Vehicle detection reminder method, system, computer device, and readable storage medium

Abstract

The present application relates to a vehicle detection reminder method, system, computer equipment, and readable storage medium, and relates to the field of vehicle detection technology. The method includes obtaining fault data of a vehicle under repair; matching the corresponding fault cause from a preset first database based on the fault data, wherein the fault cause includes the external environment, the vehicle itself, and driving habits; if the fault cause is driving habits, obtaining the first driving data of the vehicle under repair, wherein the first driving data refers to various operating parameters of the vehicle under repair during driving; analyzing the first driving data and determining the driving habits of the first driver; matching a second driver with the same driving habits from a preset second database; determining a vehicle with hidden dangers based on the second driver and the first driving data, and issuing a detection reminder to the vehicle with hidden dangers. The present application has the effect of identifying vehicles with hidden dangers in advance and reminding them to be detected in time to avoid the expansion of losses.

Description

Technical Field

[0001] The present application relates to the field of vehicle detection technology, and in particular to a vehicle detection reminder method, system, computer equipment, and readable storage medium. Background Art

[0002] Currently, users typically perform vehicle inspections and maintenance at fixed mileage or time intervals. However, the vehicle's condition can vary significantly depending on the time and mileage. Drivers often fail to monitor and inspect their vehicles before reaching the designated mileage or time interval. This can lead to subtle, unnoticeable damage to the vehicle while driving going undetected. If left unchecked, these minor faults can eventually lead to serious vehicle failures, resulting in significant losses and even endangering the driver's life.

[0003] In reality, the causes of multiple vehicle failures are somewhat similar. For example, when multiple vehicles share a bumpy road, the road conditions can cause wear and tear on the vehicle's brake system and engine. Alternatively, certain driving habits, such as high-speed low gear, low-speed high gear, or engaging a gear without depressing the clutch, can lead to powertrain failures.

[0004] The first driving data refers to the parameters of the vehicle's driving process. Speed, gear, brake, clutch (shifting is not fully depressed, half-clutch driving), restart, steering wheel fully turned,

[0005] Therefore, providing a solution that can remind the driver to inspect the vehicle before a serious fault occurs is a problem that needs to be solved now. Summary of the Invention

[0006] In view of this, the purpose of the embodiments of the present invention is to provide a vehicle detection reminder method, system, computer device, and readable storage medium. By identifying potential faults in unrepaired vehicles, the driver can be reminded to repair the vehicle in time to avoid further losses. The specific solution is as follows:

[0007] In the first aspect, the present application provides a vehicle detection reminder method using the following technical solutions:

[0008] A vehicle detection reminder method, the method comprising:

[0009] Obtain fault data of repaired vehicles;

[0010] Matching corresponding fault causes from a preset first database based on the fault data, wherein the fault causes include external environment, the vehicle itself, and driving habits;

[0011] If the fault is caused by driving habits, obtaining first driving data of the maintenance vehicle, wherein the first driving data refers to various operating parameters of the maintenance vehicle during driving;

[0012] analyzing the first driving data and determining the driving habits of the first driver;

[0013] matching a second driver having the same driving habit from a preset second database, wherein the second database stores all second drivers and their corresponding driving habits;

[0014] A potential hazard vehicle is determined based on the second driver and the first driving data, and a detection reminder is issued to the potential hazard vehicle.

[0015] Optionally, analyzing the first driving data and determining the driving habits of the first driver includes:

[0016] determining first operation data of a first driver during driving of the vehicle based on the first driving data;

[0017] parsing the first operation data to obtain first operation action data of the first driver;

[0018] The corresponding driving habit is matched from a preset third database according to the first operation action data.

[0019] Optionally, after parsing the first operation data to obtain the first operation action data of the first driver, and before matching the corresponding driving habits from a preset third database according to the first operation action data, the method further includes:

[0020] determining a first impact result of the first operating action data on the vehicle fault according to the first operating action data and the fault data, where the first impact result includes direct impact, indirect impact, and no impact;

[0021] Determining whether the first impact result is a direct impact;

[0022] If so, the corresponding driving habit is matched from a preset third database according to the first operation action data.

[0023] Optionally, after parsing the first operation data to obtain the first operation action data of the first driver, and before matching the corresponding driving habits from a preset third database according to the first operation action data, the method further includes:

[0024] Acquiring an operating pattern of the first operating action data, where the operating pattern refers to a frequency at which the driver performs the first operating action before the fault data is generated;

[0025] According to the operation rule, obtaining a hazard level corresponding to the first operation action data;

[0026] Determining whether the hazard level is greater than a preset value;

[0027] If so, the corresponding driving habit is matched from a preset third database according to the first operation action data.

[0028] Optionally, determining a potential vehicle based on the second driver and the first driving data, and issuing a detection reminder for the potential vehicle includes:

[0029] determining an uninspected vehicle matched according to the second driver from a preset fourth database as the first vehicle, wherein the fourth database stores uninspected vehicles corresponding to the second driver;

[0030] Acquire second driving data of the first vehicle, where the second driving data refers to various operating parameters of the first vehicle during driving;

[0031] Determine a vehicle with hidden danger based on the second driving data and the first driving data, and issue a detection reminder for the vehicle with hidden danger.

[0032] Optionally, determining a vehicle with hidden dangers based on the second driving data and the first driving data includes:

[0033] parsing the second driving data to obtain second operation action data of the second driver;

[0034] parsing the first driving data to obtain first operation action data of the first driver;

[0035] Obtaining the number of operations of the second operation action data that is consistent with the first operation action data corresponding to the first impact result being a direct impact or a hazard level greater than a preset value;

[0036] Determining whether the number of operations of the second operation action data exceeds a preset value;

[0037] If so, the current vehicle is determined to be a potential hazard vehicle.

[0038] Optionally, the method further includes:

[0039] Obtaining the operating status of the vehicle with hidden dangers, wherein the operating status includes a driving state and a stationary state;

[0040] When the operating state is the driving state, broadcasting a vehicle detection reminder to the second driver on the vehicle with the potential danger, and simultaneously broadcasting the driving habits that caused the vehicle failure;

[0041] When the running state is a stationary state, a vehicle detection reminder is sent to the second driver via a text message, a mini-program reminder, or the like.

[0042] In the second aspect, the vehicle detection reminder system provided by this application adopts the following technical solutions:

[0043] A vehicle detection reminder system, the system comprising:

[0044] Fault data acquisition module: used to obtain fault data of the repaired vehicle;

[0045] A fault cause acquisition module is configured to match the corresponding fault cause from a preset first database based on the fault data, wherein the fault cause includes the external environment, the vehicle itself, and driving habits;

[0046] A first driving data acquisition module is used to acquire first driving data of the maintenance vehicle when the fault cause is driving habits. The first driving data refers to various operating parameters of the maintenance vehicle during driving;

[0047] A first driving data processing module: configured to analyze the first driving data and determine the driving habits of the first driver;

[0048] A first determining module is configured to match a second driver having the same driving habit from a preset second database, wherein the second database stores all second drivers and their corresponding driving habits;

[0049] The second determining module is used to determine the potential vehicle according to the second driver and the first driving data, and issue a detection reminder to the potential vehicle.

[0050] In a third aspect, the present application provides a computer device that adopts the following technical solution:

[0051] A computer device comprises: a memory for storing a computer program; and a processor for implementing the steps of the above-mentioned vehicle detection reminder method when executing the computer program.

[0052] In a fourth aspect, the present application provides a computer-readable storage medium that employs the following technical solutions:

[0053] A computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned vehicle detection reminder method when executed by a processor.

[0054] In summary, this application includes at least one of the following beneficial technical effects:

[0055] 1. Using the first driving data of a faulty vehicle, the system identifies vehicles with similar driving conditions as potential risk vehicles and broadcasts fault detection reminders to these vehicles, notifying them to undergo repairs in advance. This prevents further damage and potential risk from spreading, effectively controlling vehicle damage.

[0056] 2. By analyzing the fault data, we can further analyze the driver's driving habits, and then match drivers with the same driving habits that caused the faults. This can help identify potential faults that may be difficult to detect directly in uninspected vehicles, and remind drivers to carry out timely inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0058] Figure 1 This is a flow chart of a vehicle detection reminder method provided by an embodiment of the present application;

[0059] Figure 2 This is a flow chart of determining driving habits of a vehicle detection reminder method provided in an embodiment of the present application;

[0060] Figure 3 This is a flowchart of a specific method for determining driving habits in a vehicle detection reminder method provided by an embodiment of the present application;

[0061] Figure 4 This is a flowchart of another specific method for determining driving habits in a vehicle detection reminder method provided in an embodiment of the present application;

[0062] Figure 5 This is a flow chart of a vehicle detection and reminder method for determining a vehicle with hidden dangers provided by an embodiment of the present application;

[0063] Figure 6 This is a flowchart of a specific method for determining a vehicle with hidden dangers in a vehicle detection reminder method provided by an embodiment of the present application;

[0064] Figure 7 This is a flow chart of sending a detection reminder of a vehicle detection reminder method provided in an embodiment of the present application;

[0065] Figure 8 This is a structural block diagram of a vehicle detection reminder system provided by an embodiment of the present application;

[0066] Figure 9 This is a hardware structure diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0067] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0068] In the description of the embodiments of the present application, words such as “exemplary,” “for example,” or “for instance,” are used to indicate examples, illustrations, or descriptions.

[0069] In the description of the embodiments of the present application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, B exists alone, and A and B exist at the same time. In addition, unless otherwise specified, the term "multiple" means two or more. For example, multiple first operation action data refers to two or more first operation action data. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0070] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0071] Example 1: A vehicle detection reminder method, referring to Figure 1 , the method comprising:

[0072] S100. Obtain fault data of the vehicle under repair.

[0073] Maintenance vehicles are those undergoing maintenance after experiencing a breakdown while driving, or at specified intervals or mileage. Fault data refers to faults discovered during maintenance of maintenance vehicles. Common vehicle faults include transmission damage, tire blowouts, engine damage, hydraulic power pump damage, brake system damage, and shock absorber damage.

[0074] S200. Match corresponding fault causes from a preset first database based on the fault data, where the fault causes include external environment, the vehicle itself, and driving habits.

[0075] Among them, the preset first database stores common automobile faults and their causes. The same automobile fault may correspond to multiple causes. Automobile faults have their causes. For example, when the road conditions are poor, it is easy to cause shock absorber damage, tire blowout and other faults; when the car is produced, parts from different manufacturers are used, and if the quality levels are uneven, it will also cause automobile faults; some irregular actions of the driver when driving the vehicle will cause gearbox damage, hydraulic power steering pump and other faults.

[0076] S300. If the fault is caused by driving habits, obtain first driving data of the vehicle under inspection, where the first driving data refers to various operating parameters of the vehicle under inspection during driving.

[0077] When the driver is driving the vehicle, the first driving data of the vehicle is recorded, including the operating parameters of various components of the vehicle, such as the relevant data of the clutch, brake, and gear during the gear shifting process; and the relevant data of the throttle and brake during the stable driving of the vehicle.

[0078] S400. Analyze the first driving data and determine the driving habits of the first driver.

[0079] The first driver is the driver who inspects the vehicle. Driving habits refer to the set of actions a driver takes while driving a vehicle, such as applying the brakes when slowing down or pressing the clutch first, then shifting gears, and then releasing the clutch when shifting gears.

[0080] After obtaining the first driving data of the vehicle under inspection, the driving habits of the first driver can be obtained based on the relevant operating data of each component of the vehicle in the first driving data. Figure 2 , this step further includes the following steps:

[0081] S410. Determine first operation data of the first driver during driving the vehicle based on the first driving data.

[0082] The first operation data refers to the instructions issued by the driver to the components to be controlled during driving, such as shifting instructions, braking instructions, etc.

[0083] After acquiring the first driving data, the operating parameters of each component of the vehicle during driving are known, and based on the operating parameters, the first operation data of the driver can be obtained. For example, when the vehicle gear shifts from first gear to second gear, it means that the driver has issued a gear shift command.

[0084] S420. Analyze the first operation data to obtain first operation action data of the first driver.

[0085] The first operation action data refers to the actions performed by the first driver to manipulate vehicle components during driving. The first operation data may correspond to one or more first operation action data. For example, when the first operation data is a shift instruction, the corresponding first operation action data may include clutch engagement, shifting, and clutch release; and when the first operation data is a brake instruction, the corresponding first operation action data may include braking.

[0086] S430. Match corresponding driving habits from a preset third database according to the first operation action data.

[0087] Among them, the third database stores all the first operation action data and their corresponding driving habits. One driving habit can correspond to multiple different first operation action data. For example, the first operation action data corresponding to the driving habit of "half-clutch shifting" include pressing the clutch, shifting gears and releasing the clutch.

[0088] In one embodiment, referring to Figure 3 , the following steps are also included between step S420 and step S430:

[0089] S4211. Determine a first impact result of the first operating action data on the vehicle fault based on the first operating action data and the fault data. The first impact result includes direct impact, indirect impact, and no impact.

[0090] The first impact result is used to characterize the degree to which the driver's specific actions while driving the vehicle affect the vehicle failure. For example, if the vehicle failure is a damaged hydraulic power steering pump, then in the first operation action data, the first impact result of turning the steering wheel all the way is a direct impact, almost directly causing the damage to the hydraulic power steering pump. If the vehicle failure is a damaged shock absorber, then in the first operation action data, the first impact result of pressing the accelerator is a direct impact, while the first impact result of pressing the brake or clutch is an indirect impact.

[0091] S4212. Determine whether the first impact result is a direct impact.

[0092] S4213. If yes, match the corresponding driving habit from a preset third database based on the first operation action data.

[0093] When the first impact result is a direct impact result, it means that the first operation action data directly causes the vehicle failure. Therefore, the driving habit matched according to the first operation action data is the root cause of the failure.

[0094] After matching the corresponding driving habits, the corresponding driver can be found based on the driving habits and whether the vehicle he is driving has hidden dangers can be detected.

[0095] In another embodiment, referring to Figure 4 , the following steps are included between step S420 and step S430:

[0096] S4221. Obtain the operating pattern of the first operating action data, where the operating pattern refers to the frequency at which the driver performs the first operating action before the fault data is generated.

[0097] The operating pattern refers to the frequency with which the driver performs the first operating action before generating fault data. For example, if the faulty vehicle is caused by a broken hydraulic power steering pump, the driver's actions may be analyzed to determine the driver's 1,000 instances of turning the steering wheel after fully turning it.

[0098] S4222. According to the operation rule, obtain the hazard level corresponding to the first operation action data.

[0099] Among them, the hazard level of the first operation action data is inversely proportional to the number of operations thereof, that is, the fewer the number of operations of the first operation action data that causes a fault, the higher the hazard level of the first operation action data. The system is preset with multiple hazard levels, which are determined according to the average number of actions that cause a fault. For example: there are 5 hazard levels preset, specifically, when the number of actions that cause a fault is 1-1000, the hazard level is 5; when the number of actions that cause a fault is 1001-2000, the hazard level is 4; when the number of actions that cause a fault is 2001-3000, the hazard level is 3; when the number of actions that cause a fault is 3001-4000, the hazard level is 2; when the number of actions that cause a fault is 4001-5000, the hazard level is 1. The average number of times that the steering wheel's hydraulic power pump fails due to turning the steering wheel all the way to the limit is 1,000 times, so the hazard level of this action is 5; the average number of times that the transmission is damaged due to half-clutch shifting is 3,000 times, so the hazard level of this action is 3.

[0100] S4223. Determine whether the hazard level is greater than a preset value.

[0101] When the hazard level is greater than the preset value, it means that the hazard level of the action is relatively high.

[0102] S4224. If yes, match the corresponding driving habit from a preset third database based on the first operation action data.

[0103] S500. Match a second driver with the same driving habit from a preset second database, wherein the second database stores all second drivers and their corresponding driving habits.

[0104] The second driver refers to the driver of the vehicle that has not been inspected. Different drivers develop different driving habits while driving a vehicle, and the specific actions they perform when maneuvering the vehicle may vary. Many drivers also have irregular driving problems. These problems do not affect the operation of the vehicle, but after prolonged and repeated operation, they often cause vehicle failures.

[0105] After matching the driving habits from the preset third database based on the first operation action data, a second driver with the driving habits is matched from the preset second database based on the driving habits. Multiple second drivers may have the same driving habits, so one or more second drivers can be matched based on the driving habits.

[0106] S600. Determine a potential vehicle based on the second driver and the first driving data, and issue a detection reminder to the potential vehicle.

[0107] After obtaining the second driver, the vehicle with hidden danger can be determined based on the first driving data of the second driver and the inspection vehicle. In this embodiment, referring to Figure 5 Specific steps to identify vehicles with potential hazards include:

[0108] S610. Determine the uninspected vehicle matched according to the second driver from the preset fourth database as the first vehicle, wherein the fourth database stores uninspected vehicles corresponding to the second driver.

[0109] The fourth database stores all drivers and their corresponding uninspected vehicles. A single driver may correspond to multiple uninspected vehicles, and a single uninspected vehicle may correspond to multiple drivers. Matching the uninspected vehicle based on the second driver involves obtaining a list of the second driver from the fourth database and then identifying the uninspected vehicle matched to the list as the first vehicle.

[0110] S620. Obtain second driving data of the first vehicle, where the second driving data refers to various operating parameters of the first vehicle during driving.

[0111] After the first vehicle is matched, second driving data of the first vehicle is obtained. The second driving data is similar in nature to the first driving data, and both correspond to various operating parameters of the vehicle during driving.

[0112] S630. Determine a vehicle with hidden dangers based on the second driving data and the first driving data, and issue a detection reminder to the vehicle with hidden dangers.

[0113] After obtaining the second driving data of the first vehicle, it is compared and analyzed with the first driving data of the faulty vehicle to determine which first vehicles are potential hidden danger vehicles and issue detection reminders for potential hidden danger vehicles. Figure 6 Specific steps to identify vehicles with potential hazards include:

[0114] S631. Analyze the second driving data to obtain second operation action data of the second driver.

[0115] The second operating action data refers to the actions performed by the second driver while manipulating vehicle components. By analyzing the second driving data of the unserviced vehicle, the system can obtain the second operating action data of the second driver performing specific maneuvers while manipulating the vehicle. Both the second operating action data and the first operating action data refer to the specific actions performed by the driver while manipulating the vehicle. The difference is that the second operating action data corresponds to an unserviced vehicle, while the first operating action data corresponds to a serviced vehicle.

[0116] S632. Analyze the first driving data to obtain first operation action data of the first driver.

[0117] Similarly, by analyzing the first driving data of the maintenance vehicle, the first operation action data corresponding to the faulty vehicle can be obtained.

[0118] S633. Obtain the number of operations of the second operation action data that is consistent with the first operation action data corresponding to the first impact result being a direct impact or a hazard level greater than a preset value.

[0119] Among all the first operation action data corresponding to the faulty vehicle, multiple first operation action data whose first impact result is a direct impact or a hazard level greater than a preset value are determined, and the second operation action data are matched with these first operation action data to find one or more second operation action data that are consistent with them, and the number of operations of these second operation action data is obtained. The number of operations refers to the number of times these second operation action data have been operated in the second driving data generated by the second driver driving the unrepaired vehicle. For example: the first operation action data that causes the hydraulic power steering pump to be damaged is turning the steering wheel to the end for steering. The system matched this action in the second operation action data corresponding to the unrepaired vehicle, and obtained that the second driver had performed this action 548 times in the past driving process.

[0120] S634: Determine whether the number of operations of the second operation action data exceeds a preset value.

[0121] Different operating actions cause faults at different frequencies, and therefore, the corresponding preset values ​​for different operating actions are also different. For example, turning the steering wheel fully will cause a hard-to-detect fault after 1,000 attempts, while a major fault such as a blocked steering wheel will occur after 1,500 attempts. When the user detects that the action has been executed 548 times, the preset value has not been exceeded.

[0122] S635. If yes, determine that the current vehicle is a potential hazard vehicle.

[0123] If the number of second action data operations exceeds a preset value, it indicates that the uninspected vehicle has likely experienced a difficult-to-detect fault. For example, if the number of steering wheel movements (turning the steering wheel all the way to the limit) exceeds the preset 1000, it indicates that the uninspected vehicle's steering wheel is likely faulty, but currently undetectable. Failure to inspect the vehicle could lead to a major fault, potentially endangering driver safety. Therefore, the uninspected vehicle is identified as a potential hazard.

[0124] In this embodiment, after the hidden danger vehicle is determined, the driver needs to be notified. Figure 7 , further comprising the following steps:

[0125] S700. Obtain the operating status of the vehicle with potential hazards, where the operating status includes a driving state and a stationary state.

[0126] The running state is used to indicate whether the potential vehicle is currently moving or stopped. When the system identifies a potential vehicle, the potential vehicle may be in a moving state or a stationary state.

[0127] S800. When the operating state is the driving state, broadcast a vehicle detection reminder to the second driver on the vehicle with hidden dangers, and at the same time broadcast the driving habits that caused the vehicle failure.

[0128] When a vehicle with a potential safety hazard is in motion, indicating a driver is on board, the system will issue a vehicle inspection reminder via vehicle radio, prompting the driver to conduct a timely inspection to prevent a minor fault from becoming a major one and causing greater losses. The system will also broadcast the driving habits that led to the vehicle failure, reminding the driver to correct the incorrect driving habits promptly.

[0129] S900. When the operating state is stationary, a vehicle detection reminder is sent to the second driver via SMS, mini-program reminder, etc.

[0130] When a vehicle with a hidden danger is stationary, it means that the driver is not in the vehicle. The system can send a vehicle inspection reminder to the driver via text message or mini program, informing the driver to conduct a vehicle inspection in time to avoid minor faults from becoming major faults and causing greater losses.

[0131] Example 2: A vehicle detection reminder system, referring to Figure 8 , the system comprises:

[0132] Fault data acquisition module: used to obtain fault data of the repaired vehicle.

[0133] After a fault is found in a maintenance vehicle, the system obtains the fault data of the maintenance vehicle through the fault data acquisition module and analyzes it.

[0134] Fault cause acquisition module: used to match the corresponding fault cause from a preset first database based on the fault data, where the fault cause includes the external environment, the vehicle itself, and driving habits.

[0135] There are many reasons for vehicle failure, which can be categorized into external environmental factors, such as uneven road surface, sharp objects on the road, etc.; vehicle internal factors, such as a component that is prone to failure due to improper installation during the vehicle production process; driving habit factors, which refer to the driver's improper operation while driving the vehicle.

[0136] The system's preset first database stores common automobile faults and their causes. After the system obtains the fault data through the fault data acquisition module, it matches the fault cause corresponding to the fault data from the first database through the fault cause acquisition module.

[0137] The first driving data acquisition module is used to obtain the first driving data of the maintenance vehicle when the fault cause is driving habits. The first driving data refers to various operating parameters of the maintenance vehicle during driving.

[0138] When the fault cause is driving habits, it means that the fault is caused by the driver's irregular driving. The system obtains the first driving data through the first driving data acquisition module and further analyzes the driver's driving habits.

[0139] The first driving data processing module is used to analyze the first driving data and determine the driving habits of the first driver.

[0140] After acquiring the first driving data of the vehicle under maintenance, the system can obtain the driving habits of the first driver by processing the relevant operating data of each component of the vehicle in the first driving data through the first driving data processing module.

[0141] The first determining module is configured to match a second driver having the same driving habit from a preset second database, wherein the second database stores all second drivers and their corresponding driving habits.

[0142] After the system determines the driving habits of the first driver through the first driving data processing module, it then matches a second driver with the same driving habits from a preset second database through the first determination module. Multiple second drivers may have the same driving habits, so one or more second drivers can be matched based on the driving habits.

[0143] The second determination module is used to determine the vehicle with hidden dangers based on the second driver and the first driving data, and issue a detection reminder to the vehicle with hidden dangers.

[0144] After acquiring the second driver, the system can determine the vehicle with hidden dangers based on the first driving data of the second driver and the inspection vehicle through the second determination module.

[0145] Example 3: A computer device, referring to Figure 9 The computer device includes a memory, a processor and a bus. The memory and the processor are connected through the bus and communicate with each other. The memory stores a computer program that can be run on the processor. When the processor executes the computer program, the application publishing method in the above embodiment is implemented.

[0146] The memory may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor.

[0147] The processor implements the vehicle detection reminder method in Example 1 by reading and executing computer program instructions stored in the memory.

[0148] Example 4: A computer-readable storage medium stores a computer program, which implements the steps of the vehicle detection reminder method in Example 1 when executed by a processor.

[0149] The examples of this specific embodiment are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, any equivalent changes made based on the structure, shape, and principle of this application should be included in the scope of protection of this application.

Claims

1. A vehicle detection reminder method, characterized in that: The method comprises: Obtain fault data of repaired vehicles; Matching corresponding fault causes from a preset first database based on the fault data, wherein the fault causes include external environment, the vehicle itself, and driving habits; If the fault is caused by driving habits, obtaining first driving data of the maintenance vehicle, wherein the first driving data refers to various operating parameters of the maintenance vehicle during driving; analyzing the first driving data and determining the driving habits of the first driver; matching a second driver having the same driving habit from a preset second database, wherein the second database stores all second drivers and their corresponding driving habits; Determining a potential vehicle based on the second driver and the first driving data, and issuing a detection reminder for the potential vehicle; The determining of the potential danger vehicle based on the second driver and the first driving data, and issuing a detection reminder for the potential danger vehicle includes: determining an uninspected vehicle matched according to the second driver from a preset fourth database as the first vehicle, wherein the fourth database stores uninspected vehicles corresponding to the second driver; Acquire second driving data of the first vehicle, where the second driving data refers to various operating parameters of the first vehicle during driving; Determine a vehicle with hidden danger based on the second driving data and the first driving data, and issue a detection reminder for the vehicle with hidden danger.

2. A vehicle detection reminder method according to claim 1, characterized in that: The analyzing the first driving data and determining the driving habits of the first driver includes: determining first operation data of a first driver during driving of the vehicle based on the first driving data; parsing the first operation data to obtain first operation action data of the first driver; The corresponding driving habit is matched from a preset third database according to the first operation action data.

3. A vehicle detection reminder method according to claim 2, characterized in that: After parsing the first operation data to obtain the first operation action data of the first driver, and before matching the corresponding driving habits from a preset third database according to the first operation action data, the method further includes: determining a first impact result of the first operating action data on the vehicle fault according to the first operating action data and the fault data, where the first impact result includes direct impact, indirect impact, and no impact; Determining whether the first impact result is a direct impact; If so, the corresponding driving habit is matched from a preset third database according to the first operation action data.

4. A vehicle detection reminder method according to claim 2, characterized in that: After parsing the first operation data to obtain the first operation action data of the first driver, and before matching the corresponding driving habits from a preset third database according to the first operation action data, the method further includes: Acquiring an operating pattern of the first operating action data, where the operating pattern refers to a frequency at which the driver performs the first operating action before the fault data is generated; According to the operation rule, obtaining a hazard level corresponding to the first operation action data; Determining whether the hazard level is greater than a preset value; If so, the corresponding driving habit is matched from a preset third database according to the first operation action data.

5. The vehicle detection reminder method according to claim 1, characterized in that: Determining a vehicle with hidden dangers according to the second driving data and the first driving data includes: parsing the second driving data to obtain second operation action data of the second driver; parsing the first driving data to obtain first operation action data of the first driver; Obtaining the number of operations of the second operation action data that is consistent with the first operation action data corresponding to the first impact result being a direct impact or a hazard level greater than a preset value; Determining whether the number of operations of the second operation action data exceeds a preset value; If so, the current vehicle is determined to be a potential hazard vehicle.

6. A vehicle detection reminder method according to claim 1, characterized in that: The method further comprises: Obtaining the operating status of the vehicle with hidden dangers, wherein the operating status includes a driving state and a stationary state; When the operating state is the driving state, broadcasting a vehicle detection reminder to the second driver on the vehicle with the potential danger, and simultaneously broadcasting the driving habits that caused the vehicle failure; When the running state is a stationary state, a vehicle detection reminder is sent to the second driver via a text message or a mini-program reminder.

7. A vehicle detection reminder system, characterized in that: A vehicle detection reminder method for executing any one of claims 1 to 6, the system comprising: Fault data acquisition module: used to obtain fault data of the repaired vehicle; A fault cause acquisition module is configured to match the corresponding fault cause from a preset first database based on the fault data, wherein the fault cause includes the external environment, the vehicle itself, and driving habits; A first driving data acquisition module is used to acquire first driving data of the maintenance vehicle when the fault cause is driving habits. The first driving data refers to various operating parameters of the maintenance vehicle during driving; A first driving data processing module: configured to analyze the first driving data and determine the driving habits of the first driver; A first determining module is configured to match a second driver having the same driving habit from a preset second database, wherein the second database stores all second drivers and their corresponding driving habits; The second determining module is used to determine the potential vehicle according to the second driver and the first driving data, and issue a detection reminder to the potential vehicle.

8. A computer device, characterized in that: The computer device includes: a memory for storing a computer program; and a processor for implementing the steps of the vehicle detection reminder method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of the vehicle detection reminder method according to any one of claims 1 to 6.

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