A cloud computing-based automobile operation fault identification system and method

Abstract

The application relates to the technical field of automobile electronic identification, and particularly discloses a cloud-computing-based automobile operation fault identification system and method, wherein a data acquisition module is responsible for collecting engine mechanical parameters, electric control system parameters and environmental parameters in real time; a data processing module filters and standardizes original data; a data analysis module subdivides mechanical system faults into three categories of intake, fuel and ignition; through construction of mathematical models under three working conditions of idling, acceleration and deceleration, corresponding fault index coefficients are obtained; meanwhile, electric control system fault coefficients are calculated by using functions; a fault identification module compares each coefficient with a preset threshold value, fault determination and positioning are realized, finally, an identification result is real-time early warned through a vehicle-mounted display screen, and is uploaded to a remote platform by means of a communication module and receives maintenance guidance, so that online real-time identification and remote expert support are organically combined.

Description

Technical Field

[0001] This invention relates to the field of automotive electronic identification technology, specifically to a cloud computing-based automotive operation fault identification system and method. Background Technology

[0002] With the rapid development of automotive electronics technology, higher requirements have been placed on the timeliness and accuracy of vehicle fault identification. Among these requirements, the engine is the most critical automotive component, so timely and accurate identification of engine faults is of great significance.

[0003] In existing technologies, engine fault identification in automobiles mainly suffers from the following two problems: 1. Traditional automobile fault diagnosis relies primarily on professional diagnostic equipment from 4S shops or repair shops for offline engine testing. This method has a significant time lag and cannot provide real-time warnings or early identification of potential engine faults during vehicle operation; 2. For faults in the engine's mechanical system, alarms are usually based on exceeding the threshold of a single parameter, which is prone to false alarms or missed alarms and cannot effectively distinguish specific faulty subsystems; 3. Engine operating conditions are significantly affected by factors such as ambient temperature, humidity, and road slope. Existing systems lack compensation mechanisms for environmental factors, which may lead to misjudging normal state changes as faults in specific environments (such as high altitudes or extreme cold), reducing the accuracy and robustness of identification.

[0004] This invention proposes a cloud computing-based vehicle operation fault identification system and method to achieve continuous monitoring and early warning of the overall health status of the engine. Summary of the Invention

[0005] The purpose of this invention is to provide a cloud computing-based vehicle operation fault identification system and method to solve the above-mentioned technical problems: The objective of this invention can be achieved through the following technical solutions: A cloud computing-based vehicle operation fault identification system, the system comprising a data acquisition module, a data processing module, a data analysis module, a fault identification and display module, and a communication module; The data acquisition module is deployed inside the vehicle and is used to collect multi-source operating parameters of the engine in real time during vehicle operation. The multi-source operating parameters include engine mechanical system parameters, engine electronic control system parameters, and environmental parameters. The data processing module, deployed on a cloud platform, is used to receive multi-source operating parameters of the engine during vehicle operation, and to perform filtering and noise reduction, outlier removal, and data format unification processing on the multi-source operating parameters to obtain standardized data. The data analysis module, deployed on a cloud platform, is used to perform real-time analysis on the processed standardized data and extract index coefficients for engine fault identification based on the analysis results. The fault identification and display module is deployed on a cloud platform and connected to the data analysis module. It is used to identify engine faults during operation based on the characteristic coefficients of engine fault identification and to display fault warnings to the driver in real time through the vehicle display screen. The communication module is used to upload multi-source operating parameters of the engine to the cloud platform in real time, and at the same time receive maintenance guidance information issued by the cloud platform.

[0006] As a further description of the technical solution of the present invention, the engine mechanical system parameters include speed, intake pressure, fuel injection quantity, and exhaust temperature; the engine electronic control system parameters include the voltage and current of the engine ECU module; and the environmental parameters include ambient temperature, humidity, and road slope.

[0007] As a further description of the technical solution of the present invention, the working process of the data analysis module includes: Engine mechanical system faults are classified into intake system faults, fuel system faults, and ignition system faults. The average engine parameters are obtained for a set time period under three operating conditions: idling, acceleration, and deceleration. Based on the obtained parameters for the three operating conditions, the index coefficients of engine intake system faults, fuel system faults, and ignition system faults are calculated in sequence.

[0008] As a further description of the technical solution of the present invention, a mathematical model of the fault index coefficients of the engine intake system is constructed, and the expression is: ； In the formula, , and The components are, in order, the intake pressure deviation, the fuel injection quantity deviation, and the exhaust temperature deviation under the i-th operating condition. , and The weighting coefficients for intake pressure, fuel injection quantity, and exhaust temperature are, in order. Let be the weighting coefficient corresponding to the i-th working condition, where , , , , and The values ​​are, in order, the measured average values ​​of intake pressure, fuel injection quantity, and exhaust temperature within a set time period under the i-th operating condition. , and These are the standard values ​​for intake pressure, fuel injection quantity, and exhaust temperature set by the system for the i-th operating condition. This is the environmental correction factor.

[0009] As a further description of the technical solution of this invention, a mathematical model of the fault index coefficients of the engine fuel system is constructed, and the expression is: ； In the formula, and These represent the maximum and minimum fuel injection amounts within a set time period under the i-th operating condition, respectively. This is an environmental correction factor; As a further description of the technical solution of this invention, a mathematical model of the fault index coefficients of the engine ignition system is constructed, and the expression is: ; In the formula, and These are the maximum and minimum engine values ​​within a set time period under the i-th operating condition, respectively. Let i be the engine speed deviation term under the i-th operating condition. , Let $\frac{i}{i}$ be the average engine speed over a given time period under the $i$-th operating condition. The standard engine speed value set by the system for the i-th operating condition. This is the environmental correction factor.

[0010] As a further description of the technical solution of the present invention, the process of obtaining the environmental correction coefficient includes: The mathematical model for the environmental correction coefficient is constructed, and its expression is as follows: ; In the formula, , and These are the influence functions of temperature, humidity, and slope, respectively; where, , The standard environmental slope range is set for the system, where S is the actual average slope during the engine parameter acquisition process. and These are the conversion factors; , The standard ambient temperature range set for the system. The actual average ambient temperature during the engine parameter collection process. and These are the conversion factors; , The standard environmental slope range set for the system. The actual average humidity during the engine parameter collection process. and These are the conversion coefficients.

[0011] As a further description of the technical solution of the present invention, the working process of the data analysis module also includes: Obtain the real-time voltage and current parameters of the engine ECU module, and construct a mathematical model of the fault index coefficients of the engine electronic control system. The expression is as follows: ； In the formula, and These are the voltage and current deviation values, respectively. and These are the weighting coefficients for voltage and current, respectively. , .

[0012] As a further description of the technical solution of the present invention, the working process of the fault identification and display module includes: The fault index coefficients of the engine intake system, engine fuel system, and engine ignition system are compared with the corresponding thresholds set by the system. If any fault index coefficient of the engine mechanical system is greater than or equal to the corresponding threshold set by the system, it indicates that there is a fault in the engine mechanical system. The fault index coefficient of the engine electronic control system is compared with the corresponding threshold set by the system. If the fault index coefficient of the engine electronic control system is greater than or equal to the corresponding threshold set by the system, it indicates that there is a fault in the engine electronic control system.

[0013] A cloud computing-based method for identifying vehicle operation faults, the method being used to implement a cloud computing-based vehicle operation fault identification system.

[0014] The beneficial effects of this invention are as follows: This invention innovatively decomposes engine mechanical system faults into three subsystems: intake, fuel, and ignition. It also constructs a dedicated mathematical model that integrates multi-condition parameter deviations and fluctuations, thereby achieving precise location and quantitative assessment of deep-seated mechanical faults, overcoming the limitations of traditional identification methods that only focus on the electronic control system. Simultaneously, the system introduces an environmental correction coefficient to dynamically compensate for the effects of temperature, humidity, and slope, significantly improving the accuracy and robustness of identification under different environments and effectively reducing the false alarm rate. Furthermore, the probabilistic evaluation of the electronic control system status using an S-shaped function enhances the sensitivity and reliability of judgment regarding core control unit faults. Ultimately, the system not only provides drivers with real-time and clear fault warnings through the in-vehicle display screen but also uploads information to a remote platform and receives maintenance guidance via a communication module, realizing a closed-loop service from local real-time identification to remote expert support, greatly improving vehicle operation safety and maintenance efficiency. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a schematic diagram of a portion of the vehicle operation fault identification system of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 As shown, the present invention provides a cloud computing-based vehicle operation fault identification system, characterized in that the system includes a data acquisition module, a data processing module, a data analysis module, a fault identification and display module, and a communication module; The data acquisition module is deployed inside the vehicle and is used to collect multi-source operating parameters of the engine in real time during vehicle operation. The multi-source operating parameters include engine mechanical system parameters, engine electronic control system parameters, and environmental parameters. The data processing module, deployed on a cloud platform, is used to receive multi-source operating parameters of the engine during vehicle operation, and to perform filtering and noise reduction, outlier removal, and data format unification processing on the multi-source operating parameters to obtain standardized data. The data analysis module, deployed on a cloud platform, is used to perform real-time analysis on the processed standardized data and extract index coefficients for engine fault identification based on the analysis results. The fault identification and display module is deployed on a cloud platform and connected to the data analysis module. It is used to identify engine faults during operation based on the characteristic coefficients of engine fault identification and to display fault warnings to the driver in real time through the vehicle display screen. The communication module is used to upload multi-source operating parameters of the engine to the cloud platform in real time, and at the same time receive maintenance guidance information issued by the cloud platform.

[0019] Through the above technical solution, this invention describes an automotive engine fault identification system based on multi-source data fusion. The system collects three types of parameters in real time through a data acquisition module: engine mechanical system parameters, engine electronic control system parameters, and environmental parameters. A data processing module standardizes the raw data, including filtering and noise reduction, outlier removal, and data format unification, providing a high-quality, standardized data foundation for subsequent accurate analysis. Based on the standardized data, a data analysis module calculates the fault index coefficients for each engine system. This module first classifies mechanical system faults into three categories: intake, fuel, and ignition, and collects the average values ​​of parameters under three typical operating conditions: idling, acceleration, and deceleration. By constructing a specialized mathematical model, the system identifies the fault index coefficients for each engine system. The actual values ​​of key parameters such as air pressure, fuel injection quantity, exhaust temperature, and speed are compared with the system standard values. Taking into account the fluctuation range of the parameters, the respective fault index coefficients are calculated. At the same time, an environmental correction coefficient is introduced to dynamically compensate for the influence of external environmental factors such as temperature, humidity, and slope on the identification results. For the electronic control system, the deviation values ​​of ECU voltage and current are processed by a function to generate electronic control fault coefficients. All these coefficients provide a quantitative decision basis for subsequent fault judgment. The fault identification and display module compares the calculated fault index coefficients with preset experience thresholds. As long as any coefficient is greater than or equal to its corresponding threshold, the fault is determined to exist, and a real-time warning is issued to the driver through the vehicle display screen.

[0020] As a further description of the technical solution of the present invention, the engine mechanical system parameters include speed, intake pressure, fuel injection quantity, and exhaust temperature; the engine electronic control system parameters include the voltage and current of the engine ECU module; and the environmental parameters include ambient temperature, humidity, and road slope.

[0021] As a further description of the technical solution of the present invention, the working process of the data analysis module includes: Engine mechanical system faults are classified into intake system faults, fuel system faults, and ignition system faults. The average engine parameters are obtained for a set time period under three operating conditions: idling, acceleration, and deceleration. Based on the obtained parameters for the three operating conditions, the index coefficients of engine intake system faults, fuel system faults, and ignition system faults are calculated in sequence.

[0022] As a further description of the technical solution of the present invention, a mathematical model of the fault index coefficients of the engine intake system is constructed, and the expression is: ； In the formula, , and The components are, in order, the intake pressure deviation, the fuel injection quantity deviation, and the exhaust temperature deviation under the i-th operating condition. , and The weighting coefficients for intake pressure, fuel injection quantity, and exhaust temperature are, in order. Let be the weighting coefficient corresponding to the i-th working condition, where , , , , and The values ​​are, in order, the measured average values ​​of intake pressure, fuel injection quantity, and exhaust temperature within a set time period under the i-th operating condition. , and These are the standard values ​​for intake pressure, fuel injection quantity, and exhaust temperature set by the system for the i-th operating condition, respectively. This is the environmental correction factor.

[0023] As a further description of the technical solution of this invention, a mathematical model of the fault index coefficients of the engine fuel system is constructed, and the expression is: ； In the formula, and These represent the maximum and minimum fuel injection amounts within a set time period under the i-th operating condition, respectively. This is an environmental correction factor; As a further description of the technical solution of this invention, a mathematical model of the fault index coefficients of the engine ignition system is constructed, and the expression is: ; In the formula, and These are the maximum and minimum engine values ​​within a set time period under the i-th operating condition, respectively. Let i be the engine speed deviation term under the i-th operating condition. , Let $\frac{i}{i}$ be the average engine speed over a given time period under the $i$-th operating condition. The standard engine speed value set by the system for the i-th operating condition. This is the environmental correction factor.

[0024] Through the above technical solution, this embodiment comprehensively evaluates the fault index coefficient of a specific subsystem by quantifying the deviation and fluctuation of key parameters of the engine under various operating conditions from their standard values. Mechanical system faults are categorized into three types: intake, fuel, and ignition. The calculations are performed uniformly under three operating conditions: idling, acceleration, and deceleration, using average parameters over a set time period, and then calculated using a formula. The engine intake system fault index coefficient is calculated using the formula, where is the weighted average relative deviation, which measures the degree of deviation of three interrelated parameters—intake pressure, fuel injection quantity, and exhaust temperature—from their standard values. This is achieved through the formula... Calculate the engine fuel system failure index coefficient, where Using squared terms to amplify systematic deviations (such as injector blockage leading to chronic under-injection, or leakage leading to over-injection) will cause problems regardless of whether the deviation is too high or too low. It measures the ratio of the fluctuation range of fuel injection quantity to the average value within a set time period. The larger this value, the more unstable the fuel supply; this is achieved through the formula... Calculate the engine fuel system failure index coefficient, where, Use the squared term to amplify the systematic deviation of the rotational speed. This measures the ratio of the fluctuation range of engine speed to the average engine speed over a set time period. The larger this value, the more unstable the engine operation, which is a typical characteristic of poor ignition.

[0025] As a further description of the technical solution of the present invention, the process of obtaining the environmental correction coefficient includes: The mathematical model for the environmental correction coefficient is constructed, and its expression is as follows: ; In the formula, , and These are the influence functions of temperature, humidity, and slope, respectively; where, , The standard environmental slope range is set for the system, where S is the actual average slope during the engine parameter acquisition process. and These are the conversion factors; , The standard ambient temperature range set for the system. The actual average ambient temperature during the engine parameter collection process. and These are the conversion factors; , The standard environmental slope range set for the system. The actual average humidity during the engine parameter collection process. and These are the conversion coefficients.

[0026] Through the above technical solution, this embodiment constructs an environmental correction coefficient to quantify and compensate for the impact of environmental factors such as temperature, humidity, and road slope on engine operating parameters, thereby ensuring the fairness and accuracy of fault identification results. This means that the total environmental correction is the product of the effects of temperature, humidity, and slope, where... The system has a preset standard slope range. Within this range, slope is considered to have no impact on identification, so the correction factor is 1. When the actual average slope S exceeds the upper limit... When climbing a steep incline, the function value is greater than 1. This means the system recognizes that a steep incline will increase engine load and cause parameter changes, thus amplifying the calculated failure coefficient. Because the same parameter deviation may be more alarming when climbing a heavily loaded incline than on a flat road, especially when the gradient S is below the lower limit. (As shown on the steep slope below) the function value is <1. This is because the engine load is low when going downhill, and some parameter deviations may be masked. Therefore, it is necessary to reduce the fault factor to reveal the truth. and These are conversion factors, which determine the strength of the influence of the correction factor when the slope deviates from the standard range. These factors need to be determined through experiments. Similar to the slope function, a standard temperature range is preset. When the ambient temperature (T) is too high, the air density decreases, potentially affecting intake efficiency; when the temperature is too low, the engine oil becomes viscous, leading to incomplete combustion. These factors can cause engine parameters to deviate from standard values. Therefore, when the temperature exceeds the ideal range, the correction factor will deviate from 1. and These are the conversion coefficients for high temperature and low temperature, respectively; Preset a standard humidity range Excessive or insufficient air humidity, i.e., excessively dry or humid environments, can affect combustion. Therefore, adjustments are necessary in excessively dry or humid environments as well.

[0027] As a further description of the technical solution of the present invention, the working process of the data analysis module also includes: Obtain the real-time voltage and current parameters of the engine ECU module, and construct a mathematical model of the fault index coefficients of the engine electronic control system. The expression is as follows: ； In the formula, and These are the voltage and current deviation values, respectively. and These are the weighting coefficients for voltage and current, respectively. , .

[0028] Through the above technical solution, this embodiment is used to determine whether a functional fault has occurred in the electronic control system. It does not focus on multiple operating conditions, but rather monitors the power supply quality of the ECU in real time. Even minor, persistent abnormalities in ECU voltage and current directly indicate that it cannot function properly, thus leading to the paralysis of the entire engine management system. The mathematical model for the engine electronic control system fault index coefficient C is as follows: In the formula, and These are the voltage and current deviation values, respectively, reflecting the degree of deviation between the measured values ​​and the standard reference values.

[0029] As a further description of the technical solution of the present invention, the working process of the fault identification and display module includes: The fault index coefficients of the engine intake system, engine fuel system, and engine ignition system are compared with the corresponding thresholds set by the system. If any fault index coefficient of the engine mechanical system is greater than or equal to the corresponding threshold set by the system, it indicates that there is a fault in the engine mechanical system. The fault index coefficient of the engine electronic control system is compared with the corresponding threshold set by the system. If the fault index coefficient of the engine electronic control system is greater than or equal to the corresponding threshold set by the system, it indicates that there is a fault in the engine electronic control system.

[0030] Through the above technical solution, this embodiment compares the various fault index coefficients calculated by the data analysis module with preset experience thresholds in a simple and efficient manner. The fault index coefficients of the engine intake system, engine fuel system, and engine ignition system are compared with the corresponding thresholds set by the system. If any fault index coefficient of the engine mechanical system is greater than or equal to the corresponding threshold set by the system, it indicates that there is a fault in the engine mechanical system. Not only can a fault be identified, but the faulty subsystem can also be located by identifying which coefficient exceeds the standard. The fault index coefficients of the engine electronic control system are compared with the corresponding thresholds set by the system. If the fault index coefficient of the engine electronic control system is greater than or equal to the corresponding threshold set by the system, it indicates that there is a fault in the engine electronic control system.

[0031] Example The system has set the following fault thresholds: Mechanical system failure threshold: =0.8, =0.7, =0.6 Fault threshold of electronic control system: =0.8 Scene 1 Compare the coefficients of the mechanical system: I(0.5)< (0.8), the intake system is normal.

[0032] F(0.9)≥ (0.7), Fuel system malfunction! L(0.7)≥ (0.6), Ignition system malfunction! Since both F and L exceed the limits, it is determined that there is a fault in the engine's mechanical system.

[0033] Compare the coefficients of the electronic control system: C(0.4)< (0.8), the electrical control system is normal.

[0034] Final diagnosis: Faults exist in the fuel system and ignition system within the mechanical system. Repair personnel are advised to focus their inspection on the fuel injectors, spark plugs, and ignition coils.

[0035] Scene 2 Comparing the mechanical system coefficients: all coefficients are well below the threshold, indicating that the mechanical system is normal.

[0036] Comparison of electronic control system coefficients: C(0.95)≥ (0.8), Electrical control system malfunction! Final diagnosis: A serious fault exists in the engine electronic control system. The problem is most likely with the ECU itself, or with its power supply or grounding, rather than with traditional mechanical components.

[0037] It should be noted that the formulas in this application are all dimensionless and numerical calculations. The formulas are obtained by software simulation based on a large amount of data and are the closest to the real situation. The thresholds, threshold ranges and coefficients involved in this application are all empirical values ​​and are selected by those skilled in the art according to the actual situation.

[0038] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A cloud computing-based vehicle operation fault identification system, characterized in that, The system includes a data acquisition module, a data processing module, a data analysis module, a fault identification and display module, and a communication module; The data acquisition module is deployed inside the vehicle and is used to collect multi-source operating parameters of the engine in real time during vehicle operation. The multi-source operating parameters include engine mechanical system parameters, engine electronic control system parameters, and environmental parameters. The data processing module, deployed on a cloud platform, is used to receive multi-source operating parameters of the engine during vehicle operation, and to perform filtering and noise reduction, outlier removal, and data format unification processing on the multi-source operating parameters to obtain standardized data. The data analysis module, deployed on a cloud platform, is used to perform real-time analysis on the processed standardized data and extract index coefficients for engine fault identification based on the analysis results. The fault identification and display module is deployed on a cloud platform and connected to the data analysis module. It is used to identify engine faults during operation based on the characteristic coefficients of engine fault identification and to display fault warnings to the driver in real time through the vehicle display screen. The communication module is used to upload multi-source operating parameters of the engine to the cloud platform in real time, and at the same time receive maintenance guidance information issued by the cloud platform.

2. The vehicle operation fault identification system based on cloud computing according to claim 1, characterized in that, The engine mechanical system parameters include engine speed, intake pressure, fuel injection quantity, and exhaust temperature; the engine electronic control system parameters include the voltage and current of the engine ECU module; and the environmental parameters include ambient temperature, humidity, and road slope.

3. The vehicle operation fault identification system based on cloud computing according to claim 1, characterized in that, The working process of the data analysis module includes: Engine mechanical system faults are classified into intake system faults, fuel system faults, and ignition system faults. The average engine parameters are obtained for a set time period under three operating conditions: idling, acceleration, and deceleration. Based on the obtained parameters for the three operating conditions, the index coefficients of engine intake system faults, fuel system faults, and ignition system faults are calculated in sequence.

4. The vehicle operation fault identification system based on cloud computing according to claim 3, characterized in that, Construct a mathematical model for the fault index coefficients of the engine intake system, the expression of which is: ； In the formula, , and The components are, in order, the intake pressure deviation, the fuel injection quantity deviation, and the exhaust temperature deviation under the i-th operating condition. , and The weighting coefficients for intake pressure, fuel injection quantity, and exhaust temperature are, in order. Let be the weighting coefficient corresponding to the i-th working condition, where , , , , and The values ​​are, in order, the measured average values ​​of intake pressure, fuel injection quantity, and exhaust temperature within a set time period under the i-th operating condition. , and These are the standard values ​​for intake pressure, fuel injection quantity, and exhaust temperature set by the system for the i-th operating condition, respectively. This is the environmental correction factor.

5. The vehicle operation fault identification system based on cloud computing according to claim 3, characterized in that, The mathematical model for the fault index coefficients of the engine fuel system is constructed, and its expression is as follows: ； In the formula, and These represent the maximum and minimum fuel injection amounts within a set time period under the i-th operating condition, respectively. This is the environmental correction factor.

6. The vehicle operation fault identification system based on cloud computing according to claim 3, characterized in that, Construct a mathematical model for the fault index coefficients of the engine ignition system, with the following expression: ； In the formula, and These are the maximum and minimum engine values ​​within a set time period under the i-th operating condition, respectively. Let i be the engine speed deviation term under the i-th operating condition. , Let $\frac{i}{i}$ be the average engine speed over a given time period under the $i$-th operating condition. The standard engine speed value set by the system for the i-th operating condition. This is the environmental correction factor.

7. The vehicle operation fault identification system according to any one of claims 4-6, characterized in that, The process of obtaining the environmental correction coefficient includes: The mathematical model for the environmental correction coefficient is constructed, and its expression is as follows: ； In the formula, , and These are the influence functions of temperature, humidity, and slope, respectively; where, , The standard environmental slope range is set for the system, where S is the actual average slope during the engine parameter acquisition process. and These are the conversion factors; , The standard ambient temperature range set for the system. The actual average ambient temperature during the engine parameter collection process. and These are the conversion factors; , The standard environmental slope range set for the system. The actual average humidity during the engine parameter collection process. and These are the conversion coefficients.

8. A cloud computing-based vehicle operation fault identification system according to claim 3, characterized in that, The data analysis module's operation also includes: Obtain the real-time voltage and current parameters of the engine ECU module, and construct a mathematical model of the fault index coefficients of the engine electronic control system. The expression is as follows: ； In the formula, and These are the voltage and current deviation values, respectively. and These are the weighting coefficients for voltage and current, respectively. , .

9. A cloud computing-based vehicle operation fault identification system according to claim 1, characterized in that, The working process of the fault identification and display module includes: The fault index coefficients of the engine intake system, engine fuel system, and engine ignition system are compared with the corresponding thresholds set by the system. If any fault index coefficient of the engine mechanical system is greater than or equal to the corresponding threshold set by the system, it indicates that there is a fault in the engine mechanical system. The fault index coefficient of the engine electronic control system is compared with the corresponding threshold set by the system. If the fault index coefficient of the engine electronic control system is greater than or equal to the corresponding threshold set by the system, it indicates that there is a fault in the engine electronic control system.

10. A cloud computing-based method for identifying vehicle operation faults, characterized in that, The method is used to implement the cloud computing-based vehicle operation fault identification system according to any one of claims 1-9.

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