Storage battery power shortage early warning method and device, storage medium and equipment

Abstract

The invention provides a storage battery power shortage early warning method and device, a storage medium and equipment, and the method comprises the steps: obtaining the power shortage time and geographic position information of each vehicle through a big data platform, and carrying out the statistics of the power shortage times of each vehicle in a preset time period, and then, according to the power shortage time, the power shortage frequency and the geographic position information of each vehicle, judging a power shortage reason of each vehicle, and sending corresponding early warning information to a user of each vehicle based on the judged power shortage reason. Therefore, the big data technology is utilized to realize efficient and accurate analysis of the power shortage reason, and the user is reminded according to the power shortage reason, so that the user can know the condition of the storage battery, unnecessary in-store maintenance of the user is reduced, the condition that the storage battery cannot be started due to power shortage is also reduced, and the user experience is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of automobile management technology, and in particular to a method, device, storage medium and equipment for early warning of low battery of a battery. Background Art

[0002] A battery drain refers to a situation where the battery is insufficient, causing electrical appliances to not receive the normal operating voltage and thus fail to work properly. The reasons for the battery drain may include long-term parking and battery life reduction. Distinguishing the cause of the battery drain is crucial for subsequent treatment measures. If the battery drain is caused by the vehicle being parked for a long time and the battery is not damaged, the user does not need to go to the store to replace the battery. If the battery drain is caused by the battery life reduction, the user needs to go to the store to replace the battery to avoid the vehicle being unable to start. Related technologies use machine learning to identify the risk of draining the battery. However, it cannot know the specific cause and therefore cannot remind the user what to do, resulting in a poor user experience. Summary of the invention

[0003] The purpose of this application is to provide a battery low power warning method, device, storage medium and equipment, aiming to solve the problem in the related art of lack of effective processing methods to identify the cause of battery low power, resulting in poor user experience.

[0004] In a first aspect, the present application provides a battery low-power warning method, comprising: obtaining the time and geographic location information of each vehicle's low-power occurrence through a big data platform, and counting the number of times each vehicle has low-power occurrence within a preset time period; the big data platform records the low-power information uploaded by each vehicle when low-power occurs; judging the cause of low-power occurrence of each vehicle based on the time of low-power occurrence of each vehicle, the number of times low-power occurrence and the geographic location information; and sending corresponding warning information to users of each vehicle based on the cause of low-power occurrence.

[0005] In the above implementation process, the time and geographical location information of each vehicle's power failure are obtained through the big data platform, and the number of power failures of each vehicle within the preset time period is counted. Then, the reason for the power failure of each vehicle is determined based on the time, number of power failures and geographical location information of each vehicle, and then the corresponding warning information is sent to the user of each vehicle based on the determined reason for the power failure. In this way, big data technology is used to achieve efficient and accurate analysis of the reasons for power failure, and users are reminded of the reasons for power failure, so that users can know the battery status, reduce unnecessary repairs to the store, and reduce the situation of failure to start due to power failure, thereby effectively improving the user experience.

[0006] Furthermore, in some examples, the cause of the power shortage of each vehicle is determined based on the time when each vehicle suffers power shortage, the number of power shortages and the geographic location information, including: if the number of power shortages of any vehicle within a preset time period is greater than a preset number, determining that the cause of the power shortage of the vehicle is battery life decay; if the number of power shortages of any vehicle within a preset time period is greater than 1 time and less than or equal to the preset number, determining whether the locations where the power shortages of the vehicles are the same based on the geographic location information, and determining the cause of the power shortage of the vehicle based on the judgment result; if the number of power shortages of any vehicle within the preset time period is 1 time, determining the cause of the power shortage of the vehicle based on the last time point of use of the vehicle corresponding to the time when the vehicle suffered power shortage.

[0007] In the above implementation process, a specific method is provided for determining the cause of the battery failure based on the time when the battery failure occurred, the number of times the battery failure occurred, and the geographical location information of the vehicle.

[0008] Further, in some examples, determining whether the locations where the vehicles ran out of power are the same based on the geographic location information includes: calculating the distance between the address locations where the vehicles ran out of power twice based on the geographic location information; if the distance is less than a preset distance, determining that the locations where the vehicles ran out of power are the same.

[0009] In the above implementation process, a specific method is provided to determine whether the locations where the vehicles ran out of power are the same, that is, comparing the calculated distance between the GPS addresses where the vehicles ran out of power twice with a preset distance. If the calculated distance is less than the preset distance, it is determined that the locations where the vehicles ran out of power are the same.

[0010] Further, in some examples, determining the cause of the vehicle's power shortage based on the judgment result includes: when it is determined that the locations where the vehicle runs low on power are the same, obtaining a time point when the vehicle was last used that exceeds a preset redundant time from the time when the vehicle first ran low on power, if the time interval between the last time the vehicle was used and the time when the first power shortage occurred exceeds a target duration, determining that the cause of the vehicle's power shortage is long-term storage; if it is determined that the locations where the vehicle runs low on power are different, determining that the cause of the vehicle's power shortage is battery damage.

[0011] In the above implementation process, for vehicles that have lost power more than once and less than or equal to the preset number of times within the preset time period, if the location where the loss of power occurs is the same, then the time of the last data that exceeds the preset redundant time from the time of the first loss of power is obtained. If the time interval between the last data time and the time of the first loss of power exceeds the target time, it indicates that the vehicle was parked in the same location for a long time without power before the first loss of power. Therefore, the loss of power of the vehicle can be attributed to long-term storage. If the location where the loss of power occurs is different, it is considered that the vehicle has been powered on after the loss of power, but the battery still loses power, so it is determined that the battery may be damaged. In this way, the cause of the loss of power of the vehicle battery can be quickly and accurately determined.

[0012] Further, in some examples, the cause of the vehicle's power shortage is determined based on the last time the vehicle was used corresponding to the time when the vehicle ran out of power, including: obtaining the last time the vehicle was used that is more than a preset redundant time away from the time when the vehicle ran out of power; if the time interval between the last time the vehicle was used and the time when the vehicle ran out of power exceeds a target duration, determining that the cause of the vehicle's power shortage is long-term storage.

[0013] In the above implementation process, for a vehicle that has one battery failure within a preset time period, the last time data was obtained that exceeded the preset redundant time from the current battery failure of the vehicle. If the time interval between the last time data was obtained and the current battery failure time exceeds the target time, it can be determined that the battery failure of the vehicle was caused by long-term storage. In this way, the cause of battery failure of the vehicle can be quickly and accurately determined.

[0014] Furthermore, in some examples, determining the cause of power shortage of each vehicle based on the time when the power shortage occurred, the number of power shortages and the geographic location information also includes: calculating the 0.9 quantile of the number of power shortages, and determining the 0.9 quantile as a target number of power shortages; if the number of power shortages of any vehicle within a preset time period is greater than the target number of power shortages, determining that the cause of the power shortage of the vehicle is battery life degradation.

[0015] In the above implementation process, after counting the number of times each vehicle loses power within a preset time period, the 0.9th percentile of the number of times the power is lost can be calculated. When the number of times a vehicle loses power within a preset time period is greater than the 0.9th percentile, it indicates that the power loss phenomenon is relatively frequent, and the power loss can be directly attributed to the battery life decay. In this way, the reasons for the power loss of some vehicles can be quickly determined, thereby effectively improving the analysis efficiency.

[0016] Furthermore, in some examples, based on the cause of power shortage, corresponding warning information is sent to the user of each vehicle, including: if the cause of power shortage of any vehicle is long-term storage, a first warning message is sent to the user of the vehicle; the first warning message is used to notify the user of the cause of power shortage; if the cause of power shortage of any vehicle is battery life degradation, a second warning message is sent to the user of the vehicle; the second warning message is used to inform the user of the cause of power shortage and remind the user to perform repairs or replacements.

[0017] In the above implementation process, different warning information is sent to the vehicle user according to the different reasons for power loss. For vehicles whose power loss is caused by long-term storage, the reason for power loss is notified to the user to remind the user not to store the vehicle for a long time. For vehicles whose power loss is caused by battery life decay, not only the user is informed of the reason for power loss, but also the user is reminded to repair or replace the battery. In this way, the user's car experience is effectively improved.

[0018] In the second aspect, the present application provides a battery low-power warning device, including: a statistical module, used to obtain the time and geographical location information of each vehicle's low-power through a big data platform, and count the number of times each vehicle has low-power within a preset time period; the big data platform records the low-power information uploaded by each vehicle when low-power occurs; a judgment module, used to judge the cause of the low-power of each vehicle based on the time of the low-power of each vehicle, the number of times the low-power occurs and the geographical location information; a sending module, used to send corresponding warning information to the users of each vehicle based on the cause of the low-power.

[0019] In a third aspect, the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of the first aspects when executing the computer program.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed on a computer, the computer executes the method as described in any one of the first aspects.

[0021] In a fifth aspect, the present application provides a computer program product, which, when running on a computer, enables the computer to execute the method as described in any one of the first aspects.

[0022] Other features and advantages disclosed in the present application will be described in the following description, or some features and advantages can be inferred or determined without doubt from the description, or can be learned by implementing the above-mentioned technology disclosed in the present application.

[0023] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 A flowchart of a battery power shortage warning method provided in an embodiment of the present application;

[0026] Figure 2 A schematic diagram of the workflow of a battery low power warning solution based on vehicle big data provided in an embodiment of the present application;

[0027] Figure 3 A block diagram of a battery low power warning device provided in an embodiment of the present application;

[0028] Figure 4 A structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0030] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0031] As described in the background technology, there is a lack of effective processing methods for identifying the cause of battery power loss in the related art, resulting in poor user experience. Based on this, the embodiment of the present application provides a battery power loss warning solution to solve the above problem.

[0032] Next, the embodiments of the present application are introduced:

[0033] like Figure 1 As shown, Figure 1 This is a flow chart of a battery low power warning method provided by an embodiment of the present application. The method can be applied to a vehicle management platform, which can use cloud services as technical support to manage vehicles.

[0034] The method comprises:

[0035] Step 101: Obtain the time and geographical location information of each vehicle when power failure occurs through the big data platform, and count the number of power failures of each vehicle within a preset time period; the big data platform records the power failure information uploaded by each vehicle when power failure occurs;

[0036] Big data platform, also known as big data acquisition system, refers to a comprehensive information technology platform with big data as the core, integrating functions such as data storage, data management, data calculation, data analysis and data mining. In this embodiment, the vehicle uploads various bus signals related to power shortage through the big data platform to form power shortage information. The power shortage information here may include the time when the vehicle suffered power shortage, geographical location information, battery voltage monitoring information, etc. The vehicle management platform can obtain the time and geographical location information of each vehicle's power shortage from the big data platform according to a preset cycle or when a vehicle is detected to have suffered power shortage, and count the frequency of power shortages that have occurred in these vehicles in history, laying a good foundation for subsequent judgment of the cause of power shortage.

[0037] The geographical location information of the vehicle when it runs out of power can be its GPS (Global Positioning System) location address; the preset time period mentioned in this step can be 90 days, 60 days, or 120 days, etc., which can be set according to the needs of the specific scenario. In addition, when implemented, the vehicle management platform can process all vehicles managed by the platform, or only vehicles whose reasons for running out of power have not yet been determined, or only a specific batch of vehicles, and this application does not limit this.

[0038] Step 102, determining the cause of the power failure of each vehicle according to the time when the power failure of each vehicle occurred, the number of power failures and the geographical location information;

[0039] In this embodiment, big data technology is used to collect statistics on the frequency of battery failure in the history of the vehicle, and the cause of the battery failure is determined by combining the time and geographical location information of the battery failure. In this way, the cause of battery failure in the vehicle can be effectively identified.

[0040] In some embodiments, this step may include: if the number of times any vehicle runs out of power within a preset time period is greater than a preset number, determining that the cause of the vehicle's power loss is battery life decay; if the number of times any vehicle runs out of power within a preset time period is greater than 1 time and less than or equal to the preset number, judging whether the locations where the vehicles run out of power are the same based on the geographic location information, and determining the cause of the vehicle's power loss based on the judgment result; if the number of times any vehicle runs out of power within the preset time period is 1 time, determining the cause of the vehicle's power loss based on the last time point of use of the vehicle corresponding to the time when the vehicle ran out of power.

[0041] That is to say, when judging the cause of the vehicle's power loss, first make a preliminary judgment based on the number of times the vehicle loses power within a preset time period; then, when the number of times a vehicle loses power within a preset time period is greater than the preset number, it indicates that its power loss phenomenon is relatively frequent, and at this time, its power loss can be directly attributed to the battery life decay; when the number of times a vehicle loses power within a preset time period is greater than 1 time and less than or equal to the preset number, it is necessary to make further judgments based on whether the location where the vehicle loses power is the same; and for a vehicle that loses power once within the preset time period, it indicates that its power loss phenomenon is not frequent, and at this time, it is necessary to further judge whether the cause of its power loss is long-term storage based on the last time point of use corresponding to the time when the vehicle lost power. In this way, the efficiency and accuracy of the analysis of the cause of battery power loss in the vehicle are improved. Among them, the preset time period can be set to nearly 90 days of the current time, and the preset number can be set to 3 times. Of course, in other embodiments, the preset time period and preset number here can also be set differently according to the needs of the specific scene, and this application does not limit this.

[0042] In some embodiments, the aforementioned determination of whether the locations where the vehicle runs out of power are the same based on the geographic location information includes: calculating the distance between the address locations where the vehicle runs out of power twice based on the geographic location information; if the distance is less than the preset distance, determining that the locations where the vehicle runs out of power are the same. That is, when determining whether the locations where the vehicle runs out of power are the same, the distance between the GPS addresses where the vehicle runs out of power twice can be calculated. If it is less than the preset distance, it is determined that the locations where the vehicle runs out of power are the same. Taking the preset distance of 100 as an example, a vehicle runs out of power three times, and the GPS addresses where the three times the power runs out are recorded as point A, point B, and point C respectively. If the straight-line distance between point A and point B is less than 100 meters, the straight-line distance between point B and point C is less than 100 meters, and the straight-line distance between point A and point C is less than 100 meters, then it can be considered that the locations where the vehicle runs out of power three times belong to the same location. Similarly, the preset distance here can also be set differently according to the needs of the specific scenario.

[0043] Further, in some embodiments, the aforementioned determination of the cause of the vehicle's power shortage based on the judgment result may include: when it is determined that the locations where the vehicle has power shortage are the same, obtaining the last time the vehicle was used more than one day away from the time when the vehicle first ran out of power, if the time interval between the last time the vehicle was used and the time when the first power shortage occurred exceeds the target duration, determining that the cause of the vehicle's power shortage is long-term storage; if it is determined that the locations where the vehicle has power shortage are different, determining that the cause of the vehicle's power shortage is battery damage. That is, for vehicles that have power shortages greater than 1 and less than or equal to the preset number of times within a preset time period, if the locations where the power shortage occurs are the same, it is necessary to obtain the last time the vehicle was used corresponding to the time when the first power shortage occurred, that is, the time when there was last data. Since the vehicle may upload data before power feeding occurs, redundancy needs to be added when the first power feeding occurs. Optionally, the preset redundancy time is 24 hours, and the target duration is 30 days. Then obtain the last time t2 with data that is more than 24 hours away from the first power loss time t1. If the time interval between t2 and t1 exceeds 30 days, it indicates that the vehicle was parked in the same position for a long time without power before the first power loss. Therefore, the power loss of the vehicle can be attributed to long-term storage. On the contrary, if the power loss location is different, it is considered that the vehicle was powered on after the power loss, but the battery still lost power, so the battery may be damaged. In this way, the cause of the vehicle battery power loss can be quickly and accurately determined.

[0044] In addition, in some embodiments, the aforementioned determination of the cause of the vehicle's battery failure based on the last time point of use corresponding to the time when the vehicle ran out of power may include: obtaining the last time point of use of the vehicle that is more than the preset redundant time from the time when the vehicle ran out of power; if the time interval between the last time point of use and the time when the vehicle ran out of power exceeds the target duration, determining that the cause of the vehicle's battery failure is long-term storage. That is, for a vehicle that has run out of power 1 times within a preset time period, obtaining the time t4 that is more than the preset redundant time from the time t3 of the vehicle's current battery failure, such as the time t4 when there is last data within 24 hours, if the time interval between t4 and t3 exceeds the target duration, such as 30 days, it can be determined that the vehicle's battery failure is caused by long-term storage. In this way, a quick and accurate judgment of the cause of the vehicle's battery failure is achieved.

[0045] In addition, in some embodiments, this step may also include: calculating the 0.9 quantile of the number of power failures, and determining the 0.9 quantile as the target number of power failures; if the number of power failures of any vehicle within a preset time period is greater than the target number of power failures, determining that the cause of the power failure of the vehicle is the battery life decay. In other words, after counting the number of power failures of each vehicle within a preset time period, the 0.9 quantile of the number of power failures can be calculated and determined as the target number of power failures, that is, in the numerical distribution of the number of power failures of each vehicle, 90% of the vehicles have a power failure number that is less than or equal to the target number of power failures. Therefore, when the number of power failures of a vehicle within a preset time period is greater than the target number of power failures, it indicates that the power failure phenomenon is relatively frequent, and the power failure can be directly attributed to the battery life decay. In this way, the cause of power failure of some vehicles can be quickly determined, thereby effectively improving the analysis efficiency.

[0046] Step 103: Based on the cause of the power shortage, corresponding warning information is sent to the user of each vehicle.

[0047] In this embodiment, after determining the cause of the vehicle's low power, the vehicle management platform can send warning information corresponding to the cause of the low power to the vehicle user, so that the user can make appropriate processing operations based on the warning information, thereby improving the user's vehicle experience.

[0048] In some embodiments, this step may include: if the reason for the power loss of any vehicle is long-term placement, a first warning message is sent to the user of the vehicle; the first warning message is used to notify the user of the reason for the power loss; if the reason for the power loss of any vehicle is the battery life decay, a second warning message is sent to the user of the vehicle; the second warning message is used to inform the user of the reason for the power loss and remind the user to repair and replace. That is to say, different warning messages can be sent to the user of the vehicle according to different reasons for power loss. For vehicles whose power loss is caused by long-term placement, the platform can inform the user of the reason for power loss to remind the user not to leave the vehicle for a long time. For vehicles whose power loss is caused by battery life decay, the platform not only informs the user of the reason for power loss, but also reminds the user to repair and replace. In this way, users are prevented from unnecessary repairs in the store, and users can also know the battery status in advance, reduce the situation of failure to start due to power loss, and effectively improve the user's car experience. Among them, the platform can notify the user by SMS, APP message, or phone, etc. Different warning messages can use the same or different notification methods, and this application does not limit this.

[0049] In the embodiment of the present application, the time and geographical location information of each vehicle's power failure are obtained through the big data platform, and the number of times each vehicle has power failure within a preset time period is counted. Then, the cause of each vehicle's power failure is determined based on the time, number of times each vehicle has power failure, and geographical location information of each vehicle. Then, based on the determined cause of power failure, corresponding warning information is sent to the user of each vehicle. In this way, big data technology is used to achieve efficient and accurate analysis of the cause of power failure, and the user is reminded of the cause of power failure, so that the user can know the battery status, reduce the user's unnecessary repair in the store, and reduce the situation of failure to start due to power failure, thereby effectively improving the user experience.

[0050] In order to explain the solution of this application in more detail, a specific embodiment is introduced below:

[0051] This embodiment provides a battery low power warning solution based on vehicle big data. The workflow of this solution is as follows: Figure 2 As shown, including:

[0052] S201. When the vehicle runs out of power, the time when the power runs out and GPS information are obtained through the big data collection system;

[0053] S202. Count the number of times each vehicle has experienced power failure in the past 90 days, and calculate the 0.9 quantile of the number of times of power failure;

[0054] S203, determining whether the number of times the vehicle has run out of power is greater than the 0.9 percentile or greater than 3 times, if yes, executing S204, otherwise executing S205;

[0055] S204, determining that the battery shortage of the vehicle is caused by battery life degradation, notifying the user of the vehicle of the battery shortage, and reminding the user to repair or replace the battery;

[0056] S205, determining whether the number of times the vehicle has been out of power is greater than 1, if yes, executing S206, otherwise executing S213;

[0057] S206, calculating the distance between the GPS addresses where the vehicle runs out of power;

[0058] S207, judging whether the locations where the vehicles have low power are the same according to the calculation results, if yes, executing S208, otherwise executing S212;

[0059] Specifically, after calculating the distances between the GPS addresses where the vehicle ran out of power, if the straight-line distances between the two are both less than 100 meters, they are considered to be the same location, otherwise they are considered to be different locations;

[0060] S208, obtaining the time t2 when the vehicle last had data more than 24 hours after the first power failure time t1;

[0061] S209, determine whether the time interval between t2 and t1 exceeds 30 days, if yes, execute S210, otherwise execute S211;

[0062] S210, determining that the reason for the battery shortage of the vehicle is that the vehicle has been left unused for a long time, notifying the user of the vehicle of the reason for the battery shortage, and reminding the user not to leave the vehicle unused for a long time;

[0063] S211, continue to observe the vehicle, and determine the cause based on subsequent observations;

[0064] S212: Determine that the battery of the vehicle may be damaged, inform the user of the vehicle of the determination result, and suggest the user to go to a store for repair in time;

[0065] S213, obtaining the time t4 when the vehicle last had data more than 24 hours before the current power failure time t3;

[0066] S214, determine whether the time interval between t4 and t3 exceeds 30 days, if yes, execute S210, otherwise execute S211.

[0067] This embodiment scheme utilizes big data technology to count the frequency of battery failure in the history of the vehicle, and identifies the possible causes of the battery failure based on the percentile of the frequency of battery failure and location information, then informs the user of the cause and gives corresponding reminders to avoid unnecessary visits to the store for repairs. It also allows the user to know the battery condition in advance and reduce the situation of being unable to start the vehicle due to battery failure, thereby improving the user's car experience.

[0068] Corresponding to the embodiments of the aforementioned method, the present application also provides embodiments of a battery low power warning device and a terminal using the same:

[0069] like Figure 3 As shown, Figure 3 : is a block diagram of a battery low power warning device provided in an embodiment of the present application, the device comprising:

[0070] The statistical module 31 is used to obtain the time and geographical location information of each vehicle when the power shortage occurs through the big data platform, and count the number of power shortages of each vehicle within a preset time period; the big data platform records the power shortage information uploaded by each vehicle when the power shortage occurs;

[0071] The judgment module 32 is used to judge the reason for the power failure of each vehicle according to the time when the power failure of each vehicle occurred, the number of power failures and the geographical location information;

[0072] The sending module 33 is used to send corresponding warning information to the users of each vehicle based on the reason for the power shortage.

[0073] The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

[0074] This application also provides an electronic device, see Figure 4 , Figure 4 A block diagram of an electronic device provided in an embodiment of the present application. The electronic device may include a processor 410, a communication interface 420, a memory 430, and at least one communication bus 440. The communication bus 440 is used to realize direct connection and communication between these components. The communication interface 420 of the electronic device in the embodiment of the present application is used to communicate signaling or data with other node devices. The processor 410 may be an integrated circuit chip with signal processing capabilities.

[0075] The processor 410 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can 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 can be a microprocessor or the processor 410 can also be any conventional processor, etc.

[0076] The memory 430 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electric erasable programmable read-only memory (EEPROM), etc. The memory 430 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 410, the electronic device can execute the above-mentioned Figure 1 The method embodiment involves various steps.

[0077] Optionally, the electronic device may further include a storage controller and an input / output unit.

[0078] The memory 430, storage controller, processor 410, peripheral interface, input and output unit components are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses 440. The processor 410 is used to execute executable modules stored in the memory 430, such as software function modules or computer programs included in the electronic device.

[0079] The input and output unit is used to provide users with the task creation and to create a start optional time period or preset execution time for the task to realize the interaction between the user and the server. The input and output unit can be, but is not limited to, a mouse and a keyboard.

[0080] Understandably, Figure 4 The structure shown is for illustration only, and the electronic device may also include Figure 4 More or fewer components as shown, or with Figure 4 Different configurations shown. Figure 4 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0081] An embodiment of the present application further provides a storage medium having instructions stored thereon. When the instructions are run on a computer, the computer program is executed by a processor to implement the method described in the method embodiment. To avoid repetition, the method will not be described here.

[0082] The present application also provides a computer program product, which, when executed on a computer, enables the computer to execute the method described in the method embodiment.

[0083] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0085] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0086] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0087] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0088] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A battery power shortage warning method, characterized in that: include: Through the big data platform, the time and geographical location information of each vehicle's power failure is obtained, and the number of power failures of each vehicle in a preset time period is counted; The big data platform records the power shortage information uploaded by each vehicle when power shortage occurs; Determine the cause of the power failure of each vehicle according to the time when the power failure of each vehicle occurred, the number of power failures, and the geographical location information; Based on the reasons for the power shortage, corresponding warning information is sent to the users of each vehicle.

2. The method according to claim 1, characterized in that The determining the reason for the power failure of each vehicle according to the power failure time of each vehicle, the power failure frequency and the geographical location information includes: If the number of times any vehicle runs out of power within a preset time period is greater than a preset number, it is determined that the reason for the vehicle's power loss is battery life attenuation; If the number of times any vehicle runs out of power within a preset time period is greater than 1 and less than or equal to the preset number, determine whether the locations where the vehicles run out of power are the same based on the geographical location information, and determine the cause of the vehicle's run out of power based on the determination result; If any vehicle runs out of power once within a preset time period, the cause of the vehicle's power failure is determined based on the last time the vehicle was used corresponding to the time when the vehicle ran out of power.

3. The method according to claim 2, characterized in that The determining, based on the geographical location information, whether the locations where the vehicles are out of power are the same includes: Calculating the distance between the locations where the vehicle ran out of power twice according to the geographical location information; If the distance is less than a preset distance, it is determined that the locations where the vehicles are out of power are the same.

4. The method according to claim 3, characterized in that The determining the reason for the battery failure of the vehicle according to the judgment result includes: When it is determined that the locations where the vehicles have run out of power are the same, obtaining a time point of last use of the vehicle that exceeds a preset redundant time from the time point of the first power loss of the vehicle, and if the time interval between the last time point of use of the vehicle and the time point of the first power loss of the vehicle exceeds a target time length, determining that the reason for the power loss of the vehicle is long-term storage; If it is determined that the location where the vehicle is running low on power is different, it is determined that the cause of the vehicle's running low on power is that the battery is damaged.

5. The method according to claim 2, characterized in that: The determining the reason for the battery shortage of the vehicle according to the last time point of use of the vehicle corresponding to the time when the battery shortage of the vehicle occurs includes: Obtaining the last time the vehicle was used when the time from when the vehicle ran out of power exceeded the preset redundancy time; If the time interval between the last time the vehicle was used and the time when the vehicle ran out of power exceeds the target time length, it is determined that the reason for the vehicle's running out of power is that it was left unused for a long time.

6. The method according to claim 2, characterized in that The determining the cause of the power shortage of each vehicle according to the time when the power shortage of each vehicle occurs, the number of power shortages and the geographical location information also includes: Calculating the 0.9 quantile of the number of power failures, and determining the 0.9 quantile as the target number of power failures; If the number of times any vehicle runs out of power within a preset time period is greater than the target number of times of running out of power, it is determined that the cause of the running out of power of the vehicle is the attenuation of the battery life.

7. The method according to claim 1, characterized in that The sending of corresponding warning information to the user of each vehicle based on the reason for the power shortage includes: If the reason for the power loss of any vehicle is that it has been left unused for a long time, a first warning message is sent to the user of the vehicle; the first warning message is used to inform the user of the reason for the power loss; If the reason for the power shortage of any vehicle is the battery life reduction, a second warning message is sent to the user of the vehicle; the second warning message is used to identify the reason for the power shortage of the user and remind the user to carry out repairs and replacements.

8. A battery low power warning device, characterized in that: include: A statistical module is used to obtain the time and geographical location information of each vehicle when the power shortage occurs through the big data platform, and to count the number of power shortages of each vehicle within a preset time period; the big data platform records the power shortage information uploaded by each vehicle when the power shortage occurs; A judgment module, used to judge the cause of the power failure of each vehicle according to the time when the power failure of each vehicle occurred, the number of power failures and the geographical location information; The sending module is used to send corresponding warning information to users of each vehicle based on the reason for the power shortage.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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