Vehicle breakdown risk prediction method, device, electronic device and storage medium

Abstract

The present application discloses a method, device, electronic device and storage medium for predicting the risk of vehicle breakdown. The method for predicting the risk of vehicle breakdown includes: obtaining the driving time point of each driving of the target vehicle within a first preset time period and the state of charge SOC at the end of each driving; for each driving, obtaining a first correlation relationship value between the driving time point of the driving and the target time point and the risk of breakdown; for each driving, obtaining a second correlation relationship value between the SOC at the end of the driving and the risk of breakdown; based on the first correlation relationship value and the second correlation relationship value of each driving, obtaining a quantitative value for predicting the risk of breakdown of the target vehicle at the target time point. The method of the embodiment of the present application uses the SOC at the end of each driving and the length of time between the driving time point of each driving and the target time point as the basis for predicting the risk of breakdown, and the obtained quantitative value for predicting the risk of breakdown is relatively accurate, which improves the accuracy of the prediction result of the risk of breakdown.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, device, electronic device and storage medium for predicting the risk of vehicle breakdown. Background Art

[0002] With the development of new energy technologies, electric vehicles have been occupying a higher and higher share in the market. In reality, electric vehicles occasionally break down, which brings inconvenience to the use of electric vehicles. In order to prevent electric vehicles from breaking down, it is necessary to predict the risk of electric vehicles breaking down. Related technologies use real-time driving parameters of electric vehicles during driving, such as vehicle speed, output torque feedback from motor controllers, and other parameters to predict the risk of electric vehicles breaking down. However, the accuracy of the prediction results of the risk of electric vehicles breaking down is not high.

[0003] The above statements are only used to provide background information related to the present application and do not necessarily constitute prior art. Summary of the invention

[0004] In view of the problem that the above-mentioned related technologies use real-time driving parameters of electric vehicles during driving to predict the risk of electric vehicle breakdown, but the accuracy of the breakdown risk prediction results is not high, the present application provides a method, device, electronic device and storage medium for predicting vehicle breakdown risk.

[0005] According to a first aspect of an embodiment of the present application, a method for predicting a vehicle breakdown risk is provided, comprising:

[0006] Obtaining the driving time point of each driving of the target vehicle within the first preset time period and the state of charge SOC at the end of each driving;

[0007] For each driving, obtaining a first correlation relationship value between the time from the driving time point of the driving to the target time point and the risk of breakdown;

[0008] For each driving, obtaining a second correlation relationship value between the SOC at the end of the driving and the breakdown risk;

[0009] Based on the first association relationship value and the second association relationship value of each driving, a predicted quantitative value of the breakdown risk of the target vehicle at the target time point is obtained.

[0010] The method for predicting the vehicle breakdown risk in the embodiment of the present application takes the state of charge SOC at the end of each driving and the length of time from the driving time point of each driving to the target time point as the basis for predicting the breakdown risk. The state of charge SOC at the end of each driving can reflect the battery status. Since the state of charge SOC at the end of each driving and the length of time from the driving time point of each driving to the target time point are both important factors related to the breakdown risk, the quantitative value of the breakdown risk prediction obtained based on the first correlation relationship value and the second correlation relationship value of each driving is relatively accurate, which greatly improves the accuracy of the breakdown risk prediction result.

[0011] In some embodiments of the present application, obtaining the predicted quantified value of the anchoring risk of the target vehicle at the target time point based on the first association relationship value and the second association relationship value of each driving includes:

[0012] Obtaining a first product of the first association relationship value and the second association relationship value in each driving;

[0013] The sum of the first products corresponding to each driving is obtained to obtain a quantitative value of the predicted risk of breakdown of the target vehicle at the target time point.

[0014] The quantitative value of the anchoring risk prediction obtained based on the first correlation relationship value and the second correlation relationship value of each driving is relatively accurate, which greatly improves the accuracy of the anchoring risk prediction result.

[0015] In some embodiments of the present application, before obtaining the predicted quantized value of the anchoring risk of the target vehicle at the target time point based on the first association relationship value and the second association relationship value of each driving, the method further includes:

[0016] Obtain each first SOC that is less than a first preset value among the SOCs at the end of each driving operation;

[0017] Acquire a third correlation relationship value between the first SOC and the anchoring risk;

[0018] The step of obtaining a predicted quantized value of the risk of breakdown of the target vehicle at the target time point based on the first association relationship value and the second association relationship value of each driving operation includes:

[0019] Based on the third association relationship value and the first association relationship value and the second association relationship value of each driving, the quantitative value of the anchoring risk prediction of the target vehicle at the target time point is obtained. This takes into account the association between the SOC distribution status at the end of each driving and the anchoring risk, further improving the accuracy of the anchoring risk prediction results.

[0020] In some embodiments of the present application, obtaining the predicted quantified value of the anchoring risk of the target vehicle at the target time point based on the third association relationship value and the first association relationship value and the second association relationship value of each driving, includes:

[0021] Obtaining a first product of the first association relationship value and the second association relationship value in each driving;

[0022] A weighted sum of the fourth sum and the third association value is obtained to obtain a quantitative value of the anchoring risk prediction of the target vehicle at the target time point, wherein the fourth sum is the sum of the first products of each driving, and comprehensively considers the association between the SOC at the end of each driving and the anchoring risk, the association between the length of time from the driving time point of each driving to the target time point and the anchoring risk, and the association between the distribution status of the SOC at the end of each driving and the anchoring risk, thereby greatly improving the accuracy of the anchoring risk prediction results.

[0023] In some embodiments of the present application, the step of obtaining a first correlation value between the time from the driving time point to the target time point and the risk of breakdown includes:

[0024] Obtain a first ratio of the opposite of the duration to the first sum, the first ratio being a first correlation value between the duration and the risk of anchoring, the first sum being the sum of the absolute value of the duration and a first preset positive number, the first correlation value being able to more accurately characterize the correlation between the duration from the driving time point of the trip to the target time point and the risk of anchoring.

[0025] In some embodiments of the present application, the obtaining of the second correlation relationship value between the SOC at the end of the driving and the risk of breakdown includes:

[0026] When the SOC at the end of the driving is greater than the second preset value, the second association relationship value is 0;

[0027] When the SOC at the end of the driving is not greater than the second preset value, a second ratio of the opposite of the SOC at the end of the driving to the second sum is obtained, the second ratio is the second association relationship value, and the second sum is the sum of the SOC at the end of the driving and the second preset positive number.

[0028] In this way, the obtained second correlation value can more accurately represent the correlation between the SOC at the end of driving and the risk of breakdown.

[0029] In some embodiments of the present application, the obtaining of a third correlation value between the first SOC and the anchoring risk includes:

[0030] Based on each of the first SOCs and a general term of a preset progression function, obtaining values ​​of each term of the preset progression function;

[0031] Obtaining the sum of the values ​​of the items to obtain the third association relationship value;

[0032] Among them, the general term of the preset series function is the ratio of the number of second SOC values ​​to the third sum, the second SOC value is the first SOC whose product with 100 is less than the first value, the third sum is the sum of the number of second SOC values ​​and a third preset positive number, the first value is any integer in the first closed interval, the left endpoint of the first closed interval is 1, and the right endpoint is the product of the first preset value and 100.

[0033] In this way, the obtained third correlation value can more accurately represent the correlation between the distribution of SOC at the end of each driving and the risk of breakdown.

[0034] In some embodiments of the present application, obtaining the driving time point of each driving of the target vehicle within the first preset time period and the state of charge SOC at the end of each driving includes:

[0035] Extracting discharge segment data from the historical driving record of the target vehicle in the first preset time period according to the charge and discharge flag;

[0036] The driving time point of each driving and the state of charge SOC at the end of each driving are extracted from the discharge segment data.

[0037] In this way, the driving time point of each driving of the target vehicle within the first preset time period and the SOC at the end of each driving can be obtained more accurately, so as to be used for subsequent prediction of the risk of vehicle breakdown.

[0038] According to a second aspect of an embodiment of the present application, a device for predicting the risk of vehicle breakdown is provided, comprising:

[0039] A historical driving data acquisition module is used to obtain the driving time point of each driving of the target vehicle within a first preset time period and the state of charge SOC at the end of each driving;

[0040] A first correlation value acquisition module is used to acquire, for each driving, a first correlation value between the time from the driving time point to the target time point and the risk of breakdown;

[0041] A second correlation relationship value acquisition module is used to acquire, for each driving, a second correlation relationship value between the SOC at the end of the driving and the risk of breakdown;

[0042] The anchoring risk prediction module is used to obtain the anchoring risk prediction quantitative value of the target vehicle at the target time point based on the first association relationship value and the second association relationship value of each driving.

[0043] The vehicle breakdown risk prediction device provided in the second aspect of the embodiment of the present application can achieve the same beneficial technical effect as the vehicle breakdown risk prediction method of the first aspect.

[0044] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the risk of vehicle breakdown as described in any embodiment of the present application.

[0045] The electronic device provided in the third aspect of the embodiment of the present application can achieve the same beneficial technical effects as the method for predicting the risk of vehicle breakdown in the first aspect.

[0046] According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement the method for predicting the risk of vehicle breakdown as described in any embodiment of the present application.

[0047] The computer-readable storage medium provided in the fourth aspect of the embodiment of the present application can achieve the same beneficial technical effects as the method for predicting the risk of vehicle breakdown in the first aspect.

[0048] The above description is only an overview of the technical solution of the embodiment of the present application. In order to more clearly understand the technical means of the embodiment of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] By reading the detailed description of the following embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The accompanying drawings are only used to illustrate the embodiments of the present application and are not to be considered as limitations of the present application. In addition, the same reference numerals are used to represent the same components throughout the drawings.

[0050] Figure 1 is a schematic diagram of the structure of a vehicle according to one or more embodiments.

[0051] Figure 2 The present invention is a flow chart of a method for predicting the risk of vehicle breakdown according to one or more embodiments.

[0052] Figure 3The present invention is a flow chart of a method for predicting the risk of vehicle breakdown according to one or more embodiments.

[0053] Figure 4 The present invention is a structural block diagram of a device for predicting the risk of vehicle breakdown according to one or more embodiments.

[0054] Figure 5 The present invention is a structural block diagram of a device for predicting the risk of vehicle breakdown according to one or more embodiments.

[0055] Figure 6 is a structural block diagram of an electronic device according to one or more embodiments. DETAILED DESCRIPTION

[0056] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0058] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two (including two), unless otherwise clearly and specifically defined.

[0059] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0060] With the rapid development of new energy technologies, power batteries are widely used in new energy vehicles, and the market share of electric vehicles is increasing. In actual use, electric vehicles occasionally break down, which brings inconvenience to the use of electric vehicles. In order to reduce the occurrence of electric vehicle breakdowns, it is necessary to predict the risk of electric vehicle breakdowns in advance and take corresponding treatment measures according to the prediction results of the risk of breakdowns to reduce the occurrence rate of breakdowns.

[0061] When predicting the risk of electric vehicle breakdown in the related art, the real-time driving parameters of the electric vehicle during driving, such as vehicle speed, output torque feedback from the motor controller, and other parameters are first obtained, and then the risk of electric vehicle breakdown is calculated based on these real-time driving parameters. However, a large part of the reason for the breakdown of electric vehicles is the poor state of the power battery; in addition, the historical driving time point before the target time point is too close to the target time point, which is also a factor that causes the electric vehicle to break down. This factor should also be considered to predict the risk of breakdown at the target time point. However, the related art does not use parameters that reflect the battery status, such as the state of charge SOC at the end of each driving, and the distance between the historical driving time point and the target time point as the basis for predicting the risk of breakdown, which results in low accuracy in the prediction of the risk of breakdown.

[0062] In response to the technical problems existing in the related technology, an embodiment of the present application provides a method for predicting the risk of vehicle breakdown, obtaining the driving time point of each driving of the target vehicle within a first preset time period and the state of charge SOC at the end of each driving, and then, for each driving, obtaining a first correlation relationship value between the duration from the driving time point of the driving time to the target time point and the risk of breakdown, and, for each driving, obtaining a second correlation relationship value between the SOC at the end of the driving time and the risk of breakdown, finally, based on the first correlation relationship value and the second correlation relationship value of each driving, obtaining a quantitative value of the predicted risk of breakdown of the target vehicle at the target time point. That is to say, the method for predicting the vehicle breakdown risk in the embodiment of the present application takes the state of charge SOC at the end of each driving and the length of time from the driving time point of each driving to the target time point as the basis for predicting the breakdown risk. The state of charge SOC at the end of each driving can reflect the battery status. Since the state of charge SOC at the end of each driving and the length of time from the driving time point of each driving to the target time point are both important factors related to the breakdown risk, compared with the related art, the prediction method of the embodiment of the present application is more accurate in the quantitative value of the breakdown risk prediction obtained based on the first correlation relationship value and the second correlation relationship value of each driving, which greatly improves the accuracy of the breakdown risk prediction result.

[0063] For the convenience of description, the following embodiments are described by taking the target vehicle of one embodiment of the present application as vehicle 1000 as an example. Figure 1 , Figure 1A schematic diagram of the structure of a vehicle 1000 provided for some embodiments of the present application. At least a part of the power source of the vehicle 1000 is a battery. The vehicle 1000 may be, for example, a pure electric vehicle, a hybrid electric vehicle, or an extended-range vehicle. A battery 100 is disposed inside the vehicle 1000. The battery 100 may be disposed at the bottom, head, or tail of the vehicle 1000. The vehicle 1000 may also include a controller 200 and a motor 300. The controller 200 is used to control the battery 100 to supply power to the motor 300, for example, for the starting, navigation, and working power requirements of the vehicle 1000 during driving. The battery 100 may be used as a driving power source for the vehicle 1000, replacing or partially replacing fuel or natural gas to provide driving power for the vehicle 1000.

[0064] A method for predicting the risk of vehicle breakdown according to an embodiment of the present application is described below in conjunction with the accompanying drawings.

[0065] refer to Figure 2 As shown, an embodiment of the present application provides a method for predicting the risk of vehicle breakdown, which may include:

[0066] S10, obtaining the driving time point of each driving of the target vehicle within the first preset time period and the state of charge SOC at the end of each driving.

[0067] The driving time point of each driving is used to mark the time when the driving occurred. According to different requirements for the marking accuracy of time, different units of time points can be used to mark the time when the driving occurred. For example, the driving time point can be date, hour, minute, etc. The date is usually used to mark the driving time point. For example, the driving time point of a certain driving is July 1, 2024. For another example, if the marking accuracy of time is accurate to hours, the driving time point can be accurate to a certain time. For example, the driving time point of a certain driving is 9 o'clock on July 1, 2024. If the driving duration of this driving is 3 hours, for example, the driving time of this driving is from 8 o'clock to 12 o'clock on July 1, 2024, then any time point can be selected from 8 o'clock to 12 o'clock on July 1, 2024 as the driving time point, for example, the driving time point can be taken as 10 o'clock on July 1, 2024. For each driving, any time point in the time interval from the start time to the end time of the driving can be selected as the driving time point. In this embodiment, the driving time point is marked with a date, that is, the driving time point is represented as a certain year, month and day.

[0068] The state of charge (SOC) is the ratio of the remaining capacity of a battery after it has been used for a period of time or has been unused for a long time to its capacity when it is fully charged. It is usually expressed as a percentage, and its value range is 0%~100%. For example, SOC=0 means that the remaining battery power is 0, and when SOC=100%, it means that the battery is fully charged.

[0069] The first preset time period may be, for example, a period of 30 days, 40 days, 60 days, etc. in the past, and may be specifically set according to actual application needs, and is not limited here.

[0070] For example, the discharge segment data can be extracted from the historical driving record of the target vehicle in the first preset period according to the charge and discharge flag, and then the driving time point of each driving and the state of charge SOC at the end of each driving can be extracted from the discharge segment data. In this way, the driving time point of each driving of the target vehicle in the first preset period and the SOC of the battery of the target vehicle at the end of each driving can be obtained more accurately, so as to be used for the subsequent prediction of the risk of vehicle breakdown.

[0071] As an optional implementation, the historical driving data of the target vehicle can be first obtained, and then the discharge segment data can be extracted from the historical driving data according to the charge and discharge flag, and finally the driving time point of each driving and the state of charge SOC at the end of each driving can be extracted from the discharge segment data.

[0072] In a specific example, the historical driving data of the target vehicle can be first obtained. The historical driving data may include the starting time point and the ending time point of each driving, the voltage and current during the driving process, the battery SOC at the starting time point, the battery SOC at the ending time point, the charge and discharge flag, and the basic parameter information data of the battery. The basic parameter information data of the battery includes data such as the mapping relationship table of the nominal capacity of the battery cell, the battery SOC and OCV, and OCV refers to the open circuit voltage (Open Circuit Voltage) of the battery. The acquired historical driving data is preprocessed to remove null values, duplicate data and abnormal value data. Then, the discharge segment data is extracted from the preprocessed historical driving data. Specifically, the discharge segment data (the discharge segment data is the actual driving data) can be extracted from the preprocessed historical driving data according to the charge and discharge flag. Finally, the starting time point of each driving and the state of charge SOC at the end of each driving are extracted from the discharge segment data, and the starting time point of the driving is used as the driving time point.

[0073] In the battery charge and discharge data, some charge and discharge flags are usually used to indicate the battery's charge or discharge status. These flags are usually expressed in digital or symbolic form. For example, the charge flag can be 1 or C, indicating that the battery is currently charging, and the discharge flag can be 0 or D, indicating that the battery is currently discharging. These charge and discharge flags are usually used to mark the battery's charge and discharge status in data logging or battery management systems.

[0074] S20: For each driving, obtain a first correlation value between the time from the driving time point to the target time point and the risk of breakdown.

[0075] The vehicle breakdown risk to be predicted by the method of this embodiment is the breakdown risk of the target vehicle at the target time point. The correlation between the time length from the driving time point to the target time point and the breakdown risk is negatively correlated, that is, the longer the time length, the smaller the breakdown risk, and the shorter the time length, the greater the breakdown risk.

[0076] Exemplarily, the first ratio of the opposite of the duration to the first sum is obtained, and the first ratio is the first correlation value between the duration and the risk of anchoring. The first sum is the sum of the absolute value of the duration and a first preset positive number. The first correlation value can more accurately characterize the correlation between the duration of the driving time point from the target time point and the risk of anchoring. In this way, the duration from the driving time point to the target time point is quantified as a temporal forgetting factor, and different durations correspond to different forgetting speeds, which conforms to the actual law of the negative correlation between the duration and the risk of anchoring. The calculation formula of the first correlation value is equivalent to the activation function of the duration, and the duration can be quantified as a temporal forgetting factor.

[0077] The calculation formula of the first association relationship value can be

[0078]

[0079] Among them, t represents the time distance between the driving time point of the target vehicle and the target time point, a represents a first preset positive number, and the value of a can be, for example, 1, 2, 3, etc., which can be set according to actual needs and is not limited here.

[0080] The duration between the driving time point of the trip and the target time point is the target time point minus the driving time point. For example, if the target time point is expressed as a date, the driving time point is the date of the trip, and the duration between the driving time point of the trip and the target time point is the date corresponding to the target time point minus the date corresponding to the driving time point, that is, the number of days between the date of the trip and the target time point.

[0081] In a specific example, assuming a=1, the calculation formula for the first association value is:

[0082]

[0083] The target time point and the driving time point of the driving are both represented by dates, and t represents the number of days between the driving time point of the target vehicle and the target time point.

[0084] For example, if you want to calculate the target vehicle's breakdown risk at the target time point on July 3, 2024, and the driving time point of this trip is July 2, 2024, then the time from the driving time point of this trip to the target time point is July 3, 2024 minus July 2, 2024, which is 1 day, that is, t=1, f(t)=f(1)=-1 / (1+1)=-0.5. If the driving time point of this trip is July 1, 2024, then the time from the driving time point of this trip to the target time point is 2 days, that is, t=2, f(t)=f(2)=-2 / (2+1)=-2 / 3. In this way, the first correlation value between the time from the driving time point of each trip to the target time point and the breakdown risk can be calculated.

[0085] It is understandable that the target time point and the driving time point of each trip can be accurately expressed to a certain day and time, and the duration from the driving time point of the trip to the target time point is expressed in hours. For example, if the target time point is 12:00 on July 3, 2024, and the driving time point is 10:00 on July 2, 2024, then the duration from the driving time point of the trip to the target time point is 26 hours. In addition, the unit of measurement of the duration can be days, hours, minutes or seconds, etc. according to actual needs, and is not limited here.

[0086] S30: For each driving, obtain a second correlation value between the SOC and the risk of breakdown at the end of the driving.

[0087] The correlation between the SOC at the end of the trip and the risk of anchoring is negatively correlated, that is, the greater the SOC at the end of the trip, the smaller the risk of anchoring, and the smaller the SOC at the end of the trip, the greater the risk of anchoring. The negative correlation between the SOC at the end of the trip and the risk of anchoring can be represented by a preset function, in which the SOC at the end of the trip is used as the independent variable and the second correlation value is used as the dependent variable, so that the SOC at the end of the trip is converted into an influencing factor of the risk of anchoring.

[0088] In addition, for the convenience of calculation, the SOC greater than the preset threshold can be determined as not affecting the prediction accuracy of the anchoring risk. Therefore, the second association value between the SOC greater than the preset threshold and the anchoring risk can be set to 0. For example, the second association value between the SOC and the anchoring risk can be set to 0 when the SOC is greater than 10%.

[0089] Exemplarily, when the SOC at the end of the driving is greater than the second preset value, the second association value is 0; when the SOC at the end of the driving is not greater than the second preset value, the second ratio of the opposite number of the SOC at the end of the driving to the second sum is obtained, and the second ratio is the second association value, and the second sum is the sum of the SOC at the end of the driving and the second preset positive number. In this way, the obtained second association value can more accurately characterize the association between the SOC at the end of the driving and the risk of breaking down. The second preset value may be equal to the first preset value, or may not be equal to it.

[0090] Specifically, the calculation formula of the second association relationship value is:

[0091] ,

[0092] Among them, b represents a second preset positive number, A represents a second preset value, b can be, for example, 0.01, 0.02, 0.03, etc., A can be, for example, 10%, 15%, 20%, etc., the values ​​of b and A can be set according to actual needs, and there is no limitation here.

[0093] Exemplarily, when the SOC at the end of the driving is greater than the second preset value, the second association value is 0; when the SOC at the end of the driving is not greater than the second preset value, a second ratio of the opposite of the second value and the second sum is obtained, and the second ratio is the second association value, wherein the second value is equal to the product of the SOC at the end of the driving and 100, and the second sum is the sum of the SOC at the end of the driving and the second preset positive number.

[0094] Specifically, the calculation formula of the second association relationship value can be:

[0095] ,

[0096] Among them, b represents a second preset positive number, A represents a second preset value, b can be, for example, 1, 2, 3, etc., A can be, for example, 10%, 15%, 20%, etc., the values ​​of b and A can be set according to actual needs and are not limited here.

[0097] For example, in a specific example, b=1, A=10%, and the calculation formula of the second association relationship value is:

[0098] .

[0099] For example, if the SOC at the end of the trip is 5%, then f(SOC)=-100×5% / (100×5%+1)

[0100] =-5 / 6; if the SOC at the end of the trip is 0, then f(SOC)=-100×0 / (100×0+1)=0.

[0101] S60: Based on the first association relationship value and the second association relationship value of each driving, a predicted quantitative value of the breakdown risk of the target vehicle at a target time point is obtained.

[0102] The first association relationship value can represent the association relationship between the time difference between the driving time point of the corresponding driving and the target time point and the risk of anchoring down. The second association relationship value can represent the association relationship between the SOC at the end of the corresponding driving and the risk of anchoring down. Therefore, the predicted quantitative value of the anchoring risk of the target vehicle at the target time point can be calculated based on the first association relationship value and the second association relationship value of each driving.

[0103] Specifically, the first product of the first association relationship value and the second association relationship value in each driving can be obtained first, and then the sum of the first products corresponding to each driving can be obtained, that is, the quantitative value of the anchoring risk prediction of the target vehicle at the target time point can be obtained. The quantitative value of the anchoring risk prediction obtained based on the first association relationship value and the second association relationship value of each driving is more accurate, which greatly improves the accuracy of the anchoring risk prediction result.

[0104] In a specific example, the calculation formula for the predicted quantitative value of the target vehicle's breakdown risk at the target time point is:

[0105]

[0106] Where n represents the total number of trips in the first preset time period, c represents the sequence number of the trip sequence, c is an integer, and 1≤c≤n, f c (SOC) represents the second correlation value between SOC and the risk of breakdown at the end of the c-th driving, f c (t) represents the first correlation value between the time from the c-th driving time point to the target time point and the risk of breakdown.

[0107] In addition, among the multiple drivings that occurred within the first preset time period, the distribution of the SOC values ​​at the end of each driving is also associated with the anchoring risk. Specifically, the greater the proportion of the number of smaller SOC values ​​at the end of driving, the greater the anchoring risk, and the smaller the SOC value at the end of driving, the greater the anchoring risk. In this way, in order to further improve the accuracy of the anchoring risk prediction results, the distribution of the SOC values ​​at the end of each driving can also be used as one of the bases for anchoring risk prediction.

[0108] Therefore, in some embodiments, reference Figure 3As shown, based on the first association relationship value and the second association relationship value of each driving, before obtaining the predicted quantified value of the anchoring risk of the target vehicle at the target time point, the method may further include steps S40 and S50:

[0109] S40, obtaining each first SOC that is less than a first preset value among the SOCs at the end of each driving operation.

[0110] The first preset value may be, for example, 10%, 15%, 20%, etc., and may be set according to actual application requirements, and is not limited here.

[0111] The SOCs less than the first preset value are selected from the SOCs of the target vehicle at the end of each driving within the first preset time period to obtain each first SOC.

[0112] S50: Obtain a third correlation value between the first SOC and the anchoring risk.

[0113] The relationship between the first SOC and the anchoring risk is that the anchoring risk is positively correlated with the total number of the first SOCs and negatively correlated with the size of each first SOC, that is, the greater the total number of the first SOCs, the greater the anchoring risk, and the smaller the first SOCs, the greater the anchoring risk; the smaller the total number of the first SOCs, the greater the anchoring risk, and the larger the first SOCs, the smaller the anchoring risk. In other words, the total number of the first SOCs and the size of the first SOCs are both associated with the anchoring risk.

[0114] In some embodiments, a preset series function is used to characterize the correlation between the first SOC and the risk of anchoring. Obtaining the third correlation value between the first SOC and the risk of anchoring may include: obtaining the values ​​of each item of the preset series function based on the general term of each first SOC and the preset series function; obtaining the sum of the values ​​of each item to obtain the third correlation value; wherein the general term of the preset series function is the ratio of the number of second SOC values ​​to the third sum, the second SOC value is the first SOC whose product with 100 is less than the first value, that is, if the product of a first SOC and 100 is less than the first value, then the first SOC is the second SOC value; the third sum is the sum of the number of second SOC values ​​and a third preset positive number, the first value is any integer in the first closed interval, the left endpoint of the first closed interval is 1, and the right endpoint is the product of the first preset value and 100. In this way, the obtained third correlation value can more accurately characterize the correlation between the distribution of SOC at the end of each driving and the risk of anchoring.

[0115] For example, the preset series function is

[0116]

[0117] Wherein, i represents the first value, the first closed interval is [1, M], M is the product of the first preset value and 100, m represents the largest integer not greater than M, and d represents a third preset positive number. The value of the third preset positive number d can be, for example, 1, 2, 3, etc., which can be set according to actual application needs and is not limited here.

[0118] For example, in a specific example, d is 1, the first preset value is 10%, then the first closed interval is [1, 10], m is 10, and the expression of the preset series function is

[0119]

[0120] Further, after obtaining the third correlation value, step S60, based on the first correlation value and the second correlation value of each driving, obtains the target vehicle's anchoring risk prediction quantitative value at the target time point, which may include: S70, based on the third correlation value and the first correlation value and the second correlation value of each driving, obtains the target vehicle's anchoring risk prediction quantitative value at the target time point. In this way, when predicting the anchoring risk, the correlation between the distribution of the SOC value at the end of each driving and the anchoring risk is taken into account, that is, the distribution of the SOC value at the end of each driving is also used as one of the bases for anchoring risk prediction, thereby further improving the accuracy of the anchoring risk prediction result.

[0121] Specifically, firstly, the first product of the first association relationship value and the second association relationship value in each driving is obtained, and then the weighted sum of the fourth sum and the third association relationship value is obtained to obtain the quantitative value of the anchoring risk prediction of the target vehicle at the target time point, wherein the fourth sum is the sum of the first product of each driving. In this way, the association between the SOC at the end of each driving and the anchoring risk, the association between the time from the driving time point of each driving to the target time point and the anchoring risk, and the association between the distribution of the SOC at the end of each driving and the anchoring risk are comprehensively considered, and used as the basis for predicting the anchoring risk, thereby greatly improving the accuracy of the anchoring risk prediction results.

[0122] In a specific example, the calculation formula for the target vehicle's breakdown risk prediction quantitative value score at the target time point is:

[0123]

[0124] Among them, α represents the weight of the fourth sum, β represents the weight of the third association relationship value, and the values ​​of α and β can be set according to the actual application needs. For example, α=1 and β=1 can be set, or α=1 and β=2 can be set, etc., which are not limited here. The weight of the fourth sum and the weight of the third association relationship value respectively represent the degree of influence of the fourth sum and the third association relationship value on the quantitative value of anchoring risk prediction. In the weighted summation, the addend with a larger weight has a greater degree of influence on the quantitative value of anchoring risk prediction.

[0125] The anchoring risk prediction quantitative value score obtained by the above calculation formula is a specific value, and the size of the anchoring risk can be determined more accurately based on the size of the value.

[0126] In some embodiments, if there are multiple vehicles, the predicted quantized value score of the breakdown risk of each vehicle at the target time point may be sorted to determine the size of the breakdown risk of each vehicle so as to select a vehicle with a smaller breakdown risk.

[0127] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0128] In a specific example, historical driving data of the target vehicle is first obtained, and the historical driving data includes historical driving records of the target vehicle. The historical driving data is preprocessed, and for the preprocessed historical driving data, discharge segment data is extracted from the historical driving records of the target vehicle in a first preset time period according to the charge and discharge flag, and then the driving time point of each driving and the SOC at the end of each driving are extracted from the discharge segment data.

[0129] For each driving, the first correlation value between the time from the driving time point to the target time point and the risk of breakdown is obtained, wherein the calculation formula of the first correlation value is:

[0130]

[0131] The target time point and the driving time point of the driving are both represented by dates, and t represents the number of days between the driving time point of the target vehicle and the target time point.

[0132] For each driving, the second correlation value between the SOC and the risk of breakdown at the end of the driving is obtained, wherein the calculation formula of the second correlation value is:

[0133] .

[0134] Each first SOC that is less than 10% of the first preset value among the SOCs at the end of each driving is obtained.

[0135] A preset series function is used to characterize the correlation between the first SOC and the risk of anchoring. Based on the general terms of each first SOC and the preset series function, the values ​​of each term of the preset series function are obtained; the sum of the values ​​of each term is obtained to obtain the third correlation value; wherein, the general term of the preset series function is the ratio of the number of second SOC values ​​to the third sum, the second SOC value is the first SOC whose product with 100 is less than the first value, the third sum is the sum of the number of second SOC values ​​and a third preset positive number, the first value is any integer in the first closed interval, the left endpoint of the first closed interval is 1, and the right endpoint is the product of the first preset value 10% and 100, that is, 10. The third preset positive number is 1. The expression of the preset series function is

[0136]

[0137] The calculation formula of the target vehicle's breakdown risk prediction quantitative value score at the target time point is:

[0138]

[0139] In this example, the target vehicle has two trips in the first preset time period. The driving time points of the two trips and the SOC at the end of the two trips are extracted from the discharge segment data, including: the driving time point of the first trip is July 1, 2024, and the SOC at the end of the trip is 0, and the driving time point of the second trip is July 2, 2024, and the SOC at the end of the trip is 5%. What needs to be predicted is the target vehicle's breakdown risk on July 3, 2024. Then f(p) in this example = 0.5+0.5+0.5+0.5+0.5+2 / 3+2 / 3+2 / 3+2 / 3+2 / 3≈5.8,

[0140] In this example f 1 (SOC)*f 1 (t)= -0 / (0+1%)*(-2 / 3)=0, f 2 (SOC)*f 2 (t)= [-5% / (5%+1%)*(-1 / 2)]≈0.83*0.5≈0.42.

[0141] Quantitative value of target vehicle's breakdown risk prediction at target time point

[0142] score=f 1 (SOC)*f 1 (t)+f 2 (SOC)*f 2 (t)+f(p) ≈[-0 / (0+1%)*(-2 / 3)]+[-5% / (5%+1%)*(-1 / 2)]+5.8≈0+0.42+5.8=6.22.

[0143] The method for predicting the vehicle breakdown risk of this example takes the state of charge SOC at the end of each driving and the length of time from the driving time point of each driving to the target time point as the basis for predicting the breakdown risk. The state of charge SOC at the end of each driving can reflect the battery status. Since the state of charge SOC at the end of each driving and the length of time from the driving time point of each driving to the target time point are both important factors related to the breakdown risk, the quantitative value of the breakdown risk prediction obtained based on the first correlation relationship value and the second correlation relationship value of each driving is relatively accurate, which greatly improves the accuracy of the breakdown risk prediction result.

[0144] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0145] refer to Figure 4 As shown, another embodiment of the present application provides a vehicle breakdown risk prediction device, which may include:

[0146] A historical driving data acquisition module is used to obtain the driving time point of each driving of the target vehicle within a first preset time period and the state of charge SOC at the end of each driving;

[0147] A first correlation value acquisition module is used to acquire, for each driving, a first correlation value between the time from the driving time point to the target time point and the risk of breakdown;

[0148] A second correlation relationship value acquisition module is used to acquire, for each driving, a second correlation relationship value between the SOC and the risk of breakdown at the end of the driving;

[0149] The anchoring risk prediction module is used to obtain a anchoring risk prediction quantitative value of a target vehicle at a target time point based on the first association relationship value and the second association relationship value of each driving.

[0150] Exemplarily, the historical driving data acquisition module may include:

[0151] A first extraction unit, used for extracting discharge segment data from the historical driving record of the target vehicle in the first preset time period according to the charge and discharge flag;

[0152] The second extraction unit is used to extract the driving time point of each driving and the state of charge SOC at the end of each driving from the discharge segment data.

[0153] Exemplarily, the anchoring risk prediction module may include:

[0154] A first product acquisition unit, used for acquiring a first product of the first association relationship value and the second association relationship value in each driving;

[0155] The anchoring risk prediction unit is used to obtain the sum of the first products corresponding to each driving, and obtain a quantitative value of the anchoring risk prediction of the target vehicle at a target time point.

[0156] For example, reference Figure 5 As shown, the prediction device may also include:

[0157] A first SOC acquisition module, used for acquiring each first SOC less than a first preset value among the SOCs at the end of each driving operation;

[0158] A third association relationship value acquisition module, used to acquire a third association relationship value between the first SOC and the anchoring risk;

[0159] The anchoring risk prediction module is further specifically used to obtain a quantitative anchoring risk prediction value of the target vehicle at a target time point based on the third association relationship value and the first association relationship value and the second association relationship value of each driving.

[0160] Exemplarily, the anchoring risk prediction module may include:

[0161] A first product acquisition unit, used for acquiring a first product of the first association relationship value and the second association relationship value in each driving;

[0162] The weighted summation unit is used to obtain the weighted sum of the fourth sum and the third association relationship value to obtain a quantitative value of the anchoring risk prediction of the target vehicle at a target time point, wherein the fourth sum is the sum of the first products of each driving.

[0163] Exemplarily, the first association relationship value acquisition module is further specifically used to obtain a first ratio of the opposite of the duration to the first sum, the first ratio being the first association relationship value between the duration and the anchoring risk, and the first sum being the sum of the absolute value of the duration and a first preset positive number.

[0164] Exemplarily, the second association relationship value acquisition module is further specifically used for:

[0165] When the SOC at the end of the driving is greater than the second preset value, the second association relationship value is 0;

[0166] When the SOC at the end of the driving is not greater than the second preset value, a second ratio of the opposite of the SOC at the end of the driving to the second sum is obtained, the second ratio is a second association relationship value, and the second sum is the sum of the SOC at the end of the driving and the second preset positive number.

[0167] Exemplarily, the third association relationship value acquisition module may include:

[0168] A first unit is used to obtain values ​​of each term of the preset series function based on each first SOC and a general term of the preset series function;

[0169] The second unit is used to obtain the sum of the values ​​of each item to obtain a third association relationship value;

[0170] Among them, the general term of the preset series function is the ratio of the number of second SOC values ​​to the third sum, the second SOC value is the first SOC whose product with 100 is less than the first value, the third sum is the sum of the number of second SOC values ​​and a third preset positive number, the first value is any integer in the first closed interval, the left endpoint of the first closed interval is 1, and the right endpoint is the product of the first preset value and 100.

[0171] The device for predicting the risk of vehicle breakdown in the embodiment of the present application takes the state of charge SOC at the end of each driving and the length of time from the driving time point of each driving to the target time point as the basis for predicting the risk of breakdown. The state of charge SOC at the end of each driving can reflect the battery status. Since the state of charge SOC at the end of each driving and the length of time from the driving time point of each driving to the target time point are both important factors related to the risk of breakdown, the quantitative value of the prediction of the risk of breakdown obtained based on the first correlation relationship value and the second correlation relationship value of each driving is relatively accurate, which greatly improves the accuracy of the prediction result of the risk of breakdown.

[0172] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0173] Another embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the risk of vehicle breakdown of any of the above-mentioned embodiments.

[0174] refer to Figure 6 As shown, the electronic device 10 may include: a processor 100, a memory 101, a bus 102 and a communication interface 103, and the processor 100, the communication interface 103 and the memory 101 are connected via the bus 102; the memory 101 stores a computer program that can be run on the processor 100, and when the processor 100 runs the computer program, it executes the method provided in any of the aforementioned embodiments of the present application.

[0175] The memory 101 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 103 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0176] The bus 102 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 101 is used to store a program, and the processor 100 executes the program after receiving an execution instruction. The method disclosed in any implementation of the aforementioned embodiment of the present application may be applied to the processor 100, or implemented by the processor 100.

[0177] The processor 100 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 100 or an instruction in the form of software. The above processor 100 may be a general-purpose processor, which may include a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 101, and the processor 100 reads the information in the memory 101 and completes the steps of the above method in combination with its hardware.

[0178] The electronic device provided in the embodiment of the present application and the method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, operated or implemented by them.

[0179] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0180] Another embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method for predicting the risk of vehicle breakdown of any of the above-mentioned embodiments.

[0181] It should be noted that examples of computer-readable storage media may include, but are not limited to, optical disks, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0182] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0183] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0184] It should be noted that:

[0185] The term "module" is not intended to be limited to a specific physical form. Depending on the specific application, a module can be implemented as hardware, firmware, software, and / or a combination thereof. In addition, different modules can share common components or even be implemented by the same components. There may or may not be clear boundaries between different modules.

[0186] The algorithm and display provided herein are not inherently related to any specific computer, virtual device or other equipment. Various general devices can also be used together with examples based on this. According to the above description, it is obvious to construct the structure required for this type of device. In addition, the application is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the application described herein, and the description made to specific languages ​​above is for the purpose of disclosing the best mode of implementation of the application.

[0187] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0188] The above embodiments only express the implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting the risk of vehicle breakdown, characterized in that: include: Obtaining the driving time point of each driving of the target vehicle within the first preset time period and the state of charge SOC at the end of each driving; Obtain a first correlation value between the time from the driving time point to the target time point and the risk of breakdown; Obtaining a second correlation relationship value between the SOC at the end of the driving and the breakdown risk; Obtain each first SOC that is less than a first preset value among the SOCs at the end of each driving operation; Acquire a third correlation relationship value between the first SOC and the anchoring risk; Based on the first association relationship value and the second association relationship value of each driving, obtaining a predicted quantized value of the breakdown risk of the target vehicle at the target time point; The step of obtaining a predicted quantized value of the risk of breakdown of the target vehicle at the target time point based on the first association relationship value and the second association relationship value of each driving operation includes: Based on the third association relationship value and the first association relationship value and the second association relationship value of each driving, obtaining a predicted quantized value of the breakdown risk of the target vehicle at the target time point; The obtaining of a third correlation value between the first SOC and the anchoring risk includes: Based on each of the first SOCs and a general term of a preset progression function, obtaining values ​​of each term of the preset progression function; Obtaining the sum of the values ​​of the items to obtain the third association relationship value; Among them, the general term of the preset series function is the ratio of the number of second SOC values ​​to the third sum, the second SOC value is the first SOC whose product with 100 is less than the first value, the third sum is the sum of the number of second SOC values ​​and a third preset positive number, the first value is any integer in the first closed interval, the left endpoint of the first closed interval is 1, and the right endpoint is the product of the first preset value and 100.

2. The method according to claim 1, characterized in that The step of obtaining a predicted quantized value of the risk of breakdown of the target vehicle at the target time point based on the first association relationship value and the second association relationship value of each driving operation includes: Obtaining a first product of the first association relationship value and the second association relationship value in each driving; The sum of the first products corresponding to each driving is obtained to obtain a quantitative value of the predicted risk of breakdown of the target vehicle at the target time point.

3. The method according to claim 1, characterized in that The step of obtaining a predicted quantized value of the anchoring risk of the target vehicle at the target time point based on the third association relationship value and the first association relationship value and the second association relationship value of each driving operation includes: Obtaining a first product of the first association relationship value and the second association relationship value in each driving; A weighted sum of the fourth sum and the third association relationship value is obtained to obtain a quantitative value of the predicted risk of anchoring of the target vehicle at a target time point, wherein the fourth sum is the sum of the first products of each driving.

4. The method according to any one of claims 1 to 3, characterized in that The step of obtaining a first correlation value between the time from the driving time point to the target time point and the risk of breakdown includes: A first ratio of the opposite of the duration to the first sum is obtained, wherein the first ratio is a first correlation relationship value between the duration and the anchoring risk, and the first sum is the sum of the absolute value of the duration and a first preset positive number.

5. The method according to any one of claims 1 to 3, characterized in that The obtaining of the second correlation relationship value between the SOC at the end of the driving and the risk of breakdown includes: When the SOC at the end of the driving is greater than the second preset value, the second association relationship value is 0; When the SOC at the end of the driving is not greater than the second preset value, a second ratio of the opposite of the SOC at the end of the driving to the second sum is obtained, the second ratio is the second association relationship value, and the second sum is the sum of the SOC at the end of the driving and the second preset positive number.

6. The method according to any one of claims 1 to 3, characterized in that The step of obtaining the driving time point of each driving of the target vehicle within the first preset time period and the state of charge SOC at the end of each driving includes: Extracting discharge segment data from the historical driving record of the target vehicle in the first preset time period according to the charge and discharge flag; The driving time point of each driving and the state of charge SOC at the end of each driving are extracted from the discharge segment data.

7. A vehicle breakdown risk prediction device, characterized in that: include: A historical driving data acquisition module is used to obtain the driving time point of each driving of the target vehicle within a first preset time period and the state of charge SOC at the end of each driving; A first correlation value acquisition module is used to acquire a first correlation value between the time from the driving time point to the target time point and the risk of breakdown; A second correlation relationship value acquisition module is used to obtain a second correlation relationship value between the SOC at the end of the driving and the risk of breakdown; A first SOC acquisition module, used for acquiring each first SOC less than a first preset value among the SOCs at the end of each driving operation; A third association relationship value acquisition module, used to acquire a third association relationship value between the first SOC and the anchoring risk; A breakdown risk prediction module, configured to obtain a predicted quantitative value of the breakdown risk of the target vehicle at the target time point based on the first association relationship value and the second association relationship value of each driving; The anchoring risk prediction module is further used to obtain an anchoring risk prediction quantitative value of the target vehicle at a target time point based on the third association relationship value and the first association relationship value and the second association relationship value of each driving; The third association relationship value acquisition module includes: A first unit is used to obtain values ​​of each term of the preset series function based on each first SOC and a general term of the preset series function; The second unit is used to obtain the sum of the values ​​of each item to obtain a third association relationship value; Among them, the general term of the preset series function is the ratio of the number of second SOC values ​​to the third sum, the second SOC value is the first SOC whose product with 100 is less than the first value, the third sum is the sum of the number of second SOC values ​​and a third preset positive number, the first value is any integer in the first closed interval, the left endpoint of the first closed interval is 1, and the right endpoint is the product of the first preset value and 100.

8. An electronic device, characterized in that: The device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 6.

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