Fault autonomous detection, identification and repair method, system and platform suitable for intelligent visual equipment

Abstract

The invention discloses a fault autonomous detection, identification and repair method, system and platform suitable for intelligent visual equipment. The method comprises the following steps: generating and acquiring first data corresponding to intelligent visual equipment in real time; wherein the first data is the running state data of the intelligent visual equipment component; analyzing, detecting and processing the first data in combination with a time sequence, and generating second data corresponding to the first data; wherein the second data is abnormal data in the operation data; a self-healing mechanism corresponding to the intelligent visual equipment is created, and the intelligent visual equipment is self-repaired and processed in real time based on the self-healing mechanism in combination with the fault type of the second data, and a system and a platform corresponding to the method can monitor the running state of the intelligent visual equipment in real time, comprehensively analyze health data of the intelligent visual equipment and peripheral equipment, and improve the safety of the intelligent visual equipment. And customized repair strategies are adopted for different types of faults or performance reduction, so that the stability and reliability of the intelligent visual equipment are improved, the maintenance cost is reduced, and continuous and stable operation of the system is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of artificial intelligence vision devices, and particularly relates to a method, system and platform for autonomous fault detection, recognition and repair applicable to intelligent vision devices. Background Technique

[0002] With the development of the Internet of Things and artificial intelligence technologies, intelligent vision devices (such as surveillance cameras, smart home cameras, etc.) have been widely used in fields such as security, smart home, and industrial inspection. However, these devices often face various faults during long-term operation, such as Wi-Fi connection interruption, lens occlusion, and degradation of hardware performance. In the prior art, intelligent vision devices usually lack efficient autonomous fault detection and repair capabilities, and mostly rely on manual intervention or simple restart operations, resulting in delayed fault response, high maintenance costs, and even loss of critical data.

[0003] In addition, intelligent vision devices usually consist of core components such as image sensors, processors, memories, and communication modules. These components may have signal abnormalities due to various factors such as external impacts during long-term operation. Once such an abnormality occurs, users often cannot detect it actively, which not only affects the continuous and stable operation of the system but also may pose security risks.

[0004] Therefore, aiming at the technical problems and defects of single fault detection, limited repair ability, and low intelligence, it is urgent to design and develop a method, system and platform for autonomous fault detection, recognition and repair applicable to intelligent vision devices. Summary of the Invention

[0005] To overcome the deficiencies and difficulties of the above-mentioned prior art, the present invention provides a method, system and platform for autonomous fault detection, recognition and repair applicable to intelligent vision devices, and realizes the full-automatic processing of device abnormalities by integrating multi-dimensional health monitoring, intelligent diagnosis and adaptive repair mechanisms, significantly improving the reliability and stability of the devices.

[0006] The first object of the present invention is to provide a method for autonomous fault detection, recognition and repair applicable to intelligent vision devices; the second object of the present invention is to provide a system for autonomous fault detection, recognition and repair applicable to intelligent vision devices; the third object of the present invention is to provide a platform for autonomous fault detection, recognition and repair applicable to intelligent vision devices;

[0007] The first object of the present invention is achieved as follows: The method includes the following steps:

[0008] Generate and obtain in real time first data corresponding to the intelligent vision device; wherein, the first data is the operation state data of the intelligent vision device components;

[0009] Detect and process the first data in combination with time series analysis, and generate second data corresponding to the first data; wherein, the second data is abnormal data in the operation data of the intelligent vision device components;

[0010] Create a self-healing mechanism corresponding to the intelligent vision device, and based on the self-healing mechanism, in combination with the fault type of the second data, autonomously repair and process the intelligent vision device in real time.

[0011] Further, the real-time generation and acquisition of the first data corresponding to the intelligent vision device further includes:

[0012] Generate and acquire hardware operation parameter data, communication status data, and environmental index data corresponding to the intelligent vision device respectively; wherein, the hardware operation parameter data includes signal-to-noise ratio data of the image sensor, device wireless transmission operation status, device operation interaction messages, temperature and load rate data of the processor, memory occupancy rate and fragmentation degree data, power supply voltage fluctuation value, battery capacity, battery temperature, etc.

[0013] Further, the combination of time series analysis to detect and process the first data, and generate second data corresponding to the first data, further includes:

[0014] Based on data statistical analysis and data mining, calculate and generate third data corresponding to the first data; wherein, the third data is the short-term change rate of the index;

[0015] Generate and acquire at least two threshold data corresponding to the operation status data of the intelligent vision device, and generate corresponding second data according to the threshold data and in combination with the third data.

[0016] Further, the combination of time series analysis to detect and process the first data, and generate second data corresponding to the first data, further includes:

[0017] Generate and acquire fourth data corresponding to the intelligent vision device; wherein, the fourth data is historical fault log data and corresponding repair result data;

[0018] Parse and extract the fourth data in sequence and generate fifth data corresponding to the fourth data; wherein, the fifth data is time domain feature data and frequency domain feature data;

[0019] Based on the fifth data, create a fault feature database corresponding to the intelligent vision device.

[0020] Further, the combination of time series analysis to detect and process the first data, and generate second data corresponding to the first data, further includes:

[0021] Construct a machine learning model corresponding to the second data in combination with a machine learning algorithm, and continuously update and process the weights of the machine learning model through online learning;

[0022] Based on the machine learning model, classify and diagnose the second data in combination with the fault feature database, and generate sixth data corresponding to the second data; wherein, the sixth data is intelligent vision device fault type data.

[0023] Further, creating a self-healing mechanism corresponding to the intelligent vision device, and based on the self-healing mechanism, in combination with the fault type of the second data, autonomously repairing the intelligent vision device in real time, further includes:

[0024] Create a first mapping relationship between the self-healing mechanism and the fault type of the second data based on the fault feature database;

[0025] Cluster and analyze the intelligent vision device fault type data in combination with the machine learning model, and optimize and process the matching weights of the autonomous repair solution in real time; wherein, the autonomous repair solution includes restarting the faulty module or restoring the most recently available configuration, switching to an alternative hardware link or downgrading the operating mode, and downloading and installing a repair patch or a complete firmware through the cloud.

[0026] Further, after creating a self-healing mechanism corresponding to the intelligent vision device, and based on the self-healing mechanism, in combination with the fault type of the second data, autonomously repairing the intelligent vision device in real time, further includes:

[0027] Generate and obtain sixth data corresponding to the intelligent vision device, and transmit the sixth data in real time; wherein, the sixth data includes intelligent vision device fault diagnosis result data and intelligent vision device self-healing status data.

[0028] The second object of the present invention is achieved as follows: The system is used to implement the method for autonomous detection, identification, and repair of faults applicable to intelligent vision devices, and the system includes:

[0029] A data generation and acquisition unit, configured to generate and acquire first data corresponding to an intelligent vision device in real time; wherein, the first data is intelligent vision device component operation status data;

[0030] A data detection and processing unit, configured to detect and process the first data in combination with time series analysis, and generate second data corresponding to the first data; wherein, the second data is abnormal data in the intelligent vision device component operation data;

[0031] A data repair processing unit for creating a self-healing mechanism corresponding to the intelligent vision device, and based on the self-healing mechanism, in combination with the fault type of the second data, autonomously and real-time repair and process the intelligent vision device.

[0032] Further, the system further includes:

[0033] A data generation and transmission unit for generating and obtaining sixth data corresponding to the intelligent vision device, and real-time transmitting the sixth data; wherein, the sixth data includes intelligent vision device fault diagnosis result data and intelligent vision device self-healing status data;

[0034] The data generation and acquisition unit further includes:

[0035] And / or, a first generation module for respectively generating and obtaining hardware operation parameter data, communication status data, and environmental index data corresponding to the intelligent vision device; wherein, the hardware operation parameter data includes signal-to-noise ratio data of the image sensor, device wireless transmission operation status, device operation interaction messages, temperature and load rate data of the processor, memory occupancy rate and fragmentation degree data, power supply voltage fluctuation value, battery capacity, battery temperature, etc. data;

[0036] And / or, the data detection and processing unit further includes:

[0037] A second generation module for calculating and generating third data corresponding to the first data based on data statistical analysis and data mining; wherein, the third data is the short-term change rate of the index.

[0038] A third generation module for generating and obtaining at least two threshold data corresponding to the intelligent vision device operation status data, and based on the threshold data, in combination with the third data, generating corresponding second data;

[0039] A fourth generation module for generating and obtaining fourth data corresponding to the intelligent vision device; wherein, the fourth data is historical fault log data and corresponding repair result data;

[0040] A fifth generation module for sequentially parsing and extracting and processing the fourth data, and generating fifth data corresponding to the fourth data; wherein, the fifth data is time-domain feature data and frequency-domain feature data;

[0041] A first construction module for creating a fault feature database corresponding to the intelligent vision device based on the fifth data;

[0042] A second construction module for constructing a machine learning model corresponding to the second data in combination with machine learning algorithms, and continuously updating and processing the weights of the machine learning model through online learning;

[0043] The first processing module is used to classify and diagnose the second data based on the machine learning model and in combination with the fault feature database, and generate sixth data corresponding to the second data; wherein, the sixth data is the intelligent vision device fault type data;

[0044] And / or, the data repair processing unit further includes:

[0045] The third construction module is used to create a first mapping relationship between the self-healing mechanism and the fault type of the second data based on the fault feature database;

[0046] The second processing module is used to perform clustering analysis on the intelligent vision device fault type data in combination with the machine learning model, and optimize the matching weight of the autonomous repair scheme in real time; wherein, the autonomous repair scheme includes restarting the faulty module or restoring the most recently available configuration, switching to an alternative hardware link or downgrading the operating mode, and downloading and installing repair patches or a complete firmware through the cloud.

[0047] The third object of the present invention is achieved as follows: It includes a processor, a memory, and a platform control program for autonomous fault detection, identification, and repair applicable to intelligent vision devices; wherein, when the processor executes the platform control program for autonomous fault detection, identification, and repair applicable to intelligent vision devices, the platform control program for autonomous fault detection, identification, and repair applicable to intelligent vision devices is stored in the memory, and the platform control program for autonomous fault detection, identification, and repair applicable to intelligent vision devices realizes the method for autonomous fault detection, identification, and repair applicable to intelligent vision devices.

[0048] The present invention generates and acquires first data corresponding to an intelligent vision device in real time through a method; wherein, the first data is the operation state data of the intelligent vision device components; the first data is detected and processed by combining time series analysis, and second data corresponding to the first data is generated; wherein, the second data is the abnormal data in the operation data of the intelligent vision device components; a self-healing mechanism corresponding to the intelligent vision device is created, and based on the self-healing mechanism, in combination with the fault type of the second data, the intelligent vision device is autonomously repaired in real time, as well as the corresponding system and platform of the method, which can monitor the operation state of the intelligent vision device in real time, comprehensively analyze the health data of the intelligent vision device and its surrounding devices, and adopt customized repair strategies for different types of faults or performance degradation, thereby improving the stability and reliability of the intelligent vision device, reducing the maintenance cost, and ensuring the continuous and stable operation of the system.

[0049] That is to say, the solution of the present invention realizes the full-automatic processing of equipment anomalies by integrating multi-dimensional health monitoring, intelligent diagnosis, and adaptive repair mechanisms, significantly improving the reliability and stability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 Schematic diagram of the process steps of a method for autonomous fault detection, identification, and repair applicable to intelligent vision devices according to the present invention;

[0052] Figure 2 Schematic diagram of the framework of the self-repair system of an intelligent vision device for a method for autonomous fault detection, identification, and repair applicable to intelligent vision devices according to the present invention;

[0053] Figure 3 Schematic diagram of the fault detection and device self-repair process for a method for autonomous fault detection, identification, and repair applicable to intelligent vision devices according to the present invention;

[0054] Figure 4 Schematic diagram of the system process of the peripheral cooperation layer for a method for autonomous fault detection, identification, and repair applicable to intelligent vision devices according to the present invention;

[0055] Figure 5 Schematic diagram of the key monitoring indicators and statistical analysis dashboard of the device state for a method for autonomous fault detection, identification, and repair applicable to intelligent vision devices according to the present invention;

[0056] Figure 6 Schematic diagram of the system framework of an intelligent data device switching to a new WiFi network after detecting a WiFi fault for a method for autonomous fault detection, identification, and repair applicable to intelligent vision devices according to the present invention;

[0057] Figure 7 Schematic diagram of the process of the network fault detection mechanism of an intelligent data device completing the self-healing strategy after detecting a WiFi fault for a method for autonomous fault detection, identification, and repair applicable to intelligent vision devices according to the present invention;

[0058] Figure 8 Schematic diagram of the system framework of a safety panel activating a WiFi hotspot and using an LTE cellular network when a WiFi fault is detected and no good WiFi network is found for a method for autonomous fault detection, identification, and repair applicable to intelligent vision devices according to the present invention;

[0059] Figure 9 This is the autonomous repair strategy process of a fault autonomous detection, identification and repair method for an intelligent vision device according to the present invention. When a WIFI fault is detected and no good WIFI network is found, the safety panel activates the WIFI hotspot and the operation flow chart using the LTE cellular network system;

[0060] Figure 10 This is the operation flow chart of the self-repair system after the intelligent vision device crashes in a fault autonomous detection, identification and repair method for an intelligent vision device according to the present invention;

[0061] Figure 11 This is the operation flow chart of the health monitoring and self-repair system in a fault autonomous detection, identification and repair method for an intelligent vision device according to the present invention;

[0062] Figure 12 This is a schematic diagram of the edge device (embodiment) of an intelligent vision device in a fault autonomous detection, identification and repair method for an intelligent vision device according to the present invention;

[0063] Figure 13 This is a schematic diagram of the panel edge device (embodiment) in a fault autonomous detection, identification and repair method for an intelligent vision device according to the present invention;

[0064] Figure 14 This is a schematic diagram of the system architecture of a fault autonomous detection, identification and repair system for an intelligent vision device according to the present invention;

[0065] Figure 15 This is a schematic diagram of the platform architecture of a fault autonomous detection, identification and repair platform for an intelligent vision device according to the present invention. Detailed implementation manners

[0066] To better understand the purpose, technical solution and advantages of the present invention more clearly, the present invention will be further described below in conjunction with the accompanying drawings and specific implementation manners. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0067] The present invention can also be implemented or applied through other different specific examples, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0068] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, the directional indications are only used to explain the relative position relationship and movement conditions between components in a certain specific posture (as shown in the accompanying drawings). If this specific posture changes, the directional indications will also change accordingly.

[0069] In addition, if there are descriptions such as "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. Secondly, the technical solutions between various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0070] Preferably, a method for autonomous detection, identification and repair of faults applicable to intelligent vision devices of the present invention is applied in one or more terminals or servers. The terminal is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0071] The terminal can be a computing device such as a desktop computer, notebook, palm computer, and cloud server. The terminal can interact with the customer through a keyboard, mouse, remote control, touchpad, cloud background to interact with instructions or voice control devices and other means for human-computer interaction.

[0072] The present invention aims to implement a method, system and platform for autonomous detection, identification and repair of faults applicable to intelligent vision devices.

[0073] As Figure 1 shown, it is a flowchart of the method for autonomous detection, identification and repair of faults applicable to intelligent vision devices provided by the embodiments of the present invention.

[0074] In this embodiment, the method for autonomous detection, identification and repair of faults applicable to intelligent vision devices can be applied to a terminal with a display function or a fixed terminal, and the terminal is not limited to personal computers, smart phones, tablet computers, desktop computers or all-in-one computers equipped with cameras, etc.

[0075] The method for autonomous detection, identification, and repair of faults applicable to intelligent vision devices can also be applied to a hardware environment composed of a terminal and a server connected to the terminal through a network. The network includes but is not limited to: wide area network, metropolitan area network, or local area network. The method for autonomous detection, identification, and repair of faults applicable to intelligent vision devices in the embodiments of the present invention can be executed by the server, can also be executed by the terminal, or can be jointly executed by the server and the terminal.

[0076] The present invention will be further described below with reference to the accompanying drawings.

[0077] As Figure 1 shown, the present invention provides a method for autonomous detection, identification, and repair of faults applicable to intelligent vision devices. The method includes the following steps:

[0078] S1. Generate and obtain first data corresponding to the intelligent vision device in real time; wherein, the first data is the operation state data of the intelligent vision device components.

[0079] S2. Detect and process the first data by combining time series analysis, and generate second data corresponding to the first data; wherein, the second data is the abnormal data in the operation data of the intelligent vision device components.

[0080] S3. Create a self-healing mechanism corresponding to the intelligent vision device, and based on the self-healing mechanism, combine the fault type of the second data to autonomously repair and process the intelligent vision device in real time.

[0081] The step of generating and obtaining first data corresponding to the intelligent vision device in real time further includes:

[0082] S11. Generate and obtain hardware operation parameter data, communication status data, and environmental index data corresponding to the intelligent vision device respectively; wherein, the hardware operation parameter data includes signal-to-noise ratio data of the image sensor, standby wireless transmission operation status, device operation interaction messages, temperature and load rate data of the processor, memory occupancy rate and fragmentation degree data, power supply voltage fluctuation value, battery capacity, battery temperature, and other data.

[0083] The step of detecting and processing the first data by combining time series analysis and generating second data corresponding to the first data further includes:

[0084] S21. Calculate and generate third data corresponding to the first data based on data statistical analysis and data mining; wherein, the third data is the short-term change rate of the index.

[0085] S22. Generate and obtain threshold data corresponding to the operation status data of the intelligent vision device, and generate corresponding second data according to the threshold data and in combination with the third data.

[0086] The step of combining time series analysis to detect and process the first data and generate second data corresponding to the first data further includes:

[0087] S23. Generate and obtain fourth data corresponding to the intelligent vision device; wherein, the fourth data is historical fault log data and corresponding repair result data.

[0088] S24. Parse and extract and process the fourth data in sequence, and generate fifth data corresponding to the fourth data; wherein, the fifth data is time domain feature data and frequency domain feature data.

[0089] S25. Create a fault feature database corresponding to the intelligent vision device based on the fifth data.

[0090] The step of combining time series analysis to detect and process the first data and generate second data corresponding to the first data further includes:

[0091] S26. Combine machine learning algorithms to construct a machine learning model corresponding to the second data, and continuously update and process the weights of the machine learning model through online learning.

[0092] S27. Based on the machine learning model, in combination with the fault feature database, classify and diagnose the second data, and generate sixth data corresponding to the second data; wherein, the sixth data is intelligent vision device fault type data.

[0093] The step of creating a self-healing mechanism corresponding to the intelligent vision device and, based on the self-healing mechanism, in combination with the fault type of the second data, performing real-time autonomous repair processing on the intelligent vision device further includes:

[0094] S31. Create a first mapping relationship between the self-healing mechanism and the fault type of the second data based on the fault feature database.

[0095] S32. Combine the machine learning model, perform clustering analysis on the intelligent vision device fault type data, and optimize and process the matching weights of the autonomous repair plan in real time; wherein, the autonomous repair plan includes restarting the faulty module or restoring the most recently available configuration, switching to an alternative hardware link or downgrading the operating mode, and downloading and installing a repair patch or a complete firmware through the cloud.

[0096] After creating a self-healing mechanism corresponding to the intelligent vision device and, based on the self-healing mechanism, combining the fault type of the second data to perform real-time autonomous repair processing on the intelligent vision device, the method further includes:

[0097] S40. Generate and obtain sixth data corresponding to the intelligent vision device, and transmit the sixth data in real time; wherein, the sixth data includes intelligent vision device fault diagnosis result data and intelligent vision device self-healing status data.

[0098] Specifically, in an embodiment of the present invention, a method for autonomous fault detection and repair of an intelligent vision device is provided, including the following steps: real-time collecting hardware operation parameters, communication status data, and environmental indicators of the device; detecting abnormal operation through multi-level threshold comparison and time series pattern analysis; classifying and diagnosing the abnormality based on a predefined fault feature library and a machine learning model; and selecting a matching solution from a preset repair strategy library according to the diagnosis result to perform a self-repair operation.

[0099] The self-repair operation includes a hierarchical execution strategy: primary repair: restarting the faulty module or restoring the most recently available configuration; intermediate repair: switching to an alternative hardware link or downgrading the operating mode; advanced repair: downloading and installing a repair patch or a complete firmware through the cloud.

[0100] The switching to an alternative hardware link includes: when the primary Wi-Fi communication is interrupted, attempting the following paths in order of priority: the 2.4 GHz or 5 GHz band of another WIFI network; if the WIFI network cannot be repaired, starting the WIFI hotspot of the gateway terminal and the cellular network. The intelligent vision device is bridged to the gateway device to achieve external network connection repair.

[0101] The method further includes a repair effect verification step: after performing the repair operation, re-monitoring the fault indicators for a duration of T; if the indicators do not return to the normal range, upgrading the repair strategy or sending a manual intervention request to the management platform, where T is a preset duration related to the device type; and implementing distributed repair through a device collaboration network: when local repair fails, broadcasting the fault information to a group of collaborative devices; and receiving available resources provided by the collaborative devices, including network bandwidth, computing power, or alternative configuration files.

[0102] The method further includes a preventive maintenance mechanism: regularly analyzing the device health trend and triggering a predictive maintenance operation before reaching the fault threshold; the health trend is modeled by a linear regression model for at least the CPU aging rate and the number of bad blocks in the memory.

[0103] The method is deployed in any of the following architectures: Fully localized execution: The entire process of detection-diagnosis-repair is independently completed by the microcontroller built into the device; Edge-cloud collaborative architecture: Fault detection and primary repair are executed at the edge, and complex diagnosis relies on cloud computing power; Blockchain evidence storage architecture: Fault events and repair records are written into blockchain nodes for auditing and traceability.

[0104] That is to say, the solution of the present invention proposes an innovative intelligent vision device system and its self-healing technology, aiming to overcome the limitations that existing intelligent vision device users cannot detect anomalies in a timely manner and rely on inefficient means such as restarting to solve problems. This system integrates advanced fault detection, diagnosis, and self-repair capabilities, can autonomously identify and repair various types of faults, ensure the continuous and stable operation of intelligent vision devices, and significantly improve the user experience and system maintenance efficiency.

[0105] Among them, the fault detection mechanism: A highly integrated fault detection module is designed to continuously monitor the operating status of the core components of the intelligent vision device (such as image sensors, processors, memories, and communication interfaces), and through analyzing the device status logs or operation data, potential fault signs are timely warned.

[0106] Fault diagnosis mechanism: When an anomaly is detected, the system immediately starts the fault diagnosis process. By deeply analyzing the log data or device operation status data, the fault characteristics are accurately identified and classified, the root cause of the fault is quickly located, and it is judged whether it is caused by a hardware fault, software error, or other peripheral factors.

[0107] Self-healing mechanism: Based on the diagnosis results, the system automatically selects and executes the most suitable repair plan. For software errors, the system will attempt to automatically restart relevant services or applications, restore default settings, or update to the latest version of the application; for hardware faults or problems affecting peripheral devices, the system will adjust working parameters or operating modes to reduce the impact of the fault, and notify users and the monitoring background to take further measures if necessary.

[0108] The intelligent vision device system with a self-healing system proposed by the solution of the present invention mainly consists of the following core components, as Figure 2 shown, Hardware layer: Includes image sensors, processors (CPU / GPU / NPU), memories (RAM / ROM), communication modules (WIFI / Bluetooth / SubG radio frequency), and power management systems. Operating system layer: Runs a customized embedded operating system, which is responsible for resource management, task scheduling, and device drivers. Middleware layer: Contains fault detection, fault diagnosis, self-healing execution engines, and user interaction interface modules. Application layer: Runs the main function applications of intelligent vision devices, such as image acquisition, processing, storage, and transmission. Peripheral collaboration layer: Includes security panels and cloud backends, which exchange device information and logs wirelessly to assist in completing fault repair and diagnosis.

[0109] An intelligent vision device system with self-healing function, where the middleware layer application is mainly responsible for fault detection and device self-healing. As Figure 3 shown, it demonstrates the fault detection and device self-healing process:

[0110] Fault detection: Real-time monitor key indicators of device status (such as image anomalies, CPU temperature, memory occupancy rate, wireless radio frequency efficiency of WiFi, etc.), and combine time series analysis to identify abnormal operation data. Fault diagnosis: Analyze system logs and device status information, identify fault characteristics and classify them, and initiate a repair request after determining the cause of the fault. Self-healing mechanism: Start the fault handling application service and adapt different recovery processes according to different faults. Interaction interface: Report the fault and repair status to users and the monitoring background.

[0111] The background and the security panel, as the peripheral collaboration layer of the self-healing mechanism, collect and analyze the real-time operation status information and logs uploaded by the device to detect the device health status, and automatically trigger the corresponding repair process and monitor the whole process when potential faults are found. As Figure 4 shown, it demonstrates the system process of the peripheral collaboration layer.

[0112] The device uploads real-time operation status information and logs, and displays key monitoring indicators through the dashboard. The background deeply analyzes and evaluates the data using methods such as statistical analysis and comparative analysis, and actively triggers the corresponding repair process when potential faults are found. As Figure 5 shown, it demonstrates some key monitoring indicators of the device status dashboard.

[0113] In the Internet of Things application, the Wi-Fi network is an important communication bridge connecting intelligent vision devices and IoT systems. However, when the Wi-Fi network fails, the intelligent vision device will not be able to work properly, thus affecting the monitoring and data transmission of the entire system. Therefore, this implementation plan proposes an innovative self-healing solution: when the Wi-Fi network interruption is detected, the intelligent vision device can automatically detect the fault, self-heal the disconnected network link and re-establish a new network connection to ensure the continuous and stable operation of the system. This solution is not only applicable to intelligent vision device products, but also can be applied to any Internet of Things product that communicates through Wi-Fi. As Figure 6 shown, it demonstrates the system framework of the intelligent vision device switching to a new network after Wi-Fi fault detection; as Figure 7 shown, it demonstrates the process of the intelligent vision device network fault detection mechanism and the peripheral collaboration layer collaborating to complete the self-healing strategy after Wi-Fi fault detection.

[0114] When the Wi-Fi router network is interrupted and there is no alternative router available, to ensure the continuous and stable operation of the system, the security panel automatically activates the built-in Wi-Fi hotspot as a backup network, and at the same time uses the LTE cellular network to maintain communication with the Internet. As Figure 8As shown, the system framework in this scenario is presented; as Figure 9 shown, the corresponding self-healing strategy process is presented.

[0115] When the intelligent vision device crashes due to an abnormal situation, the system immediately activates the watchdog mechanism to start the self-healing process: the intelligent vision device automatically performs a reset operation and restores its function. After reconnecting to the network, it transmits the event log to the background for analysis. If the analysis shows that the crash problem has been fixed in the subsequent version, the background will automatically send a version update request to the intelligent vision device. Through this self-healing process, precise adaptive management and an efficient self-repair mechanism for the intelligent vision device are achieved.

[0116] The background system analyzes the performance of the intelligent vision device using intelligent methods by continuously monitoring key device status metrics and log information. When potential problems that may affect the function are detected, such as abnormal CPU load, memory fragmentation, and degraded performance of communication modules (including but not limited to WiFi, Bluetooth, SubGRF, etc.), the problems are automatically classified and a restart request for the intelligent vision device is initiated accordingly, achieving precise adaptive management and an efficient self-healing mechanism.

[0117] Preferably, in another embodiment of the solution of the present invention, an intelligent vision device system with a self-repair function is provided, including: an intelligent vision device module: responsible for collecting image data and providing visual input for the system. A processor module: capable of not only analyzing the performance information of key components of the intelligent vision device but also detecting internal system faults. A self-repair mechanism: integrating fault detection, fault diagnosis, and self-repair functions. When the processor module identifies a system fault, it automatically triggers at least one self-repair operation, including software restart, hardware reset, or a collaborative repair process with other devices. A communication module: establishing communication with an external server when needed to obtain additional repair instructions or resources, thereby enhancing the system's self-repair ability. A peripheral cooperation mechanism: integrating with peripherals such as a panel, a background system, or other peripheral systems, and assisting the intelligent vision device to perform self-repair operations by exchanging intelligent vision device status information when a fault occurs.

[0118] Under the above framework, the system can achieve a comprehensive self-repair function without manual intervention by using integrated diagnostic algorithms and repair logic. In addition, the following key components are also equipped: an image sensor, one or more processors, one or more memories, one or more wireless communication modules, a power management system, and an embedded operating system, etc. The listed components include but are not limited to these key devices, and they jointly constitute the core functional part of the intelligent vision device system. Among them, the communication module includes components of wireless communication channels such as but not limited to LTE, WIFI, and SubG RF. This communication module is connected to peripherals and jointly constitutes a part of the self-repair function unit of the intelligent monitoring system.

[0119] Among them, the self-healing mechanism in the solution includes: Fault detection mechanism: Real-time monitoring of key indicators of the device status, and identification of abnormal operation data by combining time series analysis. Integrated system and implementation method (health monitoring and self-healing intelligent vision device); Fault diagnosis mechanism: Parsing system logs and device status information to identify fault characteristics and classify fault types. Self-healing mechanism: Starting the fault handling application service and adapting different recovery processes according to different faults. Interaction interface: Reporting the fault and repair status to the user and the monitoring background.

[0120] In addition, there are also respectively set up an intelligent vision device module: responsible for collecting image data and providing visual input for the system. Processor module: Analyzing not only the performance information of key components of the intelligent vision device, but also detecting internal system faults. Self-healing mechanism: Integrating fault detection, fault diagnosis and self-healing functions. When the processor module identifies a system fault, it automatically triggers at least one self-healing operation, including software restart, hardware reset or collaborative repair process with other devices. Communication module: Establishing communication with an external server when needed to obtain additional repair instructions or resources, thereby enhancing the system's self-healing ability. Peripheral cooperation mechanism: By integrating with peripherals such as panels, background systems or other peripheral systems, assisting the intelligent vision device to perform self-healing operations by exchanging intelligent vision device status information in case of a fault. Data acquisition and analysis mechanism: Automatically collecting multi-dimensional data of the background system, including log records, daily usage data, intelligent vision device performance parameters, key component operation performance and shooting environment-related information. Deeply mining and analyzing the collected data set through advanced data analysis techniques, aiming to identify and extract valuable information. At the same time, this mechanism conducts fault mode analysis, uses data insights to optimize the overall performance of the intelligent vision device, or provides customized self-healing suggestions based on individual differences.

[0121] Under this framework, the system integrates intelligent background data analysis for auxiliary diagnosis and can achieve comprehensive self-healing ability without manual intervention.

[0122] In another embodiment of the solution of the present invention, a method for an intelligent vision device with self-repair function includes the following steps: receiving information such as log records, daily operation data, intelligent vision device performance parameters, and key component operation efficiency from the intelligent vision device system. Using a data analysis engine to preliminarily analyze the received information to determine possible fault types and scopes. Accessing a fault knowledge database, comparing the preliminary analysis results with the data in the database, and identifying known fault cases and corresponding repair solutions that match the fault information. Generating a repair instruction according to the identified repair solution and transmitting the instruction to the self-repair module of the intelligent vision device system for execution. Receiving the feedback from the self-repair module after the repair operation is executed, verifying the repair effect and correspondingly updating the fault knowledge database. Among them, the background data analysis module further includes a machine learning module, which is configured to train a machine learning model based on historical fault data and repair results, so as to improve the accuracy of fault detection and the efficiency of generating repair solutions. After verifying the repair effect, if the repair is successful, add the current fault case and its repair solution to the fault knowledge database to enrich its content. If the repair fails or cannot be repaired currently, adjust the training parameters of the machine learning model according to the fault cause, and count the incidence rate of related problems to create a problem work order. This move aims to enhance the model's ability to detect future faults and improve its ability to automatically update software after future repairs.

[0123] To achieve the above object, the present invention also provides a system for autonomous fault detection, identification and repair applicable to intelligent vision devices, as Figure 14 shown, the system specifically includes:

[0124] A data generation and acquisition unit, configured to generate and acquire in real time first data corresponding to the intelligent vision device; wherein, the first data is the operation state data of the intelligent vision device components;

[0125] A data detection and processing unit, configured to detect and process the first data in combination with time series analysis and generate second data corresponding to the first data; wherein, the second data is the abnormal data in the operation data of the intelligent vision device components;

[0126] A data repair and processing unit, configured to create a self-healing mechanism corresponding to the intelligent vision device, and based on the self-healing mechanism, in combination with the fault type of the second data, autonomously repair and process the intelligent vision device in real time.

[0127] The system further includes: a data generation and transmission unit, configured to generate and acquire sixth data corresponding to the intelligent vision device and transmit the sixth data in real time; wherein, the sixth data includes intelligent vision device fault diagnosis result data and intelligent vision device self-healing status data;

[0128] The data generation and acquisition unit further includes:

[0129] And / or, a first generation module, configured to respectively generate and obtain hardware operation parameter data, communication status data, and environmental index data corresponding to the intelligent vision device; wherein, the hardware operation parameter data includes signal-to-noise ratio data of the image sensor, wireless transmission operation status, device operation interaction messages, temperature and load rate data of the processor, memory occupancy rate and fragmentation degree data, power supply voltage fluctuation value, battery capacity, battery temperature, and other data;

[0130] And / or, the data detection and processing unit further includes:

[0131] A second generation module, configured to perform data statistical analysis and data mining algorithms to calculate and generate third data corresponding to the first data; wherein, the third data is the short-term change rate of the index;

[0132] A third generation module, configured to generate and obtain at least two threshold data corresponding to the operation status data of the intelligent vision device, and generate corresponding second data according to the threshold data and in combination with the third data;

[0133] A fourth generation module, configured to generate and obtain fourth data corresponding to the intelligent vision device; wherein, the fourth data is historical fault log data and corresponding repair result data;

[0134] A fifth generation module, configured to sequentially analyze, extract, and process the fourth data, and generate fifth data corresponding to the fourth data; wherein, the fifth data is time-domain feature data and frequency-domain feature data;

[0135] A first construction module, configured to create a fault feature database corresponding to the intelligent vision device based on the fifth data;

[0136] A second construction module, configured to construct a machine learning model corresponding to the second data in combination with machine learning algorithms, and continuously update and process the weights of the machine learning model through online learning;

[0137] A first processing module, configured to perform classification diagnosis on the second data based on the machine learning model and in combination with the fault feature database, and generate sixth data corresponding to the second data; wherein, the sixth data is intelligent vision device fault type data;

[0138] And / or, the data repair and processing unit further includes:

[0139] A third construction module, configured to create a first mapping relationship between the self-healing mechanism and the fault type of the second data based on the fault feature database;

[0140] A second processing module, which is used to cluster and analyze the data of the intelligent vision device failure types in combination with a machine learning model, and optimize the matching weights of the autonomous repair plan in real time; wherein, the autonomous repair plan includes restarting the failed module or restoring the most recently available configuration, switching to an alternative hardware link or a degraded operating mode, and downloading and installing a repair patch or a complete firmware through the cloud.

[0141] In the embodiment of the system solution of the present invention, the method steps involved in the autonomous detection, identification, and repair of faults applicable to intelligent vision devices have been described in detail above. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiments, which will not be elaborated here.

[0142] To achieve the above object, the present invention also provides a platform for autonomous detection, identification, and repair of faults applicable to intelligent vision devices, as Figure 15 shown, including a processor, a memory, and a control program for the platform for autonomous detection, identification, and repair of faults applicable to intelligent vision devices; wherein, when the processor executes the control program for the platform for autonomous detection, identification, and repair of faults applicable to intelligent vision devices, the control program for the platform for autonomous detection, identification, and repair of faults applicable to intelligent vision devices is stored in the memory, and the control program for the platform for autonomous detection, identification, and repair of faults applicable to intelligent vision devices implements the method steps for the autonomous detection, identification, and repair of faults applicable to intelligent vision devices. For example:

[0143] S1. Generate and obtain first data corresponding to the intelligent vision device in real time; wherein, the first data is the operation status data of the intelligent vision device components.

[0144] S2. Combine time series analysis to detect and process the first data, and generate second data corresponding to the first data; wherein, the second data is the abnormal data in the operation data of the intelligent vision device components.

[0145] S3. Create a self-healing mechanism corresponding to the intelligent vision device, and based on the self-healing mechanism, combine the fault type of the second data to autonomously repair and process the intelligent vision device in real time.

[0146] The specific details of the steps have been described in detail above and will not be elaborated here.

[0147] In the embodiments of the present invention, the built-in processor of the platform for autonomous detection, identification, and repair of faults applicable to intelligent vision devices may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or it may be composed of multiple integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor uses various interfaces and circuits to connect to each component, and by running or executing programs or units stored in the memory, as well as calling data stored in the memory, it performs various functions for autonomous detection, identification, and repair of faults applicable to intelligent vision devices and processes data;

[0148] The memory is used to store program codes and various data, is installed in the platform for autonomous detection, identification, and repair of faults applicable to intelligent vision devices, and realizes high-speed and automatic access to programs or data during operation.

[0149] The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), or any other computer-readable medium capable of carrying or storing data.

[0150] The present invention generates and obtains first data corresponding to an intelligent vision device in real time through a method; wherein, the first data is the operation status data of components of the intelligent vision device; the first data is detected and processed in combination with time series analysis, and second data corresponding to the first data is generated; wherein, the second data is abnormal data in the operation data of components of the intelligent vision device; a self-healing mechanism corresponding to the intelligent vision device is created, and based on the self-healing mechanism, in combination with the fault type of the second data, the intelligent vision device is repaired and processed autonomously in real time, as well as a system and a platform corresponding to the method, which can monitor the operation status of the intelligent vision device in real time, comprehensively analyze the health data of the intelligent vision device and surrounding devices, and adopt customized repair strategies for different types of faults or performance degradation, so as to improve the stability and reliability of the intelligent vision device, reduce the maintenance cost, and ensure the continuous and stable operation of the system.

[0151] That is to say, the solution of the present invention realizes the full-automatic processing of device anomalies by integrating multi-dimensional health monitoring, intelligent diagnosis and adaptive repair mechanisms, and significantly improves the reliability and stability of the device.

[0152] The above embodiments only represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. A method for autonomous fault detection, recognition and repair applicable to intelligent vision devices, characterized in that, The method includes the steps of: Generating and obtaining in real time first data corresponding to the intelligent vision device; wherein, the first data is the operating state data of the intelligent vision device components; Combining time series analysis to detect and process the first data, and generating second data corresponding to the first data; wherein, the second data is the abnormal data in the operating data of the intelligent vision device components; Creating a self-healing mechanism corresponding to the intelligent vision device, and based on the self-healing mechanism, combining the fault type of the second data, autonomously repairing and processing the intelligent vision device in real time.

2. The method for autonomous detection, identification and repair of faults applicable to intelligent vision devices according to claim 1, characterized in that, The step of generating and obtaining in real time first data corresponding to the intelligent vision device further includes: Generating and obtaining respectively the hardware operation parameter data, communication status data and environmental index data corresponding to the intelligent vision device; wherein, the hardware operation parameter data includes the signal-to-noise ratio data of the image sensor, the wireless transmission operation status of the device, the device operation interaction message, the temperature and load rate data of the processor, the memory occupancy rate and fragmentation degree data, the power supply voltage fluctuation value, the battery capacity, and the battery temperature data.

3. A method for autonomous detection, recognition, and repair of faults applicable to intelligent vision devices according to claim 1, characterized in that, The step of combining time series analysis to detect and process the first data, and generating second data corresponding to the first data further includes: Calculating and generating third data corresponding to the first data based on data statistical analysis and data mining; wherein, the third data is the short-term change rate of the index; Generating and obtaining at least two threshold data corresponding to the operating state data of the intelligent vision device, and generating corresponding second data according to the threshold data and combining the third data.

4. A method for autonomous detection, recognition, and repair of faults applicable to intelligent vision devices according to claim 1 or 3, characterized in that, The step of combining time series analysis to detect and process the first data, and generating second data corresponding to the first data further includes: Generating and obtaining fourth data corresponding to the intelligent vision device; wherein, the fourth data is the historical fault log data and the corresponding repair result data; Parsing and extracting and processing the fourth data in sequence, and generating fifth data corresponding to the fourth data; wherein, the fifth data is the time domain feature data and the frequency domain feature data; Based on the fifth data, creating a fault feature database corresponding to the intelligent vision device.

5. A method for autonomous detection, identification and repair of faults applicable to intelligent vision devices according to claim 4, characterized in that, The step of combining time series analysis to detect and process the first data, and generating second data corresponding to the first data further includes: Combining machine learning algorithms to construct a machine learning model corresponding to the second data, and continuously updating and processing the weights of the machine learning model through online learning; Based on the machine learning model, combining the fault feature database, classifying and diagnosing the second data, and generating sixth data corresponding to the second data; wherein, the sixth data is the intelligent vision device fault type data.

6. A method for autonomous detection, identification and repair of faults applicable to intelligent vision devices according to claim 1, characterized in that, The step of creating a self-healing mechanism corresponding to the intelligent vision device, and based on the self-healing mechanism, combining the fault type of the second data, autonomously repairing and processing the intelligent vision device in real time further includes: Creating a first mapping relationship between the self-healing mechanism and the fault type of the second data based on the fault feature database. Combined with a machine learning model, clustering analysis processes the data of the intelligent vision device failure types and optimizes the matching weights of the autonomous repair solutions in real time; among them, the autonomous repair solutions include restarting the failed module or restoring the most recently available configuration, switching to an alternative hardware link or downgrading the operating mode, and downloading and installing repair patches or complete firmware through the cloud.

7. A method for autonomous detection, recognition and repair of faults applicable to intelligent vision devices according to claim 1 or 6, characterized in that, After creating the self-healing mechanism corresponding to the intelligent vision device and, based on the self-healing mechanism, combining the failure types of the second data to perform real-time autonomous repair processing on the intelligent vision device, it further includes: Generating and obtaining the sixth data corresponding to the intelligent vision device and transmitting the sixth data in real time; among them, the sixth data includes the intelligent vision device failure diagnosis result data and the intelligent vision device self-healing status data.

8. A fault self-detection, recognition and repair system suitable for intelligent vision devices, characterized in that, The system is applied to the method for autonomous detection, identification and repair of failures applicable to intelligent vision devices as described in any one of claims 1-7, and the system includes: A data generation and acquisition unit for generating and acquiring the first data corresponding to the intelligent vision device in real time; among them, the first data is the operating status data of the intelligent vision device components. A data detection and processing unit for detecting and processing the first data in combination with time series analysis and generating the second data corresponding to the first data; among them, the second data is the abnormal data in the operating data of the intelligent vision device components. A data repair and processing unit for creating the self-healing mechanism corresponding to the intelligent vision device and, based on the self-healing mechanism, combining the failure types of the second data to perform real-time autonomous repair processing on the intelligent vision device.

9. The fault self-detection, recognition and repair system for intelligent vision devices according to claim 8, wherein, The system further includes: A data generation and transmission unit for generating and obtaining the sixth data corresponding to the intelligent vision device and transmitting the sixth data in real time; among them, the sixth data includes the intelligent vision device failure diagnosis result data and the intelligent vision device self-healing status data. The data generation and acquisition unit further includes: And / or, a first generation module for respectively generating and obtaining the hardware operation parameter data, communication status data and environmental index data corresponding to the intelligent vision device; among them, the hardware operation parameter data includes the signal-to-noise ratio data of the image sensor, the temperature and load rate data of the processor, the memory occupancy rate and fragmentation degree data, the power supply voltage fluctuation value, the battery capacity, the battery temperature, etc. And / or, the data detection and processing unit further includes: A second generation module for calculating and generating the third data corresponding to the first data based on data statistical analysis and data mining; among them, the third data is the short-term change rate of the index. A third generation module for generating and obtaining at least two threshold data corresponding to the intelligent vision device operation status data, and generating the corresponding second data according to the threshold data and in combination with the third data. A fourth generation module for generating and obtaining the fourth data corresponding to the intelligent vision device; among them, the fourth data is the historical failure log data and the corresponding repair result data. A fifth generation module, configured to sequentially analyze and extract the fourth data, and generate fifth data corresponding to the fourth data; wherein the fifth data is time-domain feature data and frequency-domain feature data; A first construction module, configured to create a fault feature database corresponding to the intelligent vision device based on the fifth data; A second construction module, configured to construct a machine learning model corresponding to the second data in combination with a machine learning algorithm, and continuously update and process the weights of the machine learning model through online learning; A first processing module, configured to classify and diagnose the second data based on the machine learning model and in combination with the fault feature database, and generate sixth data corresponding to the second data; wherein the sixth data is intelligent vision device fault type data; And / or, the data repair processing unit further includes: A third construction module, configured to create a first mapping relationship between a self-healing mechanism and the fault type of the second data based on the fault feature database; A second processing module, configured to perform clustering analysis on the intelligent vision device fault type data in combination with the machine learning model, and optimize and process the matching weights of the self-repair scheme in real time; wherein the self-repair scheme includes restarting the faulty module or restoring the most recent available configuration, switching to an alternative hardware link or a degraded operating mode, and downloading and installing a repair patch or a complete firmware through the cloud.

10. A platform for autonomous fault detection, identification and repair applicable to intelligent vision devices, characterized in that, Including a processor, a memory, and a platform control program for autonomous fault detection, identification, and repair applicable to an intelligent vision device; wherein, when the processor executes the platform control program for autonomous fault detection, identification, and repair applicable to an intelligent vision device, the platform control program for autonomous fault detection, identification, and repair applicable to an intelligent vision device is stored in the memory, and the platform control program for autonomous fault detection, identification, and repair applicable to an intelligent vision device implements the method for autonomous fault detection, identification, and repair applicable to an intelligent vision device according to any one of claims 1 to 7.

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