Power distribution network live working robot core control board intelligent detection method, system, equipment and storage medium

Abstract

The application discloses a power distribution network live working robot core control board intelligent detection method, system, equipment and a storage medium, relates to the detection technical field of a power distribution network live working robot core control board, and comprises the following steps: constructing a key level evaluation model based on the function characteristics of each component of the core control board, and executing a dynamic detection process according to the grading result; constructing an abnormality processing engine in combination with a state machine model to monitor the equipment state in real time; locating the root cause of the abnormality in the equipment state data by fault tree analysis, recording fault information by using an enhanced watchdog mechanism, and dynamically adjusting the waiting time length of the function detection; and based on the response state of the real-time monitoring control board and equipment, the waiting time length of the function detection is dynamically adjusted. The method disclosed by the application improves the detection efficiency and reliability, and meets the beneficial effects of large-scale deployment of the power distribution network live working robot.

Description

Technical Field

[0001] This invention relates to the field of testing the core control board of a live-line working robot in a power distribution network, specifically to an intelligent testing method, system, equipment, and storage medium for the core control board of a live-line working robot in a power distribution network. Background Technology

[0002] In the field of testing core control boards for live-line working robots in power distribution networks, existing technologies mainly employ the following systems and methods for functional verification and reliability assessment. First, there is the semi-automated testing method dominated by manual labor, with manual operation as the core. Technicians manually measure parameters such as voltage and current on the control board using tools such as oscilloscopes and multimeters, while simultaneously using simple automated equipment to complete some continuity tests. This approach has several problems: low automation, requiring manual intervention to switch from test items to result recording; strong subjectivity, significantly affected by differences in technicians' experience and operating habits; high rate of missed detections, as manual testing cannot comprehensively cover all test points and potential faults; insufficient safety, as improper operation can easily lead to safety risks when testing live-line components; and complete reliance on local operation, lacking remote interaction capabilities, requiring technicians to be on-site during testing and unable to remotely monitor the testing progress or retrieve historical data.

[0003] Secondly, the detection method based on a fixed test case library pre-sets the detection steps and parameter ranges. It automatically completes the detection of routine items such as voltage, current, and signal transmission by connecting probes to test points on the control board, generating simple "pass / fail" results. Its main problems include: insufficient scenario coverage, failing to simulate the dynamic environment of the entire machine during operation, leading to undetected faults in actual working conditions; coarse fault localization, only able to determine whether a test item passes, unable to identify the specific faulty pin or component; weak anomaly handling capability, if a test item times out during detection, the device directly terminates the entire process without saving completed test data, requiring re-detection from the beginning; and a lack of a component criticality grading mechanism, failing to prioritize the detection of core and non-core components, affecting detection efficiency.

[0004] Secondly, there is the simplified fault diagnosis system based on threshold judgment and fixed delay. This type of system identifies anomalies by setting preset parameter thresholds, uses hard-coded fixed delays, and executes a static test process. During the testing process, regardless of the actual response of each component on the control board, it waits according to the set fixed delay, and the test process remains unchanged. The problems are as follows: weak anomaly handling capability, only able to determine "whether there is a fault," but unable to determine the specific fault point; lack of fault tracing capability, when the microcontroller program crashes and restarts through the watchdog timer, it does not record context information such as test items and parameter states before the crash; the fixed delay mechanism cannot adapt to the response characteristics of different devices, and the test results are inaccurate due to the excessively short waiting time.

[0005] Then, the single-environment performance testing scheme, which only conducts testing in a standard laboratory environment at normal temperature and pressure, verifies the control board's performance by simulating simple loads. Its limitations are: serious deficiencies in the coverage of the testing environment and scenarios, failing to simulate real-world operating conditions such as high and low temperatures and electromagnetic interference in power distribution network operations, resulting in approximately 30% of field faults not being exposed during factory testing; a lack of differentiated project testing, failing to conduct specialized tests such as high and low temperature cycling, load stress, and frequent network disconnections from multiple clients, making it impossible to assess the control board's stability under extreme conditions; finally, the offline module testing method, which uses local stand-alone operation and lacks remote interaction capabilities. All data during the testing process is stored locally, preventing technicians from remotely monitoring the testing process, viewing test data, and fault information; test reports must be manually exported and compiled, and cross-batch quality analysis reports cannot be automatically generated, making it difficult to support targeted optimization of production processes. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is that the existing detection methods for the core control board of the live-line working robot in power distribution networks have problems such as poor detection sequence optimization, low dynamic adaptability, low degree of automation, and how to perform remote interaction.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: an intelligent detection method for the core control board of a live-line working robot in a power distribution network, comprising constructing a key level evaluation model based on the functional characteristics of each component of the core control board, executing a dynamic detection process according to the grading results; constructing an anomaly handling engine by combining a state machine model to monitor the equipment status in real time; locating the root cause of the equipment status data before the anomaly through fault tree analysis, recording fault information using an enhanced watchdog mechanism, and dynamically adjusting the waiting time of functional detection; the state machine model comprises a state register and combinational logic circuits, and performs state transitions according to preset states based on control signals, the preset states covering initial state, ready state, running state, paused state, completed state, and fault state.

[0009] As a preferred embodiment of the intelligent detection method for the core control board of the live-line working robot in the distribution network described in this invention, the construction of the key level evaluation model includes: setting scoring standards and assigning weights based on the functional necessity, fault impact range, fault occurrence frequency, and maintenance cost of each component of the core control board; quantitatively evaluating each component of the core control board according to preset scoring rules; classifying the components according to the comprehensive score; and executing a dynamic detection process based on the classification results. Based on the classification results of the key level evaluation model, core modules are determined and assigned the highest detection priority, prioritizing the detection of high-level core components. The core module refers to a first-level core component, and the response status of each module is monitored in real time during the detection process.

[0010] As a preferred embodiment of the intelligent detection method for the core control board of the live-line working robot in the power distribution network described in this invention, the state machine model includes: state transition according to the control signal and a preset state; defining the equipment state; the state definition conforms to the actual equipment detection process; triggering state transitions based on various events and conditions in the detection process; when a detection start event occurs, the equipment transitions from the initial state to the ready state; when a module abnormal event occurs during the detection process, the equipment transitions from the running state to the paused state.

[0011] As a preferred embodiment of the intelligent detection method for the core control board of the live-line working robot in the distribution network described in this invention, the anomaly handling engine includes: by real-time monitoring of equipment status, when an anomaly is detected, combining the state mechanism model to trace back the equipment status data before the fault occurred, locating the key state nodes before the anomaly occurred, and then locating the root cause of the fault; for program crash scenarios, an enhanced watchdog mechanism is used to quickly record the context information before the crash and generate a fault log.

[0012] As a preferred embodiment of the intelligent detection method for the core control board of the live-line working robot in the distribution network described in this invention, the fault tree analysis includes: based on the equipment status data automatically traced back when an anomaly occurs, the analysis is performed through a closed-loop process of top event definition, dynamic fault tree modeling, logical reasoning and pruning, and root cause localization; and through a dynamic weight adjustment mechanism, the real-time traced status data is deeply integrated into the entire process of fault tree modeling and reasoning for data-driven dynamic analysis; the equipment status data includes the core control board output signals, communication interaction timing, test tool feedback data, and program running logs.

[0013] As a preferred embodiment of the intelligent detection method for the core control board of the live-line working robot in the distribution network described in this invention, the method of recording fault information using an enhanced watchdog mechanism includes: triggering a reset at the moment of program crash, synchronously capturing key data before the crash, cleaning up residual states and generating fault logs, jointly determining whether it is a crash state through core module interaction heartbeat and parameter thresholds, triggering actions only when multiple dimensions meet the crash characteristics, deeply linking with fault tree analysis, recording crash context data as input parameters for fault tree analysis, associating it with the bottom event reasoning of the top event of program crash, and performing full-link collaborative work.

[0014] As a preferred embodiment of the intelligent detection method for the core control board of the live-line working robot in the distribution network described in this invention, the dynamic adjustment of the waiting time for function detection includes: real-time monitoring of the response status of the core control board and each test device; and dynamically adjusting the waiting time for each function detection according to the response characteristics of different components and the requirements of the current test item. The response status of the device includes no response, partial response, and full response.

[0015] Another objective of this invention is to provide an intelligent detection system for the core control board of a live-line working robot in a distribution network. This system can construct a key level evaluation model based on the functional characteristics of each component of the core control board and execute a dynamic detection process according to the grading results, thus solving the problem of low detection efficiency in the current detection technology for the core control board of a live-line working robot in a distribution network.

[0016] As a preferred embodiment of the intelligent detection system for the core control board of the live-line working robot in the power distribution network described in this invention, the system includes: a core control board module, a test fixture module, a host computer remote interaction module, a cloud platform interaction module, and a regulated power supply module. The core control board module is used to execute detection process control and data processing. It connects to the test fixture module, hierarchically manages internal components through a key-level hierarchical module, plans the detection sequence through a test process definition module, coordinates detection state transitions through a state machine management module, drives peripheral devices through wiring ports, collects voltage and current parameter data in real time, interacts with the test fixture module and the host computer remote interaction module, and dynamically adjusts the detection timing based on response characteristics. The test fixture module is used for hardware interaction between the core control board and the test system. It establishes a physical connection with the core control board module under test through wiring terminals, integrates a communication interface for command transmission and data acquisition, and includes peripheral triggering. The system simulates the physical input of case operations, analyzes the operating status of the equipment through image acquisition, and transmits the detection data to the host computer remote interaction module for recording and analysis. The host computer remote interaction module coordinates the data communication and command transmission of the detection system. It establishes a physical connection with the core control board module through a communication port and serial port conversion tool, and a connection with the test fixture module through a network cable. It receives real-time operating data from each device, integrates and analyzes the received data, dynamically displays the detection progress and results, uploads the processed data to the cloud platform module, and receives control commands for centralized monitoring and remote management of the detection process. The cloud platform interaction module connects to the host computer remote interaction module, receives detection data, equipment status information, and test reports uploaded from the host computer, and stores and manages historical data and fault logs of the detection nodes. The regulated power supply module connects the core control board module and the test fixture module, providing stable and controllable DC power input.

[0017] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program as a step in implementing an intelligent detection method for the core control board of a live-line working robot in a power distribution network.

[0018] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of an intelligent detection method for the core control board of a live-line working robot in a power distribution network.

[0019] The beneficial effects of this invention are as follows: The intelligent detection method for the core control board of the live-line working robot provided by this invention constructs a key level evaluation model based on the functional characteristics of each component of the core control board. It executes a dynamic detection process according to the grading results, significantly improving detection efficiency and fault coverage, ensuring priority verification of core functional modules, and constructing an anomaly handling engine using a state machine model to monitor equipment status in real time, achieving accurate fault location and rapid recovery, avoiding secondary faults caused by program crashes. Fault tree analysis locates the root cause of the equipment status data before the anomaly, and an enhanced watchdog mechanism is used to record fault information, improving the accuracy and efficiency of fault location. This achieves full automation from anomaly detection to root cause location. This invention achieves better results in terms of comprehensive detection environment coverage, accurate fault diagnosis, and adaptive detection process. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The first embodiment of the present invention provides an overall flowchart of an intelligent detection method for the core control board of a live-line working robot in a power distribution network.

[0022] Figure 2 The diagram shows a system connection diagram for an intelligent detection method of the core control board of a live-line working robot in a power distribution network, as provided in the first embodiment of the present invention.

[0023] Figure 3 The system structure diagram is provided for a smart detection method of the core control board of a live-line working robot in a power distribution network, according to the second embodiment of the present invention. Detailed Implementation

[0024] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0025] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for intelligent detection of the core control board of a live-line working robot in a power distribution network is provided, comprising:

[0026] S1: Construct a key level evaluation model based on the functional characteristics of each component of the core control board, and execute a dynamic detection process based on the classification results.

[0027] Furthermore, the construction of the key level assessment model includes setting scoring standards and assigning weights based on the functional necessity, fault impact range, fault frequency, and maintenance cost of each component of the core control board; quantitatively evaluating each component of the core control board according to the preset scoring rules; classifying the components according to the comprehensive score; and executing a dynamic detection process based on the classification results.

[0028] Based on the classification results of the critical level assessment model, core modules are identified and assigned the highest detection priority, with high-level core components being tested first.

[0029] The core module refers to the primary core component, and the response status of each module is monitored in real time during the testing process.

[0030] It should be noted that the first step is to build and connect the system, constructing a comprehensive test platform that includes high and low temperature test chambers, electromagnetic interference generators, load simulators, network interference generators, and other equipment. The test fixtures and the core control board are physically connected through hardware interfaces to form a closed-loop detection circuit. Based on a standard laboratory environment, the platform simulates typical scenarios in power distribution network operations, such as high and low temperatures, strong electromagnetic interference, and humidity, to achieve full coverage of test scenarios.

[0031] It should also be noted that the components are classified into three levels: Level 1 core components, Level 2 important components, and Level 3 auxiliary components.

[0032] The first-level core components are those with a comprehensive score of 80 or higher. The criteria for judgment are: a functional necessity score of 25 or higher, meaning that the component is the core support for the safe operation of the robot, such as current charging and discharging circuit detection, power-on and power-off sequence detection of the main controller, gimbal, and robotic arm, and robotic arm emergency stop detection; a fault impact range score of 25 or higher, meaning that the fault will lead to the paralysis of the entire machine or cause a safety accident; a fault occurrence frequency score of 15 or higher, meaning that it is a high-risk and vulnerable component; and a maintenance cost score of 15 or higher, meaning that replacement or repair requires professional equipment and a long working time.

[0033] Level 2 critical components are those with a comprehensive score of 60 or higher but less than 80. The criteria for judgment are: a functional necessity score of 20 to 24 points, indicating that they are key functional auxiliary modules, such as sensor interfaces, AD analog quantity and IO pin detection, communication protocol conversion units, etc.; a fault impact score of 20 to 24 points, indicating that the fault will lead to a decrease in operating accuracy but not fatal; a fault occurrence frequency score of 12 to 14 points, indicating a moderate failure rate; and a maintenance cost score of 12 to 14 points, indicating a moderate repair difficulty.

[0034] Level 3 auxiliary components are those with a comprehensive score of less than 60 points. The criteria for judgment are: a functional necessity score of less than 20 points, which are non-core auxiliary function modules, such as status indicator lights, buzzer alarm units, physical button detection, etc.; a fault impact range score of less than 20 points, which means the fault only affects the human-computer interaction experience; a fault occurrence frequency score of less than 12 points, which means the fault rate is low; and a maintenance cost score of less than 12 points, which means the maintenance is simple.

[0035] It should also be noted that core modules refer to first-level core components, as they directly determine whether the robot can start normally and operate safely, and are given the highest detection priority. Non-core modules include second-level important components and third-level auxiliary components. Their functional defects or abnormalities will not directly cause the entire machine to fail. A dynamic detection process is designed based on the classification results, prioritizing the detection of core modules. When a non-core module is abnormal, the system can skip that module and continue to execute the detection of the core module to avoid process interruption.

[0036] The core module is given the highest testing priority. This priority testing is achieved through pre-setting and execution logic of the testing order in the dynamic testing process. First, the testing process is initialized. After the system starts the testing program, it identifies and classifies all modules on the core control board, confirming the specific scope of core and non-core modules to prepare for priority testing of the core module. Second, core module priority testing is executed. After initialization, the system directly enters the core module testing phase, sequentially testing its key performance parameters, startup conditions, and safety control functions according to the functional logic order of the core module in actual application. Third, non-core module subsequent testing occurs. After all core module testing is completed, the system automatically switches to the non-core module testing phase, sequentially testing secondary important components and tertiary auxiliary components according to the configuration of non-core modules in actual application and the preset testing sequence. Finally, exception handling and process continuation are handled. Relying on the constructed exception handling engine and enhanced watchdog mechanism, if a core module malfunctions during testing, the system judges the device status according to preset rules and executes corresponding processing, records the exception information, skips the malfunctioning core module, and continues testing the next core module, ensuring the integrity of the core module testing sequence.

[0037] When the core module detection is completed and the non-core module detection phase begins, if an anomaly occurs in a non-core module, the system will also use the above-mentioned anomaly handling mechanism to determine and handle the status, record the anomaly information, skip the anomaly non-core module, and continue to execute the detection of the next non-core module. Through the above anomaly handling logic, the detection process can continue to advance even if an anomaly occurs in any module, avoiding the interruption of the overall detection due to an anomaly in a single module.

[0038] It should also be noted that by constructing a critical level assessment model and realizing the quantitative classification of core control board components, the technical problems of ambiguous priorities and uneven resource allocation in traditional testing can be solved. By prioritizing the real-time monitoring and rapid fault response of core modules, the reliability and testing efficiency of the system can be significantly improved, the interference of non-core faults on the overall process can be reduced, and the integrity of the core module testing sequence can be guaranteed.

[0039] S2: Combine a state machine model to build an exception handling engine and monitor the device status in real time.

[0040] Furthermore, the state machine model includes: state transitions according to preset states based on control signals; defining device states; state definitions that fit the actual device testing process; triggering state transitions based on various events and conditions in the testing process; when a testing start event occurs, the device transitions from the initial state to the ready state; when a module abnormal event occurs during the testing process, the device transitions from the running state to the paused state.

[0041] The state machine model, as a mathematical model that abstracts the operating rules of real-world things, consists of a state register and combinational logic circuits. It can perform state transitions according to preset states based on control signals. This invention innovates and optimizes the state machine model. First, it precisely defines the equipment states, covering initial state, ready state, running state, paused state, completed state, and fault state. The state definitions are more in line with the actual equipment detection process, laying the foundation for accurate anomaly judgment and handling.

[0042] It should be noted that the exception handling engine includes real-time monitoring of device status. When an exception is detected, it combines the state mechanism model to trace back the device status data before the fault occurred, locate the key state nodes before the exception occurred, and then locate the root cause of the fault. For program crash scenarios, an enhanced watchdog mechanism is used to quickly record the context information before the crash and generate a fault log.

[0043] In terms of state transition logic design, the state machine model of this invention triggers state transitions based on various events and conditions in the detection process. For example, when a detection start event occurs, the device transitions from the initial state to the ready state; when a module abnormality event occurs during the detection process, the device transitions from the running state to the paused state. This flexible state transition logic, based on actual detection conditions, breaks through the limitations of fixed patterns in traditional models and can better adapt to complex changes in the detection process.

[0044] The anomaly handling engine monitors the device status in real time. When an anomaly is detected, it uses the optimized state machine model of this invention to trace back the device status data before the fault occurred. Traditional models are difficult to trace the state before the anomaly in a comprehensive and accurate manner, while this model, with its fine state records and efficient transition logic, can accurately locate multiple key state nodes before the anomaly occurred, providing rich and accurate data support for subsequent fault tree analysis, and thus accurately locating the root cause of the fault.

[0045] It should also be noted that an optimized state machine model is used to build an anomaly handling engine, enabling real-time monitoring of equipment status and precise anomaly localization. This solves the problems that traditional state machine models often have when dealing with anomalies in complex systems, such as insufficiently precise state definitions, inflexible state transition logic, and limited ability to trace the root cause of anomalies. Compared with traditional models in existing technologies, this anomaly handling engine has higher accuracy, flexibility, and reliability, significantly improving the ability to handle anomalies during the detection process of the core control board of the live-line working robot in the distribution network.

[0046] S3: Locate the root cause of the device status data before the anomaly through fault tree analysis, use an enhanced watchdog mechanism to record fault information, and dynamically adjust the waiting time of function detection.

[0047] Furthermore, fault tree analysis includes analyzing equipment status data automatically traced back when an anomaly occurs, based on a closed-loop process of top event definition, dynamic fault tree modeling, logical reasoning and pruning, and root cause localization. Through a dynamic weight adjustment mechanism, real-time traced status data is deeply integrated into the entire fault tree modeling and reasoning process for data-driven dynamic analysis.

[0048] Equipment status data includes core control board output signals, communication interaction timing, test tool feedback data, and program running logs.

[0049] It should be noted that the definition of the top event is based on the "fault state" determined by the state machine model (such as the core control board signal exceeding the threshold, communication interruption exceeding 5 seconds, program crash, etc.). The specific abnormal phenomena that occur during the monitoring process are defined as the "top event", which is the final fault manifestation that needs to be traced back to the root cause. The definition of the top event is deeply bound to the device status monitored in real time by the anomaly handling engine to ensure that the analysis target is completely matched with the actual fault scenario.

[0050] The dynamic fault tree modeling is pre-built based on the hardware structure, software interaction logic, and historical fault cases of the core control board. It constructs a basic fault tree with a hierarchical relationship of "top event - intermediate event - bottom event". The top event is the specific abnormal phenomenon, the intermediate event is the direct cause of the top event (such as abnormal communication interface function, poor communication cable contact, etc.), and the bottom event is the fundamental fault cause that does not need further decomposition (such as physical damage to the interface chip, power supply voltage fluctuation of more than ±10%, program driving logic error, etc.). At the same time, combined with real-time backtracking of equipment status data, the association weight of each event in the fault tree is dynamically adjusted. If a certain type of status data shows abnormality (such as multiple fluctuations in power supply voltage before the fault), the probability weight of the corresponding bottom event (such as voltage fluctuation) will automatically increase, and vice versa, so that the fault tree always adapts to the characteristics of the current fault scenario.

[0051] Logical reasoning and pruning employ Boolean logic gates (AND, OR, XOR, etc.) for qualitative reasoning of the fault tree. For faults with multiple causes and one effect (such as occasional FLASH storage failures), an AND gate is used to determine whether the upper-level event analysis is triggered. This is only triggered when all associated intermediate events (such as "abnormal VCC voltage fluctuation of the microcontroller" and "FLASH read / write error") are true simultaneously. For faults with a single cause and a wide spread (such as abnormal indicator lights), an OR gate is used to determine whether any associated bottom event (such as "optical coupler burnout in indicator light driver circuit", "abnormal control level of microcontroller IO output", "damage to the indicator light itself") is true. This triggers the analysis of the corresponding intermediate event ("abnormal indicator light driver circuit"). At the same time, irrelevant events are pruned by combining backtracking data: if the data shows that a certain parameter was normal before the fault (such as the power supply voltage being stable within ±5% of the rated value), the bottom event (such as "voltage fluctuation") and associated branches corresponding to that parameter are directly removed to reduce invalid analysis dimensions.

[0052] Root cause localization uses logical reasoning to filter out the lowest-probability events and combines them with associated state data (such as "gimbal motor communication interruption" corresponding to abnormal power supply to the gimbal motor before the fault) to output a root cause that can be directly verified.

[0053] It should also be noted that, through the dynamic weight adjustment mechanism, the real-time backtracking status data is deeply integrated into the entire process of fault tree modeling and reasoning, realizing "data-driven dynamic analysis" rather than simply relying on preset logic. For the dynamic detection process design of the core control board of the live distribution network operation robot, the analysis dimensions can be adaptively adjusted according to the detection stage (such as focusing on key nodes such as power supply and processor in the core module detection stage), which significantly improves the adaptability of dynamic scenarios. The anomaly handling engine automatically identifies the top event and can automatically expand the fault tree nodes through historical data, realizing full-process automation from "anomaly detection" to "root cause location".

[0054] It should also be noted that the enhanced watchdog mechanism for recording fault information includes triggering a reset at the moment of program crash, synchronously capturing key data before the crash, cleaning up residual states and generating fault logs, jointly determining whether a crash state is reached through core module interaction heartbeats and parameter thresholds, triggering actions only when multiple dimensions of crash characteristics are met, deeply linking with fault tree analysis, recording crash context data as input parameters for fault tree analysis, associating it with bottom event reasoning of the top event of program crash, and performing full-link collaborative work.

[0055] The watchdog mechanism monitors program anomalies through "timeout reset." A preset time threshold is used; if the program fails to send a "feed the watchdog signal" within the threshold, a program crash is detected, triggering a system reset. The enhanced watchdog mechanism builds upon this with three layers of innovation: adding context information capture, residual state cleanup, and fault log generation, forming a closed-loop mechanism of "detection-recording-recovery." It also employs "multi-dimensional judgment logic," where core module interaction heartbeats and key parameter thresholds jointly determine whether a "true crash" has occurred. Actions are only triggered when multiple dimensions meet the crash characteristics, reducing the false positive rate. Furthermore, the enhanced watchdog deeply integrates with the fault tree analysis engine. The crash context data it records is automatically used as input parameters for fault tree analysis, directly linking to the bottom-event reasoning of the "program crash" top event, achieving end-to-end collaboration of "anomaly capture - data recording - cause localization."

[0056] It should also be noted that dynamically adjusting the waiting time for functional tests includes real-time monitoring of the response status of the core control board and each test device, and dynamically adjusting the waiting time for each functional test according to the response characteristics of different components and the requirements of the current test items.

[0057] The device's response status includes no response, partial response, and full response.

[0058] It should also be noted that the response status specifically includes: a non-response status, in which the component or device does not provide any signal within the preset initial time; a partial response status, in which the feedback signal is incomplete or the parameters have not reached a stable value; and a full response status, in which the feedback signal is complete, the parameters are stable, and the response is within the expected range.

[0059] The response characteristics mainly include: signal output delay, which is the time from when the component receives the command to when it outputs the first valid signal, such as the relay activation delay and sensor data acquisition delay; parameter stabilization time, which is the duration for the component's output signal to go from fluctuation to stabilization, such as the time for the power module voltage to stabilize from startup and the stabilization time after the load simulator current is adjusted; and communication interaction frequency, which is the data interaction interval between the component and the test fixture and host computer, such as a high-frequency communication sensor sending data once every 10ms and a low-frequency communication indicator light refreshing its status once every 500ms.

[0060] For components with faster response times, the waiting time is shortened to improve efficiency; for components with slower response times, the waiting time is extended to ensure detection accuracy, balancing both efficiency and accuracy. Components with faster response times are defined as those with a signal output delay of less than or equal to 20ms, a parameter stabilization time of less than or equal to 50ms, and a communication interaction frequency of greater than or equal to 10Hz, such as platform indicator lights and button feedback modules. Components with slower response times are defined as those with a signal output delay greater than 50ms, a parameter stabilization time greater than 100ms, or a communication interaction frequency less than 1Hz, such as high-precision sensors and load regulation modules. The system dynamically adjusts the waiting time for each function detection based on these characteristics, shortening the waiting time for faster-responding components to improve efficiency and extending the waiting time for slower-responding components to ensure detection accuracy, thus balancing both efficiency and accuracy.

[0061] It should also be noted that, based on the testing phase of the core control board, corresponding differentiated testing strategies are automatically matched. The comprehensive testing platform simulates high and low temperature shocks, strong electromagnetic interference, and humid environments. Stress testing applies continuous full-load or even overload operation through a load simulator, and network interference generators simulate frequent disconnections of multiple network clients to verify the stability of the core control board. Aging testing continuously monitors the performance degradation trend of the components by running the core control board for a long time, thereby predicting the equipment lifespan.

[0062] By integrating a machine vision module into the testing fixture and combining it with automated testing programs, a "zero-human-intervention" testing process is achieved, from detection initiation, parameter configuration, project execution to result judgment, reducing the false negative rate to below 5%. Remote interaction between the testing fixture and a host computer is enabled via a serial port converter and network cable. The host computer uploads testing data to a cloud management platform in real time, supporting functions such as centralized scheduling of multi-node devices, remote parameter adjustment, and historical data mining.

[0063] It should also be noted that by integrating fault tree analysis with an enhanced watchdog mechanism, the entire chain of automated diagnosis and recording of equipment anomalies is achieved. This solves the problems of low data integration, poor adaptability, coarse detection, and weak traceability in traditional mechanisms, improving the location accuracy of fault analysis, system adaptability, and high reliability of the core control board detection of the live-line working robot. Furthermore, by monitoring the response status and characteristics of equipment in real time and dynamically adjusting the waiting time of functional testing, it solves the technical problem that traditional fixed delay cannot balance detection efficiency and accuracy. This enables differentiated and precise testing for different response components, improves the degree of test automation, reduces the false negative rate, and ensures test reliability under extreme operating conditions.

[0064] Example 2, refer to Figures 2-3As an embodiment of the present invention, an intelligent detection system for the core control board of a live-line working robot in a power distribution network is provided, including a core control board module 100, a test fixture module 200, a host computer remote interaction module 300, a cloud platform interaction module 400, and a voltage regulator module 500.

[0065] The core control board module 100 is used to execute the detection process control and data processing. It is connected to the test fixture module 200, and manages the internal components hierarchically through the key level layering module. The test process definition module plans the detection sequence, the state machine management module coordinates the detection state transition, drives the peripheral device to run through the wiring port, collects voltage and current parameter data in real time, interacts with the test fixture module 200 and the host computer remote interaction module 300, and dynamically adjusts the detection timing according to the response characteristics.

[0066] Among them, the test fixture module 200 is used for hardware interaction between the core control board and the test system. It establishes a physical connection with the core control board module 100 under test through the terminal block 201, integrates a communication interface for instruction transmission and data acquisition, includes physical input for peripheral triggering of simulated case operation, analyzes the equipment operating status through image acquisition and transmits the test data to the host computer remote interaction module 300 for recording and analysis.

[0067] The host computer remote interaction module 300 is used to coordinate the data communication and command transmission of the detection system. It establishes a physical connection with the core control board module 100 through the communication port 301 and the serial port conversion tool 302, and establishes a connection with the test fixture module 200 through the network cable 303. It receives real-time operating data from each device, integrates and analyzes the received data, dynamically displays the detection progress and results, uploads the processed data to the cloud platform interaction module 400 and receives control commands for centralized monitoring and remote management of the detection process.

[0068] The cloud platform interaction module 400 is used to connect to the host computer remote interaction module 300, receive detection data, equipment status information and test reports uploaded from the host computer, and store and manage historical data and fault logs of the detection nodes.

[0069] The voltage regulator module 500 is used to connect the core control board module 100 and the test fixture module 200, providing a stable and controllable DC power input.

[0070] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0072] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0073] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent detection of the core control board of a live-line working robot in a power distribution network, characterized in that, include: A key level assessment model is constructed based on the functional characteristics of each component of the core control board, and a dynamic detection process is executed based on the grading results. The construction of the key level assessment model includes setting scoring standards and assigning weights based on the functional necessity, fault impact range, fault frequency, and maintenance cost of each component of the core control board; quantitatively evaluating each component of the core control board according to the preset scoring rules; classifying the components according to the comprehensive score; and executing a dynamic detection process based on the classification results. Based on the classification results of the critical level assessment model, the core modules are identified and assigned the highest detection priority, with high-level core components being tested first. The core module refers to the primary core component, and the response status of each module is monitored in real time during the detection process; An exception handling engine is built by combining a state machine model to monitor the device status in real time. By analyzing fault trees, the root cause of the equipment status data before the anomaly is located. An enhanced watchdog mechanism is used to record fault information and dynamically adjust the waiting time of function detection. The fault tree analysis includes analyzing the equipment status data automatically backdated when an anomaly occurs, based on the equipment status data, through a closed-loop process of top event definition, fault tree dynamic modeling, logical reasoning and pruning, and root cause localization. Through a dynamic weight adjustment mechanism, the real-time backdated status data is deeply integrated into the entire fault tree modeling and reasoning process for data-driven dynamic analysis. Equipment status data includes core control board output signals, communication interaction timing, test fixture feedback data, and program execution logs; The dynamic adjustment of the waiting time for the function test includes real-time monitoring of the response status of the core control board and each test device, and dynamic adjustment of the waiting time for each function test according to the response characteristics of different components and the requirements of the current test item. The device's response status includes no response, partial response, and full response. The state machine model consists of a state register and combinational logic circuits. It performs state transitions according to preset states based on control signals. The preset states include initial state, ready state, running state, paused state, completed state, and fault state.

2. The intelligent detection method for the core control board of the live-line working robot in claim 1, characterized in that: The state machine model includes, According to the control signal, the state transition is carried out according to the preset state. The state definition is consistent with the actual process of equipment testing. The state transition is triggered by various events and conditions in the testing process. When the testing start event occurs, the equipment transitions from the initial state to the ready state. When a module abnormal event occurs during the testing process, the equipment transitions from the running state to the paused state.

3. The intelligent detection method for the core control board of the live-line working robot in claim 1, characterized in that: The exception handling engine includes, By monitoring the device status in real time, when an anomaly is detected, the device status data before the fault occurs is traced back using the state mechanism model to locate the key state nodes before the anomaly occurs, and then locate the root cause of the fault. For program crash scenarios, an enhanced watchdog mechanism is used to quickly record the context information before the crash and generate a fault log.

4. The intelligent detection method for the core control board of the live-line working robot in distribution networks as described in any one of claims 1 to 3, characterized in that: The method of using an enhanced watchdog mechanism to record fault information includes, The system triggers a reset the instant the program crashes, simultaneously captures key data prior to the crash, cleans up residual states and generates fault logs, and determines whether a crash state is reached through heartbeats and parameter thresholds in core module interactions. Actions are only triggered when multiple crash characteristics are met, and the system is deeply integrated with fault tree analysis. The system records crash context data and automatically uses it as input parameters for fault tree analysis, linking it to the bottom event reasoning of the top event of the program crash, and performing full-link collaborative work.

5. An intelligent detection system for the core control board of a live-line working robot in a distribution network, employing the intelligent detection method for the core control board of a live-line working robot in a distribution network as described in any one of claims 1 to 4, characterized in that: It includes a core control board module (100), a test fixture module (200), a host computer remote interaction module (300), a cloud platform interaction module (400), and a regulated power supply module (500). The core control board module (100) is used to execute the detection process control and data processing. It is connected to the test fixture module (200), and the internal components are hierarchically managed by the key level layering module. The test process definition module plans the detection sequence, the state machine management module coordinates the detection state transition, drives the peripheral device to run through the wiring port, collects voltage and current parameter data in real time, interacts with the test fixture module (200) and the host computer remote interaction module (300) to exchange data, and dynamically adjusts the detection timing according to the response characteristics. The test fixture module (200) is used for hardware interaction between the core control board and the test system. It establishes a physical connection with the core control board module (100) under test through the wiring terminal (201), integrates a communication interface for instruction transmission and data acquisition, includes physical input for peripheral triggering simulated case operation, analyzes the device operating status through image acquisition and transmits the detection data to the host computer remote interaction module (300) for recording and analysis. The host computer remote interaction module (300) is used to coordinate the data communication and command transmission of the detection system. It establishes a physical connection with the core control board module (100) through the communication port (301) and serial port conversion tool (302), and establishes a connection with the test fixture module (200) through the network cable (303). It receives real-time operating data of each device, integrates and analyzes the received data, dynamically displays the detection progress and results, uploads the processed data to the cloud platform interaction module (400) and receives control commands to perform centralized monitoring and remote management of the detection process. The cloud platform interaction module (400) is used to connect to the host computer remote interaction module (300), receive detection data, equipment status information and test reports uploaded from the host computer, and store and manage historical data and fault logs of the detection nodes; The voltage regulator module (500) is used to connect the core control board module (100) and the test fixture module (200) to provide a stable and controllable DC power input.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent detection method for the core control board of the live-line working robot in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent detection method for the core control board of the live-line working robot in any one of claims 1 to 4.

Images