Mine multi-system intelligent linkage control method and system and storage medium

Abstract

The invention discloses a mine multi-system intelligent linkage control method and system and a storage medium, and relates to the field of mine control. According to cross-system linkage control of a unified rule engine, subsystem data interfaces of mine control ports are integrated, and a linkage control strategy is generated through a predefined logic model and a dynamic learning algorithm; constructing a mine intelligent linkage control knowledge graph; based on the out-degree, in-degree and edge relation strength of nodes in the mine intelligent linkage control knowledge graph, calculating the sensitivity weight of each node by adopting a graph structure importance evaluation algorithm, and determining the deployment position of an edge calculation node according to the sensitivity weight; a rule engine instruction is analyzed in real time, and the system efficiency and the emergency response speed of each node in the mine intelligent linkage control indication diagram are improved through a dynamic bandwidth allocation and resource optimization algorithm when local control is executed. The method is suitable for intelligent upgrading of a complex mine environment, and cloud dependence is reduced.

Description

Technical Field

[0001] The present invention relates to the field of mine control, and more particularly to a mine multi-system intelligent linkage control method, system and storage medium. Background Art

[0002] In modern mine operations, safe production and efficient operations are core goals. Mine operations involve multiple complex and interconnected systems, including ventilation, drainage, hoisting, and transportation. Traditionally, these systems have mostly operated independently, monitored and controlled manually. For example, ventilation systems adjust fan speed based on preset times or manual experience, while drainage systems activate pumps when water levels reach specific thresholds. These systems lack effective coordination mechanisms.

[0003] This decentralized control model has significant drawbacks. On the one hand, faced with the complex and ever-changing environment within a mine, such as sudden increases in gas concentration or a sharp increase in water inflow, manual responses are often delayed, making it difficult to quickly and accurately respond in a coordinated manner. This poses a significant threat to the safety of mine workers and the stable operation of production equipment. On the other hand, the independent operation of each system can easily lead to resource waste. For example, the ventilation system can over-ventilate in sparsely populated areas underground, and the drainage system can maintain high power operation even when the water level is low. This results in high energy consumption and makes it difficult to effectively control production costs.

[0004] With the rapid development of information technology, the intelligent transformation of mines has become an inevitable trend. Intelligent multi-system coordinated control technology for mines is emerging. This technology aims to break down information barriers between systems. Through real-time monitoring and intelligent algorithms, it enables efficient collaboration and precise control across these systems, improving mine safety and production efficiency while reducing operating costs to meet the growing demand for coal and increasingly stringent safety standards. Summary of the Invention

[0005] In view of this, the present invention provides a method, system and storage medium for intelligent linkage control of multiple systems in a mine, which is suitable for intelligent upgrades in complex mine environments, reduces cloud dependence, and solves the problems of poor real-time performance and delayed response to safety hazards caused by the isolated operation of traditional mine subsystems and strong cloud dependence.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A mine multi-system intelligent linkage control method includes the following steps:

[0008] Based on the cross-system linkage control of the unified rule engine, the subsystem data interface of the mine control port is integrated, and the linkage control strategy is generated through the predefined logic model and dynamic learning algorithm;

[0009] Construct a knowledge graph for intelligent linkage control of mines, with subsystem data interfaces as nodes and linkage control strategies as edges between nodes;

[0010] Based on the out-degree, in-degree, and edge relationship strength of the nodes in the mine intelligent linkage control knowledge graph, a graph structure importance evaluation algorithm is used to calculate the sensitivity weight of each node, and the edge computing node deployment location is determined based on the sensitivity weight;

[0011] The rules engine instructions are parsed in real time, and dynamic bandwidth allocation and resource optimization algorithms are used when executing localized control to improve the system efficiency and emergency response speed of each node in the mine intelligent linkage control instruction diagram.

[0012] Optionally, a unified rule engine is formulated based on the actual needs and safety standards of mine production. After receiving data from each subsystem, the unified rule engine will analyze and judge the data in real time according to various unified rules, and generate corresponding control instructions based on the judgment results; mine control ports include ventilation ports, water supply and drainage ports, power supply ports, transportation ports, and safety monitoring ports.

[0013] Optionally, the graph structure importance evaluation algorithm includes node centrality calculation and edge-node correlation calculation between nodes; the node centrality calculation is based on the unified rule engine to determine the central node for the node control frequency; the edge-node correlation calculation between nodes is based on the rationality calculation of the linkage control strategy, and the higher the rationality, the higher the correlation; combining the correlation and the central node, the sensitivity weight of the node is determined, and the nodes of the mine intelligent linkage control knowledge graph are graded, and the nodes are divided into different sensitive data levels based on a preset sensitivity level threshold, and the sensitive data level of each node is marked in the mine intelligent linkage control knowledge graph.

[0014] Optionally, edge computing nodes are deployed based on the sensitive data level of each node marked in the mine intelligent linkage control knowledge graph. The higher the sensitivity, the more edge computing nodes are deployed to improve data processing and analysis capabilities and reduce data transmission delays.

[0015] Optionally, the operating status of different equipment in the mine can be monitored in real time, and the operating status and health status of the equipment can be understood through analysis of the equipment status data; an equipment collaborative optimization model can be established, taking into account the relationships and influences between equipment, and the optimal combination of equipment operating parameters can be solved through optimization algorithms; according to changes in equipment status and adjustments to production needs, the operating parameters of the equipment can be adjusted in real time to achieve collaborative optimization between equipment.

[0016] Optionally, dynamic bandwidth allocation includes assigning different priorities to different types of services, giving priority to ensuring bandwidth requirements of high-priority services.

[0017] A mine multi-system intelligent linkage control system, comprising:

[0018] Control port and control strategy generation module: used for cross-system linkage control based on a unified rule engine, integrating the subsystem data interface of the mine control port, and generating linkage control strategies through predefined logic models and dynamic learning algorithms;

[0019] Mine intelligent linkage control knowledge graph construction module: used to construct the mine intelligent linkage control knowledge graph, with subsystem data interfaces as nodes and linkage control strategies as edges between nodes;

[0020] Edge computing node deployment module: This module is used to calculate the sensitivity weight of each node based on the out-degree, in-degree, and edge relationship strength of the nodes in the mine intelligent linkage control knowledge graph, using a graph structure importance evaluation algorithm, and determine the edge computing node deployment location based on the sensitivity weight;

[0021] Control collaborative optimization module: used to parse rule engine instructions in real time and improve the system efficiency and emergency response speed of each node in the mine intelligent linkage control instruction diagram through dynamic bandwidth allocation and resource optimization algorithms when executing localized control.

[0022] A computer storage medium stores a computer program, which, when executed by a processor, implements any one of the steps of a mine multi-system intelligent linkage control method.

[0023] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a mine multi-system intelligent linkage control method, system and storage medium, which have the following beneficial effects:

[0024] 1. Build a knowledge graph for intelligent linkage control in mines, using subsystem data interfaces as nodes and linkage control strategies as edges. This intuitive graph structure displays the relationships and interactions between subsystems. This helps managers and technicians quickly understand the overall system architecture and operating logic, facilitating system analysis and troubleshooting.

[0025] 2. Calculate the sensitivity weight of each node based on a graph structure importance assessment algorithm and determine the deployment location of edge computing nodes based on the sensitivity weight. This ensures that edge computing nodes are deployed in the most critical areas where real-time data processing is most needed. This reduces data transmission delays and improves the system's response speed and processing capabilities.

[0026] 3. Real-time analysis of rule engine instructions and, when executing localized control, dynamic bandwidth allocation and resource optimization algorithms ensure rapid communication and execution of instructions. When an emergency occurs in a mine, the system can respond quickly and implement appropriate control measures to minimize the impact and losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0028] Figure 1 Schematic diagram of the method flow of the present invention;

[0029] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0031] The embodiment of the present invention discloses a method for intelligent linkage control of multiple systems in a mine. Figure 1 As shown, the following steps are included:

[0032] Step 1: Based on the cross-system linkage control of the unified rule engine, the subsystem data interface of the mine control port is integrated, and the linkage control strategy is generated through the predefined logic model and dynamic learning algorithm;

[0033] Step 2: Build a knowledge graph for intelligent linkage control of mines, with subsystem data interfaces as nodes and linkage control strategies as edges between nodes;

[0034] Step 3: Based on the out-degree, in-degree, and edge relationship strength of the nodes in the mine intelligent linkage control knowledge graph, the graph structure importance evaluation algorithm is used to calculate the sensitivity weight of each node, and the edge computing node deployment location is determined based on the sensitivity weight;

[0035] Step 4: parse the rule engine instructions in real time, and use dynamic bandwidth allocation and resource optimization algorithms when executing localized control to improve the system efficiency and emergency response speed of each node in the mine intelligent linkage control instruction diagram.

[0036] Furthermore, in step one, a unified rule engine is formulated based on the actual needs and safety standards of mine production. After receiving data from each subsystem, the unified rule engine will analyze and judge the data in real time according to each unified rule, and generate corresponding control instructions based on the judgment results; the mine control ports include ventilation ports, water supply and drainage ports, power supply ports, transportation ports, and safety monitoring ports. The rule engine generates corresponding control instructions based on various preset rules, real-time data, and logical judgments of mine production. These instructions are quickly and stably transmitted to edge computing nodes deployed in key areas underground through network communication links (such as industrial Ethernet, wireless communication, etc.). During the transmission process, it is necessary to ensure the reliability and low latency of communication to ensure that the instructions can be delivered in a timely manner.

[0037] Upon receiving a command, an edge computing node immediately parses it. This parsing process involves identifying the command format, extracting the command content, and understanding its meaning. For example, if a command is to adjust the wind speed of a ventilation system, the edge computing node needs to clearly identify key information such as the specific value to be adjusted and the time range for the adjustment. To achieve fast and accurate parsing, edge computing nodes are typically equipped with specialized parsing modules and efficient algorithms.

[0038] Furthermore, in step three, the graph structure importance evaluation algorithm includes node centrality calculation and edge-node correlation calculation between nodes; the node centrality calculation is based on the unified rule engine to determine the central node for the node control frequency; the edge-node correlation calculation between nodes is based on the rationality calculation of the linkage control strategy, and the higher the rationality, the higher the correlation; combining the correlation and the central node, the sensitivity weight of the node is determined, and the nodes of the mine intelligent linkage control knowledge graph are graded, and the nodes are divided into different sensitive data levels based on the preset sensitivity level threshold, and the sensitive data level of each node is marked in the mine intelligent linkage control knowledge graph.

[0039] Furthermore, edge computing nodes are deployed based on the sensitive data level of each node marked in the mine intelligent linkage control knowledge graph. The higher the sensitivity, the more edge computing nodes are deployed to improve data processing and analysis capabilities and reduce data transmission delays.

[0040] Furthermore, in step four, the operating status of different equipment in the mine is monitored in real time, and the operating status and health status of the equipment are understood through analysis of the equipment status data; an equipment collaborative optimization model is established, considering the relationship and influence between equipment, and the optimal equipment operating parameter combination is solved through the optimization algorithm; according to the changes in equipment status and adjustments to production needs, the equipment operating parameters are adjusted in real time to achieve collaborative optimization between equipment.

[0041] Furthermore, in this embodiment, different services within the mine are divided into different categories based on their characteristics and requirements, such as security monitoring services, production control services, and video monitoring services. Different service categories have different requirements for bandwidth and real-time performance. For example, security monitoring services have higher real-time requirements, while video monitoring services have higher bandwidth requirements.

[0042] Define corresponding bandwidth demand indicators for each service category, such as minimum bandwidth, maximum bandwidth, average bandwidth, etc. Dynamically evaluate the current bandwidth demand based on the real-time operation of the service.

[0043] The above technical solution has the following beneficial effects:

[0044] Real-time network status monitoring: The algorithm monitors bandwidth usage, network latency, packet loss rate, and other parameters within the mine network in real time. By analyzing these parameters, we can understand the real-time status and performance of the network.

[0045] Demand-based bandwidth allocation: Dynamically adjust bandwidth allocation based on the real-time bandwidth needs of different applications and devices. For example, when a security surveillance system needs to transmit large amounts of high-definition video data, the algorithm will prioritize it and allocate more bandwidth. However, for applications with lower real-time requirements, such as uploading equipment maintenance data, bandwidth allocation can be appropriately reduced.

[0046] Optimize network resource utilization: Dynamic bandwidth allocation avoids bandwidth waste and congestion, improving network resource utilization efficiency. It also ensures that critical applications and devices have sufficient bandwidth support, ensuring timely data transmission and processing.

[0047] Furthermore, dynamic bandwidth allocation includes assigning different priorities to different types of services, giving priority to ensuring the bandwidth requirements of high-priority services.

[0048] and Figure 1 Corresponding to the method shown, the present invention also discloses a mine multi-system intelligent linkage control system for Figure 1 The implementation of the method, the specific structure is as follows Figure 2 Shown, including:

[0049] Control port and control strategy generation module: used for cross-system linkage control based on a unified rule engine, integrating the subsystem data interface of the mine control port, and generating linkage control strategies through predefined logic models and dynamic learning algorithms;

[0050] Mine intelligent linkage control knowledge graph construction module: used to construct the mine intelligent linkage control knowledge graph, with subsystem data interfaces as nodes and linkage control strategies as edges between nodes;

[0051] Edge computing node deployment module: This module is used to calculate the sensitivity weight of each node based on the out-degree, in-degree, and edge relationship strength of the nodes in the mine intelligent linkage control knowledge graph, using a graph structure importance evaluation algorithm, and determine the edge computing node deployment location based on the sensitivity weight;

[0052] Control collaborative optimization module: used to parse rule engine instructions in real time and improve the system efficiency and emergency response speed of each node in the mine intelligent linkage control instruction diagram through dynamic bandwidth allocation and resource optimization algorithms when executing localized control.

[0053] Finally, this embodiment further discloses a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods for intelligent linkage control of multiple systems in a mine are implemented.

[0054] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0055] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A mine multi-system intelligent linkage control method, characterized in that: The following steps are involved: Based on the cross-system linkage control of the unified rule engine, the subsystem data interface of the mine control port is integrated, and the linkage control strategy is generated through the predefined logic model and dynamic learning algorithm; Construct a knowledge graph for intelligent linkage control of mines, with subsystem data interfaces as nodes and linkage control strategies as edges between nodes; Based on the out-degree, in-degree, and edge relationship strength of the nodes in the mine intelligent linkage control knowledge graph, a graph structure importance evaluation algorithm is used to calculate the sensitivity weight of each node, and the edge computing node deployment location is determined based on the sensitivity weight; The rules engine instructions are parsed in real time, and dynamic bandwidth allocation and resource optimization algorithms are used when executing localized control to improve the system efficiency and emergency response speed of each node in the mine intelligent linkage control instruction diagram.

2. A mine multi-system intelligent linkage control method according to claim 1, characterized in that: The unified rule engine is developed based on the actual needs and safety standards of mine production. After receiving data from each subsystem, the unified rule engine will analyze and judge the data in real time according to various unified rules, and generate corresponding control instructions based on the judgment results; mine control ports include ventilation ports, water supply and drainage ports, power supply ports, transportation ports, and safety monitoring ports.

3. A mine multi-system intelligent linkage control method according to claim 1, characterized in that: The graph structure importance evaluation algorithm includes the calculation of node centrality and the calculation of edge-node correlation between nodes; the node centrality calculation is based on the unified rule engine to determine the central node for the node control frequency; the edge-node correlation calculation is based on the rationality calculation of the linkage control strategy, and the higher the rationality, the higher the correlation; combining the correlation and the central node, the sensitivity weight of the node is determined, and the nodes of the mine intelligent linkage control knowledge graph are graded, and the nodes are divided into different sensitive data levels based on the preset sensitivity level threshold, and the sensitive data level of each node is marked in the mine intelligent linkage control knowledge graph.

4. A mine multi-system intelligent linkage control method according to claim 3, characterized in that: Edge computing nodes are deployed based on the sensitive data level of each node marked in the mine intelligent linkage control knowledge graph. The higher the sensitivity, the more edge computing nodes are deployed to improve data processing and analysis capabilities and reduce data transmission delays.

5. A mine multi-system intelligent linkage control method according to claim 1, characterized in that: Monitor the operating status of different equipment in the mine in real time, and understand the operating status and health status of the equipment through analysis of equipment status data; establish an equipment collaborative optimization model, consider the relationship and influence between equipment, and solve the optimal equipment operating parameter combination through optimization algorithm; adjust the equipment operating parameters in real time according to changes in equipment status and adjustments in production needs to achieve collaborative optimization between equipment.

6. A mine multi-system intelligent linkage control method according to claim 1, characterized in that: Dynamic bandwidth allocation includes assigning different priorities to different types of services, giving priority to ensuring the bandwidth requirements of high-priority services.

7. A mine multi-system intelligent linkage control system, characterized in that: include: Control port and control strategy generation module: used for cross-system linkage control based on a unified rule engine, integrating the subsystem data interface of the mine control port, and generating linkage control strategies through predefined logic models and dynamic learning algorithms; Mine intelligent linkage control knowledge graph construction module: used to construct the mine intelligent linkage control knowledge graph, with subsystem data interfaces as nodes and linkage control strategies as edges between nodes; Edge computing node deployment module: This module is used to calculate the sensitivity weight of each node based on the out-degree, in-degree, and edge relationship strength of the nodes in the mine intelligent linkage control knowledge graph, using a graph structure importance evaluation algorithm, and determine the edge computing node deployment location based on the sensitivity weight; Control collaborative optimization module: used to parse rule engine instructions in real time and improve the system efficiency and emergency response speed of each node in the mine intelligent linkage control instruction diagram through dynamic bandwidth allocation and resource optimization algorithms when executing localized control.

8. A computer storage medium, characterized in that The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of a mine multi-system intelligent linkage control method as described in any one of claims 1 to 6.

Images