CN114821856B - Intelligent auxiliary device connected in parallel to automobile traveling computer for rapid automobile maintenance - Google Patents
Intelligent auxiliary device connected in parallel to automobile traveling computer for rapid automobile maintenance Download PDFInfo
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Abstract
The invention belongs to the field of research and development of automobile maintenance equipment, discloses an intelligent auxiliary device for rapid automobile maintenance connected in parallel with a traveling crane computer, and can solve the problems that manual maintenance cannot be performed on specific fault parts and network connection cannot be performed to cause failure of fault diagnosis. The method comprises the following steps: through installing quick plug sensor additional, combine on-vehicle original sensor, on-vehicle computer, obtain car operation data information, transmit intelligent fault diagnosis embedded system with the help of CAN bus technique, with the help of downloading from the API model at high in the clouds, accomplish fault diagnosis. And (3) completing API model training at the cloud server, selecting a BP neural network with a genetic algorithm optimized weight threshold, and obtaining initial training data from a vehicle operation information historical database tested and constructed under laboratory conditions. And updating the database through user feedback. The device is mainly used for fault state identification and fault classification positioning of automobile operation.
Description
Technical Field
The invention belongs to the field of research and development of automobile maintenance equipment, and particularly relates to an automobile rapid maintenance intelligent auxiliary device which comprises an automobile external rapid plugging sensor, cloud service neural network model training and an intelligent fault diagnosis embedded system, is connected in parallel with a traveling computer and has the functions of fault state recognition and fault classification and positioning.
Background
With the development of automobile manufacturing industry and the increase of automobile function demands of users, the total amount of automobiles is continuously increased, and the structures of all parts of the automobiles are gradually complicated. The complex structure leads to more and more automobile fault types, and the difficulty of automobile fault identification and fault location is increased. If a practical and effective fault diagnosis method is lacked, the automobile overhaul process is complicated, the waiting time of a user is increased, and the automobile overhaul cost is also increased. Therefore, how to quickly identify and locate the automobile fault becomes a concern.
In the existing common local fault diagnosis method, an automobile fault diagnosis instrument is used for reading a fault code in a memory of an on-board computer, so that maintenance personnel can be helped to find out the cause of the vehicle fault. However, the automobile fault diagnosis instrument only completes the explanation of the fault code and indicates the system where the fault is located (for example, the fault of the power module-battery management system), but the fault cannot be accurate to a specific fault part, and maintenance personnel still need to further judge according to experience to find out a specific fault point. In the remote fault diagnosis method under the popularization background of the internet of vehicles, a vehicle-mounted end sends a fault signal to a cloud end through wireless transmission, and then detection and feedback are carried out through a detection technology stored in the cloud end. However, the car networking can only process the feedback information of the traditional vehicle-mounted sensor, and key targeted sensing information is lacked, so that the car networking diagnosis method has the defects of deficient congenital diagnosis key clue information and the like.
In summary, the existing device diagnosis technology lacks the accurate positioning capability for the fault component, and meanwhile, the car networking diagnosis technology cannot deploy sensors in a targeted manner to obtain key diagnosis clue information, so that the diagnosis efficiency and the intelligent diagnosis degree need to be improved.
Disclosure of Invention
The invention aims to design an intelligent auxiliary device for fast repairing of an automobile, which is connected in parallel with a traveling computer, aiming at the industrial problems that the existing equipment diagnosis technology lacks the accurate positioning capability of a fault part, meanwhile, the Internet of vehicles diagnosis technology cannot deploy a sensor in a targeted manner to obtain key diagnosis clue information, and the diagnosis efficiency and the intelligent diagnosis degree need to be improved. Firstly, training a plurality of conventional faults of an automobile system in an early stage by utilizing a leading edge neural network technology based on a large amount of proprietary experimental data; then, the model obtained by training is arranged in an embedded system, so that the embedded system device can identify the conventional faults of various systems of various automobiles; then, designing a device which can be quickly installed and disassembled by simply connecting the device in parallel with the vehicle-mounted computer, and effectively reading sensing information and vehicle-mounted computer alarm information in a vehicle-mounted system; finally, designing various sensors which can be rapidly deployed at key parts of the automobile and can be rapidly plugged in and pulled out of an embedded computer, and realizing direct and rapid acquisition of key fault diagnosis clue information of the automobile; the intelligent auxiliary device for automobile maintenance capable of achieving offline accurate information acquisition is constructed comprehensively, functions of fault state identification and fault classification and positioning of accident vehicles for maintenance engineers are achieved rapidly, and the intelligent auxiliary device has the advantages of being low in cost, high in precision, high in efficiency and strong in applicability.
An intelligent auxiliary device for fast maintenance of an automobile connected in parallel with a traveling crane computer comprises a fast plug-in sensor, an intelligent fault diagnosis embedded system and a cloud platform;
the quick plug-in sensor and the intelligent fault diagnosis embedded system are connected with the original vehicle-mounted sensor and the original vehicle-mounted computer of the automobile in parallel in a quick plug-in mode; the modularized quick plug-pull sensor is used for carrying out auxiliary measurement on vehicle state data; the intelligent fault diagnosis embedded system is used for receiving a quick plug sensor signal, an original vehicle-mounted sensor signal of an automobile and original accident alarm information of a vehicle-mounted computer, and carrying out automobile fault state identification and fault classification positioning according to the received information;
the cloud platform comprises cloud storage and cloud computing; the cloud storage part constructs a vehicle operation information historical database which comprises original vehicle-mounted sensor information, rapid plugging and unplugging sensor information, vehicle-mounted computer accident alarm information and corresponding fault state and fault classification and positioning information; the cloud computing part uses a cloud-stored vehicle operation information historical database to perform fault state recognition and fault positioning core API model training;
the cloud storage part updates the database according to the feedback of the user diagnosis result;
the cloud computing part is combined with a genetic algorithm to carry out BP neural network parameter optimization when fault state identification and fault positioning core API model training are carried out;
the cloud computing part updates the database according to the feedback of the user diagnosis result in the cloud storage part, then carries out fault state recognition and fault positioning core API model training again, and downloads and updates the trained model to the intelligent fault diagnosis embedded system in a networking state; downloading a fault state identification and fault positioning core API model after cloud training to an intelligent fault diagnosis embedded system of a vehicle-mounted end for judging the fault state of the vehicle and classifying and positioning the faults;
original vehicle-mounted sensor information, rapid plugging sensor information and vehicle-mounted computer accident alarm information are transmitted to an intelligent fault diagnosis embedded system through a CAN bus, and the intelligent fault diagnosis embedded system performs data preprocessing operation on the information in a database to obtain input data of a fault state identification and fault positioning core API model;
the method comprises the steps of firstly training a constructed vehicle operation information historical database in an Ali cloud server, and establishing a model for performing fault state identification and fault classification positioning by using vehicle operation information data, namely mapping from vehicle operation information to definite faults.
In the fault state identification and fault location core API model training, firstly, training samples need to be extracted from a vehicle operation information historical database, and extracted data are divided into a training set, a testing set and a verification set;
for extracted data, data preprocessing is firstly required before training, and the specific steps are as follows:
step 1: deleting samples corresponding to the detected data duplication and data deletion from the database, and re-extracting new data from the database to supplement the training samples;
step 2: in consideration of the existence of data noise, the data noise needs to be removed by adopting means including sliding filtering;
and step 3: selecting various characteristic data by adopting principal component analysis;
and 4, step 4: and (3) carrying out min-max normalization on the characteristic data selected by adopting the principal component analysis method, finishing the random extraction of the training sample, and preprocessing the data after the random extraction.
Using the preprocessed data to train the model for fault state recognition and fault classification positioning, and adopting a BP neural network and a genetic algorithm, wherein the specific steps are as follows:
step 1: constructing a plurality of BP neural network models by using the preprocessed data, wherein the activation function of the hidden layer is a tanh function, and the activation function of the output layer adopts a softmax function;
and 2, step: initializing a neural network connection weight and a threshold, and optimizing the weight and the threshold by adopting a genetic algorithm;
and step 3: carrying out real number coding on the weight and the threshold of each BP neural network diagnosis model obtained by establishing, and randomly selecting 100 initial individuals of the weight and the threshold corresponding to the real number coding to form an initial population;
and 4, step 4: calculating a loss function expressed by the sum of squared errors; taking the reciprocal of the loss function as an individual fitness function;
and 5: selecting, crossing and mutating individuals in the current population to form a new population of the next generation;
step 6: judging whether the new population obtained in the step 5 reaches a convergence condition, and finishing weight and threshold optimization if the new population reaches the convergence condition; if the convergence condition is not reached, returning to the step 5 for recalculation;
and 7: taking the data of the optimal individuals in the population as the initial weight and the threshold of the optimized BP neural network model, starting iterative training on the BP neural network model until the loss function value is smaller than the preset threshold or the number of iterations is reached, and finishing the training of the BP neural network model;
and 8: inputting the verification set into a plurality of trained BP neural network models, and selecting the neural network model with the best performance as a fault state identification and fault positioning core API model;
and step 9: and obtaining the accuracy of the API model through the test set.
In the updating process of the database content, data updating is carried out according to the following principles:
(1) When the diagnosis result of the intelligent fault diagnosis embedded system is correct and the data of the same fault state identification and fault classification positioning in the database does not reach the capacity value, directly updating the vehicle operation information data, the fault state identification and fault classification positioning data into the database;
(2) When the intelligent fault diagnosis embedded system has correct fault diagnosis results and the data corresponding to fault state identification and fault classification positioning in the database reaches a capacity value, the database is not updated;
(3) When the intelligent fault diagnosis embedded system has wrong fault diagnosis results and data corresponding to fault state identification and fault classification positioning in the database does not reach a capacity value, directly updating vehicle operation information data, fault state identification and fault classification positioning data to the database;
(4) When the intelligent fault diagnosis embedded system has wrong fault diagnosis results and the data corresponding to fault state identification and fault classification positioning in the database reaches a capacity value, the vehicle operation information data and the fault state identification and fault classification positioning data during diagnosis are used for randomly replacing a group of data corresponding to the same fault state identification and fault classification positioning in the database.
And buffering by adopting a cloud data message queue: in order to ensure that the information transmission of the intelligent fault diagnosis embedded system is matched with the updating process of the cloud database, an information queue is additionally arranged in the middle to serve as a data buffer area, so that the access pressure of a cloud server is reduced; the cloud platform receives operation information simultaneously sent by the intelligent fault diagnosis embedded systems, and the operation information is pushed to a message queue through the data storage module; the message queue is designed into a circular queue data structure, and the queue stores vehicle operation history information according to a first-in first-out sequence; starting a resident process, monitoring the data storage condition of the message queue in real time, taking out the data for updating the database once finding that new data information arrives in the queue, and then deleting the processed information in the queue.
The invention has the beneficial effects that: the sensors are quickly plugged and pulled, different sensors can be selectively used for different types of automobiles, and the flexibility of data acquisition is high and the universality is good; by combining the automobile sensor, the vehicle-mounted computer and other components, the fault state identification and the fault classification positioning can be more accurate and comprehensive; the construction of the core API model is realized by applying a cloud training method, the cost for purchasing a server is saved, and the updating of the algorithm model is more convenient by using a cloud training mode; for an embedded system at a vehicle-mounted end, a model with functions of fault state recognition and fault classification positioning is downloaded from a cloud end, so that the phenomenon that fault diagnosis cannot be performed when networking cannot be performed is avoided, and meanwhile, model training is not required for the embedded system, so that the computational cost is saved; the updating of the database is completed through the later feedback of the user, the overfitting problem of the model to the original fault and the classification and positioning problem of the model to the new fault are solved to a certain extent, and the real-time performance and the accuracy of the model are improved.
Drawings
FIG. 1 is a general block diagram of the present invention;
FIG. 2 is a fault state identification and fault location core API model training method of the present invention;
FIG. 3 is a database update method of the present invention for all vehicles;
fig. 4 is a process of API model training for fault diagnosis by using a BP cloud neural network platform according to the present invention.
Detailed Description
The invention will be further illustrated with reference to specific embodiments. It is to be understood that the examples are for illustrative purposes only and are not intended to limit the present invention.
Example 1:
the embodiment provides an intelligent auxiliary device with high reliability, which is connected in parallel with a traveling computer and has the functions of fault state identification and fault classification positioning for rapid maintenance of an automobile.
Before fault diagnosis analysis, the raw data of the operation of the vehicle is firstly acquired: the acquisition of the original data of the running automobile is completed by quickly plugging and unplugging the sensing sensor, the vehicle-mounted original sensor, the vehicle-mounted computer, the data acquisition card, the signal conditioner and other components. The method comprises the steps of collecting original data of automobile operation through a plug-in sensor, a vehicle-mounted original sensor and a vehicle-mounted computer, converting gain parameters of a combined program control amplifier into voltage signals, then performing discrete time sequence signal conversion on output continuous signals by using a data acquisition card, and outputting discrete voltage values.
Hardware equipment adopted for data acquisition is as follows:
(1) The sensor and the vehicle-mounted original sensor are quickly plugged and pulled out.
Corresponding sensors are respectively additionally arranged for the faults of components such as a fuel supply system, a cooling system, a starting system, an ignition system, a lubrication system and the like, for example, the sensors for measuring signals in the fuel supply system are as follows: oil tank level sensor: detecting whether fuel in the fuel tank is too little or the fuel level is lower than the lower opening of the upper oil pipe hole to judge whether the fuel quantity is sufficient; oil pipe vibration signal sensor that oils: the vibration signal sensor is bound around the oil feeding pipe, and if the oil feeding pipe has the phenomena of welding failure, crack, fracture or loosening of an oil pipe joint, an abnormal vibration signal can be detected; fuel pipeline liquid pressure gauge: the fuel oil pressure sensor is placed on a pipeline passage where a gasoline filter is located, and if the blockage phenomenon exists, the fuel oil pressure is abnormal.
(2) A signal conditioner.
(3) A data acquisition card.
As shown in fig. 2, the intelligent auxiliary device for quickly repairing a vehicle, which is connected in parallel to a vehicle computer and has the functions of identifying a fault state and classifying and positioning faults, according to the embodiment, firstly, vehicle operation information data, fault state identification and fault classification and positioning data measured under laboratory conditions need to be used to form a vehicle operation information historical database.
As shown in fig. 2, a training set, a test set and a verification set are obtained by randomly extracting data from a vehicle operation information historical database and distributing the extracted data according to the proportion of 70%, 15% and 15%. In the process of randomly extracting data, the fault state parameter data corresponding to the fault of each category needs to be extracted.
For the extracted data, firstly, checking is needed, repeated data and missing data are deleted from a sample, meanwhile, information is reported to a database, and the repeated data and the missing data are deleted from the database; meanwhile, in order to ensure that the total amount of the extracted samples is unchanged, a corresponding number of samples need to be extracted from the database again to be used as supplement; and then, checking repeated data and missing data, and sequentially and circularly operating until all data in the sample are qualified.
And for the sample data subjected to the repeated data and missing data detection, the data is subjected to filtering processing in consideration of the existence of data noise.
Performing feature extraction on the filtered data; considering that the extracted data features are too many and high redundancy possibly exists, selecting various feature data by adopting principal component analysis and performing data dimension reduction; and finishing the steps of randomly extracting the training samples and preprocessing the randomly extracted data.
After the data preprocessing is completed, as shown in fig. 4, the BP neural network model is trained by combining a genetic algorithm.
(1) Constructing a plurality of BP neural network models, wherein each BP neural network comprises n input neurons, d hidden layer neurons (the number of the hidden layer neurons of different BP neural network models is different) and m output neurons, the activation function of the hidden layer is a tanh activation function, and the activation function of the output layer is a softmax function.
(2) Firstly, optimizing parameters of a BP neural network model by adopting a genetic algorithm, and carrying out real number coding on the weight and the threshold of each BP neural network diagnosis model established in the step 1, wherein the coding length, namely the length of an individual chromosome is S; then a group of population with an individual specification number of 100 is randomly generated as 100 random solutions. Namely: and randomly selecting initial individuals of the weights and thresholds corresponding to 100 real number codes to form an initial population. Each initial individual represents an initial solution for finding the optimal initial weight and initial threshold.
(3) Calculating the fitness of each individual in the population (initial population) in the step 2, firstly calculating a loss function, and expressing the loss function by using the sum of squares of errors J (i), wherein the formula is as follows:
wherein i = 1.. N is the number of chromosomes, m is the number of output layer nodes, k is the number of training samples, C m Representing the actual value of the m-th output node, y m A predicted value representing the mth output node;
(4) Calculating the fitness of the individual, and taking the reciprocal of the loss function as a fitness function F (i) of the individual:
(5) And (4) carrying out selection, crossing and mutation operations on individuals in the current population (the population generated in the step 2 or the population returned in the step 6) to form a new population of the next generation. The higher the fitness of the individual, the greater the probability of being selected, the probability of each individual being selected P (i):
for the cross probability, 0.4 is taken in the training; for the mutation probability, 0.1 was taken in the training.
(6) And (5) judging whether the new population obtained in the step (5) reaches a convergence condition, finishing genetic algorithm optimization if the new population reaches the convergence condition, and returning to the step (5) to calculate again if the new population does not reach the convergence condition.
(7) And taking the optimal individual data in the population which reaches the convergence condition as the initial weight and the threshold of the corresponding BP neural network model, optimizing the initial weight and the threshold by the constructed BP neural networks through a genetic algorithm, and then training the BP neural network model.
(8) Performing iterative training on the BP neural network models, and quitting the training of the neural network when the loss function is smaller than a preset threshold value or reaches a target iteration number to obtain a plurality of neural network models;
(9) After obtaining a plurality of neural network models, in order to verify the performance of the models, each model is tested by using a test set, the model which has the best performance to the verification set is selected, the test set is input into the model which has the best performance, and the generalization capability of the model is preliminarily known.
Downloading the neural network model with the best performance to an intelligent fault diagnosis embedded system, connecting a quick plug-pull sensor with an original vehicle-mounted sensor and an original vehicle-mounted computer in parallel, inputting vehicle operation information data and original accident alarm information data into the embedded system for preprocessing (extracting the same characteristics used in the process of establishing an initial model), and then using a fault state recognition and fault positioning core API model in the embedded system for fault state recognition and fault classification positioning.
After the fault state identification and the fault classification positioning are finished, a user feeds back whether the diagnosis result is correct or not and the correct fault category through actual maintenance; the feedback result is audited by the diagnostician to ensure the validity of the data.
The database updating principle is as shown in fig. 3, along with the continuous updating of the database, the original laboratory collected data is replaced by the actual fault state identification and fault classification data, the data volume in the database is also increased to the rated capacity, and the accuracy of the fault state identification and fault positioning core API model fault identification obtained by training is improved; the intelligent auxiliary device for the rapid maintenance of the automobile is used for fault diagnosis of various automobiles, and the applicability of the trained fault state identification and fault positioning core API model is gradually enhanced along with the increase of data acquisition in the fault diagnosis process of various automobiles.
In the updating process of the database, when the accumulated diagnosis error quantity of a certain fault type reaches a set threshold value, the updated database is used for training the BP neural network model, the BP neural network model for fault state identification and fault classification positioning is obtained again, and the model is updated to the intelligent fault diagnosis embedded system of the vehicle.
TABLE 1 Fault location and Classification function Table of the invention
Claims (2)
1. An intelligent auxiliary device for fast maintenance of an automobile, which is connected in parallel with a traveling computer, is characterized by comprising a fast plugging sensor, an intelligent fault diagnosis embedded system and a cloud platform;
the quick plug-in sensor and the intelligent fault diagnosis embedded system are connected with the original vehicle-mounted sensor and the original vehicle-mounted computer of the automobile in parallel in a quick plug-in mode; the modularized quick plug-pull sensor is used for carrying out auxiliary measurement on vehicle state data; the intelligent fault diagnosis embedded system is used for receiving a quick plug-pull sensor signal, an original vehicle-mounted sensor signal of an automobile and original accident alarm information of a vehicle-mounted computer, and carrying out automobile fault state identification and fault classification positioning according to the received information;
the cloud platform comprises cloud storage and cloud computing; the cloud storage part constructs a vehicle operation information historical database which comprises original vehicle-mounted sensor information, rapid plugging and unplugging sensor information, vehicle-mounted computer accident alarm information and corresponding fault state and fault classification and positioning information; the cloud computing part performs fault state recognition and fault positioning core API model training by using a cloud-stored vehicle operation information historical database;
the cloud storage part updates the database according to the user diagnosis result feedback;
the cloud computing part is combined with a genetic algorithm to carry out BP neural network parameter optimization when fault state identification and fault positioning core API model training are carried out;
the cloud computing part updates the database according to the user diagnosis result feedback in the cloud storage part, then carries out fault state recognition and fault positioning core API model training again, and downloads and updates the trained model to the intelligent fault diagnosis embedded system in a networking state; downloading the fault state recognition and fault positioning core API model after cloud training to an intelligent fault diagnosis embedded system of a vehicle-mounted end for judging the fault state of the vehicle and classifying and positioning the fault;
original vehicle-mounted sensor information, rapid plugging sensor information and vehicle-mounted computer accident alarm information are transmitted to an intelligent fault diagnosis embedded system through a CAN bus, and the intelligent fault diagnosis embedded system performs data preprocessing operation on the information in a database to obtain input data of a fault state identification and fault positioning core API model;
the method comprises the steps that a fault state identification and fault location core API model firstly trains a constructed vehicle operation information historical database in an Alice cloud server, and establishes a model for carrying out fault state identification and fault classification location by using vehicle operation information data, namely mapping from vehicle operation information to definite faults is obtained;
in the fault state identification and fault location core API model training, firstly, training samples need to be extracted from a vehicle operation information historical database, and extracted data are divided into a training set, a test set and a verification set;
for extracted data, before training, data preprocessing is firstly required, and the method comprises the following specific steps:
step 1: deleting samples corresponding to the detected data repetition and data deletion from the database, and re-extracting new data from the database to supplement the training samples;
step 2: in consideration of the existence of data noise, the data noise needs to be removed by adopting means including sliding filtering;
and step 3: selecting various characteristic data by adopting principal component analysis;
and 4, step 4: performing min-max normalization on the characteristic data selected by adopting a principal component analysis method, finishing random extraction of the training sample, and preprocessing the data after random extraction;
using the preprocessed data to train the model for fault state recognition and fault classification positioning, and adopting a BP neural network and a genetic algorithm, wherein the specific steps are as follows:
step 1: constructing a plurality of BP neural network models by using the preprocessed data, wherein the activation function of the hidden layer is a tanh function, and the activation function of the output layer adopts a softmax function;
step 2: initializing a neural network connection weight and a threshold, and optimizing the weight and the threshold by adopting a genetic algorithm;
and step 3: carrying out real number coding on the weight and the threshold of each BP neural network diagnosis model obtained by building, and randomly selecting 100 initial individuals of the weight and the threshold corresponding to the real number coding to form an initial population;
and 4, step 4: calculating a loss function expressed by the sum of squared errors; taking the reciprocal of the loss function as an individual fitness function;
and 5: selecting, crossing and mutating individuals in the current population to form a new population of the next generation;
and 6: judging whether the new population obtained in the step 5 reaches a convergence condition, and finishing weight and threshold optimization if the new population reaches the convergence condition; if the convergence condition is not reached, returning to the step 5 for recalculation;
and 7: taking the data of the optimal individuals in the population as the initial weight and the threshold of the optimized BP neural network model, starting iterative training on the BP neural network model until the loss function value is smaller than the preset threshold or the number of iterations is reached, and finishing the training of the BP neural network model;
and 8: inputting the verification set into a plurality of trained BP neural network models, and selecting the neural network model with the best performance as a fault state identification and fault positioning core API model;
and step 9: obtaining the accuracy of the API model through a test set;
in the updating process of the database content, data updating is carried out according to the following principles:
(1) When the diagnosis result of the intelligent fault diagnosis embedded system is correct and the data of the same fault state identification and fault classification positioning in the database does not reach the capacity value, directly updating the vehicle operation information data, the fault state identification and fault classification positioning data into the database;
(2) When the intelligent fault diagnosis embedded system has correct fault diagnosis results and the data corresponding to fault state identification and fault classification positioning in the database reaches a capacity value, the database is not updated;
(3) When the intelligent fault diagnosis embedded system has wrong fault diagnosis results and data corresponding to fault state identification and fault classification positioning in the database does not reach a capacity value, directly updating vehicle operation information data, fault state identification and fault classification positioning data to the database;
(4) When the intelligent fault diagnosis embedded system has wrong fault diagnosis results and the data corresponding to fault state identification and fault classification positioning in the database reaches a capacity value, the vehicle operation information data and the fault state identification and fault classification positioning data during diagnosis are used for randomly replacing a group of data corresponding to the same fault state identification and fault classification positioning in the database.
2. The intelligent auxiliary device for automobile rapid maintenance according to claim 1, wherein the cloud data message queue is adopted for buffering: in order to ensure that the information transmission of the intelligent fault diagnosis embedded system is matched with the updating process of the cloud database, an information queue is additionally arranged in the middle to serve as a data buffer area, so that the access pressure of a cloud server is reduced; the cloud platform receives operation information simultaneously sent by the intelligent fault diagnosis embedded systems, and the operation information is pushed to a message queue through the data storage module; the message queue is designed into a circular queue data structure, and the queue stores vehicle running history information according to a first-in first-out sequence; starting a resident process, monitoring the data storage condition of the message queue in real time, taking out the data for updating the database once finding that new data information arrives in the queue, and then deleting the processed information in the queue.
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