Industrial management system with industrial configuration picture presentation

Abstract

The invention relates to the technical field of mine industry management, in particular to an industrial management system with an industrial configuration picture presentation function, which comprises a heterogeneous data acquisition module which is composed of a plurality of sensors and realizes data fusion through a multi-protocol converter; the three-dimensional dynamic configuration engine covers the geological model construction unit, the real-time data mapping unit and the visual rendering unit; the intelligent scheduling decision module is provided with a transportation path optimization sub-module, a resource allocation sub-module and an emergency response sub-module; and a man-machine interaction terminal. An edge computing node of the heterogeneous data acquisition module adopts an improved LSTM missing data interpolation model; the transportation path optimization sub-module uses an improved ant colony algorithm, the resource allocation sub-module uses an improved multi-objective optimization model, and the emergency response sub-module uses an improved rule reasoning algorithm. The problems of data fusion, path optimization, resource allocation, emergency response, visualization and the like in mine industry management are effectively solved, and the efficiency, the safety and the intelligent level of mine management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mining industry management, in particular to an industrial management system with industrial configuration screen presentation. Background Art

[0002] In the field of mining industry management, traditional management methods face many difficulties. With the continuous increase in the scale and complexity of mining, the demand for refined management of the production process is becoming increasingly urgent.

[0003] On the one hand, there are serious deficiencies in data collection. Different types of sensors and monitoring equipment often operate independently, lacking a unified data fusion mechanism. This makes data scattered and in different formats, making it difficult to conduct comprehensive analysis and effective use, and unable to provide comprehensive and accurate information support for management decisions.

[0004] On the other hand, there are outstanding problems in production scheduling and safety management. Transportation route planning usually relies on experience and judgment, which makes it difficult to achieve optimal configuration, resulting in low transportation efficiency and serious energy waste. Resource allocation lacks scientific model guidance, and it is impossible to achieve a reasonable balance between equipment operation and maintenance costs and production progress, which can easily lead to excessive use or idleness of equipment, affecting production efficiency. The emergency response mechanism is not perfect. In the face of sudden abnormal working conditions, it is difficult to quickly and accurately assess risks and take effective countermeasures, which brings huge hidden dangers to mine safety production.

[0005] In addition, due to the lack of visual management methods, it is difficult for managers to intuitively understand the overall operating status of the mine and the real-time status of the equipment, which is not conducive to timely discovering problems and making scientific decisions. Therefore, in response to the above problems, an industrial management system with industrial configuration screen presentation is proposed. Summary of the invention

[0006] The object of the present invention is to provide an industrial management system with industrial configuration screen presentation to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An industrial management system with industrial configuration screen presentation, comprising:

[0009] Heterogeneous data acquisition module: It consists of vibration sensors, gas concentration sensors, equipment status monitoring units and personnel positioning terminals deployed on the mine working surface, and realizes data fusion with PLC controllers and DCS systems through multi-protocol converters;

[0010] 3D dynamic configuration engine, including:

[0011] Geological model construction unit: Establish a three-dimensional mine model including rock structure, tunnel distribution and equipment layout based on BIM technology;

[0012] Real-time data mapping unit: establishes dynamic binding relationship between sensor data and 3D model elements;

[0013] Visual rendering unit: adopts LOD dynamic grading technology to achieve smooth rendering of large-scale scenes;

[0014] Intelligent scheduling decision module: with:

[0015] Transportation route optimization submodule: Generate the optimal material transportation route based on the improved ant colony algorithm;

[0016] The resource allocation submodule combines equipment operation status data with production plan requirements to establish a multi-objective optimization model;

[0017] Emergency response submodule: configure the abnormal operating mode library and integrate safety regulations constraints;

[0018] Prediction unit: Uses a spatiotemporal graph convolutional network model to predict the operating status of equipment, which is used to detect potential failure risks in advance and assist in optimizing production scheduling decisions;

[0019] Human-computer interaction terminal: Equipped with an industrial-grade display screen with multi-touch function, it supports dynamic annotation of configuration screens and emergency plan simulation operations.

[0020] As a preferred solution, the edge computing node of the heterogeneous data acquisition module adopts an improved LSTM missing data interpolation model, and its calculation process satisfies: ,in, express The interpolated data at time, Represents the dynamically adjusted LSTM unit weight, which is It will be adjusted according to the model's adaptive mechanism to reflect the different LSTM units in The contribution of each moment to the interpolated data, For the The historical hidden states of LSTM units, is the bias term, is the number of LSTM units;

[0021] The dynamically adjusted LSTM unit weight calculation adopts a two-stage attention mechanism: ,in, Query vector for the current moment; For the The key vector of the sensor, representing the The characteristic information of each sensor at the current moment; is the historical data value vector, which contains the valuable information contained in the historical data for the current interpolation task; It is a dimension scaling factor, which is used to scale the matrix operation results in the attention calculation process.

[0022] As a preferred solution, the transportation route optimization submodule adopts an improved ant colony algorithm, and the route selection probability formula is: ,in, Indicates at time , No. Ant slave node To Node The probability of For path pheromone concentration; , Represents a slave node To Node The heuristic function is used to measure the distance from the node To Node direct attraction; is the energy consumption factor, used to measure the path Energy consumption, is a constant coefficient used to adjust the impact of energy consumption on path selection. is the cumulative change in path slope, It is the maximum value of the accumulated path slope change in the current scenario; Indicates at time , from the node To Node pheromone concentration along the path; is the heuristic function, defined as , Represents a slave node To Node distance; is the energy consumption factor introduced by the improved ant colony algorithm, and its calculation formula is , For slave nodes To Node Cumulative change in path slope; , , are the adjustment parameters of pheromone concentration, heuristic function and energy consumption factor; The cumulative change in path slope, which is the change from the starting point along the path The accumulated value of the slope change during the process of reaching the end point.

[0023] As a preferred solution, the resource allocation submodule adopts an improved multi-objective optimization model: ; ;

[0024] in, For the The operation and maintenance cost of the equipment refers to the maintenance and operation of Costs required for such equipment; It is the real-time equipment health, reflected in Moment The operating health of the equipment; For the The production process is time-consuming, that is, Moment The time required to complete each production step; is the fuzzy membership function, which is used to describe Moment The production link is The membership degree of a fuzzy constraint condition, its value is between 0 and 1; is the decision variable, representing Moment about The decision value of each production link; For the The minimum allowed value of a fuzzy constraint.

[0025] As a preferred solution, the prediction unit adopts a spatiotemporal graph convolutional network model, and the node state update formula is: ,in, Indicates The updated state matrix of all nodes in the spatiotemporal graph convolutional network; is the activation function; For the The input feature matrix of all nodes in the layer network; For the Layer network and The learnable parameter matrix corresponding to the Chebyshev polynomial basis of order; It is the normalized graph Laplacian matrix, which is used to describe the connection relationship and topological characteristics between nodes in the graph structure; is the Chebyshev polynomial basis, which is used to perform polynomial expansion on the graph Laplacian matrix in graph convolution operations; It is the historical state memory gating parameter, which is used to control the influence of historical state information on node state update, and its value is between 0 and 1; The device topology relationship history feature includes the device topology information at the past moment and the related status features; is the highest order of the Chebyshev polynomial basis, Is an index variable for summation, ranging from 0 to .

[0026] As a preferred solution, the emergency response submodule adopts an improved rule reasoning algorithm: ,in, The weight of gas / dust / water inflow is used to measure the relative importance of gas, dust and water inflow in the calculation of emergency response warning level. It is a nonlinear normalization function used to convert the gas / dust / water inflow monitoring value Perform nonlinear transformation and normalize to the set range; is the equipment failure correlation function, used to describe the equipment failure parameters The relationship with the emergency response warning level; is the correction factor for geological conditions; is the threshold value.

[0027] It can be seen from the technical solution provided by the present invention that the present invention provides an industrial management system with industrial configuration screen presentation, which has the following beneficial effects:

[0028] Data collection and processing advantages: The heterogeneous data collection module realizes data fusion of multiple sensors and systems through multi-protocol converters, and can comprehensively collect information such as vibration, gas concentration, equipment status and personnel location of the mine working surface; the improved LSTM missing data interpolation model of its edge computing node uses a two-stage attention mechanism to dynamically adjust weights, effectively handles data missing problems, improves data integrity and reliability, and provides a more accurate data basis for subsequent analysis and decision-making;

[0029] Path optimization and resource allocation efficiency: The transportation path optimization submodule adopts an improved ant colony algorithm, which comprehensively considers factors such as pheromone concentration, heuristic function and energy consumption factor, and can plan the optimal path for material transportation, reduce detours and energy consumption, improve transportation efficiency and reduce costs; the improved multi-objective optimization model of the resource allocation submodule balances the equipment operation and maintenance costs and the time consumption of the production links, while considering fuzzy constraints, realizing the reasonable allocation of resources, ensuring the coordination and efficient operation of the production process, and improving the overall production efficiency;

[0030] Emergency response and safety assurance capabilities: The improved rule reasoning algorithm of the emergency response submodule, combined with the gas / dust / water inflow weights, nonlinear normalization function, equipment failure correlation function and geological condition correction coefficient, can quickly and accurately assess risks and issue early warnings in a timely manner; the configured abnormal operating mode library and integrated safety regulations ensure that scientific and reasonable emergency decision-making plans can be quickly generated in the event of an emergency, effectively reducing accident losses and enhancing the safety of mine production;

[0031] Visualization and interactive convenience: The 3D dynamic configuration engine uses BIM technology to build geological models, and through real-time data mapping and LOD dynamic hierarchical rendering, it realizes smooth and realistic display of large-scale mining scenes; the industrial-grade display screen and multi-touch function of the human-computer interaction terminal support dynamic annotation and plan deduction operations, which facilitates users to intuitively obtain information and perform efficient operations, improving the convenience of management and the timeliness of decision-making;

[0032] Equipment operation prediction and maintenance foresight: The spatiotemporal graph convolutional network model of the prediction unit can detect potential failure risks in advance through spatiotemporal joint analysis of equipment operation data, provide accurate prediction and decision support for equipment maintenance and production scheduling, change the traditional maintenance model, realize preventive maintenance, extend equipment service life, reduce maintenance costs, and ensure production continuity and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The present invention is a schematic diagram of the overall structure of an industrial management system with industrial configuration screen presentation. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0036] like Figure 1 As shown, an embodiment of the present invention provides an industrial management system with industrial configuration screen presentation, including a heterogeneous data acquisition module, a three-dimensional dynamic configuration engine, an intelligent scheduling decision module and a human-computer interaction terminal.

[0037] In this embodiment, the heterogeneous data acquisition module is composed of vibration sensors, gas concentration sensors, equipment status monitoring units and personnel positioning terminals deployed on the mine working surface, and realizes data fusion with the PLC controller and DCS system through a multi-protocol converter;

[0038] Furthermore, the heterogeneous data acquisition module is an important component of an industrial management system with an industrial configuration screen presentation, and plays a key role in fields such as mining industrial management; the following is a detailed description of it:

[0039] Composition structure: It consists of vibration sensors, gas concentration sensors, equipment status monitoring units and personnel positioning terminals deployed on the mine operation surface; these different types of sensors and monitoring units can collect various types of data in the mine operation process from multiple dimensions, providing a rich source of information for subsequent analysis and management; for example, vibration sensors can monitor the vibration of equipment to help determine whether the equipment is operating normally; gas concentration sensors can detect the concentration of harmful gases in the mine to ensure operation safety; equipment status monitoring units can obtain various operating parameters of equipment; and personnel positioning terminals can grasp the location information of personnel in the mine in real time;

[0040] Data fusion method: Data fusion with PLC controller and DCS system is realized through multi-protocol converter. In the mining industry environment, there are many different devices and systems, which may use different communication protocols. The multi-protocol converter is like a translator, which can convert data from different sensors and monitoring units into a unified format, so that it can interact and integrate effectively with PLC controller and DCS system. This can break the data island, realize the centralized management and comprehensive utilization of data, and improve the efficiency and accuracy of mining industry management.

[0041] Missing data interpolation model of edge computing nodes: Its edge computing nodes use an improved LSTM missing data interpolation model; during the data collection process, data may be missing due to various reasons; this model can interpolate missing data, and its calculation process satisfies the formula ,in, express The interpolated data at time, Represents the dynamically adjusted LSTM unit weight, which is It will be adjusted according to the model's adaptive mechanism to reflect the different LSTM units in The contribution of each moment to the interpolated data, For the The historical hidden states of LSTM units, is the bias term, is the number of LSTM units;

[0042] The dynamically adjusted LSTM unit weight calculation adopts a two-stage attention mechanism: ,in, Query vector for the current moment; For the The key vector of the sensor, representing the The characteristic information of each sensor at the current moment; is the historical data value vector, which contains the valuable information contained in the historical data for the current interpolation task; It is a dimension scaling factor, which is used to scale the dimension of the matrix operation results in the attention calculation process; in this way, missing data can be interpolated more accurately, the integrity and reliability of the data can be improved, and stronger support can be provided for subsequent analysis and decision-making.

[0043] In this embodiment, the three-dimensional dynamic configuration engine includes a geological model construction unit, a real-time data mapping unit and a visual rendering unit;

[0044] Among them, the geological model construction unit builds a three-dimensional mine model including rock structure, tunnel distribution and equipment layout based on BIM technology; further:

[0045] The 3D dynamic configuration engine is the core visualization component of the industrial management system with industrial configuration screen presentation. It integrates a variety of cutting-edge technologies to create a highly realistic, dynamically interactive 3D configuration environment for mining industrial management, achieving efficient integration and intuitive presentation of geological information, equipment status and production data, and effectively improving the refinement of mine management and the scientific nature of decision-making; specifically:

[0046] 1. Core technology principle:

[0047] BIM geological modeling technology: Constructing a mine geological model based on building information modeling (BIM) technology; creating a three-dimensional model containing rich geological details by digitally processing and modeling a large amount of geological exploration data, such as rock layer structure, geological faults, ore body distribution and other information; using the parametric characteristics of BIM technology to achieve dynamic updating and precise adjustment of the model, ensuring that the geological model is closely aligned with the actual mine geological conditions; for example, after new geological exploration data is obtained, the rock layer change information can be quickly updated in the model to provide an accurate basis for subsequent mining planning;

[0048] Real-time data dynamic binding technology: With the help of data interfaces and transmission protocols, a real-time dynamic binding relationship is established between the sensor data obtained by the heterogeneous data acquisition module and the three-dimensional model elements; a data-driven architecture design is adopted to ensure that the equipment, lanes and other elements in the model can dynamically change their appearance, color or display corresponding warning signs according to the real-time data transmitted by the sensor, such as equipment vibration frequency, gas concentration value, personnel location information, etc.; for example, when the gas concentration in a certain area exceeds the standard, the model part bound to the area will automatically change color and display the concentration value, intuitively reminding managers to pay attention to safety hazards;

[0049] LOD dynamic grading rendering technology: Using the level of detail (LOD) dynamic grading rendering technology, the rendering accuracy of the model is automatically adjusted according to the observer's perspective, distance and scene complexity; when browsing in a distant or large-scale scene, a low-precision model is used for fast rendering to ensure the smoothness of the picture; when the observer focuses on a specific area or equipment, it automatically switches to a high-precision model to display detailed texture, structure and other information; for example, when viewing the entire mine, the distant mountains and large mining areas use simplified models, and when zooming in to view a key equipment or tunnel, the fine structure of the equipment, the support details of the tunnel and other high-precision models can be presented, ensuring visual effects while optimizing system performance;

[0050] 2. System architecture and components:

[0051] Model building components: including geological data parsing module, model creation module and model optimization module; the geological data parsing module is responsible for reading and parsing various geological exploration data formats and converting them into data structures that can be recognized by BIM models; the model creation module uses professional modeling software tools to build an initial three-dimensional mine model based on the parsed data, and sets the basic attributes and structural relationships of the model; the model optimization module performs optimization processing such as geometric simplification and texture compression on the created model to improve the rendering efficiency and display quality of the model;

[0052] Data processing components: including data receiving module, data conversion module and data updating module; the data receiving module receives sensor data from heterogeneous data acquisition modules through network communication protocols, and performs preliminary data verification and caching; the data conversion module converts the received data of different types and formats into a unified data standard so as to bind it with the 3D model; the data updating module updates the converted data to the corresponding elements in the 3D model in real time according to the set time interval or event trigger mechanism, so as to ensure the timeliness of the model display data;

[0053] Rendering display component: It consists of a graphics rendering engine, a view management module and an interactive processing module. The graphics rendering engine uses advanced GPU acceleration technology, such as OpenGL or DirectX, to efficiently render the 3D model, achieving realistic visual effects such as light and shadow effects and material texture. The view management module is responsible for managing the user's perspective change and zoom operations, and provides a variety of preset view modes, such as plane view, perspective view, panoramic view, etc., to facilitate users to observe the mine scene from different angles. The interactive processing module processes the user's input operations such as mouse, keyboard and touch screen, and realizes interactive functions such as model rotation, translation, click query, etc., to enhance the interactivity between the user and the 3D configuration screen.

[0054] 3. Strengthening management functions:

[0055] Accurate decision support: Through the visualization of the 3D dynamic configuration engine, managers can intuitively understand the overall layout of the mine, the operating status of equipment and changes in geological conditions; when formulating production plans, they can reasonably arrange production tasks according to the equipment distribution and real-time status to avoid equipment overload or idleness; optimize the mining path according to the ore body occurrence in the geological model to improve resource recovery rate; for example, when planning a new mining area, combine the geological model with equipment location information to select the optimal mining starting point and transportation route to reduce mining costs and transportation energy consumption;

[0056] Efficient troubleshooting: When equipment fails or has safety hazards, managers can quickly locate the faulty equipment in the mine based on real-time data binding and 3D visualization, and quickly analyze the cause of the fault by viewing the surrounding environment information and the status of related equipment. For example, if a certain equipment failure triggers a chain reaction that causes abnormal surrounding gas concentration, managers can clearly view the faulty equipment and its impact range in the 3D configuration screen, and take effective countermeasures in a timely manner to reduce downtime and safety accident risks.

[0057] Improved safety management: Use 3D models to visualize the distribution of dangerous areas and safety facilities in mines; provide employees with immersive virtual scenes during safety training to enhance safety awareness and emergency response capabilities; in daily safety inspections, ensure timely discovery of safety hazards and follow-up of rectification through real-time data monitoring and 3D scene linkage; for example, 3D modeling and real-time data monitoring of the mine's ventilation system. Once poor ventilation leads to gas accumulation in a certain area, the system immediately issues an alarm in the 3D picture and prompts relevant safety operating procedures to ensure mine production safety;

[0058] The 3D dynamic configuration engine, with its advanced technology, reasonable architecture and powerful functions, has injected powerful visualization and decision-making support capabilities into the mining industrial management system, becoming a key force in promoting intelligent management of mines, and playing an indispensable and important role in ensuring safe production in mines, improving production efficiency and resource utilization efficiency.

[0059] Among them, the real-time data mapping unit establishes a dynamic binding relationship between the sensor data and the three-dimensional model elements;

[0060] Furthermore, the real-time data mapping unit is a key link in realizing the deep integration of data and three-dimensional models in industrial management systems with industrial configuration screen presentation. It accurately associates the massive real-time data collected by sensors with the corresponding elements of the three-dimensional mine model through efficient data processing and intelligent mapping algorithms, providing instant, accurate and intuitive information display and analysis basis for mine management, greatly improving the timeliness and scientificity of management decisions; specifically:

[0061] 1. Core technology principle:

[0062] Data acquisition and transmission technology: Receive various sensor data from heterogeneous data acquisition modules, including equipment vibration amplitude and frequency data from vibration sensors, harmful gas content data from gas concentration sensors, operating parameter data from equipment status monitoring units, and location coordinate data from personnel positioning terminals; use high-speed data transmission protocols, such as industrial Ethernet and wireless sensor network (WSN) protocols, to ensure that data can be stably and quickly transmitted to real-time data mapping units, ensure the timeliness and integrity of data, and avoid data loss or delays that affect subsequent mapping operations;

[0063] Data parsing and standardization technology: Parse and process the received data in various formats and types, and convert them into a unified data structure and standard format. For example, for monitoring data with specific coding rules generated by equipment from different manufacturers, a special parsing program is written to extract key data values ​​and normalize them according to a pre-set standard data template, so that data from different sources can be processed and analyzed under the same data framework, laying the foundation for subsequent accurate mapping with 3D model elements.

[0064] Intelligent mapping algorithm: Adopt an intelligent mapping algorithm based on the combination of model-driven and data-driven. In terms of model-driven, a mapping relationship rule base between data and model objects is established based on the structure and element definition of the three-dimensional mine model. For example, according to the unique identifier of the equipment in the model, the corresponding equipment operation data is accurately associated with the equipment model. In terms of data-driven, cluster analysis, association rule mining and other algorithms in machine learning are used to automatically identify the potential connection between data features and model elements. For example, through the study of historical data, it is found that the changes in gas concentration in certain specific areas are closely related to the operating status of nearby ventilation equipment, so as to automatically establish this dynamic mapping relationship and realize adaptive matching and intelligent mapping between data and models.

[0065] 2. System architecture and components:

[0066] Data receiving and caching component: responsible for establishing a stable communication connection with heterogeneous data acquisition modules, receiving real-time data and temporarily storing it in the cache area; using advanced cache management strategies, such as cache replacement algorithms based on data priority and timeliness, to ensure that important and latest data can always remain available in the cache, providing fast data access support for subsequent data processing and mapping operations, and reducing data reading delays;

[0067] Data processing engine components: including data parsing module, standardization module and mapping rule management module; the data parsing module uses a special parser to parse different types of data; the standardization module converts and normalizes the parsed data according to unified standards; the mapping rule management module is responsible for maintaining and updating the mapping relationship rule library between data and 3D model elements to ensure the accuracy and timeliness of mapping, and provides a visual mapping rule editing interface to facilitate managers to adjust and optimize according to actual needs;

[0068] Data mapping and update component: Based on the intelligent mapping algorithm and mapping rules, the processed data is accurately mapped to the corresponding elements of the 3D mine model, and the display status of the model is updated in real time; a mechanism combining event-driven and timed updates is adopted to immediately trigger the mapping update operation when new data is received or the data changes significantly; at the same time, the model data is fully refreshed at a certain time interval to ensure that the data displayed by the model is always highly consistent with the actual situation, providing users with the latest mine operation information;

[0069] 3. Functional advantages:

[0070] Real-time dynamic visualization: By accurately mapping real-time data onto the 3D model, dynamic visualization of the mine production process is achieved; managers can intuitively see the changes in the operating status of the equipment, the real-time fluctuations in gas concentration, and the movement trajectory of personnel in the mine; for example, in the 3D model, the running equipment will be displayed in different colors or animation effects according to its actual operating parameters. When the equipment is abnormal, the equipment icon on the model will immediately flash or change color, reminding the management staff to pay attention and deal with it in time, which greatly improves the timeliness and intuitiveness of problem discovery;

[0071] Accurate decision support: Provide accurate data support for mine management decisions; based on the 3D model of real-time data mapping, managers can fully understand the actual situation of each link of the mine, so as to make more scientific and reasonable decisions in formulating production plans, adjusting equipment operating parameters, arranging personnel tasks, etc.; for example, according to the ore mining progress and equipment load conditions in different areas, adjust the transportation route and equipment allocation in real time, optimize the production process, improve production efficiency and resource utilization, and reduce production costs and safety risks;

[0072] Fault diagnosis and prediction: Combining real-time data mapping and historical data trend analysis can help achieve early diagnosis and risk prediction of equipment failures. By continuously monitoring the dynamic changes of equipment operation data in the three-dimensional model and comparing it with the data pattern under normal operation, once the data is found to be abnormally deviating from the normal range, the system can issue a fault warning in time and provide possible fault cause analysis and potential risk assessment. For example, through long-term monitoring and mapping analysis of the vibration data of a certain equipment, it is found that its vibration amplitude gradually increases and the frequency changes abnormally. Combined with historical fault case data, the system can predict that the equipment may be about to fail and arrange maintenance personnel to perform maintenance in advance to avoid production interruptions and safety accidents caused by sudden equipment failures.

[0073] The real-time data mapping unit plays a vital role as a bridge in the industrial management system by virtue of its advanced technical principles, reasonable system architecture and significant functional advantages. It effectively connects the data acquisition end and the 3D model display end, and provides a solid technical guarantee and powerful support means for the intelligent management and safe production of the mining industry.

[0074] Among them, the visualization rendering unit uses LOD dynamic grading technology to achieve smooth rendering of large-scale scenes;

[0075] Furthermore, the visualization rendering unit is the core part of the industrial management system that is responsible for creating high-quality visual effects. It uses advanced graphics technology and optimization strategies to transform three-dimensional mine models and related data into intuitive, realistic and smooth image displays, providing users with clear and accurate information presentation and good interactive experience, and plays a key role in improving mine management efficiency and decision-making quality. Specifically:

[0076] 1. Core technology principle:

[0077] LOD dynamic grading technology principle: according to the observer's position, viewing direction, field of view, and the importance of model elements, the three-dimensional mine model is automatically divided into detail levels; for the model parts that are far away, in the edge of the field of view or relatively minor, such as the distant mountains, small equipment in non-critical areas, etc., low-detail level models are used for rendering to reduce the number of geometric figures and texture complexity; for elements that are close to the observer, in the focal position or have an important impact on decision-making, such as the main production equipment in operation, the intersection of key tunnels, etc., they are switched to high-detail level models to show rich textures, fine structures and realistic light and shadow effects; through this dynamic grading mechanism, while ensuring the visual effect, the system calculation amount and graphics rendering load are effectively reduced to ensure the smooth rendering of large-scale mining scenes;

[0078] Lighting and shadow rendering technology: Use physically based lighting models, such as the Phong lighting model or a more advanced global illumination model based on ray tracing, to simulate the lighting effects of the real world; consider the propagation and reflection characteristics of various light sources such as ambient light, point light, and spotlight in the mine scene, and accurately calculate the properties of the model surface such as light intensity, color, and glossiness; at the same time, combine shadow mapping technology or volume shadow algorithm to generate realistic shadow effects and enhance the three-dimensional and layered sense of the scene; for example, when simulating sunlight shining on the mine, accurately calculate the illuminated surface, backlit surface, and shadow area of ​​the mountain, building, and equipment according to the position and angle of the sun, making the entire scene more realistic and natural, helping users better perceive the spatial structure and equipment layout of the mine;

[0079] Texture mapping and material processing technology: Use high-resolution texture images to map the surface of the 3D model to enhance the realism of the model; for different geological rock layers, equipment shells, tunnel walls and other elements, use corresponding texture materials, such as rock texture, metal texture, masonry texture, etc., and use texture coordinate mapping technology to accurately fit the texture to the model surface; in terms of material processing, consider the physical properties of the model surface, such as roughness, reflectivity, transparency, etc., and use material editing tools and shader programming technology to create realistic material effects; for example, for metal equipment, set appropriate reflectivity and glossiness to make it present the luster and texture of metal under light; for transparent ventilation ducts, adjust the transparency and refractive index to achieve a realistic transparent effect and light refraction effect, and further improve the visual quality of the model;

[0080] 2. System architecture and components:

[0081] Graphics rendering engine component: As the core of the visualization rendering unit, the graphics rendering engine is developed based on the underlying graphics API (such as OpenGL or DirectX) and is responsible for performing actual graphics drawing and rendering tasks; it includes sub-modules such as the geometry processing module, the rasterization module and the pixel processing module; the geometry processing module performs pre-processing operations such as transformation, clipping and lighting calculation on the geometry data of the 3D model; the rasterization module converts the geometrically processed primitives into pixel fragments on the screen; the pixel processing module performs operations such as texture sampling, color mixing and depth testing on the pixel fragments, and finally generates a complete image frame; through the collaborative work of these sub-modules, an efficient graphics rendering process is achieved to ensure the quality and performance of the image;

[0082] Resource management component: responsible for managing various resources required in the rendering process, such as graphic textures, material data, model geometry data, and lighting data; this component includes a resource loading module, a cache management module, and a resource recycling module; the resource loading module loads the required resource files from local storage or network servers, and performs necessary parsing and preprocessing; the cache management module adopts an intelligent cache strategy to store commonly used resource data in memory to improve resource access speed and reduce repeated loading; the resource recycling module regularly cleans up unused resources, releases system memory and storage space, ensures the effective use of system resources, and avoids problems such as resource leakage and memory overflow;

[0083] Rendering optimization component: In response to the rendering performance optimization requirements of large-scale mining scenes, the rendering optimization component uses a variety of technical means for optimization, including the view frustum culling technology, which determines whether the model elements are within the view frustum of the observer and culls the model parts that are not within the field of view, thereby reducing unnecessary rendering calculations; the occlusion culling technology, which uses spatial data structures (such as octrees or BSP trees) and depth buffer information to identify and cull model elements that are occluded by other objects, further improving rendering efficiency; and the multi-threaded rendering technology, which decomposes the rendering task into multiple subtasks and executes them in parallel on multiple CPU cores or threads, making full use of the advantages of multi-core processors, accelerating the rendering process, and improving the overall performance and response speed of the system;

[0084] 3. Functional advantages:

[0085] High-quality visual presentation: Through advanced lighting, texture and material processing technologies, users are provided with highly realistic visual effects of mining scenes; whether it is complex geological structures, delicate equipment structures or real light and shadow changes, they can be clearly and accurately displayed in the configuration screen, making users feel as if they are on the scene, which helps to better understand the actual situation of the mine; for example, when viewing underground tunnels, users can clearly see the rock texture of the tunnel walls, the lighting effects of lighting equipment and the material details of the equipment surface, so as to more intuitively evaluate the safety of the tunnel and the operating status of the equipment;

[0086] Smooth interactive experience: With the help of LOD dynamic grading technology and rendering optimization components, smooth image rendering and interactive operations can be achieved on terminal devices with different hardware configurations; when users perform operations such as zooming, panning, and rotating, the picture can respond quickly without obvious freezes or delays, providing a smooth and natural interactive experience; this enables users to efficiently browse and explore various parts of the mine model, quickly obtain required information, and improve work efficiency; for example, when quickly zooming in and out of the mine panoramic view, the distant scene can quickly switch to a low level of detail for fast rendering, and when focusing on a specific device, the high level of detail model can be quickly loaded, ensuring that users can always get a smooth visual experience and efficient operation response;

[0087] Information communication and decision support: Clear and intuitive visual rendering effects help convey complex mine data and information to users in an easy-to-understand way, providing strong support for management decisions. Managers can quickly identify potential problem areas or abnormal situations, such as equipment failure hazards, geological disaster risk points, etc., by observing the rendered configuration screen, and make timely and accurate decisions based on this. For example, according to the color changes or warning signs of the equipment in the rendered screen, it can be quickly determined whether the equipment needs maintenance or replacement. By observing the ventilation effects and personnel distribution in different areas, the ventilation system and personnel scheduling plan can be reasonably adjusted to ensure the safe and efficient operation of mine production.

[0088] With its advanced technical principles, reasonable system architecture and significant functional advantages, the visualization rendering unit has achieved efficient graphics rendering and high-quality visual presentation in the industrial management system, providing powerful visualization support for mining industry management and effectively improving the level of management refinement and scientific decision-making.

[0089] In this embodiment, the intelligent scheduling decision module has a transportation path optimization submodule, a resource allocation submodule, an emergency response submodule and a prediction unit;

[0090] Among them, the transportation route optimization submodule generates the optimal material transportation route based on the improved ant colony algorithm;

[0091] Furthermore, the transportation route optimization submodule is a key component of the intelligent scheduling decision-making module in the industrial management system with industrial configuration screen presentation. It aims to plan the optimal route for mine material transportation through the combination of advanced algorithms and actual working conditions, effectively improve transportation efficiency, reduce costs and ensure production smoothness, and play an important hub role in mine industrial management; specifically:

[0092] 1. Core technology principle:

[0093] Improved ant colony algorithm mechanism: The improved ant colony algorithm used in this module is optimized based on the traditional ant colony algorithm. In the process of searching for paths, ants select paths based on the pheromone concentration, heuristic information and energy consumption factor on the path. The formula for path selection probability is: ,in, Indicates at time , No. Ant slave node To Node The probability of For path pheromone concentration, ants will tend to choose the path with high pheromone concentration, because it means that the path is considered to be a better path by the previous ants; , Represents a slave node To Node The heuristic function is used to measure the distance from the node To Node The direct attraction of the distance is greater when it is shorter. is the energy consumption factor, used to measure the path Energy consumption, is a constant coefficient used to adjust the impact of energy consumption on path selection. is the cumulative change in path slope, It is the maximum value of the cumulative change of the path slope in the current scenario. Considering the energy consumption and slope changes can make the path selection more in line with the energy consumption and equipment operation difficulty in actual transportation; Indicates at time , from the node To Node pheromone concentration along the path; is the heuristic function, defined as , Represents a slave node To Node distance; is the energy consumption factor introduced by the improved ant colony algorithm, and its calculation formula is , For slave nodes To Node Cumulative change in path slope; , , are the adjustment parameters of pheromone concentration, heuristic function and energy consumption factor. By adjusting these parameters, the algorithm can optimize the emphasis on different factors to adapt to the terrain and transportation equipment characteristics of different mines; The cumulative change in path slope, which is the change from the starting point along the path The accumulated value of the slope change during the process of reaching the end point;

[0094] Dynamic information update mechanism: After the ants complete a path search, they will update the pheromone concentration on the path according to the search results; for a better path, the pheromone concentration will be increased to guide the subsequent ants to choose this path; for a worse path, the pheromone concentration will be reduced; at the same time, the pheromone concentration will gradually evaporate over time to prevent the algorithm from falling into a local optimal solution and maintain the diversity of the search; this dynamic update mechanism enables the algorithm to continuously learn and adapt to changes in the mine transportation environment, such as changes in road conditions, adjustments to transportation tasks, etc., and continuously optimize the path selection results;

[0095] 2. System architecture and components:

[0096] Data input and preprocessing component: responsible for collecting various data related to the transportation route, including topographic data of the mine (such as altitude, slope information, etc.), performance parameters of transportation equipment (such as maximum load, travel speed, energy consumption characteristics, etc.), starting and ending locations of material transportation, and connection relationships between nodes; cleaning, converting and standardizing these raw data into a format acceptable to the algorithm; for example, converting geographic coordinate data into a unified coordinate system and normalizing equipment performance parameters to enable accurate calculation and comparison in the algorithm;

[0097] Algorithm execution component: It is the core computing part of the transportation path optimization submodule, and implements the specific logic of the improved ant colony algorithm. It includes functional modules such as ant colony initialization, ant path search process simulation, path selection probability calculation, and pheromone concentration update. During the algorithm execution process, according to the input data and set parameters, it controls the movement and path selection behavior of ants in the mine transportation network, continuously iterates and searches for the optimal path, and records the optimal solution and related statistical information of each iteration, such as path length, energy consumption, transportation time, etc., to provide data support for subsequent analysis and decision-making.

[0098] Result output and evaluation component: output the optimal transportation path and related performance indicators (such as path length, transportation time, energy consumption, etc.) obtained by executing the algorithm to other modules or user interfaces of the system; at the same time, evaluate and analyze the output results, compare them with the historical optimal results or preset standards, and judge the effectiveness and degree of improvement of this optimization result; if the result is not ideal, adjust the algorithm parameters or collect data again for optimization according to the evaluation results to ensure that the output transportation path can meet the actual needs of mine production and maximize transportation efficiency and minimize costs;

[0099] 3. Functional advantages:

[0100] Improve transportation efficiency: By accurately planning the optimal transportation route, detours and unnecessary trips in the material transportation process can be reduced, shortening the transportation time. For example, in a complex mine tunnel network, it can avoid frequent vehicle turns in narrow passages or areas with large slopes, allowing transportation vehicles to quickly and smoothly transport materials from loading points to unloading points, improving the utilization rate of transportation equipment and overall transportation efficiency, and helping to ensure the continuity and efficiency of mine production;

[0101] Reduce transportation costs: Considering factors such as the energy consumption factor of the route and the difficulty of equipment operation, the optimal route selected can effectively reduce energy consumption and equipment wear during transportation; this not only reduces fuel costs and equipment maintenance costs, but also extends the service life of equipment, reducing the total cost of mine material transportation from multiple aspects and improving the economic benefits of mining enterprises; for example, avoiding routes with frequent and large slope changes can reduce the energy consumption of transport vehicles during climbing and the loss of mechanical parts, thereby reducing operating costs;

[0102] Adaptability to complex working conditions: The improved ant colony algorithm can better adapt to the complex and changeable terrain and transportation task requirements of mines; no matter under different geological conditions, or in the face of fluctuations in transportation task volume or changes in transportation starting and ending points, it can quickly find a suitable transportation route through dynamic information update mechanism and adjustment of algorithm parameters; this flexibility and adaptability make the transportation route optimization submodule very practical in mining industry management, and can effectively cope with various complex situations in actual production and ensure the stable operation of the mine transportation system;

[0103] The transportation route optimization submodule, with its advanced technical principles, reasonable system architecture and significant functional advantages, realizes efficient transportation route planning in the mining industrial management system, provides strong support for the material transportation link of mining production, and plays a key role in improving the overall operational efficiency and economic benefits of mining enterprises;

[0104] Among them, the resource allocation submodule combines equipment operation status data with production plan requirements to establish a multi-objective optimization model;

[0105] Furthermore, the resource allocation submodule occupies a key position in the industrial management system with industrial configuration screen presentation. Its core role is to reasonably allocate various resources based on the actual situation and needs of mine production with the help of scientific models and algorithms to ensure the efficient and stable development of mine production activities, optimize resource utilization and maximize production benefits. Specifically:

[0106] 1. Core technology principle:

[0107] Improved multi-objective optimization model architecture: The improved multi-objective optimization model used in this module is: ; ;

[0108] in, For the The operation and maintenance cost of the equipment refers to the maintenance and operation of The costs required for such equipment include equipment maintenance costs, parts replacement costs, energy consumption costs, etc. To monitor and analyze the equipment's operating parameters (such as temperature, pressure, vibration frequency, etc.) in real time, comprehensively evaluate the equipment's health Moment The health status of the equipment. The higher the health of the equipment, the more stable and reliable the equipment is. For the Each production link, from raw material mining, transportation, processing to finished product output, is time-consuming and accurately recorded in Moment The time required to complete each production link is crucial for the control of the overall production progress; is the fuzzy membership function, which is used to describe Moment The production link is The membership degree of a fuzzy constraint condition, whose value is between 0 and 1. Through this function, some production constraints that are difficult to accurately quantify (such as the stability requirements of the production process, the influence of personnel operation proficiency, etc.) can be fuzzy processed so that they can be included in the optimization model; is the decision variable, representing Moment about The decision values ​​of each production link, such as the number of equipment inputs, the allocation ratio of materials, etc., are used to achieve optimal allocation of resources by adjusting these decision variables; For the The minimum allowable value of a fuzzy constraint condition ensures that the production process meets certain basic requirements and constraints;

[0109] Multi-objective trade-off and optimization strategy: During the optimization process, it is necessary to balance the two conflicting goals of reducing equipment operation and maintenance costs and shortening production time. , find the optimal solution or approximate optimal solution that meets the constraints; for example, increasing the number of equipment inputs may reduce the operation and maintenance costs of a single device, but may increase the idle time and management costs of the equipment, and may also affect the coordination and overall efficiency of the production process; while reducing the number of equipment inputs may lead to excessive use of equipment, increased operation and maintenance costs, and may extend the production process due to insufficient equipment; therefore, it is necessary to comprehensively consider various factors and use advanced optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) to solve the model in order to achieve the optimal balance of resource allocation;

[0110] 2. System architecture and components:

[0111] Data collection and processing component: responsible for collecting various data related to resource allocation, including equipment operation data, production progress data, cost data, personnel and material information, etc.; cleaning, screening, classification and statistical analysis of the collected data, extracting key information and converting it into the format required by the model; for example, obtaining the real-time operation parameters of the equipment from the equipment status monitoring system, calculating the equipment health index after data cleaning and outlier processing; extracting the time records and material consumption data of each production link from the production management system, summarizing and analyzing them, and providing accurate data support for the model;

[0112] Model building and solving component: According to the architecture and principles of the improved multi-objective optimization model, a mathematical model is constructed and the corresponding parameters are set; the model is solved using the algorithms in the optimization algorithm library, and the calculation is continuously iterated during the solving process. According to the set convergence conditions, it is determined whether the optimal solution or the approximate optimal solution that meets the requirements is found; at the same time, this component is also responsible for verifying and analyzing the solution results to ensure the rationality and effectiveness of the results; for example, when using genetic algorithms for solving, the population initialization, selection, crossover and mutation operations are reasonably set, and the optimal solution is gradually approached through multiple generations of evolution and screening, and a sensitivity analysis is performed on the final results to evaluate the impact of different parameter changes on the results;

[0113] Result output and feedback component: outputs the resource allocation plan (such as equipment allocation plan, material allocation list, personnel scheduling, etc.) obtained by solving the model to other modules or user interfaces of the system for management personnel to make decisions and execute; at the same time, collects feedback information from the actual production process, such as the execution effect of the resource allocation plan, problems and deviations, etc., and feeds this information back to the data acquisition and processing component and the model building and solving component, so as to update and optimize the model and data and improve the accuracy and adaptability of resource allocation;

[0114] 3. Functional advantages:

[0115] Cost-effectiveness optimization: By accurately analyzing the equipment operation and maintenance costs and the time consumption of production links, and rationally allocating resources, it is possible to effectively reduce equipment operation and maintenance costs and improve resource utilization efficiency while ensuring production progress, thereby improving the economic benefits of mining enterprises; for example, according to the health status of the equipment and the requirements of production tasks, the equipment operation time and maintenance plan can be dynamically adjusted to avoid additional costs caused by excessive maintenance or equipment failure, thereby achieving refined cost management and maximizing benefits;

[0116] Production process coordination: Considering the interrelationships and constraints of each production link, the resource allocation submodule can optimize resource allocation, ensure the close connection and coordinated operation of each production link, and reduce production interruptions and delays; for example, according to the material requirements and equipment capacity of different production links, it can reasonably allocate material and equipment resources to avoid material shortages or idle equipment, so as to make the entire production process smooth and efficient, and improve production efficiency and product quality;

[0117] Adapt to dynamic production environment: As factors such as equipment status, production tasks, and market demand continue to change during the mining process, the resource allocation submodule can obtain the latest data and update the model in a timely manner, quickly adjust the resource allocation plan, and adapt to the dynamic production environment; for example, when a certain type of equipment fails, the system can quickly reallocate other equipment resources, adjust the production plan, ensure the continuation of production activities, and enhance the production resilience and competitiveness of mining enterprises;

[0118] The resource allocation submodule, with its advanced technical principles, reasonable system architecture and significant functional advantages, has achieved scientific and reasonable resource allocation in the industrial management system, providing a strong guarantee for the stable and efficient operation of mining production, and playing an important role in promoting the sustainable development of mining enterprises;

[0119] Among them, the emergency response submodule configures the abnormal operating mode library and integrates the safety regulations constraints;

[0120] Furthermore, the emergency response submodule is a key component of the intelligent scheduling decision-making module in the industrial management system with industrial configuration screen presentation, and plays a core role in ensuring the safety of mine production; it integrates a variety of technologies and data resources to achieve rapid identification, risk assessment and effective response to abnormal working conditions, ensuring that timely measures can be taken in emergency situations to reduce accident losses and maintain the stability and safety of mine production. Specifically:

[0121] 1. Core technology principle:

[0122] Improved rule-based reasoning algorithm mechanism: The emergency response submodule adopts an improved rule-based reasoning algorithm, and its core formula is: ,in, The weights are gas / dust / water inflow, which are determined based on the geological conditions, mining technology, and historical accident data of the mine. They are used to measure the relative importance of gas, dust, and water inflow in the calculation of emergency response warning levels. For example, in mining areas with a higher risk of gas outburst, the weight of gas concentration will be increased accordingly to highlight its key impact on safety. It is a nonlinear normalization function. It converts the monitoring values ​​of gas, dust and water inflow into Perform nonlinear transformation and normalize to a set range, so that monitoring data of different magnitudes and properties can be compared and comprehensively evaluated under a unified standard, thereby accurately reflecting the contribution of each factor to the safety risk; is the equipment failure correlation function, by establishing the equipment failure parameters The correlation with the emergency response warning level can incorporate the equipment operation status into the overall risk assessment system; for example, when the temperature, pressure and other parameters of a key equipment exceed the normal range, the equipment failure correlation function will affect the emergency response level according to the preset rules, indicating the safety risks that may be caused by the equipment failure; is the geological condition correction factor, which takes into account the differences in geological complexity and stability in different areas of the mine and corrects the final warning level; The threshold is used as a criterion for evaluating the comprehensive calculation results of gas, dust, water inflow and other monitoring factors when calculating the emergency response warning level (i.e. ) Whether the level of early warning is reached; in areas with complex geological conditions and prone to geological disasters, the geological condition correction factor will be increased to enhance the sensitivity and accuracy of early warning and ensure that effective early warning signals can be issued in a timely manner in high-risk geological environments;

[0123] Abnormal operating mode library and safety regulations integration mechanism: The emergency response submodule is equipped with a rich abnormal operating mode library, which covers various typical abnormal situations that may occur in the mine production process, such as gas explosion, water seepage accident, serious equipment failure, etc.; each abnormal operating mode has detailed records of the corresponding characteristic parameters, occurrence conditions, possible development trends and corresponding emergency treatment measures; at the same time, the national and industry safety regulations and constraints are integrated into the system to ensure that all decisions and actions strictly comply with safety regulations during the emergency response process; for example, when the monitoring data matches the abnormal operating mode of gas explosion, the system will immediately start the corresponding ventilation, power off, personnel evacuation and other emergency procedures according to the safety regulations to ensure the safety of personnel and mine facilities;

[0124] 2. System architecture and components:

[0125] Data monitoring and collection component: responsible for real-time collection of various data from the mine production site, including environmental monitoring data such as gas concentration, dust content, water inflow, and equipment operating parameters, temperature, pressure, vibration and other status data; seamlessly connected with the mine automation system through sensor networks, data acquisition equipment, to ensure timely and accurate data collection; at the same time, preliminary verification, filtering and preprocessing of the collected data to remove noise and outliers and improve data reliability; for example, using high-precision gas sensors and data acquisition cards to collect gas concentration data at regular intervals, and to verify the data in real time to ensure the authenticity and validity of the data;

[0126] Risk assessment and decision-making component: Based on the improved rule reasoning algorithm and abnormal operating mode library, the collected data is comprehensively analyzed and risk assessed; when the data triggers the warning condition, the emergency response warning level is calculated according to the preset rules and weights, and the corresponding emergency decision plan is generated in combination with the safety regulations and actual conditions; this component includes a risk assessment algorithm module, a decision rule engine and an emergency plan library; the risk assessment algorithm module performs specific calculation and analysis tasks; the decision rule engine matches the corresponding decision rules according to the risk assessment results and safety regulations; the emergency plan library stores the detailed processing procedures and measures under various emergency scenarios to provide a reference for decision-making; for example, when assessing the risk of excessive gas concentration, the risk assessment algorithm module calculates the warning level according to the formula, and the decision rule engine retrieves the gas leakage emergency processing process from the emergency plan library according to the level, including notifying relevant personnel, starting ventilation equipment, and organizing personnel to evacuate dangerous areas.

[0127] Instruction issuance and execution component: The generated emergency decision instructions are issued to the relevant departments and equipment control systems of the mine in a timely manner to ensure that the emergency measures can be quickly executed; this component includes an instruction transmission module and an execution feedback module; the instruction transmission module sends instructions to the corresponding target equipment and personnel terminals through wired or wireless communication, such as ventilation systems, drainage systems, transportation equipment, monitoring centers, and handheld terminals of on-site staff; the execution feedback module is responsible for receiving feedback information on execution results, monitoring the execution of emergency measures, such as whether the equipment is started normally, whether the personnel have been safely evacuated, etc., and timely passing the feedback information to the risk assessment and decision-making component to adjust and optimize the decision; for example, when an instruction to increase the ventilation volume is issued to the ventilation system, the execution feedback module will monitor the operating status and air volume changes of the ventilation equipment in real time. If it is found that the equipment does not respond normally or the air volume does not meet the requirements, it will promptly notify the risk assessment and decision-making component to take further measures;

[0128] 3. Functional advantages:

[0129] Fast and accurate early warning: Through real-time monitoring of multiple key factors and advanced algorithm analysis, early warning signals can be quickly issued at the early stage of abnormal situations, and the warning level accurately reflects the degree of risk; this buys valuable time for mine managers, enabling them to take timely measures to prevent further deterioration of accidents; for example, when the gas concentration begins to show an abnormal upward trend, the system can issue an early warning in time to remind staff to strengthen ventilation and conduct safety inspections to avoid explosion accidents caused by gas accumulation;

[0130] Scientific and reasonable decision-making: Based on a rich library of abnormal operating conditions and strict safety regulations, scientific and reasonable emergency decision-making plans can be quickly generated after an early warning occurs. These plans take into account various factors and actual conditions to ensure that the measures taken in an emergency are both effective and in compliance with safety regulations, minimizing accident losses. For example, when facing the risk of fire caused by equipment failure, the system can formulate a series of detailed and reasonable emergency measures including fire extinguishing, personnel evacuation, equipment isolation, etc. according to factors such as equipment location, fire development trend, and surrounding environment, to ensure personnel safety and reduce property losses.

[0131] Efficient and coordinated response: The emergency response submodule can effectively coordinate the actions of various departments and equipment in the mine to ensure the efficient implementation of emergency measures; through the command issuance and execution components, a rapid response mechanism from decision-making to execution is realized, so that ventilation, drainage, transportation and other systems and on-site staff can work closely together to form an organic emergency response system; for example, when a flooding accident occurs, the system can quickly coordinate the drainage equipment to drain water at full capacity, and at the same time organize the orderly evacuation of personnel in the threatened area, avoiding delays and confusion caused by poor communication or uncoordinated actions between departments, and improving the mine's ability and efficiency in responding to emergencies;

[0132] With its advanced technical principles, reasonable system architecture and significant functional advantages, the emergency response submodule has built a solid safety line in the mining industrial management system, providing a strong guarantee for the safe and stable operation of mining production, effectively reducing the risk and loss of accidents, and is of vital importance to the sustainable development of mining enterprises.

[0133] The prediction unit uses a spatiotemporal graph convolutional network model to predict the equipment operation status, which is used to detect potential failure risks in advance and assist in optimizing production scheduling decisions.

[0134] Furthermore, as a key part of the industrial management system, the prediction unit uses the spatiotemporal graph convolutional network model to accurately predict the equipment operation status, playing a core role in ensuring production continuity, reducing equipment failure rate, and optimizing production scheduling, providing a forward-looking decision-making basis for industrial management. Specifically:

[0135] 1. Core technology principle:

[0136] Architecture of the spatiotemporal graph convolutional network model: The spatiotemporal graph convolutional network model used by the prediction unit constructs a graph structure containing the topological relationship of the devices, regards the devices as nodes, and the connection relationship between the devices as edges; in this graph structure, the graph convolution operation is used to extract the spatial correlation features between the devices, and at the same time, the time series information is combined to perform a spatiotemporal joint analysis of the device operation status; the node status update formula is: ,in, Indicates The updated state matrix of all nodes in the layer spatiotemporal graph convolutional network, which comprehensively reflects the operating status characteristics of the equipment after being processed by the network layer; Introducing nonlinear factors into the activation function enhances the model's expressiveness and enables the model to better fit the changing laws of complex equipment operating states; For the The input feature matrix of all nodes in the layer network. These features include various operating parameters of the equipment (such as temperature, pressure, vibration frequency, etc.) and related features extracted from historical data, providing a rich source of information for the model; For the Layer network and The learnable parameter matrix corresponding to the Chebyshev polynomial basis of 10 orders is continuously adjusted through learning and training of a large amount of historical data, so that the model can accurately capture the changes in the spatiotemporal characteristics of the equipment's operating status; The normalized graph Laplacian matrix is ​​used to describe the connection relationship and topological characteristics between nodes in the graph structure. It plays a key role in graph convolution operations and determines the propagation mode and weight distribution of information between device nodes. It is the Chebyshev polynomial basis, which is used to perform polynomial expansion of the graph Laplacian matrix in graph convolution operations, further enriching the model's ability to express and process device topological relationships; It is the historical state memory gating parameter, which is used to control the influence of historical state information on node state update. Its value is between 0 and 1. It is used to control the influence of historical state information on node state update. , the model can flexibly use historical information according to actual conditions, avoid overfitting or underfitting, and make the prediction results more accurate and reliable; The historical characteristics of the device topology relationship include the topological structure information of the device at the past moment and the related status characteristics. This historical information is crucial for capturing the long-term trend and periodic changes of the device operation status, and provides an important reference for the model. is the highest order of the Chebyshev polynomial basis, Is an index variable for summation, ranging from 0 to , which together determine the complexity and accuracy of graph convolution operations;

[0137] Data-driven learning and prediction mechanism: During the model training phase, the prediction unit collects a large amount of historical equipment operation data, including normal operating status and abnormal data before a fault occurs; through preprocessing and feature engineering of these data, they are converted into a format suitable for model input and input into the spatiotemporal graph convolutional network model for training; the model uses the back-propagation algorithm to continuously adjust its own parameters to minimize the error between the prediction result and the actual data; during the prediction process, the model predicts the equipment operation status in the future based on the currently input equipment operation data and the learned spatiotemporal feature patterns; for example, by analyzing the temperature and pressure change trends of a certain equipment in the past period of time and the correlation with adjacent equipment, it is predicted whether the equipment may fail in the next few hours or days, and the probability of failure and possible failure types are given, providing timely early warning information for equipment maintenance and production scheduling;

[0138] 2. System architecture and components:

[0139] Data collection and preprocessing component: responsible for collecting equipment operation data from heterogeneous data collection modules and other data sources, and performing preprocessing operations such as cleaning, denoising, and normalization on the data; this component uses data quality detection algorithms to identify and process abnormal values ​​and missing values ​​in the data, ensuring that the data input into the prediction model has high quality and reliability; for example, for the collected equipment temperature data, if it is found that the temperature value at a certain moment is significantly deviated from the normal range, the data collection and preprocessing component will judge and process it according to the preset rules, such as using mean filling or time series-based interpolation methods to repair missing values, and using filtering algorithms to remove noise interference, so that the data can accurately reflect the actual operating status of the equipment, providing a solid data foundation for subsequent model training and prediction;

[0140] Model training and optimization component: builds and manages spatiotemporal graph convolutional network models, including model initialization, parameter setting, control of the training process, and model evaluation and optimization; this component uses large-scale historical equipment operation data for model training, and uses optimization algorithms such as small batch gradient descent to continuously adjust the model parameters to improve the model's prediction performance; at the same time, the model is evaluated through technical means such as cross-validation to monitor the generalization ability and accuracy of the model on different data sets; for example, during the model training process, after each certain training round, the model training and optimization component will use the validation data set to evaluate the performance of the model. If it is found that the loss function value of the model on the validation set no longer decreases or overfitting occurs, the training parameters (such as learning rate, regularization coefficient, etc.) will be adjusted in time or the model fusion and other technologies will be used to optimize the model to ensure that the model has good prediction ability and stability;

[0141] Prediction result output and visualization component: Output the equipment operation status results predicted by the model and display them to the user in an intuitive and visual way; this component can generate charts such as equipment failure risk probability curve and future operation status trend chart to help users quickly understand the meaning and trend of the prediction results; at the same time, the prediction results are compared with the preset threshold. If the prediction results exceed the threshold, an early warning signal will be issued in time to remind managers to take corresponding measures; for example, when the probability of a certain device failing in the next 24 hours is predicted to exceed 50%, the prediction result output and visualization component will display a red warning sign on the human-computer interaction terminal and provide a detailed prediction report, including possible failure cause analysis and recommended countermeasures, to assist managers in making scientific and reasonable decisions;

[0142] 3. Functional advantages:

[0143] Early warning of faults: Through real-time monitoring and spatiotemporal joint analysis of the equipment's operating status, potential risks can be discovered in the early stages before a fault occurs, and early warning signals can be issued in advance. Compared with traditional methods based on threshold judgment or post-analysis, the prediction unit can capture subtle changes and abnormal trends in the equipment's operating status earlier, gaining valuable time for equipment maintenance and repair, and effectively reducing production losses caused by sudden equipment failures. For example, for some key equipment, such as mine ventilators or hoists, through early warnings from the prediction unit, maintenance personnel can prepare spare parts and tools in advance, perform preventive maintenance before a fault occurs, and avoid production interruptions and safety hazards caused by equipment downtime.

[0144] Accurate prediction and decision support: Based on the powerful prediction ability of the spatiotemporal graph convolutional network model, it can accurately predict the operating status of the equipment in the future, including the performance change trend of the equipment, the possible fault types and the time range of the fault. These accurate prediction results provide an important basis for production scheduling decisions. Managers can reasonably arrange production tasks, adjust equipment operation plans, optimize resource allocation, and improve production efficiency and equipment utilization based on the prediction information. For example, if it is predicted that multiple equipment on a production line may have different degrees of failure in the next few days, managers can adjust the production plan in advance, assign tasks to other normally operating equipment, or arrange equipment maintenance time to ensure the continuity and stability of production.

[0145] Improve equipment maintenance efficiency: The application of prediction units has changed the traditional equipment maintenance model from passive fault repair to active preventive maintenance. By understanding the potential failure risks of equipment in advance, maintenance personnel can formulate maintenance plans and programs in a targeted manner, reduce unnecessary maintenance operations and resource waste, and improve the efficiency and quality of equipment maintenance. At the same time, maintenance strategies based on prediction results can extend the service life of equipment, reduce the maintenance cost of the equipment throughout its life cycle, and bring significant economic benefits to enterprises. For example, based on the equipment health status information provided by the prediction unit, maintenance personnel can conduct regular inspections and maintenance of equipment, replace wearing parts in a timely manner, avoid serious failures caused by excessive use of equipment or long-term operation in poor conditions, and improve the reliability and stability of equipment.

[0146] Relying on its advanced spatiotemporal graph convolutional network model, reasonable system architecture and significant functional advantages, the prediction unit has achieved efficient prediction and precise management of equipment operating status in the industrial management system, providing strong technical support for the safe, stable and efficient operation of industrial production, which is of great significance to enhancing the core competitiveness of enterprises.

[0147] In this embodiment, the human-computer interaction terminal is equipped with an industrial-grade display screen with multi-touch function, which supports dynamic annotation of configuration screens and plan deduction operations;

[0148] Furthermore, as a direct operation interface of the industrial management system with industrial configuration screen presentation, the human-machine interaction terminal plays a key role as a bridge between information display and command input in the entire system. It provides users with a convenient, efficient and intuitive interactive experience by integrating advanced hardware equipment and feature-rich software systems, greatly improving the convenience and accuracy of industrial management; specifically:

[0149] 1. Hardware equipment composition:

[0150] Industrial-grade display screen: Industrial-grade display screens with high resolution, high brightness and high contrast are used to adapt to the light conditions and usage requirements in complex industrial environments such as mines. Its rugged and durable design can effectively resist adverse factors such as dust, moisture and vibration to ensure long-term stable operation. For example, in open-air working areas of mines with strong light or underground tunnels with a certain amount of dust, the display screen can still clearly present configuration images and various information, providing users with reliable visual support. The screen size is usually selected according to the actual application scenario and the convenience of operation. It can meet the large-size requirements for macro data viewing and overall system operation in the monitoring center, and can also meet the small-size requirements for portable operation by on-site staff.

[0151] Touch function module: equipped with advanced multi-touch function, supports users to interact with the configuration screen through finger touch, sliding, zooming and other gesture operations; touch technology has high sensitivity and accuracy, can quickly respond to operation instructions, and achieve a smooth user experience; for example, users can easily view the local details or overall layout of the mine model through two-finger zoom operation; quickly switch different function pages or data display areas through sliding gestures, greatly improving the operation efficiency and convenience, reducing the dependence on traditional external input devices such as mouse and keyboard, so that command input and information query can be completed quickly in on-site operation or emergency situations;

[0152] Processor and storage device: Built-in high-performance processor, with powerful data processing capabilities and computing speed, can quickly load and render complex configuration screens to ensure smooth system operation; at the same time, equipped with large-capacity memory and storage devices for storing system software, configuration screen data, historical records, and temporary cache data; sufficient storage capacity ensures that the system can still operate normally in the case of unstable or temporarily interrupted network connection, and maintains data integrity and availability for a certain period of time, avoiding the impact of data loss or system freeze on the normal progress of industrial management work;

[0153] Communication module: Integrate multiple communication interfaces and wireless communication modules, such as Ethernet interface, USB interface, Wi-Fi module, Bluetooth module and 4G / 5G communication module (select according to the actual application scenario and network coverage); these communication modules ensure that the human-computer interaction terminal can perform stable and high-speed data transmission and communication with other devices in the system (such as data acquisition equipment, servers, etc.); for example, connect to the local area network inside the mine through the Ethernet interface or Wi-Fi module to achieve real-time data synchronization and command interaction with the server; in the mobile operation scenario, use the 4G / 5G communication module to transmit data with the remote server to ensure that the latest information can be obtained and operation instructions can be uploaded in time at different locations, realizing remote monitoring and management functions;

[0154] 2. Software system functions:

[0155] Configuration screen display and management: The software system has powerful configuration screen display and management functions, and can display the mine model, equipment status information, real-time data charts, etc. generated by the three-dimensional dynamic configuration engine on the display screen in an intuitive and beautiful way; users can easily select different configuration screen views through the operation interface, such as the overall mine layout view, equipment detailed information view, production process monitoring view, etc., and personalize the display parameters of the screen (such as color, font, transparency, etc.) to meet the visual needs and work priorities of different users; at the same time, it supports hierarchical display and dynamic update of the screen. When new data is received or the system status changes, the corresponding screen elements can be refreshed in time to ensure that users always see the latest information;

[0156] Dynamic annotation function: Provides convenient dynamic annotation tools, users can mark and annotate key equipment, areas or data points on the configuration screen in real time; the annotation content can include text information such as equipment name, operating parameters, precautions, maintenance records, and customized graphic symbols or icons; these annotation information can not only help users better understand the content of the screen, but also serve as important work records and reminders; for example, during equipment maintenance, staff can mark maintenance time, maintenance personnel, maintenance items and other information on the configuration screen corresponding to the equipment, which is convenient for subsequent viewing and management; when abnormal gas concentration in a certain area is found, warning information and recommended measures can be marked in the area to improve work safety and accuracy;

[0157] Plan simulation operation support: Supports emergency plan simulation operation. Users can simulate various possible emergency situations on the human-computer interaction terminal and conduct drills and evaluations according to the preset emergency plan. During the plan simulation process, the system can dynamically display the response process and results of the mine system according to the simulated events and operation steps input by the user, including changes in the operating status of the equipment, the evacuation path of personnel, the allocation of resources, etc. In this way, users can familiarize themselves with the process and operation points of the emergency plan in advance, discover potential problems and deficiencies, and make timely adjustments and improvements to improve their response capabilities and decision-making levels in actual emergency situations. For example, when simulating a gas leakage accident, users can start ventilation equipment and organize personnel evacuation according to the emergency plan, and observe the system's response in each link, evaluate the effectiveness and feasibility of the emergency plan, and make full preparations for actual emergency work.

[0158] 3. Application scenario advantages:

[0159] Convenient on-site operation: At the mine production site, workers can easily obtain information such as equipment operation status and production data through the human-computer interaction terminal, and directly input and control operation instructions on the terminal; for example, equipment operators can view the real-time temperature, pressure and other parameters of the equipment through the terminal on site. When abnormal parameters are found, they can quickly start the equipment's self-test program or adjust the operating parameters through touch operation without returning to the central control room for operation, which greatly improves work efficiency and response speed, and reduces production problems that may be caused by information transmission delays and inconvenient operations;

[0160] Training and teaching assistance: In the process of employee training and teaching, the rich functions of the human-computer interaction terminal play an important auxiliary role; by displaying the three-dimensional mine model and the configuration screen of the actual production process, combined with dynamic annotation and plan deduction functions, it can provide employees with a more vivid and intuitive learning experience; trainers can explain the structural principles, operation methods, safety precautions and emergency plans of the equipment in detail on the terminal, and let employees conduct simulation exercises through actual operation of the terminal to deepen their understanding and mastery of knowledge and skills, improve training effects and the overall quality of employees;

[0161] Remote collaboration and management: With the remote communication capability of the communication module, managers can log in to the system remotely through the human-computer interaction terminal in a different location, view the production situation and system status of the mine in real time, and remotely collaborate and communicate with on-site staff. For example, when encountering complex technical problems or emergencies, experts can view detailed configuration images and data information through the remote terminal, guide on-site staff to conduct troubleshooting and emergency response, realize efficient cross-regional collaboration and management, and improve the flexibility and timeliness of industrial management.

[0162] With its advanced hardware equipment, powerful software system and significant advantages in different application scenarios, the human-computer interaction terminal provides users of industrial management systems with all-round, efficient and convenient interactive services, which effectively promotes the intelligent and efficient development of industrial management work.

[0163] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An industrial management system with industrial configuration screen presentation, characterized in that: include: Heterogeneous data acquisition module: It consists of vibration sensors, gas concentration sensors, equipment status monitoring units and personnel positioning terminals deployed on the mine working surface, and realizes data fusion with PLC controllers and DCS systems through multi-protocol converters; 3D dynamic configuration engine, including: Geological model construction unit: Establish a three-dimensional mine model including rock structure, tunnel distribution and equipment layout based on BIM technology; Real-time data mapping unit: establishes dynamic binding relationship between sensor data and 3D model elements; Visual rendering unit: adopts LOD dynamic grading technology to achieve smooth rendering of large-scale scenes; Intelligent scheduling decision module: with: Transportation route optimization submodule: Generate the optimal material transportation route based on the improved ant colony algorithm; The resource allocation submodule combines equipment operation status data with production plan requirements to establish a multi-objective optimization model; Emergency response submodule: configure the abnormal operating mode library and integrate safety regulations constraints; Prediction unit: Uses a spatiotemporal graph convolutional network model to predict the operating status of equipment, which is used to detect potential failure risks in advance and assist in optimizing production scheduling decisions; Human-computer interaction terminal: Equipped with an industrial-grade display screen with multi-touch function, it supports dynamic annotation of configuration screens and emergency plan simulation operations.

2. The industrial management system with industrial configuration screen presentation according to claim 1, characterized in that: The edge computing node of the heterogeneous data acquisition module adopts an improved LSTM missing data interpolation model, and its calculation process satisfies: ,in, express The interpolated data at time, Represents the dynamically adjusted LSTM unit weight, which is It will be adjusted according to the model's adaptive mechanism to reflect the different LSTM units in The contribution of each moment to the interpolated data, For the The historical hidden states of LSTM units, is the bias term, is the number of LSTM units; The dynamically adjusted LSTM unit weight calculation adopts a two-stage attention mechanism: ,in, Query vector for the current moment; For the The key vector of the sensor, representing the The characteristic information of each sensor at the current moment; is the historical data value vector, which contains the valuable information contained in the historical data for the current interpolation task; It is a dimension scaling factor, which is used to scale the matrix operation results in the attention calculation process.

3. The industrial management system with industrial configuration screen presentation according to claim 1, characterized in that: The transport path optimization submodule adopts an improved ant colony algorithm, and the path selection probability formula is: ,in, Indicates at time , No. Ant slave node To Node The probability of For path pheromone concentration; , Represents a slave node To Node The heuristic function is used to measure the distance from the node To Node direct attraction; is the energy consumption factor, used to measure the path Energy consumption, is a constant coefficient used to adjust the impact of energy consumption on path selection. is the cumulative change in path slope, It is the maximum value of the accumulated path slope change in the current scenario; Indicates at time , from the node To Node pheromone concentration along the path; is the heuristic function, defined as , Represents a slave node To Node distance; is the energy consumption factor introduced by the improved ant colony algorithm, and its calculation formula is , For slave nodes To Node Cumulative change in path slope; , , are the adjustment parameters of pheromone concentration, heuristic function and energy consumption factor; The cumulative change in path slope, which is the change from the starting point along the path The accumulated value of the slope change during the process of reaching the end point.

4. The industrial management system with industrial configuration screen presentation according to claim 1, characterized in that: The resource allocation submodule adopts an improved multi-objective optimization model: ; ; in, For the The operation and maintenance cost of the equipment refers to the maintenance and operation of Costs required for such equipment; It is the real-time equipment health, reflected in Moment The operating health of the equipment; For the The production process is time-consuming, that is, Moment The time required to complete each production link; is the fuzzy membership function, which is used to describe Moment The production link is The membership degree of a fuzzy constraint condition, its value is between 0 and 1; is the decision variable, representing Moment about The decision value of each production link; For the The minimum allowed value of a fuzzy constraint.

5. The industrial management system with industrial configuration screen presentation according to claim 1, characterized in that: The prediction unit adopts a spatiotemporal graph convolutional network model, and the node state update formula is: ,in, Indicates The updated state matrix of all nodes in the spatiotemporal graph convolutional network; is the activation function; For the The input feature matrix of all nodes in the layer network; For the Layer network and The learnable parameter matrix corresponding to the Chebyshev polynomial basis of order; It is the normalized graph Laplacian matrix, which is used to describe the connection relationship and topological characteristics between nodes in the graph structure; is the Chebyshev polynomial basis, which is used to perform polynomial expansion on the graph Laplacian matrix in graph convolution operations; It is the historical state memory gating parameter, which is used to control the influence of historical state information on node state update, and its value is between 0 and 1; The device topology relationship history feature includes the device topology information at the past moment and the related status features; is the highest order of the Chebyshev polynomial basis, Is an index variable for summation, ranging from 0 to .

6. The industrial management system with industrial configuration screen presentation according to claim 1, characterized in that: The emergency response submodule adopts an improved rule-based reasoning algorithm: ,in, The weight of gas / dust / water inflow is used to measure the relative importance of gas, dust and water inflow in the calculation of emergency response warning level. It is a nonlinear normalization function used to convert the gas / dust / water inflow monitoring value Perform nonlinear transformation and normalize to the set range; is the equipment failure correlation function, used to describe the equipment failure parameters The relationship with the emergency response warning level; is the correction factor for geological conditions; is the threshold value.

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