Modular robotic connection state monitoring and automatic repair system and method

Abstract

The present application relates to the technical field of modular robots, and particularly relates to a modular robot connection state monitoring and automatic repair system and method, which comprises a control module for receiving external commands and managing the operation of each module, a communication module for data transmission and information exchange between modules, a perception module for environmental perception and fault detection to provide real-time monitoring data, a fault detection module for identifying and diagnosing possible connection faults or communication errors in the system, and a calculation and analysis module for executing graph convolution network and other high-precision data analysis tasks for fault classification and priority sorting. The present application uses deep learning technologies such as graph convolution network to process the connection topology graph of the module, automatically identifies the connection abnormality or fault point, and classifies and prioritizes, thereby improving the accuracy and efficiency of fault detection.

Description

Technical Field

[0001] This invention relates to the field of modular robot technology, and specifically to a modular robot connection status monitoring and automatic repair system and method. Background Technology

[0002] With the continuous development of robotics technology, the concept of modular robots has received increasing attention. Modular robots consist of multiple functional modules that can be connected and separated through standardized interfaces, enabling rapid assembly, disassembly, and reconfiguration. This design gives modular robots high flexibility, scalability, and adaptability, allowing them to be applied in various complex and changing environments. During the operation of a modular robot, the connection status between modules is crucial. Any abnormal or faulty module connection will directly affect the robot's normal operation. Therefore, real-time monitoring and identification of module connection status, and rapid repair in case of faults, are key to the reliable and safe operation of modular robots.

[0003] The existing patent announcement number CN118769227A, entitled "A Modular Wiring Harness Management System and Method for Industrial Robots," preprocesses data collected by various sensors, performs periodic analysis on the preprocessed data to identify periodic patterns in wiring harness state changes, and simultaneously performs multivariate time-series analysis. Based on a multivariate autoregressive model, it analyzes the mutual influence between different sensor data, combining periodic analysis and multivariate time-series analysis to detect the state of the modular wiring harness of the industrial robot. It acquires adaptive management information generated during the modular wiring harness state detection process, determines the state of the modular wiring harness based on this adaptive management information, generates different signals, and adjusts the modular wiring harness management of the industrial robot based on the generated signals. This optimizes resource allocation and reduces the maintenance costs of the industrial robot.

[0004] However, its detection method is singular, relying solely on magnetic sensors and a simple threshold judgment mechanism. This may fail to accurately detect complex connection states (e.g., weak magnetic interference, nonlinear magnetic field distribution), and the threshold judgment cannot handle complex abnormal patterns (e.g., gradually deteriorating connection states or intermittent failures). Furthermore, it is highly sensor-dependent, with performance significantly degrading in environments with sensor failure or strong magnetic interference. It also lacks supplementary data sources (e.g., mechanical or visual sensors) to compensate for insufficient sensor data, resulting in insufficient robustness. Simultaneously, it lacks intelligent analysis; the data analysis method is based solely on static threshold judgment, lacking adaptability and learning capabilities, making it difficult to cope with dynamic environments and complex modular robot structures. Finally, its primary function is connection state detection, without encompassing subsequent repair strategies or path planning, limiting its overall application scenarios. Therefore, we provide a modular robot connection state monitoring and automatic repair system and method. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the existing technology, the first objective of the present invention is to provide a modular robot connection status monitoring and automatic repair system and method to solve the problems in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A modular robot connectivity status monitoring and automatic repair system and method includes a control module for controlling and coordinating the entire system, receiving external commands, and managing the operation of each module; a communication module for data transmission and information exchange between modules; a perception module for environmental perception and fault detection, providing real-time monitoring data; a fault detection module for identifying and diagnosing potential connectivity failures or communication errors in the system; a calculation and analysis module for performing graph convolutional networks and other high-precision data analysis tasks to classify and prioritize faults; a reconstruction path planning module for using dynamic programming algorithms to calculate the optimal repair path to ensure rapid system reconstruction in fault conditions; a repair execution module for handling the actual physical repair process; and a verification and recovery module for verifying the system status after repair by checking the connectivity status through sensors to ensure the system returns to normal operating conditions.

[0008] The invention is further configured such that: the control module coordinates the operation of each module and sends instructions to the communication module to ensure smooth data transmission between modules; the communication module receives environmental monitoring data transmitted by the sensing module to ensure that real-time monitoring information can be accurately transmitted to other modules for processing; and the sensing module provides real-time environmental data for the fault detection module to analyze in order to identify and diagnose possible connection faults or communication errors.

[0009] The present invention is further configured such that: the fault detection module transmits the detected fault information to the calculation and analysis module for deep analysis through graph convolutional networks, classifies and sorts the priority of the faults; the calculation and analysis module transmits the results of fault classification and priority sorting to the reconstruction path planning module to calculate the optimal repair path based on the analysis results to ensure rapid repair.

[0010] The present invention is further configured such that: the reconstruction path planning module provides the optimal repair path, and the repair execution module performs physical repair operations according to the path; after the repair execution module completes the repair, the verification and recovery module detects the connection status again through sensors to ensure that the system returns to normal working state and restarts the task.

[0011] The present invention is further configured to include the following steps:

[0012] S1. Real-time sensing of the connection status of the magnetic sensor array module;

[0013] S2. High-precision data analysis for fault detection and classification;

[0014] S3. Graph convolutional networks automatically identify connection anomalies or fault points, classify and prioritize them.

[0015] S4. Dynamic programming algorithm generates the optimal reconstruction path;

[0016] S5, self-locking module interface and automatic refactoring mechanism are implemented for repair;

[0017] S6. Multimodal sensing fusion improves system robustness and adaptability;

[0018] S7. Self-diagnosis and self-repair mechanisms adapt to unknown environments and extend mission lifespan.

[0019] The present invention is further configured such that: in step S1, real-time sensing of the connection status of the modules by the magnetic sensor array, and in step S2, fault detection and classification by high-precision data analysis, a magnetic sensor array and a high-precision data analysis method are used to sense the connection status between the modules in real time by the magnetic sensor array, and to detect and classify faults by the high-precision data analysis method.

[0020] The present invention is further configured such that: in step S3, the graph convolutional network automatically identifies connection anomalies or fault points, classifies and prioritizes them, and adopts graph convolutional network deep learning technology to automatically identify connection anomalies or fault points, and performs classification and priority ranking. GCN can handle complex module connection topologies.

[0021] The present invention is further configured such that: in step S4, generating the optimal reconstruction path using the dynamic programming algorithm, the optimal reconstruction path is generated by considering connection location, module load and environmental constraints. Generating the optimal reconstruction path through the dynamic programming algorithm can improve the flexibility and adaptability of the repair strategy.

[0022] The present invention is further configured such that: in the repair process of step S5, the self-locking module interface and the automatic reconfiguration mechanism are used to simplify the monitoring and repair process of the module connection status. The self-locking module interface can provide a high-strength mechanical connection and support rapid separation, thereby simplifying the monitoring and repair process of the module connection status.

[0023] The present invention is further configured such that: in step S6, multimodal sensing fusion to improve system robustness and adaptability, and in step S7, self-diagnosis and self-repair mechanism to adapt to unknown environment and extend task life, a self-diagnosis and self-repair mechanism is adopted, enabling the modular robot to adapt to unknown environment and self-repair to extend task life, through the self-diagnosis and self-repair mechanism.

[0024] Beneficial effects

[0025] Compared with known public technologies, the technical solution provided by this invention has the following beneficial effects:

[0026] 1. This invention employs deep learning techniques such as graph convolutional networks to process the module connection topology graph, automatically identifying connection anomalies or fault points, and classifying and prioritizing them, thereby improving the accuracy and efficiency of fault detection. Compared with existing technologies that rely on manually designed feature extraction and classifiers, this invention can more effectively cope with environmental noise and interference, reducing false alarms and false negatives. When repairing module connection faults, a dynamic programming algorithm is used to generate the optimal reconstruction path, taking into account connection location, module load, and environmental constraints to optimize reconstruction efficiency. Compared with existing technologies that rely on preset paths and strategies, this invention has higher flexibility and adaptability, and can quickly and effectively solve problems, especially in complex and changing environments.

[0027] 2. This invention employs a low-power magnetic sensor array to improve the real-time performance and reliability of fault detection. It also combines multimodal sensing data from magnetic sensors, IMUs, and visual information, using multimodal sensing fusion technology to enhance the system's robustness and adaptability. Compared to existing technologies where magnetic sensors lack accuracy or data acquisition speed is insufficient, this invention offers significant improvements in real-time performance and robustness. Its modular design provides high flexibility and adaptability, enabling its application in various complex and dynamic environments such as disaster search and rescue, deep space exploration, and industrial robotics. Compared to existing technologies, this invention better adapts to different application scenarios and needs.

[0028] 3. After detecting a fault, the present invention can automatically activate a self-repair mechanism by removing the faulty module or reconnecting the failed module, or by initiating a backup module replacement process when the module cannot be repaired, thereby improving the reliability and security of the system. Compared with the prior art, the present invention can automatically repair when a fault occurs, reducing manual intervention and maintenance time, and improving the availability and task continuity of the system. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the system modules of a modular robot connection status monitoring and automatic repair system according to the present invention;

[0030] Figure 2 This is a schematic diagram of a direct connection module of a modular robot connection status monitoring and automatic repair system according to the present invention;

[0031] Figure 3 This is a flowchart illustrating a modular robot connection status monitoring and automatic repair method according to the present invention.

[0032] Figure 4 This is an initial structural schematic diagram of a modular robot connection status monitoring and automatic repair method according to the present invention;

[0033] Figure 5 This is a schematic diagram of the obstacle-connection process of a modular robot connection status monitoring and automatic repair method according to the present invention;

[0034] Figure 6 This is a schematic diagram of the optimal reconfiguration path for a modular robot connection status monitoring and automatic repair method according to the present invention. Detailed Implementation

[0035] To make the technical problems, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the described embodiments are only a part of the embodiments of this application, not all of them. The specific embodiments described herein are only used to explain the invention and are not intended to limit the invention. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] It should be further noted that the accompanying drawings and embodiments of the present invention mainly describe the concept of the present invention. Based on this concept, some specific forms and arrangements of connection relationships, positional relationships, power mechanisms, power supply systems, hydraulic systems and control systems may not be fully described. However, under the premise that those skilled in the art understand the concept of the present invention, they can implement the above-mentioned specific forms and arrangements in a well-known manner.

[0037] When a component is referred to as being "fixed to" or "set on" another component, it can be directly on or indirectly on that other component. When a component is referred to as being "connected to" another component, it can be directly connected to or indirectly connected to that other component.

[0038] The directional terms "inner" and "outer" refer to the inner and outer contours of each component itself. The terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0039] For ease of description, spatial relative terms such as "above," "over," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "above" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways, and the spatial relative descriptions used herein will be interpreted accordingly.

[0040] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, and "several" means one or more, unless otherwise explicitly specified.

[0041] The present invention will be further described below with reference to embodiments.

[0042] Example 1

[0043] like Figure 1-6 As shown, a modular robot connection status monitoring and automatic repair system includes a control module for controlling and coordinating the entire system, receiving external commands, and managing the operation of each module; a communication module for data transmission and information exchange between modules; a perception module for environmental perception and fault detection, providing real-time monitoring data; a fault detection module for identifying and diagnosing possible connection failures or communication errors in the system; a calculation and analysis module for performing graph convolutional networks and other high-precision data analysis tasks to classify and prioritize faults; a reconstruction path planning module for using dynamic programming algorithms to calculate the optimal repair path to ensure rapid reconstruction of the system in case of failure; a repair execution module for the actual physical repair process; and a verification and recovery module for verifying the system status after repair by checking the connection status through sensors to ensure the system returns to normal working condition.

[0044] The control module coordinates the operation of each module and sends instructions to the communication module to ensure smooth data transmission between modules; the communication module receives environmental monitoring data transmitted by the sensing module to ensure that real-time monitoring information can be accurately transmitted to other modules for processing; the sensing module provides real-time environmental data for the fault detection module to analyze in order to identify and diagnose possible connection faults or communication errors.

[0045] The fault detection module transmits the detected fault information to the calculation and analysis module for deep analysis using a graph convolutional network, classifying and prioritizing the faults. The calculation and analysis module then transmits the results of fault classification and priority ranking to the reconstruction path planning module, which calculates the optimal repair path based on the analysis results to ensure rapid repair.

[0046] The reconstruction path planning module provides the optimal repair path, and the repair execution module performs physical repair operations according to the path. After the repair execution module completes the repair, the verification and recovery module detects the connection status again through sensors to ensure that the system returns to normal working state and restarts the task.

[0047] In this embodiment, it is assumed that a modular robot consists of 16 modules, which are connected to form a complex mesh topology (e.g., Figure 4 (As shown). Each module uses a magnetic sensor to monitor its connection status in real time. Connection topology: Each module is connected via a self-locking interface.

[0048] Main path: Module 1 --- Module 2 --- Module 3 --- Module 4 --- Module 5

[0049] Parallel path 1: Module 2 --- Module 6 --- Module 7 --- Module 8 --- Module 5

[0050] Parallel path 2: Module 3 --- Module 9 --- Module 10 --- Module 11 --- Module 4

[0051] Cross-connection: Module 6---Module 9, Module 7---Module 10, Module 8---Module 11

[0052] Extended Network: Module 5 --- Module 12 --- Module 13 --- Module 14 Module 12 --- Module 15 --- Module 16

[0053] Suppose that during operation, the connections between module 7 and module 10, and between module 8 and module 11, fail simultaneously, causing the data stream to fail to be transmitted normally.

[0054] Such multi-point failures may be caused by loose connections, localized communication failures, or external interference. In this case, a magnetic sensor array and high-precision data analysis methods are used to perceive the connection status between modules in real time, and to detect and classify failures through high-precision data analysis. The magnetic sensor array consists of 10 low-power magnetic sensors distributed at various key locations on the modular robot to monitor the module connection status in real time. The high-precision data analysis method employs a graph convolutional network (GCN) to automatically identify connection anomalies or failure points through deep learning of the module connection topology, and then classifies and prioritizes them. The magnetic sensor array not only detects the physical connection status between modules but also identifies loose connection anomalies through magnetic field strength distribution (e.g., detecting a 30% drop in magnetic field strength between modules 7-10), and analyzes abnormal nodes in the topology graph through GCN (e.g., a sudden increase in communication delay between modules 8-11), classifying them as intermittent communication failures. This process covers anomaly types not addressed in existing technologies, verifying the comprehensiveness of the detection scope of this patent.

[0055] A dynamic programming algorithm is used to generate the optimal reconstruction path. Considering connection locations, redundant paths in the mesh topology are prioritized; regarding module load, a load balancing algorithm is used to distribute tasks and avoid excessive load on single points; and regarding environmental constraints, communication bottlenecks are avoided to optimize reconstruction efficiency. The dynamic programming algorithm calculates the optimal set of paths from the initial state to the target state by establishing the state transition equation F(i,j)=min{F(i-1,k)+C(k,j)} (where C(k,j) is the repair cost from module k to module j, considering multiple path selections in the mesh topology).

[0056] The planning result was to bypass the faulty connection, and the system automatically selected the optimal reconfiguration path: Module 7 -> Module 8 -> Module 5 -> Module 4 -> Module 11.

[0057] This route was chosen based on the following advantages:

[0058] Existing stable connections (7-8, 8-5, 5-4, 4-11) were utilized.

[0059] Minimizes path length and reduces communication latency

[0060] All faulty connection points (7-10 and 8-11) were avoided.

[0061] A reliable data transmission channel is provided through the backbone network (modules 4-5).

[0062] from Figure 6As can be seen, the reconstructed path fully utilizes the redundancy characteristics of the mesh topology, achieving the selection of the shortest path while ensuring connection reliability. This path reconstruction not only solves the communication interruption problem but also provides a better load balancing effect because it distributes the nodes through which data flows, avoiding the situation of excessive load on a single point.

[0063] In terms of performance metrics, the dynamic programming algorithm in mesh topology has a time complexity of O(n^3), and the replanning time is approximately 2.5 seconds due to the need to consider combinations of multiple paths. However, compared to chain topology, mesh structure offers higher fault tolerance and more flexible replanning options.

[0064] After completing the optimal path planning (module 7 -> module 8 -> module 5 -> module 4 -> module 11), the modular robot's self-repair process is initiated to repair the module connection status. A self-locking module interface and an automatic reconfiguration mechanism simplify the monitoring and repair process for module connection status. The self-locking module interface provides a high-strength mechanical connection while supporting rapid separation (by removing the faulty connection points of modules 7-10 and 8-11 through a high-strength mechanical connection (maximum load capacity 500N) and a rapid separation mechanism, a reinforced connection is established on the planned new path). In the mesh topology, due to the existence of multiple parallel paths and cross connections, the system can execute the reconfiguration process more efficiently. The response time of the backup module is optimized to 10 seconds, and the overall reconfiguration process completion time is shortened to 14 seconds. In terms of performance metrics, the module replacement success rate in multi-point failure scenarios is improved to 99.2%, with a total repair time of approximately 16 seconds (including path planning and module replacement).

[0065] After the connectivity status verification and repair are completed, the newly established path (7-8-5-4-11) is verified using magnetic sensors and GCN. Because the mesh topology provides more reference points and verification paths, the system can more accurately assess connectivity quality. In terms of performance metrics, the verification time for complex topologies is optimized to 0.3 seconds, and the system recovery success rate is improved to 99.5%.

[0066] Example 2

[0067] like Figure 3 As shown, a modular robot connection status monitoring and automatic repair method includes the following steps:

[0068] S1. Real-time sensing of the connection status of the magnetic sensor array module;

[0069] S2. High-precision data analysis for fault detection and classification;

[0070] S3. Graph convolutional networks automatically identify connection anomalies or fault points, classify and prioritize them.

[0071] S4. Dynamic programming algorithm generates the optimal reconstruction path;

[0072] S5, self-locking module interface and automatic refactoring mechanism are implemented for repair;

[0073] S6. Multimodal sensing fusion improves system robustness and adaptability;

[0074] S7. Self-diagnosis and self-repair mechanisms adapt to unknown environments and extend mission lifespan;

[0075] In step S1, where the magnetic sensor array senses the connection status of the modules in real time, and in step S2, where high-precision data analysis is used for fault detection and classification, a magnetic sensor array and a high-precision data analysis method are employed. The magnetic sensor array senses the connection status between the modules in real time, and the high-precision data analysis method is used for fault detection and classification.

[0076] In step S3, the graph convolutional network automatically identifies connection anomalies or fault points, classifies and prioritizes them. The graph convolutional network deep learning technology is used to automatically identify connection anomalies or fault points and classify and prioritize them. GCN can handle complex module connection topologies.

[0077] In step S4, generating the optimal reconstruction path using dynamic programming algorithm, the optimal reconstruction path is generated by considering connection location, module load, and environmental constraints. Generating the optimal reconstruction path through dynamic programming algorithm can improve the flexibility and adaptability of the repair strategy.

[0078] In step S5, the self-locking module interface and automatic reconfiguration mechanism are used to perform the repair, which simplifies the monitoring and repair process of the module connection status. The self-locking module interface can provide a high-strength mechanical connection and supports rapid separation, thereby simplifying the monitoring and repair process of the module connection status.

[0079] In step S6, multimodal sensing fusion improves system robustness and adaptability, and in step S7, self-diagnosis and self-repair mechanisms adapt to unknown environments and extend task lifespan, self-diagnosis and self-repair mechanisms are adopted to enable the modular robot to adapt to unknown environments and self-repair to extend task lifespan.

[0080] In this embodiment, a magnetic sensor array monitors the connection status between modules in real time and performs fault detection and classification through high-precision data analysis. When a fault occurs, a graph convolutional network (GCN) automatically identifies connection anomalies or fault points and sorts them according to priority to handle complex module connection topologies. Then, a dynamic programming algorithm generates the optimal repair path, considering connection locations, module load, and environmental constraints to improve the flexibility and adaptability of the repair strategy.

[0081] During the repair process, the self-locking module interface and automatic reconfiguration mechanism simplify the module connection and repair process, ensuring high-strength mechanical connection and rapid separation. Through multimodal sensor fusion and self-diagnosis and self-repair mechanism, the system can automatically adapt to unknown environments and extend the mission life, achieving efficient self-repair and improved system robustness.

[0082] Working principle

[0083] In practical use, the repair execution module and the verification and recovery module of this invention are responsible for the actual physical repair and the functional verification after repair, respectively. The connection status between the various functional modules is monitored in real time through magnetic sensors, and high-precision data analysis and graph convolutional networks (GCN) are used to identify and locate faults, thereby realizing the modular robot's rapid self-repair capability during task execution.

[0084] At the fault detection level, the system primarily relies on a magnetic sensor array to perceive the module connection status in real time. The magnetic sensor array boasts a sensitivity of up to 10^-6 T, capable of capturing even the smallest connection anomalies between modules at a sampling frequency of 100 Hz. The signals collected by the sensors undergo preliminary processing and fault detection by a high-precision data analysis module. Subsequently, this data is input into a graph convolutional network (GCN) for deep learning. The GCN leverages its topology awareness to automatically identify abnormal connections or fault points, classifying and prioritizing them according to fault type. This step achieved a detection accuracy of approximately 99.2% in testing and can complete topology analysis and fault detection within 0.2 seconds.

[0085] After fault identification, the system's reconfiguration path planning module uses dynamic programming (DP) to quickly generate the optimal repair path. Dynamic programming establishes a state transition equation F(i,j) = min{F(i-1,k) + C(k,j)}, considering multiple factors such as connection location, module load, and environmental constraints, to provide the optimal repair solution from the initial state to the target state. This ensures that when a fault occurs, existing effective modules are reused as much as possible, and adjacent nodes are prioritized for rapid connection, avoiding more disconnections or resets. The time complexity of this planning process is approximately O(n^2), and in actual testing, it took an average of about 1.5 seconds, quickly obtaining the optimal reconfiguration strategy and greatly improving repair efficiency.

[0086] Repair operations primarily rely on self-locking module interfaces and an automatic reconfiguration mechanism to perform physical-level operations. The self-locking interface has a maximum load capacity of 500N, providing high-strength connections during normal operation while also supporting rapid disconnection for timely replacement of faulty modules or termination of faulty connections. The automatic reconfiguration mechanism executes according to the optimal repair path generated by dynamic programming, removing or reconnecting failed modules when necessary. If a specific module completely fails, the system invokes a backup module replacement procedure. The backup module response time is approximately 10 seconds. The entire repair process, including path planning and module replacement, takes approximately 14 seconds, with a module replacement success rate of up to 98.5%.

[0087] After repair, the system re-verifies the connectivity using a combination of magnetic sensors and GCN to ensure all modules have returned to normal function. This verification process takes approximately 0.2 seconds. If the topology matches expectations, the control module will instruct the robot to resume task execution. Through multimodal sensor fusion and self-diagnosis / self-repair mechanisms, the entire system can autonomously cope with faults in unknown environments, extend task lifespan, and maintain high robustness. Final test data shows that, by combining magnetic sensors, GCN, and dynamic programming algorithms, the system achieves a fault detection accuracy of up to 99.2%, significantly improves reconstruction efficiency, and truly realizes efficient modular robot automatic fault detection and rapid repair.

[0088] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0089] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0090] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

Claims

1. A modular robot connection status monitoring and automatic repair system, characterized in that, It includes a control module responsible for controlling and coordinating the entire system, receiving external commands and managing the operation of each module; a communication module responsible for data transmission and information exchange between modules; a perception module for environmental awareness and fault detection, providing real-time monitoring data; a fault detection module for identifying and diagnosing possible connection failures or communication errors in the system; a calculation and analysis module for performing graph convolutional networks and other high-precision data analysis tasks to classify and prioritize faults; a reconstruction path planning module for using dynamic programming algorithms to calculate the optimal repair path to ensure rapid reconstruction of the system in case of failure; a repair execution module responsible for the actual physical repair process; and a verification and recovery module for verifying the system status after repair by checking the connection status through sensors to ensure that the system returns to normal working status. The fault detection module transmits the detected fault information to the calculation and analysis module for deep analysis using a graph convolutional network, classifying and prioritizing the faults. The calculation and analysis module then transmits the results of fault classification and priority ranking to the reconstruction path planning module, which calculates the optimal repair path based on the analysis results to ensure rapid repair. The reconstruction path planning module provides the optimal repair path, and the repair execution module performs physical repair operations based on the path. After the repair execution module completes the repair, the verification and recovery module uses sensors to detect the connection status again, ensuring that the system returns to normal working status and restarts the task.

2. The modular robot connection status monitoring and automatic repair system according to claim 1, characterized in that, The control module coordinates the operation of each module and sends instructions to the communication module to ensure smooth data transmission between modules; the communication module receives environmental monitoring data transmitted by the sensing module to ensure that real-time monitoring information can be accurately transmitted to other modules for processing. The sensing module provides real-time environmental data for the fault detection module to analyze, in order to identify and diagnose possible connection faults or communication errors.

3. A modular robot connection status monitoring and automatic repair method, employing a modular robot connection status monitoring and automatic repair system according to any one of claims 1-2, characterized in that, The method includes the following steps: S1. Real-time sensing of the connection status of the magnetic sensor array module; S2. High-precision data analysis for fault detection and classification; S3. Graph convolutional networks automatically identify connection anomalies or fault points, classify and prioritize them. S4. Dynamic programming algorithm generates the optimal reconstruction path; S5, self-locking module interface and automatic refactoring mechanism are implemented for repair; S6. Multimodal sensing fusion improves system robustness and adaptability; S7. Self-diagnosis and self-repair mechanisms adapt to unknown environments and extend mission lifespan.

4. The modular robot connection status monitoring and automatic repair method according to claim 3, characterized in that: In step S1, where the magnetic sensor array is used to sense the connection status of the modules in real time, and in step S2, where high-precision data analysis is used for fault detection and classification, a magnetic sensor array and a high-precision data analysis method are employed. The magnetic sensor array is used to sense the connection status between modules in real time, and the high-precision data analysis method is used for fault detection and classification.

5. The modular robot connection status monitoring and automatic repair method according to claim 3, characterized in that: In step S3, the graph convolutional network automatically identifies connection anomalies or fault points, classifies them, and prioritizes them. The graph convolutional network uses deep learning technology to automatically identify connection anomalies or fault points, classifies them, and prioritizes them. The graph convolutional network can handle complex module connection topologies.

6. The modular robot connection status monitoring and automatic repair method according to claim 3, characterized in that: In step S4, the dynamic programming algorithm is used to generate the optimal reconstruction path. The optimal reconstruction path is generated by considering connection location, module load and environmental constraints, thereby improving the flexibility and adaptability of the repair strategy.

7. The modular robot connection status monitoring and automatic repair method according to claim 3, characterized in that: In step S5, the self-locking module interface and automatic reconfiguration mechanism are used to perform the repair, which simplifies the monitoring and repair process of the module connection status. The self-locking module interface can provide a high-strength mechanical connection and supports rapid separation, thereby simplifying the monitoring and repair process of the module connection status.

8. The modular robot connection status monitoring and automatic repair method according to claim 3, characterized in that: In step S6, multimodal sensing fusion improves system robustness and adaptability, and in step S7, self-diagnosis and self-repair mechanism adapts to unknown environments and extends task lifespan, a self-diagnosis and self-repair mechanism is adopted, enabling the modular robot to adapt to unknown environments and self-repair to extend task lifespan.

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