediction model based on 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 startup to the current startup;

[0113] The prediction unit 332 is configured 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, and generate prediction result information.

[0114] In one possible implementation, the charging 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] Performing a charge prediction process on the battery information and the actual temperature value based on the updated starting voltage prediction model 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;

[0117] According to the updated power battery health prediction model, the power battery information is subjected to a charging prediction process 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 indicate 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 configured to:

[0121] If it is determined that any one of the voltage prediction result information, battery health prediction result information, and 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 effects are similar and will not be described in detail in this embodiment.

[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 further 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 the at least one processor 501 performs the above method.

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

[0126] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), 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 present invention may be directly implemented by a hardware processor or implemented 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 (NVM), such as at least one disk memory.

[0128] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just 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 memory 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-purpose 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 an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (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 merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

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

[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 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments 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, and other media that can store program code.

[0137] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0138] Finally, it should be noted that those skilled in the art will readily identify 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 that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A power supply control method, characterized in that: include: 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; Determining an actual temperature value of the area where the target vehicle terminal is located based on the location information; According to a preset charging prediction model, the vehicle condition data and the actual temperature value are subjected to charging prediction processing to generate prediction result information; wherein the prediction result information indicates whether the target vehicle terminal meets the preset charging 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; The method of 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: updating a preset power replenishment prediction model based on 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; According to the updated power replenishment prediction model, power replenishment prediction processing is performed on the vehicle condition data and the actual temperature value to generate prediction result information.

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 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, power replenishment prediction processing is performed on the vehicle condition data and the actual temperature value to generate prediction result information, including: Performing a charge prediction process on the battery information and the actual temperature value according to the updated starting voltage prediction model 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; According to the updated power battery health prediction model, the power battery information is subjected to a charging prediction process to generate the battery health prediction result information in the prediction result information; wherein, the battery health prediction result information indicates whether the health value of the power battery of the target vehicle terminal is greater than a preset battery health threshold.

4. The method according to any one of claims 1 to 3, 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 indicate 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.

5. The method according to claim 4, 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.

6. A power supply control device, characterized in that: include: A receiving module, configured 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, configured to determine an actual temperature value of the area where the target vehicle terminal is located based on 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 indicates that the power replenishment rule information is satisfied; a sending module, configured to send the charging request to the target vehicle terminal; wherein the charging request is used to indicate a charging operation; The prediction module is specifically configured to update a preset power supply prediction model based on the vehicle condition data and the actual temperature value; wherein the power supply 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; and based on the updated power supply prediction model, power supply prediction processing is performed on the vehicle condition data and the actual temperature value to generate prediction result information.

7. 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 5.

8. 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 5 when executed by a processor.

9. 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 5 is implemented.

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