CN105867351B - Method and device for real-time collection of vehicle fault codes and analysis and diagnosis of historical data - Google Patents

Abstract

A method and a device for real-time collection and historical data analysis and diagnosis of vehicle fault codes belong to the field of fault code collection, solve the problem of classification alarm, and are technically characterized in that: the method comprises the steps that each time a vehicle is ignited, fault codes are collected by equipment, the fault codes collected by the equipment in real time on the vehicle are uploaded to a cloud platform through a mobile communication network, the cloud platform receives the fault code data and stores the fault code data into a database, the cloud platform combines the current uploaded fault data with stored fault historical data to perform big data analysis, the faults obtained by analyzing and diagnosing the fault code data are classified, and fault reminding is decided according to the classification. The effect is as follows: the alarm decision is determined by classification, so that the phenomenon of alarm credibility reduction caused by all alarms can be avoided.

Description

Method and device for real-time collection of vehicle fault codes and analysis and diagnosis of historical data

technical field

The invention belongs to the field of fault code collection, and relates to a method for collecting, analyzing and diagnosing fault codes.

Background technique

When the vehicle fails, especially the electrical failure (electrical failure refers to the failure that occurs in the ECU, circuit or induction components, which is different from a kind of failure in the mechanical failure.), the general practice is to go to the 4s shop or repair factory for repairs. Inspection, can quickly find faults that can be reproduced or frequently occur, but there are some occasional or hidden faults, or faults that are not easy to locate quickly, and can only be repaired after knowing it and waiting until they occur. Diagnostic products are mainly:

Original factory diagnostic instrument: This method is mainly used by 4S shops to detect faults. This method is to use the car manufacturer's original diagnostic instrument to connect the car through wired connection or wireless (Bluetooth and other technologies) to obtain the vehicle status when the car is in the 4s store. The price of the original diagnostic instrument is higher, and it is the standard configuration required by car manufacturers for 4s stores.

OBD diagnostic instrument: This method is mainly used by auto repair shops, and the price is low. The fault code is limited to the OBD type, and it also detects the current condition of the vehicle, and cannot locate the fault that has occurred before the vehicle.

The above solution cannot store the collected fault codes in real time for big data analysis. However, after the real-time collection, storage and uploading of fault codes, new technical problems arise. Alarm, or just display, and how to select the alarm for the fault classification corresponding to the fault code, not only can enhance the alarm efficiency, but also improve the reliability of the alarm. And no matter what kind of fault is carried out, the alarm will often make the car owner paralyzed, and the real urgently needed alarm will be ignored.

SUMMARY OF THE INVENTION

In order to solve the above problem of classified alarm, the present invention provides a method for real-time collection of vehicle fault codes and analysis and diagnosis of historical data. The fault code is uploaded to the cloud platform by the mobile communication network. The cloud platform receives the fault code data and stores the fault code data in the database. The cloud platform combines the currently uploaded fault data with the stored fault historical data for big data analysis. Classify the faults obtained by analyzing and diagnosing the fault code data, and make fault reminders according to the classification.

At the same time, the present invention also relates to a device for real-time collection of vehicle fault codes and analysis and diagnosis of historical data, including equipment, a classification module, and a reminder module, the fault codes collected by the vehicle ignition equipment each time, and the fault codes collected by the equipment in real time on the vehicle, It is uploaded to the cloud platform from the mobile communication network, the cloud platform receives the fault code data, and stores the fault code data in the database. The faults obtained by code data analysis and diagnosis are classified, and fault reminders are made according to the classification decisions.

Beneficial effects: the present invention can realize real-time collection, upload and storage of vehicle fault codes, and use big data processing to classify faults corresponding to vehicle fault codes, and determine alarm decision by classification, which can avoid alarm reliability caused by all alarms. decrease phenomenon.

Description of drawings

FIG. 1 is a process schematic diagram 1 of the method.

Figure 2 is a flow chart of the method.

Detailed ways

Embodiment 1: In order to get a foresight or to know the vehicle fault in a way that it can be handled at any time at any time, this embodiment introduces a method by monitoring the vehicle condition in real time, especially the vehicle fault code (the vehicle fault code refers to the failure of the car. After analyzing the fault codes reflected by the ECU of the car computer, the usual fault codes are the big data analysis of the changes caused by the sensor failure and the poor work of the sensor to carry out real-time diagnosis of the car, as shown in Figures 1 and 2: a vehicle The method for real-time collection of fault codes and analysis and diagnosis of historical data is characterized in that, each time the vehicle is on fire, the equipment collects the fault codes, and the fault codes collected by the equipment in real time on the vehicle are uploaded to the cloud platform by the mobile communication network, and the cloud platform receives the fault. code data, store the fault code data in the database, and the cloud platform combines the fault data uploaded at the current time with the stored fault history data for big data analysis. This embodiment does not use popular big data analysis technology, and the key analysis The tool is to classify the same fault code or similar fault code through experience accumulation, and let the technician do the corresponding diagnosis and final confirmation. Compared with the prior art, the diagnosis is not limited to the real-time fault code phenomenon, but also refers to historical fault information, vehicle attribute information, vehicle driving parameter information, and the like. This kind of analysis method will more effectively identify the real fault, and the diagnosis accuracy will be higher. At the same time, according to the severity of the fault and the degree of urgency, it will be pushed to the maintenance unit such as the owner or the dealer, so as to more effectively ensure the timely maintenance of the vehicle.

Embodiment 2: The described method for classifying faults is:

S1. Compare the fault knowledge base to determine the fault category: the fault code category needs to refer to several knowledge bases, 1. Associated fault code table: the table of associated fault storage, the faults stored in this table are all non-important associated fault codes, Such failures all occur when other failures occur, or are caused by the addition of other components to the vehicle, and are non-deterministic failures. 2. Negligible high-frequency minor faults, such faults occur frequently, but the degree of damage to the vehicle is extremely slight and can be ignored. For the high frequency, a high frequency threshold can be set, and those exceeding the threshold can be regarded as high frequency. In the same way, a minor threshold can also be set, and a minor fault is lower than the minor threshold. Major fault table, such faults will affect the driving safety of the vehicle, or increase the degree of damage to the vehicle, which can be set by the technicians.

If the current fault is the information recorded in the fault knowledge base, the fault type can be directly determined. If the current fault is fault information that is not included in the fault knowledge base, the following step S2 needs to be further discriminated.

S2.. Count the occurrence frequency and occurrence trend of fault codes to determine the fault category. This type of discrimination process is mainly performed by the technician using the remote analysis real-time diagnosis system to check the fault occurrence trend and occurrence frequency, and the fault information collected through experience and manufacturers and other channels as a reference. , and finally determine its fault frequency threshold, and save the fault to the relevant knowledge base.

Among them: for the classification of real faults and major faults, the cloud platform sends real-time reminders to the remote analysis real-time diagnosis system. The system mainly plays two roles. One is to provide an interface for real-time remote diagnosis for diagnostic technicians. The system includes vehicles. The basic information, maintenance information, fault information, various parameters during the driving of the vehicle, etc., so that the technician can view the real-time and historical driving conditions of the vehicle, so as to analyze the connection between the specific fault code and the actual fault, and retain the technician's information. The diagnostic opinions and results are finally saved in the fault knowledge base, so as to achieve the effect of gradually improving the fault knowledge base. 2 is as the trigger of the fault diagnosis result, pushing or providing an interface for calling the diagnosis result to other systems or terminals that need the diagnosis result. Those classified as low-frequency faults and non-major faults are only retained in the big data analysis library of the cloud platform as a reference factor for fault code data analysis, continue to monitor and add the analysis and diagnosis results in the next data analysis.

The diagnostic analysis includes real-time analysis and analysis of fault code data collected by the vehicle within a certain period of time to obtain real-time analysis results and stage analysis results. The real-time analysis results can reflect the real-time and timeliness of the diagnosis report. For car owners, the real-time and timeliness of vehicle failures, especially major failures, is their most concerned and crucial. Stage analysis focuses on presenting staged diagnostic results for the vehicle for later archiving and reference for the next analysis. The real-time analysis results of vehicles with major faults and the stage analysis results of each vehicle are sent to the remote analysis real-time diagnosis system. The remote analysis real-time diagnosis system stores the big data analysis results of the cloud platform and presents them to users when they are retrieved. Or, presented through various charts or data representations.

Embodiment 3: A device for real-time collection of vehicle fault codes and historical data analysis and diagnosis, which is characterized in that it includes equipment, a classification module, and a reminder module. Every time the vehicle is fired, the equipment collects fault codes, and the equipment collects faults in real time on the vehicle. The fault code is uploaded to the cloud platform by the mobile communication network. The cloud platform receives the fault code data and stores the fault code data in the database. The cloud platform combines the currently uploaded fault data with the stored fault historical data for big data analysis. Classify the faults obtained by analyzing and diagnosing the fault code data, and make fault reminders according to the classification. The apparatus described is used to execute and implement the methods in Embodiments 1 and/or 2.

Due to the method of fault collection and judgment in the prior art, just like a patient going to a hospital to see a doctor and checking an electrocardiogram, what is observed is only a very short period of time in the store, and the previous faults of specific vehicles are even occasional. The faults cannot be located. Such a bottleneck has formed an insurmountable obstacle for the technicians to analyze the real fault causes of the vehicle and follow-up vehicle tracking observation. To sum up, the inability to observe and collect the trouble codes of the vehicle at all times is the fatal flaw of the previous diagnostic instrument. The method and device introduced by the invention achieve the purpose of checking vehicle history at any time and tracking vehicle status in real time by collecting vehicle fault code information in real time and uploading it to the cloud, through cloud storage and regular big data analysis.

In this embodiment, vehicle fault codes and mobile phone fault information are detected in real time and uploaded to the cloud for cloud storage. Through big data analysis of a large amount of stored fault code information, the vehicle fault status can be determined and judged according to the changes of relevant parameters of the fault code, and hidden dangers or accidental faults can be identified, and false faults can be eliminated. The present invention can be practically applied to the actual vehicle repairing industry of 4s shops and auto repair shops, and is of great help to help technicians analyze the actual vehicle failures that have occurred or may occur, and can almost directly locate the problem. The invention can help manufacturers to collect the probability of occurrence of failure after the actual use of the vehicle and to analyze the improvement point of the vehicle, which is of great help.

The above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited to this. The equivalent replacement or modification of the created technical solution and its inventive concept shall be included within the protection scope of the present invention.

Claims (1)

1. a kind of vehicle trouble code acquires the method with historical data analysis diagnosis in real time, which is characterized in that vehicle is struck sparks every time, Equipment acquires error code, and the error code that equipment acquires in real time on vehicle uploads to cloud platform, cloud platform by mobile communications network Error code data are received, the storage of error code data into database, cloud platform is the fault data for working as secondary upload and has stored Malfunction history data combine carry out big data analysis, the failure obtained to error code data analyzing and diagnosing is classified, root It is reminded according to categorised decision failure；

Described to be to the method that failure is classified: control fault knowledge library differentiates its fault category；

The knowledge base of the differentiation reference of error code classification includes: association failure code table, insignificant high frequency minor failure table and again Major break down table；If current failure is the fault message that do not include in fault knowledge library, occurrence frequency and the generation of error code are counted Its fault category of trend discrimination；

Differentiation process mainly checks failure occurrence tendency and occurrence frequency using remote analysis real-time diagnosis system by technician, collects Fault message finally determines its fault occurrence frequency threshold value as reference, and saves failure to relevant knowledge library；

Described being classified as true fault and significant trouble, cloud platform, which issues, reminds in real time to remote analysis real-time diagnosis system, The system provides the interface of real time remote diagnosis, includes essential information, repair message, fault message and the vehicle of vehicle in system All kinds of parameter informations during traveling, as the basis contacted for analyzing specific error code and physical fault generation, and storage is examined Disconnected opinion and diagnostic result, are finally saved in fault knowledge library；It is described being classified as low frequency failure and insignificant failure, only make Reference factor is analyzed for error code data to be retained in the big data analysis library of cloud platform, continues monitoring and in data next time point The analyzing and diagnosing result is added when analysis；

Remote analysis real-time diagnosis system is the touching originator of fault diagnosis result, and system or the end of diagnostic result are needed to other End push provides the interface for calling diagnostic result；

Diagnostic analysis is obtained including analyzing and being analyzed the error code data that vehicle acquires within a certain period of time in real time Analysis result and phase analysis in real time is as a result, the real-time analysis result of the vehicle with significant trouble and the stage point of each vehicle Analysis result is sent to remote analysis real-time diagnosis system, big data analysis knot of the remote analysis real-time diagnosis system cloud platform Fruit stores, and is presented to user when being transferred, and is presented by the form of expression of all kinds of charts or data；

Vehicle trouble code acquire in real time with historical data analysis diagnosis device, which is characterized in that including equipment, categorization module, Reminding module, vehicle strike sparks every time equipment acquisition error code, the error code that equipment acquires in real time on vehicle, by mobile radio communication Network uploads to cloud platform, and cloud platform receives error code data, the storage of error code data into database, cloud platform when time on The fault data of biography combines carry out big data analysis with stored malfunction history data, obtains to error code data analyzing and diagnosing To failure classify, according to categorised decision failure remind；

Reminded according to categorised decision failure described is to the method that failure is classified: control fault knowledge library differentiates its failure classes Not；The knowledge base of the differentiation reference of error code classification includes: association failure code table, insignificant high frequency minor failure table and great Bug list；

If current failure is the fault message that do not include in fault knowledge library, the occurrence frequency and occurrence tendency for counting error code are sentenced Its other fault category；Differentiation process mainly checks failure occurrence tendency and generation using remote analysis real-time diagnosis system by technician Frequency collects fault message and finally determines its fault occurrence frequency threshold value as reference, and saves failure to relevant knowledge library；

Described being classified as true fault and significant trouble, cloud platform, which issues, reminds in real time to remote analysis real-time diagnosis system, The system provides the interface of real time remote diagnosis, includes essential information, repair message, fault message and the vehicle of vehicle in system All kinds of parameter informations during traveling, as the basis contacted for analyzing specific error code and physical fault generation, and storage is examined Disconnected opinion and diagnostic result, are finally saved in fault knowledge library；It is described being classified as low frequency failure and insignificant failure, only make Reference factor is analyzed for error code data to be retained in the big data analysis library of cloud platform, continues monitoring and in data next time point The analyzing and diagnosing result is added when analysis.