Fault early warning method, device and equipment based on similarity vehicles and storage medium

Abstract

The invention discloses a fault early warning method, device and equipment based on similar vehicles and a storage medium, and relates to the field of intelligent early warning. The method comprises the following steps of: determining similar vehicles of each vehicle according to attribute information of each vehicle by taking the same attribute information of the vehicles as a determination condition; when the vehicle breaks down, the vehicle with the high similarity in all the similar vehicles of the current vehicle is determined as the early warning vehicle of the current vehicle; and carrying out early warning on the early warning vehicle according to the fault. When vehicle early warning is carried out, a vehicle sensor is not needed, the early warning cost is remarkably reduced, and the early warning duration is guaranteed; early warning is carried out directly according to the basic attribute information of the vehicle, and the method is simple and efficient; and the basic attribute information of the vehicle is necessary information of the vehicle, so that the method is basically suitable for all vehicles.

Description

Technical Field

[0001] The present invention relates to the field of intelligent early warning, and in particular to a fault early warning method, device, equipment and storage medium based on similar vehicles. Background Art

[0002] Automobile fault warning is a very important technology that can help drivers detect component failures in time and avoid unexpected situations during driving.

[0003] At present, the conventional automobile fault warning methods are generally the following two:

[0004] (1) By monitoring the vehicle sensor signals in real time to see if they are normal, it is possible to determine whether a fault has occurred or is about to occur, and to provide early warning.

[0005] The defects of method (1) are: the warning time is short, and it is difficult to perform fault repair in time according to the warning to avoid the situation of sending faults; at the same time, there are many types of vehicle faults. If you want to respond to multiple or every type of fault, you need too many vehicle sensors, which is costly.

[0006] (2) Early warning models are constructed through machine learning and other models. In this process, various model samples (such as training samples, reference samples, etc.) need to be collected, and the model needs to be continuously learned and optimized.

[0007] The drawbacks of method (2) are: the warning process is relatively complicated, and the vehicle needs to have the corresponding signal required by the warning model, that is, it cannot be applied to vehicles that do not have the corresponding signal. Summary of the invention

[0008] In view of the defects existing in the prior art, the technical problem solved by the present invention is: when providing fault warning for an automobile, how to reduce the warning cost and simplify the warning process while ensuring that the warning time is sufficient.

[0009] To achieve the above objectives, in the first aspect, an embodiment of the present application provides a fault warning method based on similar vehicles, the method comprising the following steps: taking the same attribute information of the vehicles as a determination condition, determining the similar vehicles of each vehicle according to the attribute information of each vehicle; when a vehicle breaks down, among all similar vehicles of the current vehicle, a vehicle with a higher degree of similarity is determined as a warning vehicle of the current vehicle; and warning the warning vehicle according to the fault that occurs.

[0010] In combination with the first aspect, in one embodiment, the method further comprises the following steps before determining the warning vehicle: setting a corresponding weight for each attribute information according to the probability of a failure;

[0011] The process of determining a vehicle with a higher similarity among all similar vehicles to the current vehicle as a warning vehicle for the current vehicle includes: determining the similarity of each similar vehicle among all similar vehicles to the current vehicle based on the same attribute information and corresponding weight as the current vehicle; and determining a vehicle with a similarity above a warning threshold or corresponding to the largest N similarities as a warning vehicle.

[0012] In combination with the first aspect, in one implementation, the similarity is the sum of weights corresponding to each piece of attribute information that is the same as the current vehicle.

[0013] In combination with the first aspect, in one embodiment, the process of determining the similarity of each vehicle based on the attribute information of each vehicle includes: constructing an attribute topology map for each vehicle, the attribute topology map includes a parent node representing the current vehicle, and each attribute information of the current vehicle is used as a child node of the parent node; other vehicles with at least one attribute information identical to the current vehicle are used as similar nodes of the parent node; the parent node is associated with each similar node through the child nodes corresponding to the same attributes.

[0014] In combination with the first aspect, in one implementation, the process of issuing an early warning to a warning vehicle based on a fault that has occurred includes: determining whether the early warning vehicle has previously experienced the fault, and if so, not issuing an early warning to the early warning vehicle, otherwise issuing an early warning to the early warning vehicle.

[0015] In combination with the first aspect, in one implementation, when a warning vehicle is warned, the warning content includes the fault and a solution determined after the fault is repaired according to the vehicle where the fault occurs; the solution includes the cause of the fault.

[0016] In combination with the first aspect, in one embodiment, the attribute information acquisition process includes: collecting attribute information, aggregating dynamically changing attribute information, and updating static attribute information; the attribute information includes: vehicle speed, engine speed, brake pedal switch status, battery voltage, mileage, fuel consumption, operating time, factory time and vehicle purpose.

[0017] In a second aspect, an embodiment of the present application provides a fault warning device based on similar vehicles, the device comprising a similar vehicle determination module and a warning module;

[0018] The similarity vehicle determination module is used to: execute the process provided in the first aspect of determining similarity vehicles of each vehicle according to the attribute information of each vehicle, taking the same attribute information of the vehicles as the determination condition;

[0019] The warning module is used to: execute the process provided in the first aspect of determining a vehicle with a higher similarity as a warning vehicle of the current vehicle among all similar vehicles of the current vehicle; and warn the warning vehicle according to the occurred fault.

[0020] In the third aspect, an embodiment of the present application provides a fault warning device based on similar vehicles, wherein the fault warning device based on similar vehicles comprises a processor, a memory, and a fault warning program based on similar vehicles stored in the memory and executable by the processor, wherein when the fault warning program based on similar vehicles is executed by the processor, the steps of the fault warning method based on similar vehicles provided in the first aspect are implemented.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which is stored a fault warning program based on similar vehicles. When the fault warning program based on similar vehicles is executed, the steps of the fault warning method based on similar vehicles provided in the first aspect are implemented.

[0022] Compared with the prior art, the advantages of the present invention are:

[0023] The present invention directly determines similar vehicles based on basic attribute information of the vehicle; when a vehicle breaks down, the present invention directly determines a warning vehicle based on the similar vehicles of the vehicle, and issues a warning based on the breakdown that occurs.

[0024] It's obvious:

[0025] (1) When the present invention performs vehicle warning, no vehicle sensor is required, which significantly reduces the warning cost and ensures the warning duration;

[0026] (2) Compared with the prior art in which warning is performed through a warning model, the present invention directly performs warning based on the basic attribute information of the vehicle, which is simple and efficient. Moreover, the basic attribute information of the vehicle is essential information of the vehicle, and therefore it is basically applicable to all vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A schematic diagram of a basic attribute topology map database in an embodiment of the present invention;

[0029] Figure 2Schematic diagram of the process of the fault warning method based on similar vehicles in an embodiment of the present invention;

[0030] Figure 3 The figure is a schematic diagram of the hardware structure of a fault warning device based on similar vehicles involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0032] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0033] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0034] First, the research and development process of the present invention is briefly described.

[0035] Regarding the problem of high cost of early warning through sensors in the prior art, if the cost is reduced by reducing the number of sensors, the scope of early warning will be narrowed, that is, early warning can only be carried out through the early warning model in the prior art.

[0036] The applicant studied the existing early warning model and tried to clarify and streamline the working logic of the early warning model; however, the applicant found that the result of the simplification would not only lead to a smaller warning range or a lower accuracy, but also, under the limitations of the early warning model, even if the above working logic was streamlined, it would be difficult to significantly shorten the early warning process (i.e., it is still very complicated).

[0037] Afterwards, during the applicant's continued exploration, experienced professional technicians proposed a method for immediately issuing an early warning to a vehicle that is highly similar to the current vehicle when a current vehicle fails.

[0038] In this context, inspired by the framework of how to overcome the technical difficulties proposed by the above-mentioned professional and technical personnel (for example, how to locate similar vehicles, how to locate warning vehicles from similar vehicles, etc.), the applicant conducted specific research and development and formed specific means to match this direction.

[0039] It should be noted that the original intention of the above-mentioned association is to locate the common group of the problem and prevent it after discovering the problem. However, the basic conditions for thinking of the above-mentioned association and applying it to vehicle fault warning are: on the basis of having professional technical capabilities, at least thinking of the framework of how to overcome the above-mentioned technical difficulties through creative labor.

[0040] Therefore: For ordinary technicians in this field, if they simply think of the above-mentioned original intention of association, but because they do not have the ability to think of how to overcome the above-mentioned technical difficulties, even if they think of it, they cannot apply it to the scenario of vehicle warning, that is, the above-mentioned original intention of association is difficult to be thought of and applied.

[0041] On this basis, in the first aspect, an embodiment of the present application provides a fault warning method based on similar vehicles, the steps of the method including: taking the same basic attribute information of the vehicles as a determination condition, determining the similar vehicles of each vehicle according to the basic attribute information of each vehicle; when a vehicle breaks down, among all similar vehicles of the current vehicle, a vehicle with a higher degree of similarity is determined as a warning vehicle of the current vehicle; and warning the warning vehicle according to the above-mentioned fault.

[0042] It can be seen that the present invention directly determines similar vehicles based on the basic attribute information of the vehicle (such as mileage, fuel consumption, purpose, etc.); when a vehicle breaks down, the present invention directly determines the warning vehicle based on the similar vehicles of the vehicle, and issues a warning based on the occurred failure.

[0043] It's obvious:

[0044] (1) When the present invention performs vehicle early warning, no vehicle sensor is required, which significantly reduces the early warning cost;

[0045] (2) Compared with the prior art in which warning is performed through a warning model, the present invention directly performs warning based on the basic attribute information of the vehicle, which is simple and efficient. Moreover, the basic attribute information of the vehicle is essential information of the vehicle, and therefore it is basically applicable to all vehicles.

[0046] The above method is described below through several embodiments.

[0047] In one embodiment, the process of obtaining the basic attribute information of the vehicle in the above method includes: collecting basic attribute information at regular or real-time intervals, aggregating dynamically changing basic attribute information (such as vehicle mileage, load, operating time, etc.), and updating static basic attribute information (such as purpose, production time, component model, etc.).

[0048] Basic attribute information generally includes: vehicle speed, engine speed, brake pedal switch status, battery voltage, mileage, fuel consumption, fuel consumption per 100 kilometers, operating time, factory time and purpose, etc. This is taken as an example. In actual use, the basic attribute information will also include a variety of other information. The definition conditions of the basic attribute information are common knowledge in this field and will not be elaborated here.

[0049] The above-mentioned means of collecting basic attribute information may be:

[0050] For static information: it is uploaded regularly by the vehicle-mounted T-BOX (Telematic BOX, the intelligent vehicle-mounted terminal in the vehicle networking system);

[0051] For dynamic information: After the corresponding data (vehicle speed, engine speed, brake pedal switch status, elevation, battery voltage, etc.) is calculated by the body controller, it is collected through the on-board T-BOX and uploaded to the big data cloud platform, which aggregates the uploaded data.

[0052] In one embodiment, the process of determining the similarity of each vehicle according to the basic attribute information of each vehicle in the above method includes: Figure 1 As shown, a basic attribute topology database (relational database) is constructed for each vehicle. The basic attribute topology includes a parent node representing the current vehicle, and each basic attribute information of the current vehicle is used as a child node of the parent node; other vehicles with at least one basic attribute information identical to the current vehicle are used as similar nodes of the parent node, that is, the vehicle corresponding to each similar node is a similar vehicle of the current vehicle; the parent node is associated with each similar node through the child nodes corresponding to the above-mentioned same basic attributes.

[0053] In one embodiment, the above method further comprises the following steps before determining the warning vehicle: according to the probability of failure, a corresponding weight is set for each basic attribute information, and the specific weight value and failure information can be intuitively reflected in the basic attribute topology map. Figure 1 As shown, in this embodiment, the weight value is reflected in the connection between the parent node and each child node (for example, weight=0.2, etc.); the fault information is used as the fault node and is associated with the parent node.

[0054] On this basis, the process of determining the vehicle with higher similarity as the warning vehicle of the current vehicle among all similar vehicles of the current vehicle in the above method includes: determining the similarity of each similar vehicle among all similar vehicles of the current vehicle according to the same basic attribute information and corresponding weight as the current vehicle; and determining the vehicle with similarity above the warning threshold or corresponding to the largest N similarities as the warning vehicle.

[0055] It should be noted that: based on the fact that the weight setting rule is set according to the probability of failure, ordinary technicians in this field can design specific weights and corresponding alarm thresholds according to different needs and conventional knowledge.

[0056] Specifically, the similarity of the similar vehicles is the sum of the weights corresponding to each basic attribute information that is the same as that of the current vehicle.

[0057] See also Figure 1 As shown, vehicle 1 and vehicle 2 have three basic attribute information in common, namely: cumulative fuel consumption, with a weight of 0.2; average load, with a weight of 0.1; and total operating time, with a weight of 0.3.

[0058] Then the similarity between vehicle 1 and vehicle 2 is 0.2+0.1+0.3=0.6.

[0059] In one embodiment, the process of issuing an early warning to the warning vehicle according to the fault in the above method includes: determining whether the early warning vehicle has had the above fault before, if so, not issuing an early warning to the warning vehicle, otherwise issuing an early warning to the warning vehicle.

[0060] In one embodiment, the warning content in the above method also includes: a solution determined after the fault of the faulty vehicle is repaired, the solution at least includes the cause of the fault, and may also include a corresponding maintenance plan; that is, the above warning period is after the fault of the faulty vehicle is repaired.

[0061] The effect of such implementation is: comprehensive notification of possible faults and corresponding solutions during early warning. Since the solution is obtained after the faulty vehicle is repaired (i.e. determined based on the actual maintenance conditions of similar vehicles), the solution is more accurate.

[0062] The objects of comparison with higher accuracy here are mainly the early warning models in the prior art. The solutions to the faults given by the early warning models are obtained through theoretical calculations, and there are multiple causes of the faults in the solutions, which makes it difficult for maintenance personnel to locate the specific causes. In comparison, the present invention significantly improves the early warning accuracy (the accuracy of measures to avoid or solve faults).

[0063] It should be noted that according to this embodiment, it is necessary to wait until a solution is obtained before issuing an early warning, which will appropriately extend the warning time. In actual use, it can be adjusted according to different priorities. For example, an early warning can be issued directly, and the early warning content includes several basic attribute information with higher weight values.

[0064] The above method is described below through a specific embodiment.

[0065] S1: Collect basic attribute information regularly, aggregate dynamically changing basic attribute information, and update static basic attribute information.

[0066] S2: Build a basic attribute topology database for each vehicle based on the basic attribute information. The basic attribute database includes:

[0067] Parent node, representing the current vehicle;

[0068] Several child nodes of the parent node, each child node represents the basic attribute information of the current vehicle;

[0069] Similar nodes at the same level as the parent node represent other vehicles with at least one basic attribute information that is the same as the current vehicle. Similar nodes are child nodes corresponding to the same basic attributes. The weight of the basic attribute information is marked between the child node and the connected parent node and the fault node.

[0070] Fault nodes associated with parent nodes and / or similar nodes represent fault information and are added after a fault occurs.

[0071] S3: After a vehicle breaks down, all similar vehicles are determined in the basic attribute topology database of the current vehicle. The sum of the weights corresponding to each basic attribute information that is the same as that of the current vehicle is used as the similarity of each similar vehicle. Vehicles with similarities above the alarm threshold or with the largest N similarities are used as warning vehicles.

[0072] S4: Determine whether the warning vehicle has had the above-mentioned fault before. If so, do not issue a warning to the warning vehicle and end; otherwise, go to S5.

[0073] S5: After receiving the solution uploaded by the vehicle having the above-mentioned fault after the fault is repaired, an early warning is issued to the warning vehicle, and the warning content includes the above-mentioned fault and the corresponding solution.

[0074] In a second aspect, an embodiment of the present application further provides a fault warning device based on similar vehicles, which is used to: implement the method mentioned in the first aspect.

[0075] Specifically, the device includes:

[0076] The basic attribute collection module is used to: collect basic attribute information regularly or instantly, aggregate dynamically changing basic attribute information (such as vehicle mileage, load, operating time, etc.), and update static basic attribute information (such as purpose, production time, component model, etc.).

[0077] A similar vehicle determination module is used to: execute the process provided in the first aspect of determining the similar vehicles of each vehicle based on the attribute information of each vehicle with the same attribute information of the vehicles as the determination condition; specifically: construct a basic attribute topology map database (relational database) for each vehicle, the basic attribute topology map includes a parent node representing the current vehicle, and each basic attribute information of the current vehicle is used as a child node of the parent node; other vehicles with at least one basic attribute information identical to the current vehicle are used as similar nodes of the parent node, that is, the vehicle corresponding to each similar node is a similar vehicle of the current vehicle; the parent node is associated with each similar node through the child nodes corresponding to the above-mentioned same basic attributes.

[0078] The warning module is used to: execute the process provided in the first aspect to determine the vehicle with the highest similarity as the warning vehicle of the current vehicle among all similar vehicles of the current vehicle; and to warn the warning vehicle according to the fault that has occurred; specifically: determine whether the warning vehicle has had the above fault before, if so, do not warn the warning vehicle, otherwise warn the warning vehicle. The warning content includes: the fault that has occurred, and the solution determined after the fault is repaired according to the faulty vehicle, the solution at least includes the cause of the fault, and may also include the corresponding maintenance plan; that is, the above warning period is after the fault of the faulty vehicle is repaired.

[0079] On the third aspect, an embodiment of the present application provides a fault warning device based on similar vehicles. The fault warning device based on similar vehicles can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0080] Reference Figure 3 , Figure 3 Schematic diagram of the hardware structure of the fault warning device based on similar vehicles involved in the embodiment of the present application. In the embodiment of the present application, the fault warning device based on similar vehicles may include a processor, a memory, a communication interface and a communication bus.

[0081] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0082] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to realize the interconnection of devices inside the fault warning device based on similarity vehicles, and an interface for realizing the interconnection of the fault warning device based on similarity vehicles with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0083] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0084] The processor may be a general-purpose processor, which may call the fault warning program based on similar vehicles stored in the memory and execute the fault warning method based on similar vehicles provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the fault warning program based on similar vehicles is called may refer to the various embodiments of the fault warning method based on similar vehicles of the present application, which will not be described in detail here.

[0085] Those skilled in the art will understand that Figure 2 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0086] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0087] The computer-readable storage medium of the present application stores a fault warning program based on similar vehicles, wherein when the fault warning program based on similar vehicles is executed by a processor, the steps of the fault warning method based on similar vehicles as described above are implemented.

[0088] Among them, the method implemented when the fault warning program based on similar vehicles is executed can refer to the various embodiments of the fault warning method based on similar vehicles in this application, and will not be repeated here.

[0089] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0090] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0091] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0092] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0093] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0094] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0095] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0096] The above are only specific implementations of the embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the embodiments of the present invention, and these modifications or replacements should be included in the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be based on the protection scope of the claims.

Claims

1. A fault warning method based on similar vehicles, characterized in that: The method includes the following steps: taking the same attribute information of the vehicles as a determination condition, determining the similar vehicles of each vehicle according to the attribute information of each vehicle; when a vehicle breaks down, among all similar vehicles of the current vehicle, a vehicle with a higher degree of similarity is determined as a warning vehicle of the current vehicle; and issuing a warning to the warning vehicle according to the occurrence of the fault.

2. The fault warning method based on similar vehicles as claimed in claim 1, characterized in that: The method further comprises the following steps before determining the warning vehicle: setting a corresponding weight for each attribute information according to the probability of failure; The process of determining a vehicle with a higher similarity among all similar vehicles to the current vehicle as a warning vehicle for the current vehicle includes: determining the similarity of each similar vehicle among all similar vehicles to the current vehicle based on the same attribute information and corresponding weight as the current vehicle; and determining a vehicle with a similarity above a warning threshold or corresponding to the largest N similarities as a warning vehicle.

3. The fault warning method based on similar vehicles as claimed in claim 2, characterized in that: The similarity is the sum of the weights corresponding to each piece of attribute information that is the same as the current vehicle.

4. The fault warning method based on similar vehicles as claimed in claim 2, characterized in that: The process of determining the similarity of each vehicle based on the attribute information of each vehicle includes: constructing an attribute topology map for each vehicle, the attribute topology map includes a parent node representing the current vehicle, and each attribute information of the current vehicle is used as a child node of the parent node; other vehicles having at least one attribute information identical to the current vehicle are used as similar nodes of the parent node; the parent node is associated with each similar node through the child nodes corresponding to the same attributes.

5. The fault warning method based on similar vehicles as claimed in claim 1, characterized in that: The process of issuing an early warning to the early warning vehicle according to the fault that has occurred includes: determining whether the early warning vehicle has previously had the fault, if so, not issuing an early warning to the early warning vehicle, otherwise issuing an early warning to the early warning vehicle.

6. The fault warning method based on similar vehicles as claimed in claim 1, characterized in that: When a warning is issued to a warning vehicle, the warning content includes the fault and a solution determined after the fault is repaired according to the vehicle having the fault; The solution includes the reason why the failure occurred.

7. The fault warning method based on similar vehicles according to any one of claims 1 to 6, characterized in that: The process of acquiring the attribute information includes: collecting attribute information, aggregating dynamically changing attribute information, and updating static attribute information; the attribute information includes: vehicle speed, engine speed, brake pedal switch status, battery voltage, mileage, fuel consumption, operating time, factory time and vehicle purpose.

8. A fault warning device based on similar vehicles, characterized in that: The device includes a similarity vehicle determination module and a warning module; The similarity vehicle determination module is used to: execute the process of determining similarity vehicles of each vehicle according to the attribute information of each vehicle, taking the same attribute information of the vehicles as a determination condition as described in any one of claims 1 to 7; The warning module is used to: perform the steps of any one of claims 1 to 7 to determine a vehicle with a higher similarity as a warning vehicle of the current vehicle among all similar vehicles of the current vehicle; The process of warning vehicles based on the faults that occur.

9. A fault warning device based on similar vehicles, characterized in that: The similarity vehicle-based fault warning device comprises a processor, a memory, and a similarity vehicle-based fault warning program stored in the memory and executable by the processor, wherein when the similarity vehicle-based fault warning program is executed by the processor, the steps of the similarity vehicle-based fault warning method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a fault warning program based on similar vehicles, wherein when the fault warning program based on similar vehicles is executed, the steps of the fault warning method based on similar vehicles as claimed in any one of claims 1 to 7 are implemented.

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