Self-diagnosis system for unmanned vehicle system and operation method

Abstract

The invention provides a self-diagnosis system for an unmanned vehicle system and an operation method, and the system comprises a real-time monitoring system, a sensor data fusion module, a sensor data processing module, a fault recognition module, and an LLAMA fault diagnosis model. The real-time monitoring system is used for monitoring the function operation condition of the vehicle in real time and collecting real-time monitoring data of the vehicle; the sensor data fusion module is used for fusing data acquired by multiple types of sensors of the vehicle; the sensor data processing module is used for receiving, processing and analyzing data acquired by various sensors of the vehicle; the fault identification module is used for carrying out fault identification on the real-time monitoring data; and the LLAMA fault diagnosis module is used for interacting with an operation user, analyzing the associated text information of the vehicle, performing fault diagnosis on the vehicle and generating a fault diagnosis report. Through the vehicle fault diagnosis method and device, the diagnosis efficiency, accuracy and reliability of fault diagnosis can be improved, and the accessibility and availability of a vehicle system are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of unmanned driving technology, and in particular, to a self-diagnosis system and an operation method for an unmanned driving vehicle system. Background Art

[0002] China is not only a country with a large population, but also one of the fastest-growing countries in the world. By 2022, the number of civilian cars in my country will exceed 300 million, and cars have become an indispensable tool in our lives. However, according to statistics, the number of traffic accidents is still relatively large, among which the accident rate caused by human factors accounts for a relatively high proportion (80%). Therefore, how to avoid traffic accidents more efficiently is still a difficult problem that people are concerned about. In order to solve this problem, smart electric vehicles have begun to receive widespread attention from academic researchers. However, the current fault diagnosis of driverless vehicles has the following defects:

[0003] (1) High maintenance cost: Traditional intelligent vehicle fault diagnosis systems usually require professional maintenance personnel to maintain and update, which is costly and takes a long time, affecting the sustainability and economy of the system.

[0004] (2) Lack of adaptability: Previous intelligent vehicle fault diagnosis systems lacked adaptability and were unable to dynamically adjust according to vehicle operating conditions and environmental changes, resulting in poor diagnostic results;

[0005] (3) Sensor error and failure: Sensors are key components of smart car systems, but sensor drift errors and failures affect the accuracy and reliability of fault diagnosis.

[0006] (4) Fault diagnosis knowledge barrier: The current fault diagnosis system has the defect of high knowledge barrier, which makes the fault identification and diagnosis process complicated and requires high professional knowledge. Summary of the invention

[0007] In view of the defects in the prior art, the purpose of the present disclosure is to provide a self-diagnosis system for an unmanned vehicle system.

[0008] To achieve the above object, according to one aspect of the present disclosure, a self-diagnosis system for an unmanned vehicle system is provided, comprising: a real-time monitoring system, a sensor data fusion module, a sensor data processing module, a fault identification module, and a LLAMA fault diagnosis model;

[0009] The real-time monitoring system is used to monitor the functional operating status of the vehicle in real time and collect real-time monitoring data of the vehicle, and the real-time monitoring data of the vehicle is data collected by multiple types of sensors of the vehicle;

[0010] The sensor data fusion module is used to fuse the data collected by multiple types of sensors of the vehicle to obtain comprehensive information of the vehicle;

[0011] The sensor data processing module is used to receive, process and analyze the comprehensive information of the vehicle and perform fault diagnosis on the vehicle using a preset diagnosis algorithm;

[0012] The fault identification module is used to perform fault identification on the real-time monitoring data of the vehicle to identify the fault of the vehicle;

[0013] The LLAMA fault diagnosis module is used to interact with the operating user and analyze the associated text information of the vehicle, perform fault diagnosis on the vehicle and generate a fault diagnosis report.

[0014] Optionally, an automatic alarm module is also included, which is connected to the sensor data processing module, the fault identification module, and the LLAMA fault diagnosis module respectively, and the automatic alarm module is used to issue a signal warning when a fault occurs in the vehicle.

[0015] Optionally, the signal warning method of the automatic alarm module includes sound alarm, visual alarm, mobile terminal information prompt, and email prompt.

[0016] Optionally, it further includes a remote monitoring and control system, which includes a remote monitoring module, a global positioning module, a remote control module, a mobile application module, and a data storage and analysis module;

[0017] The remote monitoring module is used to collect vehicle data and transmission data of the vehicle;

[0018] The global positioning module is used to determine the location information of the vehicle;

[0019] The remote control module is used to remotely access the vehicle through a network and control the vehicle to perform functions;

[0020] The mobile application module is used to remotely monitor the location information of the vehicle through a mobile terminal;

[0021] The data storage and analysis module is used to store and analyze vehicle data of the vehicle.

[0022] Optionally, a cloud platform is also included, and the cloud platform is used to communicate with the vehicle.

[0023] Optionally, the cloud platform includes a remote data storage module, a remote system update module, and a system self-learning module;

[0024] The remote data storage module is used to store all data corresponding to the vehicle;

[0025] The remote system update module is used to update system algorithms, fix known defects or deploy new functions;

[0026] The system self-learning module is used to provide machine learning algorithms and artificial intelligence technology, and perform self-learning through iterative optimization.

[0027] Optionally, an unmanned driving sensor is also included, and the unmanned driving sensor includes a lidar sensor and a visual sensor, and the unmanned driving sensor is used to collect surrounding environment information of the vehicle.

[0028] According to a second aspect of the present disclosure, there is provided a method for operating a self-diagnosis system for an unmanned vehicle system, comprising:

[0029] In the system startup phase, a self-diagnosis system for the unmanned vehicle system is started, the self-diagnosis system for the unmanned vehicle system is initialized and performs a self-diagnosis of the vehicle fault;

[0030] During the vehicle operation phase, the self-diagnosis system for the unmanned vehicle system monitors the functional operation status of the vehicle and the status of the unmanned vehicle system in real time, and performs fault self-diagnosis.

[0031] Optionally, the monitoring results of the system startup phase and the monitoring results of the vehicle operation phase are displayed on a display screen of the vehicle.

[0032] Optionally, the method further comprises:

[0033] During the system cloud update phase, the update model file is uploaded to the server, and the update model is deployed in the self-diagnosis system for the unmanned vehicle system using a blue-green deployment or canary release strategy.

[0034] Compared with the prior art, the embodiments of the present disclosure have at least one of the following beneficial effects:

[0035] Through the above technical scheme, a real-time monitoring system is set up to realize efficient real-time monitoring, timely discover potential faults or abnormal conditions, and improve the speed and accuracy of fault diagnosis; a sensor data fusion module and a sensor data processing module are set up to fuse and process sensor data to obtain comprehensive and accurate vehicle information, timely detect sensor faults, improve the comprehensiveness and reliability of fault diagnosis, and set up a fault identification module to identify faults in the real-time monitoring data of the vehicle and identify the faults of the vehicle; an LLAMA fault diagnosis module is set up to identify the potential faults and abnormalities of the vehicle by interacting with the operating user and analyzing the associated text information of the vehicle, thereby lowering the diagnostic threshold, improving the accessibility and availability of the system, reducing the labor intensity of fault diagnosis, and improving the reliability and maintenance efficiency of unmanned vehicles.

[0036] In the embodiments of the present disclosure, a remote monitoring and control system is provided, so that operators can remotely access the vehicle, discover and solve faults in a timely manner, and reduce the parking time caused by the faults.

[0037] In the embodiments of the present disclosure, a system self-learning module is set on the cloud platform to optimize the diagnosis algorithm based on the actual operating conditions of the vehicle, thereby improving the efficiency and accuracy of vehicle fault diagnosis.

[0038] The embodiments of the present disclosure accurately identify the failure modes and abnormal conditions of the vehicle based on rules, models, statistical analysis and machine learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Other features, objects and advantages of the present disclosure will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0040] Figure 1 The figure is a schematic diagram of the overall process of a self-diagnosis system for an unmanned vehicle system according to an exemplary embodiment.

[0041] Figure 2 The present invention is a flowchart of an operation method of a self-diagnosis system for an unmanned vehicle system according to an exemplary embodiment.

[0042] Figure 3 The diagram is a schematic diagram of the structure of an operation mode of a self-diagnosis system for an unmanned vehicle system according to an exemplary embodiment.

[0043] Figure 4 The present invention is a schematic diagram of a diagnostic process of a self-diagnosis system for an unmanned vehicle system according to an exemplary embodiment. DETAILED DESCRIPTION

[0044] The present disclosure is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present disclosure, but are not intended to limit the present disclosure in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements may be made without departing from the concept of the present disclosure. These all fall within the scope of protection of the present disclosure.

[0045] The present disclosure provides a self-diagnosis system for an unmanned vehicle system, which maintains platform independence and can run on different hardware platforms or operating systems.

[0046] A self-diagnosis system for an unmanned vehicle system includes: a real-time monitoring system, a sensor data fusion module, a sensor data processing module, a fault identification module, and a LLAMA fault diagnosis model.

[0047] The real-time monitoring system is used to monitor the functional operating status of the vehicle in real time and collect the real-time monitoring data of the vehicle.

[0048] The real-time monitoring data of the vehicle is the data collected by various types of sensors of the vehicle. The real-time monitoring system monitors various components of the vehicle and the unmanned vehicle system in real time, and collects data from various types of sensors, control units, and drive systems as real-time monitoring data.

[0049] The multiple types of sensors may include but are not limited to lidar, cameras, GPS antennas and component sensors, wherein lidar and cameras are used to detect the contour of the vehicle ahead, GPS is used for vehicle positioning, and component sensors are used to detect the operating parameters of various components of the vehicle.

[0050] Those skilled in the art should understand that in other embodiments, other corresponding kits may also be used to implement the above functions.

[0051] The sensor data fusion module is used to fuse the data collected by multiple types of sensors of the vehicle to obtain comprehensive information about the vehicle.

[0052] The comprehensive information of the vehicle may include the surrounding environment information of the vehicle and the vehicle's own information.

[0053] When a sensor installed on a vehicle fails, the data collected by the sensor will also be abnormal. It is necessary to fuse the data collected by multiple types of sensors installed on the vehicle to facilitate subsequent fault diagnosis of the comprehensive information of the fused vehicle.

[0054] The integrated vehicle's comprehensive information can also assist the self-diagnosis system of the unmanned vehicle system to fully understand the vehicle and its surrounding conditions, thereby improving the accuracy and reliability of unmanned vehicle driving.

[0055] The sensor data processing module is used to receive, process and analyze comprehensive information of the vehicle, and use preset diagnostic algorithms to perform fault diagnosis on the vehicle.

[0056] Among them, some fault diagnosis algorithms are preset in the sensor data processing module to perform primary fault diagnosis on multiple types of sensors of the vehicle.

[0057] The sensor data processing module can process large amounts of data collected by multiple types of sensors installed on the vehicle in real time, and can quickly and accurately identify possible faults or abnormal conditions in the vehicle and provide corresponding solutions to the faults or abnormal conditions.

[0058] The fault identification module is used to perform fault identification on the real-time monitoring data of the vehicle and identify the vehicle's faults.

[0059] Among them, the fault identification module includes a preset complete fault identification algorithm, which completely analyzes and processes the real-time monitoring data to identify possible faults or abnormal conditions of the vehicle.

[0060] As an example, the preset fault identification algorithm can use machine learning algorithms and multi-level screening strategies to perform probabilistic calculations on abnormal data, learn and analyze large amounts of data, and identify sensor faults through statistical analysis and machine learning without establishing specific mathematics.

[0061] As another example, the preset fault identification algorithm may also use a neural network combining principal component analysis and a firefly algorithm to identify sensor faults.

[0062] The fault identification module can identify the following fault types: battery fault, sensor fault, electric system fault, etc.

[0063] The LLAMA fault diagnosis module is used to interact with the operating user and analyze the associated text information of the vehicle, diagnose the vehicle faults and generate a fault diagnosis report.

[0064] The LLAMA fault diagnosis module can interact with the operating user, the operating user asks questions to the LLAMA fault diagnosis module manually, and the LLAMA fault diagnosis module answers the questions of the operating user, forming a question-answer sequence of manual answers.

[0065] The associated text information of the vehicle may include the vehicle's log files, the vehicle's historical fault reports, and text information of operating user feedback.

[0066] The LLAMA fault diagnosis module learns the associated text information of the vehicle to achieve a deep understanding of the vehicle status and fault diagnosis, and generates fault solutions and suggestions to assist technicians in fault diagnosis and repair, improve vehicle fault diagnosis efficiency, and save vehicle maintenance time.

[0067] In a possible embodiment, a self-diagnosis system for an unmanned vehicle system also includes an automatic alarm module, which is respectively connected to the sensor data processing module, the fault identification module, and the LLAMA fault diagnosis module. The automatic alarm module is used to issue a signal warning when a vehicle fault occurs.

[0068] Among them, the signal warning methods of the automatic alarm module include sound alarm, visual warning, mobile terminal information prompt, and email prompt.

[0069] As an example, when the vehicle has an abnormal battery status, sensor failure, or abnormal vehicle position, the fault identification module or the sensor data processing module can send a signal to the automatic alarm module; or, when the LLAMA fault diagnosis module diagnoses that the vehicle is in a dangerous state or fault mode, the LLAMA fault diagnosis module sends a signal to the automatic alarm module, and the automatic alarm module can issue a signal warning based on the received signal and preset alarm rules.

[0070] Among them, the LLAMA fault diagnosis module includes the LLAMA model, which can accurately determine whether the vehicle is in a dangerous state based on the learned vehicle experience and vehicle professional knowledge data; the LLAMA model can also analyze the vehicle's log files, the vehicle's historical fault reports, and the text information of the operating user feedback to identify potential fault modes.

[0071] In a possible embodiment, a self-diagnosis system for an unmanned vehicle system also includes a remote monitoring and control system, which includes a remote monitoring module, a global positioning module, a remote control module, a mobile application module, and a data storage and analysis module.

[0072] The remote monitoring module is used to collect vehicle data and transmit data.

[0073] The global positioning module is used to determine the location information of the vehicle.

[0074] The remote control module is used to remotely access the vehicle through the network and control vehicle execution functions.

[0075] The mobile application module is used to remotely monitor the vehicle's location information through a mobile terminal.

[0076] As an example, the vehicle's location information can be remotely monitored by installing a mobile application module on a smartphone or tablet.

[0077] The data storage and analysis module is used to store and analyze vehicle data of the vehicle.

[0078] By setting up a remote monitoring and control system, the present disclosure enables operators to remotely access vehicles, discover and solve faults in a timely manner, and reduce parking time caused by faults.

[0079] In a possible embodiment, a self-diagnosis system for an unmanned vehicle system further includes a cloud platform, and the cloud platform is used to communicate with the vehicle.

[0080] The cloud platform includes a remote data storage module, a remote system update module, and a system self-learning module.

[0081] The remote data storage module is used to store all data corresponding to the vehicle.

[0082] Among them, all data corresponding to the vehicle may include data collected by multiple types of sensors, vehicle log files, vehicle historical fault reports, text information feedback from operating users, and other vehicle-related data.

[0083] The remote system upgrade module is used to update system algorithms, fix known defects or deploy new features.

[0084] As an example, the remote system upgrade module can update system software and system algorithms to fix known defects or deploy new functions.

[0085] The system self-learning module is used to provide machine learning algorithms and artificial intelligence technologies, and perform self-learning through iterative optimization.

[0086] As an example, the LLAMA model can be combined with artificial intelligence algorithms or models for higher-level self-learning.

[0087] As another example, the system self-learning module is combined with a deep learning model and a reinforcement learning model to perform self-learning through data training and iterative optimization.

[0088] Through the self-learning of the system self-learning module, the intelligent diagnostic capability of the self-diagnosis system for the unmanned vehicle system is further improved, the diagnostic algorithm is optimized based on the actual operating conditions of the vehicle, and the efficiency and accuracy of vehicle fault diagnosis are improved.

[0089] In a possible embodiment, a self-diagnosis system for an unmanned vehicle system further includes an unmanned driving sensor, which includes a lidar sensor and a visual sensor, and the unmanned driving sensor is used to collect information about the surrounding environment of the vehicle.

[0090] As an example, the unmanned driving sensor can perceive the vehicle's surrounding environment in real time, and accurately obtain the position relationship information of the front vehicle relative to the rear vehicle and the contour information of the front vehicle.

[0091] In a possible embodiment, the sensor data fusion module can fuse the data collected by the unmanned driving sensor to obtain comprehensive information about the vehicle, and the sensor data processing module can use a tracking algorithm to continuously analyze and process the data collected by the unmanned driving sensor.

[0092] In a possible embodiment, a self-diagnosis system for an unmanned vehicle system further includes an autonomous charging diagnosis module for regularly checking the vehicle battery charging status and the connection between the vehicle and the charging pile to ensure normal vehicle charging.

[0093] Figure 1 The figure is a schematic diagram of the overall process of a self-diagnosis system for an unmanned vehicle system according to an exemplary embodiment.

[0094] like Figure 1 As shown, the operation and maintenance personnel can publish the model to the self-diagnosis system for the unmanned vehicle system through the cloud platform, and collect data for the real-time monitoring system in the self-diagnosis system of the unmanned vehicle system; the sensor data fusion module and the sensor data processing module are used for data processing, and the LLAMA fault diagnosis module and the fault identification module are used for decision analysis, and the LLAMA fault diagnosis module performs response processing and feedback to the operating user, and forms a fault diagnosis report and system log, which are visualized on the web interface of the visual display screen.

[0095] The present disclosure provides a self-diagnosis system for an unmanned vehicle system, which can be installed on a mobile terminal.

[0096] In a possible embodiment, a self-diagnosis system for an unmanned vehicle system is established, including:

[0097] Step 1: Obtain the expert knowledge base and the complete training set as model training data.

[0098] Among them, the expert knowledge base includes the knowledge and experience of experts in the field, for example, about vehicle systems, sensor working principles, and common fault information; the complete training set includes training samples of multiple fault information and solutions corresponding to multiple fault information. The complete training set is used for model training and learning.

[0099] Step 2: Use deep reinforcement learning algorithm and model training data to train the model.

[0100] Among them, a deep reinforcement learning algorithm is used to simulate the model so that the intelligent agent takes actions in the environment and obtains rewards to learn the optimal strategy.

[0101] As an example, in the self-diagnosis system for unmanned vehicle systems, the LLAMA model is introduced, and the deep reinforcement learning algorithm and model training data are used to train and learn the LLAMA model, so that fault diagnosis can be performed based on data collected by real-time multi-type sensors and a preset fault identification algorithm. It can also identify the vehicle's potential failure modes and abnormal behaviors by analyzing the vehicle's associated text information, and warn of potential failures.

[0102] Step 3: Establish an explanation library between the output results of the deep reinforcement learning knowledge system and the fault types.

[0103] Among them, the explanation library stores the deep reinforcement learning knowledge system's diagnosis of different fault conditions and their corresponding explanations, providing reference and basis for subsequent fault diagnosis.

[0104] Step 4: Obtain data collected by multiple types of sensors on the vehicle, images of the vehicle, and equipment configuration data on the vehicle.

[0105] Among them, the image of the vehicle may include a real-time image of the vehicle's surrounding environment; the data collected by multiple types of sensors may include the real-time readings and working status of the sensors; and the equipment configuration data may include the configuration information of the vehicle system.

[0106] Data collected by multiple types of vehicle sensors, images of the vehicle, and equipment configuration data on the vehicle are used as input to the deep reinforcement learning knowledge system for fault diagnosis.

[0107] Step 5: Preprocess the data to be processed as the data to be detected.

[0108] Among them, data preprocessing includes data cleaning, denoising, and normalization operations, and data quality and availability are improved through data preprocessing.

[0109] The data to be tested is used as the input of the deep reinforcement learning knowledge system, and the deep reinforcement learning knowledge system is trained for fault detection and fault diagnosis, and a self-diagnosis system for the unmanned vehicle system is established.

[0110] Figure 2 The present invention is a flowchart of an operation method of a self-diagnosis system for an unmanned vehicle system according to an exemplary embodiment.

[0111] like Figure 2 As shown, the present disclosure also provides an operating method for a self-diagnosis system of an unmanned vehicle system, including S11 to S12.

[0112] S11, during the system startup phase, a self-diagnosis system for the unmanned vehicle system is started, the self-diagnosis system for the unmanned vehicle system is initialized and a fault self-check is performed on the vehicle.

[0113] Among them, in the system startup phase, the operating user starts and waits for the self-diagnosis system for the unmanned vehicle system to complete the initialization process and fault self-check process.

[0114] The user interacts with the self-diagnostic system for the unmanned vehicle system through a designated user interface of the client, and the user can provide authentication when the user logs into the system to obtain access rights.

[0115] After the vehicle's self-diagnosis system for the unmanned vehicle system is activated, it will start to collect vehicle data in real time, including but not limited to vehicle status, environmental conditions, sensor readings, and use the vehicle data as input to the self-diagnosis system for the unmanned vehicle system for fault detection and diagnosis. The system will analyze and process the data through pre-trained models and algorithms to detect any potential faults or abnormal conditions in a timely manner;

[0116] After the diagnostic process is completed, the system will provide the user with the diagnostic results and possible solutions. The user can view the diagnostic report through the system interface to understand the status of the vehicle and any problems found. If there is a fault that needs to be repaired, the system may suggest appropriate actions to the user or provide further guidance so that the user can deal with the vehicle's fault in a timely manner.

[0117] S12, during the vehicle operation phase, the self-diagnosis system for the unmanned vehicle system monitors the functional operation status of the vehicle and the status of the unmanned vehicle system in real time, and performs fault self-diagnosis.

[0118] In a possible embodiment, the monitoring results of the system startup phase and the monitoring results of the vehicle operation phase are displayed on a display screen of the vehicle.

[0119] The operating user can view the real-time monitoring results of the vehicle on the system interface displayed on the vehicle's display screen. The real-time monitoring results may include various sensor data, battery power, vehicle location, etc.

[0120] The operating user uses the self-diagnosis system for the unmanned vehicle system to diagnose vehicle faults based on the vehicle startup stage and vehicle operation stage, and promptly discovers potential faults or abnormal conditions so as to take necessary measures to protect the safe operation of the vehicle.

[0121] like Figure 2 As shown, in a possible embodiment, a method for operating a self-diagnosis system for an unmanned vehicle system further includes S13.

[0122] S13, in the system cloud update phase, the update model file is uploaded to the server, and the update model is deployed in the self-diagnosis system for the unmanned vehicle system using a blue-green deployment or canary release strategy.

[0123] As an example, the updated model file needs to be evaluated before being uploaded to the server. After passing the evaluation, the updated model file is uploaded to the server, the API interface is updated, or the old model is replaced in the local system of the unmanned vehicle. The updated model file is deployed using a blue-green deployment or canary release strategy to prevent affecting the normal operation of the vehicle.

[0124] Figure 3 The diagram is a schematic diagram of the operation structure of a self-diagnosis system for an unmanned vehicle system according to an exemplary embodiment.

[0125] like Figure 3 As shown, the R&D department develops the LLAMA model and deploys it in the self-diagnosis system for the unmanned vehicle system through the workstation and the cloud platform; the management personnel interact with the cloud platform through the PC website; the users interact with the cloud platform through the application program of the mobile terminal; the self-diagnosis system for the unmanned vehicle system interacts with the vehicle, and the self-diagnosis system for the unmanned vehicle system also interacts with the cloud platform.

[0126] Therefore, R&D personnel, management personnel, and users all interact with the self-diagnosis system for the unmanned vehicle system provided by this application through the cloud platform.

[0127] Figure 4 The present invention is a schematic diagram of a diagnostic process of a self-diagnosis system for an unmanned vehicle system according to an exemplary embodiment.

[0128] like Figure 4 As shown, the present disclosure provides a self-diagnosis system for an unmanned vehicle system, which first detects faults on the vehicle and determines the existence and time of the faults; secondly, it identifies the detected faults of the vehicle and determines the severity and cause of the faults; then it uses the LLAMA model to perform model processing, analyze the fault data, and propose solutions; the user performs fault processing based on the solution proposed by the LLAMA model.

[0129] The present disclosure provides a self-diagnosis system for an unmanned vehicle system, which accurately identifies vehicle failure modes and abnormal conditions based on rules, models, statistical analysis, and machine learning.

[0130] In a possible embodiment, the user starts the vehicle by scanning the QR code of the vehicle; during the use of the vehicle, if the user encounters a situation where the vehicle cannot be started and the automatic alarm module issues a signal warning, the user can directly initiate a self-service diagnosis request to the self-diagnosis system for the unmanned vehicle system through the application of the mobile terminal; the LLAMA model in the LLAMA fault diagnosis module in the self-diagnosis system for the unmanned vehicle system will recommend possible faults and solutions to the user based on the text information fed back by the user and combined with the associated text information of the vehicle, such as the fault database.

[0131] If the problem reported by the user is relatively simple, such as low battery, the self-diagnosis system of the unmanned vehicle system will provide charging suggestions; if the problem reported by the user is more complex, such as electronic system failure, the self-diagnosis system of the unmanned vehicle system will give corresponding maintenance suggestions or suggest that the user contact professional maintenance personnel for inspection.

[0132] If the battery level of the vehicle is lower than a preset threshold while the user is driving, maintenance personnel can remotely access the vehicle through the network through the remote control module of the remote monitoring and control system to diagnose the vehicle and perform repairs based on the diagnosis results.

[0133] Users can check the vehicle status and charging status at any time through the mobile terminal application to ensure the efficient operation of the unmanned vehicle system.

[0134] In a possible embodiment, during the system startup phase, the user can start the self-diagnosis system for the unmanned vehicle system provided by the present disclosure in the application of the mobile terminal. The user turns on the self-diagnosis system, and the system initializes and performs self-fault detection to ensure that all components and systems are working properly. The user waits for the self-diagnosis system to complete the initialization process and the fault diagnosis process. According to the user's selection, the self-diagnosis system executes the diagnostic program, performs fault diagnosis on the vehicle, checks the data collected by various sensors, and analyzes the vehicle status.

[0135] In a possible embodiment, during the vehicle operation phase, the self-diagnosis system for the unmanned vehicle system monitors the various components of the vehicle and the status of the unmanned vehicle system in real time, such as data collected by various sensors, battery power, and vehicle location, and the user can view the real-time monitoring results through the display screen.

[0136] After the diagnostic program is executed, the self-diagnosis system of the unmanned vehicle system generates a fault diagnosis report, which can be viewed by the user through the display screen. The fault diagnosis report includes the fault problem, fault code, and recommended repair plan.

[0137] The dispatching lead vehicle sends a follow-up driving instruction to all vehicles in the fleet, and all vehicles follow the lead vehicle until the next low-battery vehicle.

[0138] In a possible embodiment, the user can take corresponding actions based on the diagnosis results. If the problem can be solved by the user, such as restarting the system, replacing a damaged sensor, etc., the user can follow the guidance provided by the system. If the problem requires professional technical support or the vehicle needs further maintenance, the user can contact the relevant service department or technical support team for processing.

[0139] If you still experience vehicle failure or other problems after repairing the problem, you can perform the system self-diagnosis procedure again to determine if there is another problem or whether the previous problem has been resolved.

[0140] The present disclosure provides a self-diagnosis system for a system of unmanned vehicles. The user first needs to start the system self-diagnosis system of the unmanned vehicle. This can usually be done by starting the power of the vehicle or a specific button on the control panel, and the system can also be started with the power on. Once the system is started, it will initialize and self-check for faults. The user needs to wait for the system to complete these steps to ensure that the system is in normal operation. If the system detects any potential faults or abnormal conditions, an alarm will be issued and the user will be prompted to pay attention. The alarm can be implemented by sound, flashing lights or warning messages on the display. The user can further view detailed diagnostic information through the system interface of the display, including the type of fault, location, possible causes, etc. The LLAMA model can automatically learn and extract key information from the user's description, and combine the fault database and diagnostic algorithm in the system to provide users with more accurate diagnostic results and solutions. If the user needs, the manager can remotely access the vehicle's diagnostic information and remotely control the vehicle to perform necessary operations or repairs through the remote monitoring and control system provided by the system.

[0141] The above describes the specific embodiments of the present disclosure. It should be understood that the present disclosure is not limited to the above specific embodiments, and those skilled in the art can make various deformations or modifications within the scope of the claims, which does not affect the essence of the present disclosure. The above preferred features can be used in any combination without conflicting with each other.

Claims

1. A self-diagnosis system for an unmanned vehicle system, characterized in that: include: Real-time monitoring system, sensor data fusion module, sensor data processing module, fault identification module, LLAMA fault diagnosis model; The real-time monitoring system is used to monitor the functional operating status of the vehicle in real time and collect real-time monitoring data of the vehicle, and the real-time monitoring data of the vehicle is data collected by multiple types of sensors of the vehicle; The sensor data fusion module is used to fuse the data collected by multiple types of sensors of the vehicle to obtain comprehensive information of the vehicle; The sensor data processing module is used to receive, process and analyze the comprehensive information of the vehicle and perform fault diagnosis on the vehicle using a preset diagnosis algorithm; The fault identification module is used to perform fault identification on the real-time monitoring data of the vehicle to identify the fault of the vehicle; The LLAMA fault diagnosis module is used to interact with the operating user and analyze the associated text information of the vehicle, perform fault diagnosis on the vehicle and generate a fault diagnosis report.

2. The self-diagnosis system for an unmanned vehicle system according to claim 1, characterized in that: It also includes an automatic alarm module, which is connected to the sensor data processing module, the fault identification module, and the LLAMA fault diagnosis module respectively, and is used to issue a signal warning when a fault occurs in the vehicle.

3. The self-diagnosis system for unmanned vehicle system according to claim 2, characterized in that: The signal warning methods of the automatic alarm module include sound alarm, visual alarm, mobile terminal information prompt, and email prompt.

4. The self-diagnosis system for an unmanned vehicle system according to claim 1, characterized in that: It also includes a remote monitoring and control system, which includes a remote monitoring module, a global positioning module, a remote control module, a mobile application module, and a data storage and analysis module; The remote monitoring module is used to collect vehicle data and transmission data of the vehicle; The global positioning module is used to determine the location information of the vehicle; The remote control module is used to remotely access the vehicle through a network and control the vehicle to perform functions; The mobile application module is used to remotely monitor the location information of the vehicle through a mobile terminal; The data storage and analysis module is used to store and analyze vehicle data of the vehicle.

5. The self-diagnosis system for unmanned vehicle system according to claim 1, characterized in that: It also includes a cloud platform, which is used to communicate with the vehicle.

6. The self-diagnosis system for unmanned vehicle system according to claim 5, characterized in that: The cloud platform includes a remote data storage module, a remote system update module, and a system self-learning module; The remote data storage module is used to store all data corresponding to the vehicle; The remote system update module is used to update system algorithms, fix known defects or deploy new functions; The system self-learning module is used to provide machine learning algorithms and artificial intelligence technology, and perform self-learning through iterative optimization.

7. The self-diagnosis system for unmanned vehicle system according to claim 1, characterized in that: It also includes an unmanned driving sensor, which includes a lidar sensor and a visual sensor, and is used to collect surrounding environment information of the vehicle.

8. A method for operating a self-diagnosis system for an unmanned vehicle system, characterized in that: include: In the system startup phase, a self-diagnosis system for the unmanned vehicle system is started, the self-diagnosis system for the unmanned vehicle system is initialized and performs a self-diagnosis of the vehicle fault; During the vehicle operation phase, the self-diagnosis system for the unmanned vehicle system monitors the functional operation status of the vehicle and the status of the unmanned vehicle system in real time, and performs fault self-diagnosis.

9. The method according to claim 8, characterized in that The monitoring results of the system startup phase and the monitoring results of the vehicle operation phase are displayed on the display screen of the vehicle.

10. The method according to claim 8, characterized in that The method further comprises: During the system cloud update phase, the update model file is uploaded to the server, and the update model is deployed in the self-diagnosis system for the unmanned vehicle system using a blue-green deployment or canary release strategy.

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