Intelligent mechanical safety protection system based on industrial Internet of Things equipment

Abstract

The invention provides an intelligent mechanical safety protection system based on industrial Internet of Things equipment, which comprises a dynamic topology modeling module, a risk propagation analysis module, a dynamic protection generation module and a collaborative triggering execution module, the composite topological graph comprises a physical layer and a logic layer; the risk propagation analysis module is used for analyzing a propagation path of the risk along a physical connection edge and a logic dependence edge based on the composite topological graph, and generating a risk propagation full-link sub-graph; the dynamic protection generation module is used for calculating a minimum necessary protection set based on the risk propagation path and generating a hierarchical response strategy; and the cooperative triggering execution module is used for sending a cooperative control instruction to the equipment in the minimum necessary protection set based on the hierarchical response strategy. By adopting the system, a risk propagation path under a complex scene can be automatically identified, and the safety protection level of equipment is improved.

Description

Technical Field

[0001] The present invention belongs to the field of production safety management, and particularly relates to an intelligent mechanical safety protection system based on industrial Internet of Things devices. Background Art

[0002] With the continuous deepening of industrial production automation, the interconnection and interoperability between devices have become an irreversible trend, and the intelligent demand for mechanical safety protection has become increasingly urgent. Safety protection is not only a guarantee for production continuity but also the bottom line for avoiding casualties and economic losses. However, many current solutions still remain at the single-device level of protection and are difficult to adapt to a highly collaborative industrial environment, which makes the potential of the industrial Internet of Things in the safety field not fully released.

[0003] Existing methods have shown obvious limitations when dealing with complex production scenarios. Traditional safety protection systems are mostly designed based on individual devices and lack overall consideration of the coordinated operation of multiple devices. When a fault or risk occurs in a certain link, other associated devices often cannot respond in time, resulting in insufficient coverage of protective measures. This isolated protection logic is unable to cope with chain reactions or multi-point trigger risks, especially in highly automated production lines, where the speed of risk spread and the scope of influence often exceed the control ability of single-device protection.

[0004] In this context, the intelligent mechanical safety protection of industrial Internet of Things devices faces several core challenges. Among them, the coordinated trigger mechanism between devices has become one of the key technical bottlenecks. Due to the complexity of production line layout and process flow, how to achieve real-time linkage response of multiple devices has become a difficult problem. In addition, the automatic identification of potential risks and the precise matching of associated devices are also crucial, which requires the system to not only sense abnormalities at a single node but also quickly analyze the global impact and make decisions. Without solving these technical factors, it is difficult to upgrade safety protection from single-point protection to systematic defense, and thus expose the shortcoming of chain risk prevention and control in a complex process environment.

[0005] However, current intelligent mechanical safety protection systems are often single-device protection and lack multi-device coordination, resulting in insufficient protection in the face of chain risks or complex scenarios, affecting equipment and personal safety. Summary of the Invention

[0006] Based on this, it is necessary to provide an intelligent mechanical safety protection system based on industrial Internet of Things devices for the above technical problems, which can automatically identify the risk propagation path in complex scenarios and improve the equipment safety protection level.

[0007] In a first aspect, the present application provides an intelligent mechanical safety protection system based on industrial Internet of Things devices, including a dynamic topology modeling module, a risk propagation analysis module, a dynamic protection generation module, and a collaborative trigger execution module:

[0008] The dynamic topology modeling module is used to generate a composite topology map according to the device physical connection relationship and production process logic. The composite topology map includes a physical layer and a logical layer;

[0009] The risk propagation analysis module is used to analyze the risk propagation path along the physical connection edges and logical dependency edges based on the composite topology map, and generate a risk propagation full-link sub-graph;

[0010] The dynamic protection generation module is used to calculate the minimum necessary protection set based on the risk propagation path and generate a hierarchical response strategy;

[0011] The collaborative trigger execution module is used to send collaborative control instructions to the devices within the minimum necessary protection set based on the hierarchical response strategy.

[0012] In a possible embodiment, the dynamic topology modeling module includes:

[0013] The physical topology construction unit is used to obtain device coordinate data through a UWB positioning base station and generate a physical connection relationship diagram between devices based on the material transmission path;

[0014] The logical topology analysis unit is used to analyze the work order process data of the manufacturing execution system, extract the start sequence dependency relationship between devices, and construct a logical dependency graph;

[0015] The map fusion unit is used to align the nodes of the physical connection relationship diagram and the logical dependency graph to obtain a composite topology map.

[0016] In a possible embodiment, the map fusion unit includes:

[0017] The attribute annotation sub-unit is used to annotate the process attribute labels for the device nodes in the physical connection relationship diagram based on the graph database, and generate a physical topology map with process attributes;

[0018] The weight annotation sub-unit is used to annotate the start priority weights of the device nodes in the logical dependency graph based on the process time sequence constraints, and generate a logical topology map with priorities;

[0019] The similarity matching sub-unit is used to match the corresponding devices in the physical connection relationship diagram and the logical dependency graph by using a node similarity algorithm, and generate a composite topology map.

[0020] In a possible embodiment, the risk propagation analysis module includes:

[0021] Anomaly detection unit, which is used to perform wavelet transform processing on the device vibration signal through an edge computing node, extract the fault characteristic frequency, and match it with a preset risk mode library to generate an anomaly type identifier;

[0022] Path deduction unit, which is used to simulate the diffusion path of risks along physical connection edges and logical dependency edges based on a composite topology map and an anomaly type identifier, and generate an initial propagation path set by using a random walk algorithm;

[0023] Impact assessment unit, which is used to calculate the probability distribution matrix of the risk impact range according to the process criticality weights of the devices in the initial propagation path set; based on the probability distribution matrix, filter out the propagation paths with the impact probability exceeding a preset threshold, and merge them to generate a full-link subgraph of risk propagation.

[0024] In a possible embodiment, the path deduction unit includes:

[0025] Initial node mapping sub-unit, which is used to map the detected anomaly type to an initial risk source node;

[0026] Breadth-first search sub-unit, which is used to perform breadth-first search along the physical connection edges in the physical connection relation graph to generate a physical propagation path set;

[0027] Depth-first search sub-unit, which is used to perform reverse depth-first search along the logical dependency edges in the logical dependency graph to generate a logical conduction path set;

[0028] Path fusion sub-unit, which is used to fuse the physical propagation path set and the logical conduction path set to obtain an initial propagation path set.

[0029] In a possible embodiment, the dynamic protection generation module includes:

[0030] Key node screening unit, which is used to calculate the betweenness centrality of each node and sort them based on the full-link subgraph of risk propagation to generate a node priority list;

[0031] Protection set optimization unit, which is used to calculate the proportion of risk paths covered by each node based on the full-link subgraph of risk propagation; use a greedy algorithm to select a set of nodes with a coverage exceeding a preset threshold, and screen out the set with the smallest number of devices to determine the minimum necessary protection set;

[0032] Strategy classification unit, which is used to divide the emergency stop, speed reduction, and area isolation three-level response strategies according to the risk diffusion speed and the equipment shutdown loss cost.

[0033] In a possible embodiment, the collaborative trigger execution module includes:

[0034] An instruction compilation unit for converting the device IDs of the minimum necessary protection set into a standardized control instruction set supported by the target device;

[0035] A multi-channel sending unit for synchronously sending the control instruction set through the main channel of the high-real-time communication network and the standby channel of the hard-wired emergency stop loop;

[0036] A redundancy verification unit for obtaining the response instruction of the target device and sending a forced shutdown signal to the hard-wired loop when the response instruction is not obtained within the preset time.

[0037] In a second aspect, the present application further provides an intelligent mechanical safety protection method based on industrial Internet of Things devices, including:

[0038] Generating a composite topology map according to the device physical connection relationship and the production process logic, where the composite topology map includes a physical layer and a logical layer;

[0039] Based on the composite topology map, analyzing the propagation paths of risks along the physical connection edges and the logical dependency edges to generate a full-link sub-graph of risk propagation;

[0040] Calculating the minimum necessary protection set based on the risk propagation path and generating a hierarchical response strategy;

[0041] Sending collaborative control instructions to the devices within the minimum necessary protection set based on the hierarchical response strategy.

[0042] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned intelligent mechanical safety protection method based on industrial Internet of Things devices.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the above-mentioned intelligent mechanical safety protection method based on industrial Internet of Things devices.

[0044] The above intelligent mechanical safety protection system based on industrial Internet of Things devices integrates the physical connection relationships of devices and the production process logic, constructs a composite topology map including a physical layer and a logical layer, and provides a multi-dimensional topology framework for risk propagation analysis; based on this map, it analyzes the propagation paths of risks along physical connection edges and logical dependency edges, generates a full-link sub-graph of risk propagation, and realizes the automatic identification of risk conduction paths in complex scenarios; calculates the minimum necessary protection set according to the risk propagation path and generates a hierarchical response strategy, accurately demarcates the protection scope and matches differential control strategies; sends collaborative control instructions to the devices within the protection set according to the hierarchical strategy, and forms a systematic protection response with multi-device linkage. The above system can automatically identify the risk propagation path in a complex industrial Internet of Things environment, and significantly improve the safety protection level of devices through dynamic, hierarchical protection strategies and collaborative control mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a schematic structural diagram of an intelligent mechanical safety protection system based on industrial Internet of Things devices provided by an embodiment of the present invention;

[0047] Figure 2 It is a schematic flowchart of an intelligent mechanical safety protection method based on industrial Internet of Things devices provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] In order to make the objectives, technical solutions and advantages of the present application more clear, the following further details the present application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] First, a brief introduction is made to the nouns involved in the embodiments of the present application.

[0050] A topology map is a graphical tool used to represent the relationships and structures among elements in a system. It abstractly depicts the connection relationships and interdependencies of various components in the system (such as devices, components, nodes, etc.) through the combination of nodes and edges. In fields such as industrial Internet of Things, network communication, and computer science, the topology map can clearly show the physical connections between devices, data transmission paths, and logical dependencies, helping technicians quickly understand the overall architecture and operating status of the system, and at the same time providing intuitive visual support for tasks such as fault troubleshooting, risk analysis, and system optimization.

[0051] A graph database is a database management system specifically designed for storing and querying graph-structured data. It uses nodes, edges, and attributes as the basic data model and can efficiently represent and process complex relationship networks. Compared with traditional relational databases, graph databases have significant advantages in processing highly correlated data, such as in scenarios like social network analysis, recommendation systems, knowledge graph construction, and path analysis of complex networks. It stores data through an intuitive graph structure, making the relationships between data clearer and the query efficiency higher, and can quickly respond to complex association query requirements, providing a powerful tool for solving complex data relationship problems.

[0052] Based on the above noun explanations, the implementation environment of an intelligent mechanical safety protection system based on industrial Internet of Things devices provided by an embodiment of this application is described. Schematically, this implementation environment includes: a storage device, a processor, and a terminal. Among them, the processor communicates with the storage device and the terminal through a network. The terminal is deployed with a data collector, which can be an intelligent sensor or a log collector; the storage device can be a distributed storage device or a disk array; the processor includes but is not limited to a central processing unit, a graphics processing unit, an artificial intelligence chip, etc., which is not limited here.

[0053] Combined with the above noun explanations and implementation environment, the application scenarios of an embodiment of this application are described. The intelligent mechanical safety protection system based on industrial Internet of Things devices provided in an embodiment of this application can be applied to the following scenarios including but not limited to:

[0054] In a highly automated automotive welding and assembly production line, robotic arms, conveyor belts, and quality inspection equipment are interconnected through the industrial Internet of Things. When an abnormal vibration is caused by tool wear in a certain robotic arm, this system can identify its physical connection relationship and process logic dependencies with adjacent stamping machines and quality inspection workstations through dynamic topology modeling, predict that the vibration may cause misalignment of stamping dies or distortion of quality inspection data, and then trigger the minimum protection set (only deactivate the faulty robotic arm and adjacent stamping machines), and synchronously adjust the detection logic of the downstream quality inspection workstation to avoid full-line shutdown and ensure production capacity stability.

[0055] In the working scenario of an intelligent warehousing and logistics center, in a warehouse where automated guided vehicles (AGVs) cooperate with stacker cranes, this system can construct a dynamic topology of devices through UWB positioning. When an AGV approaches the personnel operation area due to incorrect path planning, the risk analysis module, based on the physical connection edge (the distance between the AGV and the fence) and the logical dependency edge (the priority of the work order), immediately triggers the motor to stop, the sound and light alarm, and the standby AGV to take over the task, thus avoiding collisions and maintaining the logistics throughput efficiency.

[0056] In the scenario where the cooling system of a nuclear power plant is linked with the turbine unit, this system integrates the data of the sensor network and the industrial control system, and analyzes the influence path of abnormal pump vibration on the overall thermal cycle in real time. If the main pump bearing failure is detected, the protection module accurately isolates the faulty pump and starts the standby pump, and at the same time adjusts the turbine load logic to prevent the reactor from overheating and ensure the safety redundancy of key facilities.

[0057] Schematically, the intelligent mechanical safety protection system based on industrial Internet of Things devices provided in the embodiments of the present application can also be applied to other application scenarios. Only examples are given here, and the specific application scenarios are not limited.

[0058] In an exemplary embodiment, as Figure 1 shown, an intelligent mechanical safety protection system 10 based on industrial Internet of Things devices is provided. In this embodiment, taking the application of this system to the terminal in the foregoing implementation environment as an example, it can be understood that this system can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. An intelligent mechanical safety protection system 10 proposed in the embodiments of the present application includes a dynamic topology modeling module 11, a risk propagation analysis module 12, a dynamic protection generation module 13, and a collaborative trigger execution module 14:

[0059] The dynamic topology modeling module 11 is used to generate a composite topology map according to the physical connection relationship of devices and the production process logic. The composite topology map includes a physical layer and a logical layer.

[0060] Specifically, based on the physical connection relationship of industrial Internet of Things devices and the production process logic information, and then generating a composite topology map according to this information. This map includes a physical layer and a logical layer. Among them, the physical layer is used to represent the physical connection relationship between devices, such as connections through cables, mechanical transmissions, etc.; the logical layer represents the logical dependency relationship between devices, such as the sequence in the production process, data interaction, etc. By integrating the information of the physical layer and the logical layer, the dynamic topology modeling module 11 can provide a comprehensive and accurate network structure model for subsequent risk propagation analysis.

[0061] The risk propagation analysis module 12 is used to analyze the risk propagation paths along the physical connection edges and logical dependency edges based on the composite topology map, and generate a sub-graph of the full risk propagation link.

[0062] Specifically, through a graph traversal algorithm or other path analysis algorithms, identify the possible risk propagation paths from the source device to other devices, and generate a sub-graph of the full risk propagation link. This sub-graph clearly shows the complete risk propagation path, providing clear risk propagation path information for the dynamic protection generation module.

[0063] The dynamic protection generation module 13 is used to calculate the minimum necessary protection set based on the risk propagation path and generate a hierarchical response strategy.

[0064] Specifically, through an optimization algorithm, determine the set of devices that need to take protection measures on the risk propagation path, ensure the pertinence and effectiveness of the protection measures, and generate a hierarchical response strategy. For example, according to the severity of the risk and the importance of the device, formulate corresponding protection measures for different devices or device groups, such as emergency shutdown, adjustment of operating parameters, or issuance of an alarm.

[0065] The collaborative trigger execution module 14 is used to send collaborative control instructions to the devices within the minimum necessary protection set based on the hierarchical response strategy.

[0066] Specifically, according to the hierarchical response strategy formulated by the dynamic protection generation module 13, send collaborative control instructions to the devices within the minimum necessary protection set. This module ensures that multiple devices can execute protection measures collaboratively according to the predetermined strategy. For example, when a device fails, the collaborative trigger execution module 14 can send instructions to relevant devices simultaneously to take measures such as emergency shutdown, isolation, or adjustment of operating parameters to prevent the risk from spreading further.

[0067] The above intelligent mechanical safety protection system based on industrial Internet of Things devices integrates the physical connection relationship of devices and the production process logic, constructs a composite topology map including the physical layer and the logical layer, provides a multi-dimensional topology framework for risk propagation analysis; based on this map, analyzes the risk propagation paths along the physical connection edges and logical dependency edges, generates a sub-graph of the full risk propagation link, and realizes the automatic identification of risk conduction paths in complex scenarios; calculates the minimum necessary protection set based on the risk propagation path and generates a hierarchical response strategy, accurately demarcates the protection scope and matches differential control strategies; sends collaborative control instructions to the devices within the protection set according to the hierarchical strategy, forming a systematic protection response of multi-device linkage. The above system can automatically identify the risk propagation path in a complex industrial Internet of Things environment, and significantly improve the safety protection level of devices through dynamic and hierarchical protection strategies and collaborative control mechanisms.

[0068] In a possible embodiment, the dynamic topology modeling module 11 may include:

[0069] The physical topology construction unit 111 is used to obtain device coordinate data through the UWB positioning base station and generate a physical connection relationship diagram between devices based on the material transmission path.

[0070] Specifically, the device coordinate data can be collected in real time by the UWB positioning base stations deployed in the industrial field to generate a physical connection relationship diagram between devices. For example, through the UWB signal strength and time difference positioning technology, the spatial coordinates of devices such as robotic arms, conveyor belts, and machining centers can be obtained with millimeter-level accuracy; based on the preset material transmission paths (such as the driving trajectory of AGV carts and the linked areas of conveyor belts), the unit automatically identifies the physical connection relationships between devices using graph theory algorithms, connects adjacent device nodes with directed edges, and assigns edge weights (such as conveyor belt lengths and robotic arm cooperation distances). The finally generated physical connection relationship diagram is stored in the form of an adjacency matrix, dynamically reflecting the real-time physical interaction status between devices.

[0071] The logical topology analysis unit 112 is used to analyze the work order process data of the manufacturing execution system, extract the start sequence dependency relationships between devices, and construct a logical dependency graph.

[0072] Specifically, by docking with the work order database of the manufacturing execution system (MES), the production process logic is analyzed and a logical dependency graph is constructed. The unit reads the work order process data in the MES system (such as "Process A is executed by device X, and only after completion can process B of device Y be triggered"), extracts the start sequence dependency relationships between devices; further, Petri net modeling technology can be used to map the process execution sequence to nodes and directed edges in the logical dependency graph, where the nodes represent devices and the directed edges represent process trigger conditions (such as time constraints and data ready status). This unit also marks the priority weights of the logical edges (such as the priority coefficients of emergency work orders) to construct a multi-layer topology structure containing device logical dependency relationships.

[0073] The graph fusion unit 113 is used to align the nodes of the physical connection relationship diagram and the logical dependency graph to obtain a composite topology graph.

[0074] Specifically, by aligning the device nodes, this unit integrates the physical connection relationship and the logical dependency relationship into the same graph, obtaining a composite topology graph containing the physical layer and the logical layer, enabling the graph to comprehensively reflect the physical connections and logical dependency relationships between devices, providing an accurate and complete topological structure basis for subsequent risk propagation analysis and protection strategy generation.

[0075] In a possible embodiment, the graph fusion unit 113 may include:

[0076] The attribute annotation subunit 1131 is used to annotate the process attribute labels for the device nodes in the physical connection relationship graph based on the graph database, and generate a physical topology graph with process attributes.

[0077] Specifically, based on the graph database, each device node in the physical connection relationship graph is annotated with process attribute labels, and the labels can include information such as the type, function, and the process flow to which the device belongs. Through the powerful storage and query capabilities of the graph database, this subunit can efficiently add detailed process attribute labels to each device node, thereby generating a physical topology graph with process attributes, providing a rich device information basis for subsequent graph fusion, ensuring that each device node not only contains physical connection information but also has a process-level description. For example, in risk propagation analysis, according to the process type and function of the device, its role and impact in risk propagation can be more accurately evaluated, thereby improving the pertinence and effectiveness of safety protection measures.

[0078] The weight annotation subunit 1132 is used to annotate the start priority weights of the device nodes in the logical dependency graph based on the process sequence constraints, and generate a logical topology graph with priorities.

[0079] Specifically, by parsing the work order process data of the MES system, the temporal dependency relationship between processes (such as "Process A must start within 5 minutes after Process B is completed") is extracted, and the time constraint is converted into the priority weight value of the logical edge (such as weight = 1 / remaining time); for the parallel process scenario, the subunit uses a competitive resource scheduling algorithm to calculate the preemption priority of the device nodes (such as high-priority work order weight + 20%). The annotated logical topology graph is stored in the format of a weighted adjacency list, and the logical edge weights are used to represent the urgency of process execution and the resource competition state, enabling the logical dependency graph to more accurately reflect the logical relationship between devices. Especially in risk propagation analysis, according to the start priority weights of the devices, the propagation path and influence range of risks along the logical path can be more accurately predicted.

[0080] The similarity matching subunit 1133 is used to match the corresponding devices in the physical connection relationship graph and the logical dependency graph by using the node similarity algorithm, and generate a composite topology graph.

[0081] Specifically, the device coordinates, process tags of the physical topology map, and the process priority weights of the logical topology map can be extracted to construct a multi-dimensional feature vector. An improved cosine similarity algorithm is used to calculate the feature matching degree between physical nodes and logical nodes, and a threshold (such as similarity ≥ 0.85) is set to determine the same device. For dynamic scenarios (such as AGV position updates), the subunit monitors the physical topology changes in real time and triggers incremental similarity calculations to update the node mapping relationship. Further, the generated composite topology map is stored in the form of RDF triples. The physical layer records the device spatial relationship, the logical layer annotates the process execution priority, and two-way mapping of the two-layer data is realized through a unified device identifier.

[0082] In a possible embodiment, the risk propagation analysis module 12 includes:

[0083] Anomaly detection unit 121, configured to perform wavelet transform processing on the device vibration signal through an edge computing node, extract the fault characteristic frequency, and match it with a preset risk pattern library to generate an anomaly type identifier.

[0084] Specifically, the vibration signal of the device can be obtained in real time through the edge computing node deployed locally on the device for fault feature extraction and risk matching. The original vibration signal is subjected to wavelet transform processing and decomposed into time-frequency components of different frequency bands to extract the fault characteristic frequencies characterizing bearing wear, gear tooth breakage, and motor imbalance. The characteristic frequencies are matched with the preset risk pattern library (including typical fault spectrum templates), and the dynamic time warping (DTW) algorithm is used to calculate the matching degree score. If the score exceeds the threshold (such as ≥ 85%), an anomaly type identifier is generated to quickly identify the potential fault type of the device and provide initial risk source information for subsequent risk propagation path analysis.

[0085] Path deduction unit 122, configured to simulate the risk diffusion path along the physical connection edge and the logical dependency edge based on the composite topology map and the anomaly type identifier, and generate an initial propagation path set.

[0086] Specifically, based on the composite topology map and the anomaly type identifier, the risk diffusion path is simulated. The unit first maps the anomaly type identifier to the initial risk source node in the composite topology map, sets the physical connection edge and the logical dependency edge as the propagation channels, and uses the random walk algorithm to simulate the risk diffusion process. For example, starting from the initial node, the risk is transmitted along the physical edge with a probability of 0.7 (such as mechanical vibration spreading along the conveyor belt) and along the logical edge with a probability of 0.3 (such as process delay causing data errors in downstream devices). After 1000 Monte Carlo simulations, the frequency of each node being triggered is statistically calculated to generate an initial propagation path set including physical conduction paths, logical conduction paths, and their superimposed paths to comprehensively cover the possible propagation directions of the risk and provide rich path information for subsequent risk impact range assessment.

[0087] An impact assessment unit 123 is configured to calculate a probability distribution matrix of the risk impact range according to the process criticality weights of the devices in the initial propagation path set; based on the probability distribution matrix, filter the propagation paths with impact probabilities exceeding a preset threshold, and merge them to generate a sub-graph of the full risk propagation link.

[0088] Specifically, by quantitatively analyzing the impact range of the initial propagation path, a sub-graph of the full risk propagation link is generated. This unit combines the process criticality weights of the devices with the trigger frequencies of each node in the initial propagation path set to calculate the risk impact probability distribution matrix, and based on a preset probability threshold, filters high-risk paths and merges them to generate a sub-graph of the full risk propagation link. This sub-graph marks the risk level in the form of a topological sub-graph and eliminates the end-device paths with low-probability impacts to ensure that the protection decision focuses on the core risk link.

[0089] In a possible embodiment, the path deduction unit 122 may include:

[0090] An initial node mapping sub-unit 1221 is configured to map the detected abnormal type to an initial risk source node.

[0091] Specifically, it can parse the device unique code (such as device ID or RFID tag) in the abnormal type identifier, retrieve the matching node in the composite topology map. If the abnormal type involves multi-device association (such as a conveyor belt drive motor failure affecting the connected robotic arm), the sub-unit expands the mapping range according to the process attribute tags, and synchronously marks the adjacent device nodes with close physical connection and high process association degree as initial risk sources. The mapping result is stored in the form of a node list as the starting point for subsequent path search.

[0092] A breadth-first search sub-unit 1222 is configured to perform a breadth-first search along the physical connection edges in the physical connection relation graph to generate a set of physical propagation paths.

[0093] Specifically, it traverses the paths along the physical connection edges in the physical connection relation graph to generate a set of physical propagation paths. Starting from the initial risk source node, based on the adjacency list structure of the physical connection relation graph, it accesses the adjacent device nodes layer by layer and records the material transfer and energy interaction paths between the nodes. In each search iteration, it dynamically updates the weights of the physical edges (such as distance, connection strength), filters the valid connection edges with weights exceeding a preset threshold and adds them to the path set. After the traversal is completed, the set of physical propagation paths contains all the device links that may be affected by physical factors such as mechanical vibration and energy overload conduction risks.

[0094] A depth-first search sub-unit 1223 is configured to perform a reverse depth-first search along the logical dependency edges in the logical dependency graph to generate a set of logical conduction paths.

[0095] Specifically, a reverse deep traversal is performed along the logical dependency edges of the logical dependency graph to generate a set of logical conduction paths. Starting from the initial risk source node, the logical dependency edges are accessed in reverse process execution order to identify upstream process equipment nodes (such as the equipment in the previous link that caused the current process abnormality). In each deep search, the search direction is dynamically adjusted according to the priority weight of the logical edge (such as the urgency of the process), and high-weight dependency paths are tracked first. After the traversal is completed, the set of logical conduction paths covers equipment links that may cause cascading failures due to logical factors such as data errors and process delays.

[0096] The path fusion subunit 1224 is used to fuse the physical propagation path set and the logical conduction path set to obtain an initial propagation path set.

[0097] Specifically, the physical propagation path set and the logical conduction path set are integrated across dimensions to generate an initial propagation path set. A path similarity algorithm can be used to remove duplicate paths (such as the same device node is included in both the physical and logical paths), and path attributes are labeled according to the path type (pure physical conduction, pure logical conduction, mixed conduction). For mixed paths, the superposition influence coefficient of physical and logical conduction is calculated, and the final path set is generated by sorting them by coefficient size. Furthermore, the fused initial propagation path set is stored in a graph structure to support risk probability calculation and visualization, thereby improving path focus.

[0098] In a possible embodiment, the dynamic protection generation module 13 includes:

[0099] The key node screening unit 131 is used to calculate and sort the betweenness centrality of each node based on the risk propagation full-link subgraph to generate a node priority list.

[0100] Specifically, by traversing all the paths in the subgraph, the number of nodes passed by each path is counted; the hub role of each node in risk propagation is calculated according to the betweenness centrality formula. The higher the value, the more significant the effect of the node in blocking the risk. The nodes are sorted from high to low according to the betweenness centrality value, and isolated nodes and non-critical path nodes are eliminated to generate a priority list. The list marks the blocking efficiency weight of each node, which provides a decision-making basis for the subsequent optimization of the protection set.

[0101] The protection set optimization unit 132 is used to calculate the proportion of risk paths covered by each node based on the risk propagation full-link subgraph; use a greedy algorithm to select a set of nodes whose coverage exceeds a preset threshold, and filter the set with the smallest number of devices to determine it as the minimum necessary protection set.

[0102] Specifically, according to the priority list and the risk propagation path coverage requirements, a minimum necessary protection set is generated. The unit first calculates the proportion of risk paths covered by each node (the proportion of paths that can be eliminated after the node is blocked), and sets a coverage threshold. Exemplarily, a greedy algorithm is used to iteratively select the node with the highest current coverage to be added to the protection set, while updating the coverage status of the remaining paths until the threshold is met or all candidate nodes are traversed, screening out the node set with the least number of devices and meeting the coverage standard, generating a minimum necessary protection set, improving the efficiency and economy of the protection measures. By selecting key nodes to cover as many risk paths as possible while minimizing the number of devices that need to take protection measures, the protection cost is reduced.

[0103] The strategy classification unit 133 is used to classify the emergency stop, speed reduction, and area isolation three-level response strategies according to the risk diffusion speed and the equipment downtime loss cost.

[0104] Specifically, the three-level response strategies can be classified in combination with the risk diffusion speed and the equipment downtime loss cost. The unit matches the diffusion model according to the risk type (for example, the risk diffusion speed of mechanical collision is 5m / s, and the temperature anomaly is 0.5m / s). The risk with a diffusion speed exceeding the safety threshold is classified as a first-level response, triggering an emergency stop instruction; the risk with a medium diffusion speed and a high downtime loss is classified as a second-level response, triggering a speed reduction operation instruction; the risk with a low diffusion speed and local isolation is classified as a third-level response, triggering an area isolation instruction. The strategy rule library supports dynamic adjustment to adapt to the safety level requirements of different production scenarios.

[0105] In a possible embodiment, the collaborative trigger execution module 14 includes:

[0106] The instruction compilation unit 141 is used to convert the device IDs in the minimum necessary protection set into a standardized control instruction set supported by the target device.

[0107] Specifically, the device IDs in the minimum necessary protection set can be converted into a standardized control instruction set supported by the target device, parsing the communication protocol type corresponding to the device ID (such as OPC UA, Modbus-TCP), encapsulating the device operation instructions into standardized data packets according to the protocol specifications, including emergency stop instructions, speed reduction instructions, and isolation instructions; for heterogeneous devices (such as PLCs and robotic arms), the instruction format is unified through a protocol conversion middleware to ensure that the instruction set is compatible with the control interfaces of devices from different manufacturers; further, the converted instruction set embeds a timestamp and a digital signature to prevent network tampering or replay attacks.

[0108] The multi-channel distribution unit 142 is used to synchronously send the control instruction set through the main channel of the high-real-time communication network and the standby channel of the hardwired emergency stop loop.

[0109] Specifically, the control instruction set is synchronously sent through the main channel of the high-real-time communication network and the spare channel of the hard-wired emergency stop loop. When the main channel is working properly, the high-real-time communication network is used to quickly transmit control instructions. At the same time, the hard-wired emergency stop loop is used as a spare channel to ensure that control instructions can still be sent in a timely manner when the main channel fails or communication is abnormal, guaranteeing the safe shutdown of the equipment. The above technical solution improves the reliability and security of instruction issuance through a dual-channel redundancy mechanism.

[0110] The redundancy verification unit 143 is used to obtain the response instruction of the target device. When the response instruction is not obtained within the preset time, a forced shutdown signal is sent to the hard-wired loop.

[0111] Specifically, the response of the target device to the control instruction is monitored in real time. If the confirmation response of the device is not received within the specified time, the forced shutdown mechanism of the hard-wired loop is immediately triggered to ensure that the device can be safely shut down in the shortest time and prevent the risk from spreading further. The above technical solution further enhances the security and reliability of the system through a redundancy verification mechanism.

[0112] In summary, an intelligent mechanical safety protection system based on industrial Internet of Things devices provided by the embodiments of the present application integrates the physical connection relationship of devices and the production process logic, constructs a composite topology map including the physical layer and the logical layer, and provides a multi-dimensional data basis for risk propagation analysis; based on the composite topology map, the random walk algorithm is used to simulate the diffusion path of risks along the physical connection edges and logical dependence edges, and a full-link sub-graph with marked risk levels is generated by combining the process criticality weights, accurately depicting the conduction link in complex scenarios; through betweenness centrality evaluation and greedy algorithm optimization, the minimum necessary protection set covering the core risk path is screened from the full-link sub-graph, and the emergency stop, speed reduction, and isolation three-level response strategies are divided according to the diffusion speed and shutdown loss; through multi-channel instruction issuance and redundancy verification mechanisms, it is ensured that the protection strategy is accurately executed. The above technical solution breaks through the limitations of traditional single-point protection, and through physical-logical two-dimensional modeling, dynamic path deduction, and intelligent strategy generation, realizes a full-link closed loop from risk source identification, propagation path prediction to global collaborative protection, improves the accuracy and systematicness of equipment safety protection in the industrial Internet of Things environment, and effectively curbs the spread of chain risks.

[0113] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of the steps or stages in other steps or other steps.

[0114] Based on the same inventive concept, an embodiment of the present application also provides an intelligent mechanical safety protection method based on industrial Internet of Things devices for implementing the intelligent mechanical safety protection system based on industrial Internet of Things devices described above. The implementation solutions provided by this method to solve problems are similar to the implementation solutions recorded in the above system. Therefore, the specific limitations in one or more embodiments of the intelligent mechanical safety protection method based on industrial Internet of Things devices provided below can refer to the limitations on the intelligent mechanical safety protection system based on industrial Internet of Things devices in the above text, and will not be repeated here.

[0115] In an exemplary embodiment, as Figure 2 shown, an intelligent mechanical safety protection method based on industrial Internet of Things devices is provided, including:

[0116] Step 101, generate a composite topology map according to the device physical connection relationship and production process logic. The composite topology map includes a physical layer and a logical layer.

[0117] Step 102, based on the composite topology map, analyze the propagation path of risks along the physical connection edges and logical dependency edges, and generate a risk propagation full-link sub-graph.

[0118] Step 103, calculate the minimum necessary protection set based on the risk propagation path, and generate a hierarchical response strategy.

[0119] Step 104, based on the hierarchical response strategy, send collaborative control instructions to the devices in the minimum necessary protection set.

[0120] In a possible embodiment, generating a composite topology map according to the device physical connection relationship and production process logic includes:

[0121] Step 201, obtain device coordinate data through a UWB positioning base station, and generate a physical connection relationship map between devices based on the material transmission path.

[0122] Step 202: Parse the work order process data of the manufacturing execution system, extract the startup sequence dependency relationships between devices, and construct a logical dependency graph.

[0123] Step 203: Align the nodes of the physical connection relationship graph and the logical dependency graph to obtain a composite topology graph.

[0124] In a possible embodiment, aligning the nodes of the physical connection relationship graph and the logical dependency graph to obtain a composite topology graph includes:

[0125] Step 301: Label the device nodes in the physical connection relationship graph with process attribute tags based on the graph database to generate a physical topology graph with process attributes.

[0126] Step 302: Label the startup priority weights of the device nodes in the logical dependency graph based on the process timing constraints to generate a logical topology graph with priorities.

[0127] Step 303: Use the node similarity algorithm to match the corresponding devices in the physical connection relationship graph and the logical dependency graph to generate a composite topology graph.

[0128] In a possible embodiment, based on the composite topology graph, analyze the propagation paths of risks along the physical connection edges and logical dependency edges to generate a full-link subgraph of risk propagation, including:

[0129] Step 401: Perform wavelet transform processing on the device vibration signals through the edge computing node, extract the fault characteristic frequencies, and match them with the preset risk pattern library to generate an abnormal type identifier.

[0130] Step 402: Based on the composite topology graph and the abnormal type identifier, use the random walk algorithm to simulate the diffusion paths of risks along the physical connection edges and logical dependency edges to generate an initial set of propagation paths.

[0131] Step 403: Calculate the probability distribution matrix of the risk impact range according to the process criticality weights of the devices in the initial set of propagation paths; based on the probability distribution matrix, screen the propagation paths with the influence probability exceeding the preset threshold, and merge them to generate a full-link subgraph of risk propagation.

[0132] In a possible embodiment, based on the composite topology graph and the abnormal type identifier, use the random walk algorithm to simulate the diffusion paths of risks along the physical connection edges and logical dependency edges to generate an initial set of propagation paths, including:

[0133] Step 501: Map the detected abnormal type to the initial risk source node.

[0134] Step 502: Perform breadth-first search along the physical connection edges in the physical connection relationship graph to generate a set of physical propagation paths.

[0135] Step 503: Perform reverse depth-first search along the logical dependency edges in the logical dependency graph to generate a set of logical conduction paths.

[0136] Step 504: Merge the set of physical propagation paths and the set of logical conduction paths to obtain an initial set of propagation paths.

[0137] In a possible embodiment, calculate the minimum necessary protection set based on the risk propagation path and generate a hierarchical response strategy, including:

[0138] Step 601: Based on the risk propagation full-link subgraph, calculate the betweenness centrality of each node and sort them to generate a node priority list.

[0139] Step 602: Based on the risk propagation full-link subgraph, calculate the proportion of risk paths covered by each node; use the greedy algorithm to select the set of nodes with a coverage exceeding a preset threshold and filter out the set with the smallest number of devices to determine the minimum necessary protection set.

[0140] Step 603: Divide into three-level response strategies of emergency stop, speed reduction, and regional isolation according to the risk diffusion speed and the equipment shutdown loss cost.

[0141] In a possible embodiment, based on the hierarchical response strategy, send collaborative control instructions to the devices in the minimum necessary protection set, including:

[0142] Step 701: Convert the device IDs in the minimum necessary protection set into a standardized control instruction set supported by the target device.

[0143] Step 702: Synchronously send the control instruction set through the main channel of the high-real-time communication network and the standby channel of the hard-wired emergency stop loop.

[0144] Step 703: Obtain the response instruction of the target device. When the response instruction is not obtained within the preset time, send a forced shutdown signal to the hard-wired loop.

[0145] In an embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of an intelligent mechanical safety protection method based on industrial Internet of Things devices as described above are implemented.

[0146] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0147] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial descriptions of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution. A person of ordinary skill in the art can understand and implement it without creative work.

[0148] The above embodiments only represent several implementation manners of the embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the embodiments of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. An intelligent mechanical safety protection system based on industrial Internet of Things devices, characterized in that, The system includes a dynamic topology modeling module, a risk propagation analysis module, a dynamic protection generation module, and a collaborative trigger execution module: The dynamic topology modeling module is used to generate a composite topology map according to the physical connection relationship of devices and the production process logic, and the composite topology map includes a physical layer and a logical layer; The risk propagation analysis module is used to analyze the propagation path of risks along physical connection edges and logical dependency edges based on the composite topology map, and generate a risk propagation full-link sub-graph; The dynamic protection generation module is used to calculate the minimum necessary protection set based on the risk propagation path and generate a hierarchical response strategy; The collaborative trigger execution module is used to send collaborative control instructions to the devices within the minimum necessary protection set based on the hierarchical response strategy.

2. The system according to claim 1, wherein The dynamic topology modeling module includes: A physical topology construction unit, which is used to obtain device coordinate data through a UWB positioning base station and generate a physical connection relationship diagram between devices based on the material transmission path; A logical topology analysis unit, which is used to analyze the work order process data of the manufacturing execution system, extract the start sequence dependency relationship between devices, and construct a logical dependency diagram; A map fusion unit, which is used to align the nodes of the physical connection relationship diagram and the logical dependency diagram to obtain the composite topology map.

3. The system according to claim 2, wherein The map fusion unit includes: An attribute annotation sub-unit, which is used to annotate process attribute tags for device nodes in the physical connection relationship diagram based on a graph database to generate a physical topology map with process attributes; A weight annotation sub-unit, which is used to annotate the start priority weights of device nodes in the logical dependency diagram based on process time sequence constraints to generate a logical topology map with priorities; A similarity matching sub-unit, which is used to match the corresponding devices in the physical connection relationship diagram and the logical dependency diagram by using a node similarity algorithm to generate the composite topology map.

4. The system according to claim 2, wherein The risk propagation analysis module includes: An anomaly detection unit, which is used to perform wavelet transform processing on the device vibration signal through an edge computing node, extract the fault characteristic frequency, and match it with a preset risk mode library to generate an anomaly type identifier; A path deduction unit, which is used to simulate the diffusion path of risks along physical connection edges and logical dependency edges based on the composite topology map and the anomaly type identifier by using a random walk algorithm to generate an initial propagation path set; An impact assessment unit, which is used to calculate the probability distribution matrix of the risk impact range according to the process criticality weights of the devices in the initial propagation path set; based on the probability distribution matrix, filter out the propagation paths with an impact probability exceeding a preset threshold and merge them to generate the risk propagation full-link sub-graph.

5. The system according to claim 4, characterized in that, The path deduction unit includes: An initial node mapping sub-unit, which is used to map the detected anomaly type to an initial risk source node; A breadth-first search sub-unit, which is used to perform breadth-first search along the physical connection edges in the physical connection relationship diagram to generate a physical propagation path set; A depth-first search sub-unit, which is used to perform reverse depth-first search along the logical dependency edges in the logical dependency diagram to generate a logical conduction path set; A path fusion subunit, configured to fuse the physical propagation path set and the logical conduction path set to obtain the initial propagation path set.

6. The system according to claim 1, wherein The dynamic protection generation module includes: A key node screening unit, configured to calculate the betweenness centrality of each node based on the full-link subgraph of risk propagation, sort the nodes, and generate a node priority list. A protection set optimization unit, configured to calculate the proportion of risk paths covered by each node based on the full-link subgraph of risk propagation; select a set of nodes with a coverage exceeding a preset threshold using a greedy algorithm, and screen the set with the smallest number of devices to determine the minimum necessary protection set. A strategy classification unit, configured to divide the emergency stop, speed reduction, and area isolation three-level response strategies according to the risk diffusion speed and the equipment downtime loss cost.

7. The system according to claim 1, wherein The collaborative trigger execution module includes: An instruction compilation unit, configured to convert the device IDs of the minimum necessary protection set into a standardized control instruction set supported by the target device. A multi-channel transmission unit, configured to synchronously transmit the control instruction set through the main channel of the high-real-time communication network and the standby channel of the hard-wired emergency stop loop. A redundancy verification unit, configured to obtain the response instruction of the target device, and when the response instruction is not obtained within a preset time, send a forced stop signal to the hard-wired loop.

8. An intelligent mechanical safety protection method based on industrial Internet of Things devices, characterized in that, The method includes: Generating a composite topology map according to the device physical connection relationship and the production process logic, where the composite topology map includes a physical layer and a logical layer. Based on the composite topology map, analyzing the risk propagation paths along the physical connection edges and the logical dependency edges to generate a full-link subgraph of risk propagation. Calculating the minimum necessary protection set based on the risk propagation path and generating a hierarchical response strategy. Based on the hierarchical response strategy, sending a collaborative control instruction to the devices within the minimum necessary protection set.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the method described in claim 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the method described in claim 8 is implemented.

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