Vehicle fault early warning method, device and system and vehicle

Abstract

The invention provides a vehicle fault early warning method, device and system and a vehicle, and relates to the technical field of vehicles, and the vehicle fault early warning method comprises the steps: obtaining monitoring data of the vehicle, determining the fault degree of a part or a functional system corresponding to the monitoring data based on the monitoring data of the vehicle, and carrying out fault early warning based on the fault degree. Therefore, before the vehicle breaks down, fault early warning can be carried out according to the fault degree of parts or functional systems uploaded by the vehicle, and the influence on normal driving of the vehicle after the fault occurs is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicles, and in particular, to a vehicle fault warning method, device, system and vehicle. Background Art

[0002] With the increasingly fierce competition in the domestic automobile market, new models have more and more functions. The more functions there are, the greater the probability of vehicle failures. Therefore, higher requirements are put forward for vehicle fault handling.

[0003] In the related art, vehicle fault diagnosis monitors whether the working state data of the vehicle is abnormal through fixed logical rules. For example, it judges whether the working state data reaches a certain threshold, and when abnormal data is detected, it gives a fault prompt by reporting a fault code, etc. However, when the working state data of the vehicle is detected to be abnormal, the vehicle fault usually has already occurred, thus affecting the normal driving of the vehicle.

[0004] Therefore, there is a certain need for vehicle fault warning at present. Summary of the Invention

[0005] The problem solved by the present invention is how to realize vehicle fault warning.

[0006] To solve the above problems, the present invention provides a vehicle fault warning method, device, system and vehicle.

[0007] In a first aspect, the present invention provides a vehicle fault warning method, and the vehicle fault warning method includes:

[0008] Obtain the monitoring data of the vehicle; the monitoring data is the working state data of the components or functional systems monitored by the vehicle;

[0009] Based on the monitoring data of the vehicle, determine the fault degree of the components or functional systems corresponding to the monitoring data; the fault degree is used to characterize the probability of a fault occurring in the corresponding components or functional systems;

[0010] Perform fault warning based on the fault degree.

[0011] Optionally, the determining the fault degree of the components or functional systems corresponding to the monitoring data based on the monitoring data of the vehicle includes:

[0012] Obtain a plurality of preset fault degree conditions corresponding to the monitoring data; each preset fault degree condition corresponds to a fault degree;

[0013] Obtain the comparison result between the monitoring data and its corresponding preset target data;

[0014] Determine a preset fault degree condition satisfied by the comparison result among the multiple preset fault degree conditions;

[0015] According to a preset fault degree condition satisfied by the determined comparison result, determine the fault degree of the component or functional system corresponding to the monitoring data.

[0016] Optionally, each of the preset fault degree conditions corresponds to a threshold range, and the threshold ranges corresponding to the multiple preset fault degree conditions are continuous and non - overlapping;

[0017] The determining a preset fault degree condition satisfied by the comparison result among the multiple preset fault degree conditions includes:

[0018] Determine the preset fault degree condition corresponding to the threshold range in which the comparison result is located among the multiple preset fault degree conditions.

[0019] Optionally, it further includes:

[0020] Upload the fault degree of the component or functional system corresponding to the monitoring data to the cloud platform;

[0021] Receive the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected output by the cloud platform; wherein, the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected are determined by the cloud platform based on the statistical analysis result of the fault degrees uploaded by multiple vehicles;

[0022] Based on the correction coefficient, correct the preset fault degree condition to be corrected.

[0023] Optionally, the cloud platform is used to feedback the statistical analysis result of the fault degrees uploaded by the multiple vehicles to the user, and in response to the user's input operation, determine the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected; or,

[0024] The cloud platform is used to automatically analyze the statistical analysis result of the fault degrees uploaded by the multiple vehicles by using a preset algorithm, and determine the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected.

[0025] Optionally, the cloud platform is used to obtain the actual fault degree of the corresponding component or functional system of the multiple vehicles;

[0026] The cloud platform is further configured to determine the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected according to the comparison result between the fault degree of the components or functional systems corresponding to the monitoring data of the multiple vehicles and the actual fault degree of the components or functional systems corresponding to the multiple vehicles.

[0027] Optionally, receiving the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition output by the cloud platform includes:

[0028] Receiving a predetermined signal cyclically generated and output by the cloud platform based on the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition at a preset frequency; the predetermined signal has a predetermined number of digits, wherein the first number of digits in the predetermined number of digits of the predetermined signal is used to represent the preset fault degree condition to be corrected, and the second number of digits in the predetermined number of digits of the predetermined signal is used to represent the correction coefficient of the preset fault degree condition to be corrected.

[0029] Optionally, the monitoring data of the vehicle includes at least one of time-based monitoring data, frequency-based monitoring data, and location-based monitoring data.

[0030] In a second aspect, the present invention provides a vehicle fault warning device, including:

[0031] A data acquisition module, configured to acquire monitoring data of a vehicle; the monitoring data is the working state data of components or functional systems monitored by the vehicle;

[0032] A fault degree determination module, configured to determine the fault degree of the components or functional systems corresponding to the monitoring data based on the monitoring data of the vehicle;

[0033] A fault warning module, configured to perform fault warning based on the fault degree.

[0034] In a third aspect, the present invention provides a vehicle, including a memory and a processor;

[0035] The memory is configured to store a computer program;

[0036] The processor is configured to implement the vehicle fault warning method as described in the first aspect when executing the computer program.

[0037] In a fourth aspect, the present invention provides a vehicle fault warning system, including a cloud platform and a vehicle, wherein the vehicle is wirelessly communicatively connected to the cloud platform, and the vehicle includes a vehicle-end controller, and the vehicle-end controller is configured to implement the vehicle fault warning method as described in the first aspect.

[0038] The beneficial effects of a vehicle fault warning method, device, system, and vehicle according to the present invention are as follows: Obtain the monitoring data of the vehicle, where the monitoring data is the working state data of the components or functional systems monitored by the vehicle, providing data support for subsequent determination of the fault degree of the vehicle components or functional systems. Based on the monitoring data of the vehicle, determine the fault degree of the components or functional systems corresponding to the monitoring data. The fault degree is used to characterize the probability of a fault occurring in the corresponding components or functional systems. Before a fault occurs, predict the probability of a fault occurring in the vehicle components or functional systems, so as to facilitate subsequent fault warning. Perform fault warning based on the fault degree, enabling fault warning to be carried out according to the fault degree of the vehicle components or functional systems uploaded by the vehicle before the vehicle fails, avoiding the impact on normal driving of the vehicle after a fault occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 FIG. 6 is a schematic structural diagram of a vehicle fault warning system according to an embodiment of the present invention;

[0040] Figure 2 FIG. 10 is a flowchart of a vehicle fault warning method according to an embodiment of the present invention;

[0041] Figure 3 FIG. 14 is a flowchart of determining the corresponding fault degree based on the monitoring data according to an embodiment;

[0042] Figure 4 FIG. 18 is a schematic diagram of determining the corresponding fault degree based on the monitoring data according to an embodiment;

[0043] Figure 5 FIG. 22 is an interaction flowchart of the cloud platform and the vehicle terminal controller of the vehicle for the received fault degree of the vehicle components or functional systems according to an embodiment;

[0044] Figure 6 FIG. 26 is a schematic curve diagram of vehicle quantity - fault degree according to an embodiment;

[0045] Figure 7 FIG. 30 is a schematic diagram of correcting the delay condition according to an embodiment;

[0046] Figure 8 FIG. 34 is a schematic diagram of an 8-bit predetermined signal according to an embodiment;

[0047] Figure 9 FIG. 38 is a schematic diagram of the analysis value of the signal value corresponding to different counter values in the predetermined signal according to an embodiment;

[0048] FIG. 10(a) is a schematic diagram of the analysis rule of multiplication correction 1 according to an embodiment;

[0049] FIG. 10(b) is a schematic diagram of the analysis rule of addition correction 1 according to an embodiment;

[0050] Figure 11 Schematic structural diagram of a vehicle fault warning device according to an embodiment of the present invention;

[0051] Figure 12 Schematic structural diagram of an electronic device according to an embodiment of the present invention. Detailed implementation manners

[0052] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following will describe in detail the specific embodiments of the present invention with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0053] It should be understood that the various steps recorded in the method embodiments of the present invention can be executed in different orders and / or executed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0054] The terms "including" and its variants used herein are open-ended, that is, "including but not limited to"; the term "based on" is "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules, or units, and are not used to limit the order of the functions executed by these devices, modules, or units or their interdependent relationships.

[0055] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly stated in the context, it should be understood as "one or more".

[0056] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0057] In related technologies, the early warning of vehicle failures is mostly achieved through neural network models. For example, in a related technology, the charging trip data of a vehicle is uploaded to a cloud platform, and a warning model is used on the cloud platform to predict possible failures. However, the warning model relies on a large amount of data for training, especially on failure feature data. If the amount of data used for training is too small, the prediction accuracy will be reduced. For another example, in a related technology, the sampling data of the ignition plugs of a vehicle engine is first obtained, then an offline model is used for analysis to determine the model parameters, and finally the model is solidified into the vehicle terminal controller to achieve failure early warning at the vehicle terminal. This model also requires a large number of experiments to determine the failure early warning threshold, and the prediction accuracy of the offline model at the vehicle terminal is poor. For still another example, in a related technology, the historical experience database is searched according to the failure data to predict the cause and treatment method of the failure. This method actually predicts the cause of the failure for the already occurred failure and takes relevant measures, which can achieve the effect of quickly solving the problem, rather than failure early warning.

[0058] In view of the problems existing in the above related technologies, the embodiments of the present invention provide a vehicle failure early warning method, device, system and vehicle.

[0059] As Figure 1 shown, the embodiments of the present invention provide a vehicle failure early warning system, including a cloud platform 100 and a vehicle 200.

[0060] Among them, the cloud platform 100 realizes data interaction with the vehicle 200 through wireless communication technology. Optionally, no limitation is imposed on the wireless communication technology in this embodiment, and it can be one or more of wireless communication technologies under any protocol.

[0061] Specifically, the cloud platform 100 can manage based on the failure degree corresponding to the monitoring data uploaded by the vehicle 200 and perform failure early warning when needed.

[0062] Specifically, the vehicle 200 is used to indicate one or more vehicles interconnected with the cloud platform 100 through wireless communication technology, and does not specifically refer to a certain vehicle in motion. The vehicle 200 is configured with a monitoring and sensing device for monitoring the operation data of each functional system of the vehicle or the operation data of components. The vehicle 200 includes a vehicle terminal controller 201, that is, the center of the vehicle, for controlling the vehicle.

[0063] The embodiments of the present invention determine the failure degree of each monitoring data in the vehicle 200 and perform failure early warning based on the failure degree, without relying on a large amount of data training of a deep learning model, and realizing failure early warning.

[0064] As Figure 2 shown, a vehicle failure early warning method provided by the embodiments of the present invention includes:

[0065] S210: Obtain the monitoring data of vehicle 200.

[0066] Specifically, the monitoring data is the working state data of components or functional systems monitored by the vehicle. In some embodiments, the monitoring data can be data collected by sensors. The sensors can be anti-theft indication sensors, throttle sensors, coolant sensors, door sensors, power sensors, turn signal sensors, tire pressure sensors, seat belt sensors, handbrake sensors, trunk sensors, and windshield wiper sensors in the vehicle, etc. The working states of components or functional systems can include: anti-theft indication, brakes, throttle, coolant, doors, power, turn signals, tire pressure, seat belts, handbrake, trunk, windshield wipers, and so on. In other embodiments, the monitoring data can also be control data in the vehicle-mounted controller. The vehicle-mounted controller is used to monitor and control the vehicle, such as operations like acceleration, deceleration, and steering.

[0067] In some embodiments, the monitoring data can be time-based monitoring data, such as the time when the supercharger valve opens; in other embodiments, the monitoring data can also be position-based monitoring data, such as the zero position voltage of the pedal. In still other embodiments, the monitoring data can also be count-based monitoring data, such as the number of teeth of the engine gear that have not rotated to a predetermined position.

[0068] S220: Based on the monitoring data of vehicle 200, determine the degree of failure of the components or functional systems corresponding to the monitoring data.

[0069] Specifically, the degree of failure is used to characterize the probability of a corresponding component or functional system having a failure. For example, a 70% degree of failure means that the probability of the component or functional system corresponding to this monitoring data having a failure is 70%, and a 90% degree of failure means that the probability of the component or functional system corresponding to this monitoring data having a failure is 90%.

[0070] S230: Conduct a failure warning based on the degree of failure.

[0071] Specifically, it is judged whether a failure warning needs to be conducted according to the level of the degree of failure. If the degree of failure is high, for example, greater than 90%, then send a failure warning for the corresponding component or functional system to the background server for subsequent failure processing; if the degree of failure is low, no failure warning is conducted.

[0072] In some embodiments, the above steps S210 to S230 are all executed in the vehicle-end controller 201 of vehicle 200. That is, the vehicle-end controller 201 performs the entire process of monitoring data acquisition, degree of failure determination, and failure warning, and when it is determined to conduct a failure warning, it gives a prompt to the vehicle user through other means such as the display module in vehicle 200.

[0073] In some other embodiments, the above-mentioned step S210 and step S220 may be executed in the vehicle terminal controller 201 of the vehicle 200, and step S230 is executed in the cloud platform 100. Specifically, S230: Performing a fault warning based on the fault degree may include: The vehicle terminal controller 201 uploads the fault degree of the component or functional system corresponding to the monitoring data to the cloud platform 100, and the cloud platform 100 performs a fault warning based on the fault degree. When the cloud platform 100 determines that a warning is required for the fault degree, it may prompt the vehicle user by sending a prompt message to the vehicle 200, or notify the after-sales center to contact the vehicle user for vehicle inspection.

[0074] In this embodiment, the monitoring data of the vehicle is obtained, where the monitoring data is the working state data of the components or functional systems monitored by the vehicle, providing data support for subsequent determination of the fault degree of the vehicle components or functional systems. Based on the monitoring data of the vehicle, the fault degree of the component or functional system corresponding to the monitoring data is determined. The fault degree is used to characterize the probability of a corresponding component or functional system having a fault. Before a fault occurs, the probability of a fault occurring in the vehicle components or functional systems is predicted, so as to facilitate subsequent fault warning. Performing a fault warning based on the fault degree enables a fault warning to be performed according to the fault degree of the components or functional systems uploaded by the vehicle before the vehicle has a fault, avoiding the impact on normal vehicle driving after a fault occurs.

[0075] Optionally, as Figure 3 shown, in S220, determining the fault degree of the component or functional system corresponding to the monitoring data based on the monitoring data of the vehicle includes:

[0076] S310: Obtain a plurality of preset fault degree conditions corresponding to the monitoring data; wherein, each preset fault degree condition corresponds to a fault degree.

[0077] In some embodiments, for each type of monitoring data, there may be multiple fault degrees, for example, the fault degrees are: 90%, 80%, 70%, 60%, etc., and each fault degree corresponds to a preset fault degree condition.

[0078] S320: Obtain the comparison result between the monitoring data and its corresponding preset target data.

[0079] In some embodiments, the monitoring data is the actually monitored data, and the preset target data is the target data corresponding to the monitoring data. For example, for the pedal zero voltage, the monitoring data is the voltage corresponding to the actual pedal zero position, and the preset target data is 0V.

[0080] In some embodiments, the comparison result between the monitoring data and its corresponding preset target data may be the difference between the monitoring data and its corresponding preset target data, or may be the ratio of the monitoring data to its corresponding preset target data. This embodiment does not make specific limitations on this.

[0081] S330: Determine a preset fault degree condition satisfied by the comparison result among multiple preset fault degree conditions.

[0082] S340: Determine the fault degree of the component or functional system corresponding to the monitoring data according to a preset fault degree condition satisfied by the determined comparison result.

[0083] Specifically, the fault degree corresponding to a preset fault degree condition satisfied by the comparison result between the monitoring data and its corresponding preset target data is the fault degree of the component or functional system corresponding to the determined monitoring data. For example, when the comparison result satisfies the preset fault degree condition corresponding to a 90% fault degree, the fault degree of the component or functional system corresponding to the monitoring data is 90%.

[0084] It should be noted that for multiple preset fault degree conditions, the comparison result between the monitoring data and its corresponding preset target data only satisfies one of the preset fault degree conditions.

[0085] In this optional embodiment, each fault degree corresponds to a preset fault degree condition. According to the preset fault degree condition satisfied by the comparison result between the monitoring data and its corresponding preset target data, the fault degree of the component or functional system corresponding to the monitoring data is determined, and different fault degrees can be determined according to different monitoring data.

[0086] Optionally, each preset fault degree condition corresponds to a threshold range, and the threshold ranges corresponding to multiple preset fault degree conditions are continuous and non-overlapping.

[0087] For example, the threshold range corresponding to preset fault degree condition 1 is [A1, A2), the threshold range corresponding to preset fault degree condition 2 is [A2, A3), and the threshold range corresponding to preset fault degree condition 3 is [A3, A4).

[0088] What is included in S330 to determine a preset fault degree condition satisfied by the comparison result among multiple preset fault degree conditions is:

[0089] Determine the preset fault degree condition corresponding to the threshold range in which the comparison result is located among multiple preset fault degree conditions.

[0090] In some embodiments, taking the opening degree of the pressure boosting valve as the monitoring data as an example, as Figure 4As shown, there are 4 degrees of failure, namely failure degree 1, failure degree 2, failure degree 3, and failure degree 4. Among them, the preset failure degree condition corresponding to failure degree 1 is delay condition 1, the preset failure degree condition corresponding to failure degree 2 is delay condition 2, the preset failure degree condition corresponding to failure degree 3 is delay condition 3, and the preset failure degree condition corresponding to failure degree 4 is delay condition 4. If the difference between the actual opening and the target opening of the pressure boosting valve exceeds the deviation threshold, that is, the deviation exceeds the limit state, and the deviation exceeding the limit state means that the pressure boosting valve is in the open state, then when the time for the pressure boosting valve to be in the open state meets the above-mentioned delay condition 1, it is determined that the failure degree corresponding to the monitoring data is failure degree 1, and so on, the failure degree corresponding to the monitoring data can be determined.

[0091] In this optional embodiment, compared with the method of directly outputting a failure code during a failure, this embodiment outputs the corresponding failure degree according to the monitoring data, and the corresponding failure degree can be obtained before a component or functional system of the vehicle fails, so that when the failure degree is relatively large, a failure warning can be given in advance to avoid the occurrence of a failure.

[0092] Optionally, as Figure 5 shown, the cloud platform 100 and the vehicle-end controller 201 of the vehicle 200 perform the following interaction processing on the received failure degree of the components or functional systems of the vehicle:

[0093] S510: The vehicle-end controller 201 of any vehicle 200 uploads the failure degree corresponding to each item of monitoring data to the cloud platform 100.

[0094] S520: The cloud platform 100 receives the failure degree corresponding to each item of monitoring data uploaded by the vehicle-end controllers 201 of multiple vehicles 200.

[0095] Specifically, the multiple vehicles 200 received by the cloud platform 100 are all vehicles that communicate and interconnect with the cloud platform 100.

[0096] S530: The cloud platform 100 performs statistical analysis on the failure degrees uploaded by multiple vehicles 200 to obtain the statistical analysis result of the failure degree.

[0097] Specifically, the statistical analysis result of the failure degree can be the number of vehicles corresponding to each failure degree. As Figure 6 shown, a curve graph of vehicle quantity - failure degree can be established.

[0098] S540: The cloud platform 100 determines the preset failure degree condition to be corrected and the correction coefficient of the preset failure degree condition to be corrected based on the statistical analysis result of the failure degree.

[0099] Under normal circumstances, the number of vehicles corresponding to different degrees of failure should satisfy a certain regular distribution, that is, the lower the degree of failure, the more the number of vehicles corresponding to that degree of failure, and the higher the degree of failure, the fewer the number of vehicles corresponding to that degree of failure.

[0100] Based on the above rule, the cloud platform 100 can determine whether the statistical analysis results of the failure degrees of the various monitoring data uploaded by the multiple vehicles 200 satisfy the above rule based on the statistical analysis results of the failure degrees of the various monitoring data received from the multiple vehicles 200. If it is satisfied, it indicates that the preset failure degree condition in the vehicle terminal controller 201 of the vehicle 200 is reasonable. If not, it indicates that the preset failure degree condition in the vehicle terminal controller 201 of the vehicle 200 is not reasonable, and the corresponding preset failure degree condition needs to be corrected by a correction coefficient.

[0101] In some embodiments, the cloud platform 100 feeds back the statistical analysis results of the failure degrees uploaded by the multiple vehicles to the user. The user can be a back-end personnel and can be displayed to the user through a user terminal (PC, tablet computer, smart phone, etc.). When the user discovers an anomaly based on the statistical analysis results, the user inputs the preset failure degree condition to be corrected and the correction coefficient of the preset failure degree condition to be corrected through the input device of the user terminal, so as to determine the preset failure degree condition to be corrected and the correction coefficient of the preset failure degree condition to be corrected in response to the user's input operation.

[0102] In some embodiments, the cloud platform 100 automatically analyzes the statistical analysis results of the failure degrees uploaded by the multiple vehicles by using a preset AI algorithm to automatically determine the preset failure degree condition to be corrected and the correction coefficient of the preset failure degree condition to be corrected.

[0103] S550: The cloud platform 100 outputs the preset failure degree condition to be corrected and the correction coefficient of the preset failure degree condition to be corrected to the vehicle terminal controller 201.

[0104] In some embodiments, the correction coefficient includes a correction formula and a coefficient. The correction formula generally includes multiplication and addition, and the coefficient is the coefficient of multiplication or addition. By default, the correction coefficient is multiplication correction 1 and / or addition correction 0. When the cloud platform 100 detects that the statistical analysis result of the failure degree is abnormal (does not satisfy the above rule), the default correction coefficient is modified, and the new correction coefficient is output to the vehicle terminal controller 201 in the vehicle 200.

[0105] S560: The vehicle terminal controller 201 receives the preset failure degree condition to be corrected and the correction coefficient of the preset failure degree condition to be corrected output by the cloud platform 100, and corrects the threshold range corresponding to the preset failure degree condition to be corrected based on the correction coefficient.

[0106] For example, as Figure 7 shown, the delay condition 1 (preset fault degree condition) is corrected, and the correction factors are multiplicative correction 1 and additive correction 1. That is, 1 is added to the threshold range corresponding to the original delay condition 1. For example, if the original delay condition 1 is [A1, A2), then the delay condition 1 after being corrected by the correction factor is [A1 + 1, A2 + 1).

[0107] The vehicle-end controller 201 determines the fault degree based on the corrected preset fault degree condition, and the vehicle-end controller 201 can perform fault warning according to the re-determined fault degree.

[0108] In this optional embodiment, the cloud platform 100 performs statistical analysis based on the fault degrees uploaded by multiple vehicles 200. When it is found that the statistical analysis result is abnormal, a correction factor is generated and output to the vehicle-end controller 201 of the vehicle 200. The vehicle-end controller 201 corrects the threshold range of the preset fault degree condition to be corrected based on the correction factor, so as to obtain a more accurate fault degree, thereby realizing accurate fault warning.

[0109] Optionally, the cloud platform 100 is used to obtain the actual fault degrees of the corresponding components or functional systems of multiple vehicles.

[0110] The cloud platform 100 is further used to determine the preset fault degree condition to be corrected and the correction factor of the preset fault degree condition to be corrected according to the comparison result between the fault degrees of the components or functional systems corresponding to the monitoring data of multiple vehicles and the actual fault degrees of the corresponding components or functional systems of multiple vehicles.

[0111] Specifically, when the cloud platform 100 determines that the fault degree of the component or functional system corresponding to the monitoring data is relatively high, it can first transmit the fault degree to the after-sales personnel of the vehicle. According to the fault degree of the vehicle transmitted by the cloud platform 100, the after-sales personnel can contact the vehicle user to drive the vehicle to the after-sales store for inspection. When the comparison result between the actual fault degree of the inspected vehicle and the fault degree transmitted by the cloud platform 100 exceeds the threshold, the actual fault degree of the vehicle and the fault degree transmitted by the cloud platform 100 are uploaded to the cloud platform 100. The cloud platform 100 determines the preset fault degree condition to be corrected and the correction factor of the preset fault degree condition to be corrected according to the comparison result between the actual fault degree of the vehicle and the fault degree transmitted by the cloud platform 100.

[0112] For example: If the fault degree corresponding to the monitoring data is 90%, while the actual fault degree is 50%, it indicates that the numerical value of the threshold range of the preset fault degree condition is too small, resulting in the predicted fault degree being higher than the actual situation. Therefore, the correction factor can be set to increase the upper and lower thresholds of the threshold range of the preset fault degree condition.

[0113] If the fault level corresponding to the monitoring data is 50%, while the actual fault level is 90%, it indicates that the numerical value of the threshold range of the preset fault level condition is too large, resulting in the predicted fault level being lower than the actual situation. Therefore, a correction coefficient can be set to reduce the upper and lower thresholds of the threshold range of the preset fault level condition.

[0114] Optionally, limited by the signal transmission limitation between the network bandwidth and the vehicle terminal controller - CAN bus - load, it is inconvenient for the cloud platform 100 to send multiple groups of signals to the vehicle terminal controller 201 simultaneously. Therefore, in this embodiment, the cloud platform 100 cyclically generates and outputs a predetermined signal to the vehicle terminal controller 201 based on the preset fault level condition to be corrected and the correction coefficient of the preset fault level condition to be corrected at a preset frequency. In one embodiment, the preset frequency can be 15 seconds each time.

[0115] The preset fault level condition to be corrected and the correction coefficient of the preset fault level condition to be corrected output by the cloud platform 100 are a predetermined signal with a predetermined number of digits. Among them, the first number of digits in the predetermined number of digits of the predetermined signal is used to represent the preset fault level condition to be corrected, and the second number of digits in the predetermined number of digits of the predetermined signal is used to represent the correction coefficient of the preset fault level condition to be corrected.

[0116] In one embodiment, the predetermined number of digits can be 8 bits. The first number of digits in the predetermined number of digits is the first 3 bits (high 3 bits) of the 8 bits, and the second number of digits in the predetermined number of digits is the last 5 bits (low 5 bits) of the 8 bits. As Figure 8 shown:

[0117] The first 3 bits (bit7, bit6, bit5) are defined as the counter. 000 to 111 can respectively represent 0 to 7, a total of 8 count values, and each count value represents a preset fault level condition.

[0118] The last 5 bits (bit4, bit3, bit2, bit1, bit0) are defined as the signal value. 00001 to 11110 can respectively represent 1 to 30, a total of 30 values. These 30 values, combined with additional parsing rules, can implement 30 different correction coefficients.

[0119] In addition, when the last 5 bits are 00000, it is parsed as no data sent by the cloud platform 100; when the last 5 bits are 11111, it is parsed as a fault state.

[0120] As Figure 9 shown, Figure 9 It exemplifies the different parsed values of the 5 bits of the signal value corresponding to each value of the counter.

[0121] After receiving a predetermined signal, the vehicle end controller 201 will analyze the predetermined signal, and a corresponding parsed value can be obtained. The parsed value can be in 30 cases. Each parsed value can obtain an expected multiplication correction coefficient or addition correction coefficient according to specific parsing rules. As shown in FIGS. 10(a) and 10(b), they are the parsing rules for multiplication correction 1 and addition correction 1 respectively.

[0122] As Figure 11 shown, a vehicle fault warning device 1100 provided by an embodiment of the present invention includes:

[0123] A data acquisition module 1110, configured to acquire monitoring data of the vehicle; the monitoring data is the working state data of components or functional systems monitored by the vehicle;

[0124] A fault degree determination module 1120, configured to determine the fault degree of the components or functional systems corresponding to the monitoring data based on the monitoring data of the vehicle;

[0125] A fault warning module 1130, configured to perform fault warning based on the fault degree.

[0126] Optionally, the determining the fault degree of the components or functional systems corresponding to the monitoring data based on the monitoring data of the vehicle includes:

[0127] Obtaining a plurality of preset fault degree conditions corresponding to the monitoring data; each of the preset fault degree conditions corresponds to a fault degree;

[0128] Obtaining a comparison result between the monitoring data and its corresponding preset target data;

[0129] Determining a preset fault degree condition satisfied by the comparison result among the plurality of preset fault degree conditions;

[0130] Determining the fault degree of the components or functional systems corresponding to the monitoring data according to a preset fault degree condition satisfied by the determined comparison result.

[0131] Optionally, each of the preset fault degree conditions corresponds to a threshold range, and the threshold ranges corresponding to the plurality of preset fault degree conditions do not overlap at all;

[0132] The determining a preset fault degree condition satisfied by the comparison result among the plurality of preset fault degree conditions includes:

[0133] Determining a preset fault degree condition corresponding to the threshold range in which the comparison result is located among the plurality of preset fault degree conditions.

[0134] Optionally, it further includes:

[0135] Receive the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition output by the cloud platform; wherein, the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected are determined by the cloud platform based on the statistical analysis results of the fault degrees uploaded by multiple vehicles.

[0136] Based on the correction coefficient, correct the preset fault degree condition to be corrected.

[0137] Optionally, the cloud platform feeds back the statistical analysis results of the fault degrees uploaded by the multiple vehicles to the user, and determines the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected in response to the user's input operation; or,

[0138] The cloud platform automatically analyzes the statistical analysis results of the fault degrees uploaded by the multiple vehicles by using a preset algorithm, and determines the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected.

[0139] Optionally, the cloud platform is used to obtain the actual fault degrees of the corresponding parts or functional systems of the multiple vehicles.

[0140] The cloud platform is further used to determine the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected according to the comparison result between the fault degrees of the parts or functional systems corresponding to the monitoring data of the multiple vehicles and the actual fault degrees of the corresponding parts or functional systems of the multiple vehicles.

[0141] Optionally, the receiving the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition output by the cloud platform includes:

[0142] Receiving a predetermined signal cyclically generated and output by the cloud platform based on the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected at a preset frequency; the predetermined signal has a predetermined number of digits, wherein the first number of digits in the predetermined number of digits of the predetermined signal is used to represent the preset fault degree condition to be corrected, and the second number of digits in the predetermined number of digits of the predetermined signal is used to represent the correction coefficient of the preset fault degree condition to be corrected.

[0143] Optionally, the monitoring data of the vehicle includes at least one of time-based monitoring data, frequency-based monitoring data, and location-based monitoring data.

[0144] Such as Figure 12As shown, a vehicle 1200 provided by an embodiment of the present invention includes a memory 1210 and a processor 1220; the memory 1210 is used to store a computer program; the processor 1220 is used to implement the vehicle fault warning method as described above when executing the computer program.

[0145] Or, a vehicle 1200 includes a memory 1210 and a processor 1220 coupled to the memory 1210; the memory 1210 is configured to store a computer program; the processor 1220 is configured to perform the following operations when executing the computer program:

[0146] Obtain the monitoring data of the vehicle; the monitoring data is the working state data of components or functional systems monitored by the vehicle;

[0147] Based on the monitoring data of the vehicle, determine the fault degree of the components or functional systems corresponding to the monitoring data; the fault degree is used to characterize the probability of a fault occurring in the corresponding components or functional systems;

[0148] Perform a fault warning based on the fault degree.

[0149] The vehicle 1200 includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The computing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0150] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. The parts or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0151] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the protection scope of the present invention.

Claims

1. A vehicle fault warning method, characterized in that: The vehicle fault early warning method comprises: Acquiring monitoring data of the vehicle; the monitoring data is working status data of components or functional systems monitored by the vehicle; Based on the monitoring data of the vehicle, determining the degree of failure of the component or functional system corresponding to the monitoring data; the degree of failure is used to characterize the probability of failure of the corresponding component or functional system; A fault warning is performed based on the fault degree.

2. The vehicle failure warning method according to claim 1, characterized in that: Determining the degree of failure of a component or a functional system corresponding to the monitoring data based on the monitoring data of the vehicle includes: Acquire a plurality of preset fault degree conditions corresponding to the monitoring data; each of the preset fault degree conditions corresponds to a fault degree; Obtaining a comparison result of the monitoring data and its corresponding preset target data; Determining a preset fault degree condition satisfied by the comparison result among the plurality of preset fault degree conditions; The failure degree of the component or functional system corresponding to the monitoring data is determined based on a preset failure degree condition satisfied by the determined comparison result.

3. The vehicle failure warning method according to claim 2, characterized in that: Each of the preset fault degree conditions corresponds to a threshold range, and the threshold ranges corresponding to the multiple preset fault degree conditions are continuous and do not overlap; Determining a preset fault degree condition satisfied by the comparison result among the plurality of preset fault degree conditions comprises: A preset fault degree condition corresponding to a threshold range in which the comparison result is located is determined among the plurality of preset fault degree conditions.

4. The vehicle failure warning method according to claim 2, characterized in that: Also includes: Uploading the failure degree of the component or functional system corresponding to the monitoring data to the cloud platform; Receiving the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected output by the cloud platform; wherein the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected are determined by the cloud platform based on the statistical analysis results of the fault degrees uploaded by multiple vehicles; Based on the correction coefficient, the preset fault degree condition to be corrected is corrected.

5. The vehicle fault warning method according to claim 4, characterized in that: The cloud platform is used to feed back the statistical analysis results of the fault levels uploaded by the multiple vehicles to the user, and determine the preset fault level condition to be corrected and the correction coefficient of the preset fault level condition to be corrected in response to the user's input operation; or, The cloud platform is used to automatically analyze the statistical analysis results of the fault levels uploaded by the multiple vehicles using a preset algorithm, and determine the preset fault level conditions to be corrected and the correction coefficients of the preset fault level conditions to be corrected.

6. The vehicle failure warning method according to claim 4, characterized in that: The cloud platform is used to obtain the actual failure degree of the corresponding components or functional systems of the multiple vehicles; The cloud platform is also used to determine the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected based on the comparison results of the fault degree of the components or functional systems corresponding to the monitoring data of the multiple vehicles and the actual fault degree of the corresponding components or functional systems of the multiple vehicles.

7. The vehicle failure warning method according to claim 4, characterized in that: The receiving of the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected output by the cloud platform includes: A predetermined signal is received which is cyclically generated and outputted by the cloud platform according to a preset frequency based on the preset fault degree condition to be corrected and the correction coefficient of the preset fault degree condition to be corrected; the predetermined signal has a predetermined number of bits, wherein the first number of bits in the predetermined number of bits of the predetermined signal is used to characterize the preset fault degree condition to be corrected, and the last second number of bits in the predetermined number of bits of the predetermined signal is used to characterize the correction coefficient of the preset fault degree condition to be corrected.

8. The vehicle failure warning method according to any one of claims 1 to 7, characterized in that: The vehicle monitoring data includes at least one of time monitoring data, frequency monitoring data and position monitoring data.

9. A vehicle fault warning device, characterized in that: include: A data acquisition module, used to acquire vehicle monitoring data; The monitoring data is the working status data of the components or functional systems monitored by the vehicle; A fault degree determination module, used to determine the fault degree of the component or functional system corresponding to the monitoring data based on the monitoring data of the vehicle; A fault warning module is used to provide a fault warning based on the fault degree.

10. A vehicle, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the vehicle fault warning method as described in any one of claims 1 to 8 when executing the computer program.

11. A vehicle fault warning system, characterized in that: It includes a cloud platform and a vehicle, wherein the vehicle is connected to the cloud platform via wireless communication, and the vehicle includes a vehicle-side controller, and the vehicle-side controller is used to execute the vehicle fault warning method as described in any one of claims 1 to 8.

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