Mine rock-soil body cracking and local collapse treatment monitoring system based on satellite remote sensing

Abstract

The invention discloses a mine rock-soil body cracking and local collapse treatment monitoring system based on satellite remote sensing, and the system comprises a satellite remote sensing unit which is used for carrying out the full-range three-dimensional monitoring of a mine region, and obtaining the live-action three-dimensional data of the mine region; the data analysis unit is used for analyzing the live-action three-dimensional data to obtain an analysis result, and the analysis result comprises illegal production construction behaviors, local potential risk points and potential safety hazard types of the mine; the monitoring and early warning unit is used for generating early warning information for the current potential safety hazard type based on the analysis result, and the potential safety hazard type comprises rock-soil body cracking and local collapse; and the governance management unit is used for formulating a corresponding mine governance scheme according to the current early warning information in combination with mine design data and development specifications. Scientific early warning information with high pertinence is provided, and precious hidden danger disposal time is won for mine managers, so that the accident rate is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of geological monitoring, and in particular to a monitoring system for cracking and local collapse of a mining rock and soil body based on satellite remote sensing. Background Art

[0002] During mining, the stability of rock and soil is directly related to production safety and environmental protection. Traditional mine monitoring technology mainly relies on on-site field inspections and visual inspections, which have many limitations. First, field inspections are difficult to cover the entire range of the mine, especially in high-altitude working areas, complex terrains and dangerous areas. There are blind spots that are "invisible and inaccessible", which makes it difficult to discover and deal with safety hazards in a timely manner. Secondly, relying on manual monitoring is easily affected by factors such as personnel quality and subjective judgment. The supervision effect is unstable and there are large errors and omissions. In addition, the frequency and efficiency of on-site inspections are limited, which makes it difficult to meet the rapidly changing needs of mine environmental monitoring, increasing the risk of accidents.

[0003] In the existing technology, although the traditional remote sensing monitoring method can provide a wide range of surface change information, it still has deficiencies in resolution, accuracy and real-time performance, and it is difficult to accurately identify and analyze safety hazards such as subtle rock and soil cracks and local collapse in mines. Especially under complex geological conditions, the processing and analysis of traditional remote sensing data requires high professional skills and complex algorithm support, which limits its popularization and application in grassroots regulatory departments. In addition, the existing remote sensing technology is difficult to achieve a comprehensive, objective and true restoration of the overall picture and details of the mine, and cannot fully meet the needs of safety supervision business.

[0004] Therefore, there is an urgent need for a monitoring system for the cracking and local collapse of mining rock and soil based on satellite remote sensing. Summary of the invention

[0005] The present invention provides a satellite remote sensing-based mining rock and soil cracking and local collapse management monitoring system to solve the above problems existing in the prior art.

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

[0007] The mine rock and soil cracking and local collapse control monitoring system based on satellite remote sensing includes:

[0008] Satellite remote sensing unit, used to conduct full-range stereoscopic monitoring of the mining area and obtain real-life three-dimensional data of the mining area;

[0009] A data analysis unit is used to analyze the real-scene three-dimensional data and obtain analysis results, which include illegal production and construction behaviors of the mine, local potential risk points and types of safety hazards;

[0010] A monitoring and early warning unit is used to generate early warning information for the current types of safety hazards based on the analysis results. The types of safety hazards include cracking of rock and soil and local collapse;

[0011] The governance management unit is used to formulate corresponding mine governance plans based on the current early warning information, combined with mine design data and development specifications.

[0012] Among them, the satellite remote sensing unit includes:

[0013] The orbit planning module is used to plan the optimal observation orbit of the satellite according to the geographical characteristics and monitoring requirements of the mining area to ensure comprehensive coverage of the mining area;

[0014] The data collection trigger module is used to synchronously trigger the data collection process when the satellite passes through the mining area based on the orbit planning results, and record the collected time and location information in real time;

[0015] A data acquisition module, used to acquire lidar data and multispectral data after triggering the data acquisition process;

[0016] The stereo image synthesis module is used to fuse lidar data with multispectral data based on three-dimensional modeling technology to generate real-life three-dimensional image data of the mining area.

[0017] The data analysis unit includes:

[0018] A data receiving module, used for receiving real-scene three-dimensional data and extracting an initial analysis object from the real-scene three-dimensional data;

[0019] The initial analysis determination module is used to determine the analysis results of the initial analysis object, including the illegal production and construction behaviors of the mine, local potential risk points and corresponding safety hazard types;

[0020] A second analysis and determination module is used to identify and classify multiple new potential analysis objects based on the analysis results, and determine the analysis results for each new potential analysis object until the continuous number of local potential risk points identified in the new analysis results reaches a preset number threshold, or the current analysis object is the last analysis object;

[0021] The analysis result report generation module is used to generate a comprehensive analysis result report based on each analysis result and the corresponding safety hazard type, and output the report content. The report includes a list of illegal production and construction behaviors in the mine, details of local potential risk points, and a classification description of safety hazard types.

[0022] The monitoring and early warning units include:

[0023] The first early warning module is used to judge the potential safety hazard based on the analysis result and obtain the potential safety hazard judgment result;

[0024] The early warning information generation module generates early warning information for the current safety hazard type based on the safety hazard judgment result, wherein the safety hazard type includes rock and soil cracking and local collapse.

[0025] The governance management units include:

[0026] The early warning assessment module is used to assess the severity of potential safety hazards based on the current early warning information;

[0027] The treatment plan formulation module is used to determine the reinforcement or support measures to be taken in combination with the mine design data when there is a risk of cracking of the rock and soil mass in the early warning information;

[0028] When there is a risk of local collapse in the early warning information, formulate corresponding protection or repair plans according to the development specifications;

[0029] The governance plan optimization module is used to comprehensively consider the requirements of mine design data and development specifications, and optimize the preliminary governance plan. If multiple safety hazards in the early warning information exist at the same time, the governance plan optimization module will comprehensively formulate comprehensive governance measures to improve the overall governance effect;

[0030] The governance plan generation module generates the corresponding mine governance plan based on the optimized governance measures. The governance plan includes specific implementation steps, required materials, time schedule and expected results.

[0031] Among them, the safety hazard judgment includes:

[0032] If the illegal production and construction behavior of the mine exceeds the illegal production threshold and is less than the preset illegal production limit, obtain the local potential risk points;

[0033] If the local potential risk point exceeds the potential risk point threshold and is less than the preset risk point limit, a safety hazard judgment result is generated.

[0034] Among them, the early warning information generated for the current safety hazard type includes:

[0035] Obtain the severity level associated with each hidden danger type as a data group to be analyzed;

[0036] After sorting each data group to be analyzed according to the hidden danger type, the data corresponding to each hidden danger type and severity level is stored in the corresponding data group to be analyzed, thereby obtaining the hidden danger data to be analyzed;

[0037] Obtain safety hazard warning specification data, and obtain warning rule data from the safety hazard warning specification data;

[0038] After noise elimination processing is performed on the warning rule data, the rule feature vector is constructed, and the specification-related data is obtained from the safety hazard warning specification data;

[0039] After clustering the rule feature vectors according to the normative associated data, a data group to be trained is obtained. After question-answering training is performed on the initial model using the data group to be trained, an early warning analysis model is obtained.

[0040] Input the hidden danger data to be analyzed into the preset early warning analysis model one by one to generate early warning information of the current safety hidden dangers;

[0041] Generate graded warning information based on the type and severity of potential safety hazards;

[0042] Input the graded warning information into the warning analysis model and integrate it to generate the final monitoring and warning information data.

[0043] Among them, the early warning information of current safety hazards is generated, including:

[0044] Inputting the first hidden danger data to be analyzed into the early warning analysis model, performing similarity matching on the first hidden danger data to be analyzed in the early warning analysis model, and outputting a first early warning result according to the matching result;

[0045] Starting from the second hidden danger data to be analyzed, the previous warning result is input into the warning analysis model together to obtain the corresponding associated warning result, and the first warning result and all associated warning results are integrated to generate warning information for the current safety hazard type.

[0046] Among them, graded warning information is generated according to the type and severity level of safety hazards, including:

[0047] When the hidden danger type is rock and soil cracking and the severity level is low, the primary warning information is triggered;

[0048] When the hidden danger type is rock and soil cracking and the severity level is high, the intermediate warning information is triggered;

[0049] When the hidden danger type is local collapse and the severity level is low, the intermediate warning information is triggered;

[0050] When the hidden danger type is local collapse and the severity level is high, an advanced warning message is triggered.

[0051] Among them, the preliminary governance plan was optimized, including:

[0052] At each warning moment, identify the types of potential safety hazards that exist and analyze the relationships between them;

[0053] Based on the mutual impact of hidden dangers, optimize the preliminary treatment plan and adjust the details of the treatment measures;

[0054] In the case of multiple potential safety hazards, comprehensive governance measures are formulated to improve the overall governance effect, including:

[0055] When there is a risk of cracking and local collapse of the rock mass, the optimization of the treatment plan includes taking anchor support measures in priority according to the risk of cracking, and implementing concrete reinforcement plans according to the risk of collapse to ensure the treatment effect;

[0056] Based on the relationship between different hidden dangers, combined with the severity of hidden dangers, risk assessment results and governance technical requirements, a joint governance plan is formulated to avoid duplication of governance and improve resource utilization efficiency;

[0057] Adjust the treatment plan in real time according to the actual monitoring data and treatment effect of each hidden danger to ensure the coordination and effectiveness of various measures;

[0058] In the comprehensive treatment measures, according to the temporal and spatial characteristics of different hidden dangers, the corresponding support structure, reinforcement technology and protective materials are adjusted to ensure the feasibility and economy of the treatment plan;

[0059] The optimized governance solution is based on the following steps:

[0060] Obtain the monitoring data of each current hidden danger, and combine the interactions between hidden dangers to calculate the comprehensive treatment effect of the current hidden dangers using the preset dynamic model;

[0061] Based on the calculated comprehensive treatment effect, adjust the specific parameters of the treatment measures of anchor support and reinforcement scheme to form the final optimized treatment plan;

[0062] If there is a deviation between the actual effect of the optimization plan and the expected effect, the plan will be re-optimized based on dynamic monitoring data and real-time feedback to form a closed-loop feedback mechanism to improve the governance effect.

[0063] Compared with the prior art, the present invention has the following advantages:

[0064] The satellite remote sensing-based monitoring system for cracking and local collapse of rock and soil in mines includes: a satellite remote sensing unit, which is used to conduct full-range stereoscopic monitoring of the mining area and obtain real-life three-dimensional data of the mining area; a data analysis unit, which is used to analyze the real-life three-dimensional data and obtain analysis results, including illegal production and construction behaviors, local potential risk points and types of safety hazards in the mine; a monitoring and early warning unit, which is used to generate early warning information for the current types of safety hazards based on the analysis results, including cracking of rock and soil and local collapse; and a governance management unit, which is used to formulate corresponding mine governance plans based on the current early warning information, combined with mine design data and development specifications. Providing scientific and targeted early warning information can help mine managers gain valuable time to deal with hidden dangers, thereby reducing the incidence of accidents.

[0065] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention.

[0066] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0068] Figure 1 It is a structural diagram of a mining rock and soil cracking and local collapse management monitoring system based on satellite remote sensing in an embodiment of the present invention;

[0069] Figure 2 This is a structural diagram of a satellite remote sensing unit in an embodiment of the present invention;

[0070] Figure 3 4 is a structural diagram of a data analysis unit in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0072] The embodiment of the present invention provides a monitoring system for cracking and local collapse of rock and soil in a mine based on satellite remote sensing, including:

[0073] Satellite remote sensing unit, used to conduct full-range stereoscopic monitoring of the mining area and obtain real-life three-dimensional data of the mining area;

[0074] A data analysis unit is used to analyze the real-scene three-dimensional data and obtain analysis results, which include illegal production and construction behaviors of the mine, local potential risk points and types of safety hazards;

[0075] A monitoring and early warning unit is used to generate early warning information for the current types of safety hazards based on the analysis results. The types of safety hazards include cracking of rock and soil and local collapse;

[0076] The governance management unit is used to formulate corresponding mine governance plans based on the current early warning information, combined with mine design data and development specifications.

[0077] The working principle of the above technical solution is as follows: the satellite remote sensing unit uses high-resolution satellite imaging technology to scan the entire mining area, and obtains three-dimensional real-life data of the mining area through stereo imaging. Remote sensing technology can perceive the shape and material of surface objects through the reflection and absorption characteristics of electromagnetic waves. For example, satellite sensors (such as optical imaging or radar sensors) are used to perform multi-angle imaging of the terrain in the mining area, and a high-precision three-dimensional model is generated through data fusion and stereo reconstruction technology. Remote sensing technology: technology that uses electromagnetic waves (such as light, infrared or microwaves) to perceive and analyze ground objects. Stereo imaging: technology that generates a three-dimensional effect by taking images of the same area from multiple angles.

[0078] The data analysis unit processes and analyzes three-dimensional data based on artificial intelligence (AI) and big data technology. The algorithm detects changes in the topography of the mining area, abnormal shapes of buildings, and changes in the mechanical parameters of the rock and soil, thereby identifying violations and potential hazards. For example, by comparing three-dimensional data from different periods, the system found that the surface of a certain area suddenly showed obvious subsidence, which was a geological problem caused by illegal mining. The AI ​​algorithm can also classify and identify the types of hidden dangers, such as cracks in the rock and soil or landslide risks.

[0079] Based on the results of the analysis unit, the monitoring and early warning unit generates corresponding early warning information through risk models and rule bases. The early warning system transmits the types of hidden dangers, possible consequences and recommended measures to relevant personnel. Assuming that the system detects cracks on the edge of a mine slope, combined with the risk assessment model, the system will determine whether the development of the cracks may lead to local collapse and generate early warning information. Risk model: A mathematical model used to assess potential risks based on historical data and experience. Rule base: A knowledge base containing information such as risk identification and disposal recommendations.

[0080] The governance management unit will formulate targeted governance plans based on early warning information and mine design data, combined with industry specifications (such as the Technical Regulations for Mine Safety Production). The plan may include geological reinforcement, drainage optimization, mining adjustments, etc. If the risk of slope landslide is found to be high, the governance management unit will combine the design data and make suggestions: install drainage facilities on the top of the slope to reduce instability caused by rainwater infiltration, and strengthen the support structure around the slope. Support structure: a device used to support the mine slope or tunnel structure, such as a steel mesh or retaining wall.

[0081] The beneficial effects of the above technical solutions are as follows: the satellite remote sensing unit realizes full-range, uninterrupted monitoring of the mining area, especially providing accurate and timely data support for mines in remote areas, reducing the blind spots of traditional manual inspections. The data analysis unit uses artificial intelligence technology to quickly identify mining violations and potential hidden dangers, greatly improving the efficiency of hidden danger investigation and reducing errors in human judgment. The monitoring and early warning unit provides scientific and targeted early warning information based on real-time data and analysis results, which helps mine managers gain valuable time to deal with hidden dangers, thereby reducing the accident rate.

[0082] In another embodiment, the satellite remote sensing unit comprises:

[0083] The orbit planning module is used to plan the optimal observation orbit of the satellite according to the geographical characteristics and monitoring requirements of the mining area to ensure comprehensive coverage of the mining area;

[0084] The data collection trigger module is used to synchronously trigger the data collection process when the satellite passes through the mining area based on the orbit planning results, and record the collected time and location information in real time;

[0085] A data acquisition module, used to acquire lidar data and multispectral data after triggering the data acquisition process;

[0086] The stereo image synthesis module is used to fuse lidar data with multispectral data based on three-dimensional modeling technology to generate real-life three-dimensional image data of the mining area.

[0087] Among them, planning the best observation orbit of the satellite includes:

[0088] Obtain information on the geographic characteristics and monitoring needs of the mining area;

[0089] Generate multiple first track planning strategies based on geographic features and monitoring requirements;

[0090] Traversing the first track planning strategy in sequence, and obtaining the feature-performance value library corresponding to the traversed first track planning strategy each time;

[0091] Splitting the monitoring requirement information into a plurality of second monitoring requirement items;

[0092] Extracting features of the second monitoring requirement item to obtain a plurality of third features;

[0093] Based on the feature-performance value library, determine the performance value corresponding to the third feature, and associate it with the corresponding second monitoring requirement item;

[0094] Accumulate and calculate the performance values ​​associated with the second monitoring requirement item to obtain a performance value sum;

[0095] Obtain the performance value and threshold corresponding to the traversed first track planning strategy. If the performance value and the threshold are greater than or equal to the performance value and the threshold, use the corresponding second monitoring requirement item as the third monitoring requirement item.

[0096] Obtaining an adaptation performance model corresponding to the traversed first track planning strategy, and based on the adaptation performance model and according to the third monitoring requirement item, performing performance adaptation on the traversed first track planning strategy, obtaining an adaptation value, and associating the adaptation value with the traversed first track planning strategy;

[0097] When the traversal of the first track planning strategy ends, the adaptation value associated with the first track planning strategy is accumulated and calculated to obtain the adaptation value sum;

[0098] The adaptation value and the maximum first track planning strategy are used as the second track planning strategy;

[0099] Based on the second orbit planning strategy, the optimal observation orbit of the satellite is planned to ensure comprehensive coverage of the mining area, especially for the monitoring needs of cracking and local collapse of rock and soil in the mine, to obtain the final observation orbit.

[0100] The working principle of the above technical solution is: the orbital planning module uses orbital dynamics calculations and the geographical characteristics of the mining area (such as terrain complexity, area, etc.), combined with monitoring requirements (monitoring frequency, monitoring time period), to plan the optimal orbit of the satellite to ensure comprehensive coverage of the observation range and efficient use of satellite resources. Assuming that a mining area needs to be monitored, the area is located in a mountainous area and has a large area. The orbital planning module will calculate the optimal path of the low earth orbit (LEO) so that the satellite can pass through the area multiple times at the optimal angle. At the same time, the module will avoid excessive overlapping observations and save satellite resources. Orbital dynamics: a discipline that studies the forces and motion laws of satellites in orbit. Low Earth Orbit (LEO): A satellite orbit that is relatively close to the surface of the earth (about 200-2000 kilometers).

[0101] When the satellite passes through the target area according to the orbit planning results, the data acquisition trigger module will synchronously trigger the remote sensing equipment on the satellite to start collecting data, and record the collection time and the satellite's position coordinates. This ensures that the data collection is consistent in time and space. For example, when the satellite enters the planned orbit and approaches the mining area, the system will automatically trigger the lidar and multispectral sensors to collect surface information and record the timestamp and location information of the data. This information is subsequently used for analysis and modeling.

[0102] The data acquisition module uses LiDAR and multispectral imaging technology to collect surface information of the mining area. LiDAR captures terrain elevation data, and multispectral imaging obtains ground object reflection information (such as vegetation, rock and soil types) in different spectral bands. Assuming that a mine has large terrain undulations and complex vegetation coverage, LiDAR can generate accurate terrain elevation maps, while multispectral data captures the spectral characteristics of different surface materials, such as the infrared reflection intensity of green vegetation and the visible light reflection intensity of rocks. LiDAR: A technology that measures the distance and shape of a target object by emitting lasers and receiving reflected signals. Multispectral imaging: A technology that collects target information in multiple spectral bands (such as visible light and infrared light).

[0103] The stereo image synthesis module fuses the lidar data and multispectral data, and uses 3D modeling technology to generate a real-life 3D image of the mining area. The fused image contains both high-precision 3D structural information of the terrain and rich surface material and spectral properties. The system uses an algorithm to spatially align the lidar elevation point cloud data with the multispectral ground object reflectance data to generate a 3D model with real material mapping. For example, you can see the true shape of the mine slope and whether there is exposed soil or missing vegetation on the slope surface. Data fusion: Combine data from multiple sources to generate more comprehensive information. Point cloud data: A large number of 3D coordinate points generated by lidar scanning, representing the surface shape of an object.

[0104] Geographical features and monitoring requirements information: First, it is necessary to collect basic information about the mining area, including topography, climate, rock and soil types, mining methods, etc. (these are "geographical features"), as well as potential problems such as cracking and collapse of rock and soil bodies that may occur during mining (these are "monitoring requirements"). First orbit planning strategy: Based on geographical features and monitoring requirements, multiple satellite orbit schemes are designed (i.e., "first orbit planning strategies"). These strategies will be set according to the geographical characteristics of the mining area, the coverage of satellite orbits, etc. Feature-performance value library and performance value: For each orbit planning strategy, there will be a corresponding set of feature-performance value libraries. This library contains the relationship between different orbit strategies and monitoring requirements, and the performance value represents the effectiveness of these orbit strategies for specific monitoring tasks. For example, a certain orbit strategy is particularly suitable for monitoring cracking, but not for collapse. Second monitoring requirement item: Decompose the monitoring requirement information into smaller, specific requirements. For example, cracking monitoring can be refined into small requirements such as "ground crack width" and "crack development speed". Feature extraction and the third feature: For each requirement item, the features related to these requirements, such as "crack morphology" or "soil settlement", are extracted by analyzing the remote sensing data. These features can help determine whether specific monitoring requirements are met. Performance value calculation and threshold determination: Based on the feature-performance value library, the performance value corresponding to each requirement item is calculated and accumulated. If the accumulated value exceeds a certain threshold, the requirement item is considered to be met under this orbital strategy. Adaptation value calculation: The adaptation value of each orbital strategy under specific requirements is calculated through the adaptation performance model. The adaptation value represents the actual performance of the orbital strategy. The higher the value, the better the adaptability of the strategy to the monitoring task. Final orbital planning: Traverse all orbital strategies and select the orbital strategy with the largest adaptation value as the final second orbital planning strategy. Based on this strategy, the optimal satellite orbit is designed to ensure full coverage of the mining area, with special consideration given to the monitoring needs of cracking and local collapse.

[0105] The beneficial effects of the above technical solution are as follows: the track planning module ensures full coverage of the target area, while reducing redundant observations and improving monitoring efficiency. For mining areas with complex terrain and changeable meteorological conditions, the module can dynamically adjust the observation plan to ensure the timeliness and integrity of data acquisition. The data acquisition trigger module uses an automated trigger mechanism to quickly start the remote sensing equipment when the satellite reaches the target area, ensuring the accuracy and spatiotemporal consistency of data acquisition. Time and location records facilitate subsequent data calibration and analysis. The data acquisition module provides multi-dimensional surface information through lidar and multispectral imaging technology, which can not only generate high-precision terrain data, but also obtain the spectral characteristics of vegetation, soil, water bodies, etc. in the mining area, providing comprehensive basic data for mine safety and ecological environment assessment. The three-dimensional real-scene image data generated by the stereoscopic image synthesis module contains both terrain structure and real ground material, which can intuitively reflect the current status of the mine. This image is not only suitable for mine monitoring, but also can be used for visualization, disaster warning and ecological restoration planning.

[0106] In another embodiment, the data analysis unit comprises:

[0107] A data receiving module, used for receiving real-scene three-dimensional data and extracting an initial analysis object from the real-scene three-dimensional data;

[0108] The initial analysis determination module is used to determine the analysis results of the initial analysis object, including the illegal production and construction behaviors of the mine, local potential risk points and corresponding safety hazard types;

[0109] A second analysis and determination module is used to identify and classify multiple new potential analysis objects based on the analysis results, and determine the analysis results for each new potential analysis object until the continuous number of local potential risk points identified in the new analysis results reaches a preset number threshold, or the current analysis object is the last analysis object;

[0110] The analysis result report generation module is used to generate a comprehensive analysis result report based on each analysis result and the corresponding safety hazard type, and output the report content. The report includes a list of illegal production and construction behaviors in the mine, details of local potential risk points, and a classification description of safety hazard types.

[0111] The working principle of the above technical solution is as follows: the data receiving module is used to receive real-scene three-dimensional data input from the outside. Real-scene three-dimensional data refers to real-scene three-dimensional model data generated by remote sensing, laser scanning, drones and other technologies, including terrain, landforms, buildings and other information. The module will pre-process these data and extract the initial analysis objects. For example, key points in the mining area, mining equipment, slope morphology and other targets that may be at risk are extracted from the three-dimensional mining scene as analysis objects. If the three-dimensional model of the mining scene shows that the angle of a slope exceeds the design safety standard, the area becomes the initial analysis object.

[0112] The initial analysis determination module analyzes the initial analysis object and generates analysis results, including: Illegal production and construction behaviors: such as mining beyond the planned area and unregistered new facilities. Local potential risk points: such as unstable slope areas and equipment overload operation. Safety hazard types: classified according to standards, such as slope collapse, subsidence, equipment failure, etc. If unauthorized extended mining is detected in a mining area, and the slope angle near the area is close to the critical value, it is marked as a "high-risk area."

[0113] The second analysis and determination module further identifies and classifies new potential analysis objects based on the analysis results and repeats the above analysis process. The second analysis and determination module will continue to mine areas or objects associated with the initial risk until one of the following conditions is met:

[0114] The number of consecutive events reaches the preset threshold: for example, 5 risk points are identified.

[0115] Object Exhaustive: All relevant objects have been analyzed.

[0116] If the initial risk point is a slope, the system may further analyze the surrounding drainage facilities, transportation roads, mining equipment, etc., and discover multiple hidden dangers related to the slope.

[0117] The analysis result report generation module summarizes all analysis results and generates a comprehensive analysis result report in a structured format: List of illegal production behaviors: such as unauthorized equipment installation or area expansion. Details of local potential risk points: including location, impact range, etc. Classification of safety hazard types: such as "slope stability risk: 3 locations", "equipment operation risk: 2 locations".

[0118] Output reports for users to review or make further decisions. If the report shows that the slope on the east side of the mine is unstable and there is a risk of landslide; the aging of equipment in the southwest leads to transportation interruption.

[0119] The beneficial effects of the above technical solution are: based on three-dimensional data, high-precision analysis of mine scenes is achieved, which is more efficient than traditional manual inspections. Multiple potential risk points can be automatically identified to avoid missing hidden dangers. Automatic classification and gradual expansion of analysis areas ensure comprehensive coverage of mine risks. By setting threshold conditions, the analysis depth can be dynamically adjusted to optimize resource utilization.

[0120] In another embodiment, the monitoring and early warning unit includes:

[0121] The first early warning module is used to judge the potential safety hazard based on the analysis result and obtain the potential safety hazard judgment result;

[0122] The early warning information generation module generates early warning information for the current safety hazard type based on the safety hazard judgment result, wherein the safety hazard type includes rock and soil cracking and local collapse.

[0123] The working principle of the above technical solution is as follows: the first early warning module, based on the analysis results generated by the previous module, makes a comprehensive judgment on the safety hazards in the mine scene and generates a safety hazard judgment result. Safety hazard judgment: refers to a detailed analysis of risks based on the type of hazard, probability of occurrence and possible consequences. For example: using a three-dimensional data model combined with mechanical analysis to determine whether a slope collapse has occurred. Hazard judgment criteria: usually based on industry specifications, historical data and real-time monitoring parameters (such as slope inclination angle, crack width, etc.). Assuming that multiple new cracks appear on the slope of a certain area, and the crack width gradually expands, the module combines the geomechanical model to determine that there is a high probability of local collapse hazards in the area and generates a "high risk" judgment result.

[0124] The early warning information generation module generates early warning information according to the judgment results of the first early warning module. Early warning information: risk warnings transmitted to managers or relevant personnel, including the type of hidden dangers, location, scope of impact and recommended measures. Types of safety hazards: mainly include: Rock and soil cracking: cracks appear in the rocks and soil on the surface or slopes due to stress, which may further develop into landslides and collapses. Local collapse: sudden instability of the rock and soil or structure, resulting in collapse of some areas. For example: For the identified rock and soil cracking hazard, the early warning information generated by the module is: "Warning: Cracks have appeared on the north slope, with a crack depth of 1.5 meters. It is recommended to immediately arrange personnel to check and take support measures."

[0125] The beneficial effects of the above technical solution are as follows: the module judges hidden dangers in real time and generates early warnings, which reduces the reaction time before accidents occur and helps managers take quick action. Through early intervention, hidden dangers are controlled within a controllable range to avoid further escalation of accidents. Information on the type, specific location and severity of hidden dangers is provided to reduce fuzzy judgments and improve the accuracy of early warnings. Targeted early warnings are provided for common problems such as "rock cracking" and "local collapse". Automated early warning information generation reduces manual intervention and improves the efficiency of mine safety management. The early warning information is clear and structured, which is convenient for subsequent analysis and decision-making.

[0126] In another embodiment, the governance management unit includes:

[0127] The early warning assessment module is used to assess the severity of potential safety hazards based on the current early warning information;

[0128] The treatment plan formulation module is used to determine the reinforcement or support measures to be taken in combination with the mine design data when there is a risk of cracking of the rock and soil mass in the early warning information;

[0129] When there is a risk of local collapse in the early warning information, formulate corresponding protection or repair plans according to the development specifications;

[0130] The governance plan optimization module is used to comprehensively consider the requirements of mine design data and development specifications, and optimize the preliminary governance plan. If multiple safety hazards in the early warning information exist at the same time, the governance plan optimization module will comprehensively formulate comprehensive governance measures to improve the overall governance effect;

[0131] The governance plan generation module generates the corresponding mine governance plan based on the optimized governance measures. The governance plan includes specific implementation steps, required materials, time schedule and expected results.

[0132] The working principle of the above technical solution is: the early warning assessment module assesses the severity of potential safety hazards based on the early warning information and provides a basis for the formulation of the governance plan.

[0133] How the early warning assessment module works: The module reads the type of hidden danger in the early warning information (such as "rock cracking" or "partial collapse"). Combined with the hidden danger parameters (such as crack width, depth, expansion speed or collapse area), the severity level (such as minor, moderate, severe) is calculated through industry standards or empirical models. If the width of the slope crack is 2 cm and the length is more than 20 meters, the module assesses it as a "serious" hidden danger and passes it to the next module.

[0134] The control plan formulation module preliminarily formulates control measures based on the type of hidden dangers and mine design data. Rock and soil cracking hazards: read mine design data (such as geological structure, slope angle), and recommend reinforcement or support measures, such as anchor reinforcement or shotcrete. Local collapse hazards: according to development specifications, protection (such as setting up protective nets) or repair (such as backfill reinforcement) plans are recommended. If a local collapse hazard is identified, the module generates a preliminary plan: "It is recommended to use steel mesh to cover the collapsed area, and use concrete for backfill and support."

[0135] The governance scheme optimization module optimizes the initially formulated governance scheme to improve the governance effect. Comprehensively consider the mine design data and development specifications to adjust the details of the plan. When multiple hidden dangers exist at the same time: formulate a joint governance plan based on the relationship between the hidden dangers (such as cracks leading to collapse), reduce repeated governance, and improve resource utilization efficiency. If a slope has both cracks and collapse risks, the optimization module recommends: first anchor support the cracks, and then use concrete to reinforce the collapsed area to ensure the governance effect.

[0136] The governance plan generation module generates specific mine governance plans based on the optimized measures.

[0137] Specific contents of the treatment plan generation module: Implementation steps: such as the installation order of support materials. Required materials: such as anchor specifications and concrete quantity. Time schedule: such as daily construction volume and construction period. Expected effect: such as the improvement data of slope stability after support. For example: A specific description of a treatment plan: "In the first stage, 25mm diameter anchors are used to fix the crack area, which requires 200 anchors and is expected to take 5 days. In the second stage, high-strength concrete is used to cover the collapsed area, and the stability is expected to increase by 95%."

[0138] The beneficial effects of the above technical solutions are as follows: The early warning assessment module conducts quantitative assessment of hidden dangers based on data and specifications, provides a scientific basis, and avoids subjective judgment. The governance plan formulation module can tailor the most suitable preliminary governance plan according to the type of hidden dangers and mining conditions, avoiding the waste of one-size-fits-all or excessive governance. The governance plan optimization module formulates a comprehensive governance plan when multiple hidden dangers coexist, effectively reducing governance conflicts and improving the efficiency of governance resource utilization and overall effect. The plan output by the governance plan generation module contains specific steps, materials and time planning, providing clear guidance for construction personnel to ensure the implementation of governance measures.

[0139] In another embodiment, the potential safety hazard determination includes:

[0140] If the illegal production and construction behavior of the mine exceeds the illegal production threshold and is less than the preset illegal production limit, obtain the local potential risk points;

[0141] If the local potential risk point exceeds the potential risk point threshold and is less than the preset risk point limit, a safety hazard judgment result is generated.

[0142] The working principle of the above technical solution is as follows: Illegal production threshold: used to measure whether the mine production has reached the standard value of preliminary violation. Preset illegal production limit: a higher standard value, indicating the severity of illegal production behavior.

[0143] Local potential risk points are calculated based on production data or behavior patterns in a specific area of ​​a mine and are indicators of potential safety risks in that area. For example, if the mining depth in a certain area exceeds the regulations, the equipment aging rate is high, or there are non-compliant behaviors in the personnel operation records, it may be determined as a local potential risk point.

[0144] Judgment process, the first step: real-time monitoring of the production and construction data of the mine to identify whether it exceeds the illegal production threshold. For example, the daily mining volume of a mine shall not exceed 10,000 tons. If the actual mining volume is 12,000 tons, it exceeds the threshold, but does not reach the preset illegal production limit of 20,000 tons, which will trigger the next step of analysis. The second step: Under the premise of illegal production, analyze whether the local potential risk points in a specific area exceed the set potential risk point threshold. For example, the risk point threshold of a certain area of ​​the mine is 80 points (based on indicators such as equipment aging, mining depth, etc.), but if the area scores 90 points (less than the risk point limit of 100 points), it is determined that there are potential safety hazards in the area.

[0145] Generate safety hazard judgment results, integrate the analysis results of violations and local risk points, and generate a systematic safety hazard report. For example, if there are potential risk points in two areas, the system will mark the two areas for priority treatment and generate a risk level assessment report for decision makers' reference.

[0146] The beneficial effects of the above technical solution are: through clear threshold and limit settings, the risk management process of the mine is systematized and standardized, thereby reducing the deviation of human subjective judgment. The system can capture illegal production and potential risk points in real time, issue early warnings in time, and avoid further expansion of safety hazards. For example, the risk of collapse in areas with high mining depths can be identified at an early stage, and reinforcement measures can be taken. Through the generated safety hazard reports, managers can give priority to the areas with the highest risks, saving manpower and equipment resources. Intervention at the hidden danger stage effectively avoids the occurrence of major safety accidents. For example, timely treatment of equipment aging areas can prevent catastrophic consequences caused by mechanical failures.

[0147] In another embodiment, generating early warning information for the current potential safety hazard type includes:

[0148] Obtain the severity level associated with each hidden danger type as a data group to be analyzed;

[0149] After sorting each data group to be analyzed according to the hidden danger type, the data corresponding to each hidden danger type and severity level is stored in the corresponding data group to be analyzed, thereby obtaining the hidden danger data to be analyzed;

[0150] Obtain safety hazard warning specification data, and obtain warning rule data from the safety hazard warning specification data;

[0151] After noise elimination processing is performed on the warning rule data, the rule feature vector is constructed, and the specification-related data is obtained from the safety hazard warning specification data;

[0152] After clustering the rule feature vectors according to the normative associated data, a data group to be trained is obtained. After question-answering training is performed on the initial model using the data group to be trained, an early warning analysis model is obtained.

[0153] Input the hidden danger data to be analyzed into the preset early warning analysis model one by one to generate early warning information of the current safety hidden dangers;

[0154] Generate graded warning information based on the type and severity of potential safety hazards;

[0155] Input the graded warning information into the warning analysis model and integrate it to generate the final monitoring and warning information data.

[0156] Among them, clustering the regular feature vectors includes:

[0157]

[0158] J represents minimizing the sum of squared distances from each point to the center of its cluster, n represents the total number of data points, K represents the number of clusters, and r represents the number of clusters. ik Indicates whether the data point i belongs to cluster k (if it belongs, it is 1, otherwise it is 0), x i represents a data point, μ k represents the center of cluster k.

[0159] The working principle of the above technical solution is: obtain the hidden danger type (such as cracking of rock and soil, local collapse, etc.) and associate the severity level (such as low, medium, and high). Sort the hidden danger data of each type according to the hidden danger type (such as by occurrence frequency or historical data). Store the sorted data into the corresponding hidden danger type analysis group to form the hidden danger data to be analyzed.

[0160] Early warning specifications and rule processing, obtain safety hazard early warning specification data, including a series of rule data for monitoring and evaluation. Eliminate noise from rule data (eliminate redundant or abnormal information), extract key rule features, and construct rule feature vectors. According to the characteristics of hazard types and rule data, obtain specification-related data (such as the impact of different environments and materials).

[0161] Clustering of rule feature vectors and model training: Use standard associated data to cluster rule feature vectors and group them into data sets to be trained. Conduct question-and-answer training on the initial model (simulating actual scenario questions and answers to improve the model's ability to understand different hidden danger situations) to obtain a more accurate early warning analysis model.

[0162] Hidden danger data input and early warning information generation: The hidden danger data to be analyzed (such as rock and soil cracking frequency, crack width change, local collapse location, etc.) are input into the early warning analysis model one by one. The model generates early warning information of the current hidden danger based on the input data. Based on the hidden danger type and severity level, graded early warning information (such as red warning for high-risk areas) is generated.

[0163] Integrate and generate monitoring and early warning information, input the graded early warning information into the early warning analysis model again, and generate the final monitoring and early warning information data through integration and processing. The output information can be used for real-time monitoring, alarm, and formulation of response measures. For example: cracking and local collapse of rock and soil in mines.

[0164] Hazard type analysis: The severity of rock and soil cracking is divided into low (initial cracks, no expansion), medium (slow crack expansion) and high (rapid crack expansion or associated collapse) based on crack width, depth and expansion speed. Rule feature vector: Construct features based on historical data of the mining area, such as the probability of crack expansion during the rainy season, soil strength, and surface water level changes. Cluster training: Crack data is grouped and trained according to expansion speed and frequency so that the model can judge the risk level of similar situations. Warning information generation: Input real-time monitoring data, such as crack dynamics and vibration sensor data, to generate step-by-step warning information (such as "cracks have expanded after recent heavy rains, reinforcement is recommended").

[0165] The beneficial effects of the above technical solution are: complex hidden danger data are classified, sorted, graded and clustered to achieve systematic management of hidden danger information and help enterprises quickly grasp the status of safety hazards. Through noise processing and the construction of rule feature vectors, false alarms and missed alarms are reduced to ensure the high accuracy of early warning information. Multi-level early warning information is generated based on real-time input hidden danger data to support dynamic decision-making. Question-and-answer training is used to continuously optimize the early warning analysis model to adapt it to more types of hidden danger scenarios and complex risk environments. Graded early warning information enables enterprises to allocate resources reasonably. High-risk areas are given priority, and medium and low-risk areas are subject to conventional monitoring to save manpower and materials.

[0166] In another embodiment, generating early warning information of current safety hazards includes:

[0167] Inputting the first hidden danger data to be analyzed into the early warning analysis model, performing similarity matching on the first hidden danger data to be analyzed in the early warning analysis model, and outputting a first early warning result according to the matching result;

[0168] Starting from the second hidden danger data to be analyzed, the previous warning result is input into the warning analysis model together to obtain the corresponding associated warning result, and the first warning result and all associated warning results are integrated to generate warning information for the current safety hazard type.

[0169] The working principle of the above technical solution is: input of a single hidden danger data and preliminary warning, the first hidden danger data (such as the record of increased crack width in the rock and soil of the mine) is input into the early warning analysis model. These data are usually collected by sensors and monitoring equipment (such as displacement meters or crack meters). The model calculates the similarity between the input data and the hidden danger feature library through feature analysis. The feature library contains the characteristics of common hidden dangers, such as "crack width change rate ≥ 1mm / d, which may indicate local instability". For example, if the crack width increases from 1mm to 5mm, the matching result shows: "Crack expansion may induce local collapse, with a matching degree of 90%". Output the first warning result: The model generates a preliminary warning based on the matching results. For example: "The cracks in the rock and soil body have expanded significantly, and there may be a risk of instability. It is recommended to strengthen monitoring."

[0170] Iterative analysis of associated hidden danger data, starting from the second hidden danger data: when new hidden danger data is input (such as an increase in local settlement of the rock and soil mass), the previous warning result (crack expansion risk) is input into the model as additional information. Generation of associated warning results: The model conducts a comprehensive analysis of the new data and the previous warning results. For example, "crack expansion + significant increase in settlement" may indicate a more serious hidden danger (such as local instability or collapse). Output result example: "The expansion of cracks in the rock and soil mass is accompanied by an increase in settlement, the comprehensive risk level is high, and it may induce local collapse."

[0171] Integrate warning information, integrate the first warning result (crack expansion risk) with the subsequent related warning results (settlement-induced instability risk) to generate the final warning information. Generate warning reports for hidden danger types: Comprehensively analyze all data and generate unified risk assessment reports and recommendations for specific hidden danger types (such as mine rock and soil instability). For example: "The expansion of cracks in the mine rock and soil (5mm) and settlement (15cm) are increasing simultaneously, and the possibility of instability is high. It is recommended to stop operations and start emergency reinforcement measures."

[0172] The first hidden danger data (crack expansion):

[0173] Input: The crack width expands from 1mm to 5mm, with a rate of change of 1mm / d.

[0174] Similarity matching:

[0175] The match degree with the known hidden danger feature library "rapidly expanding cracks may lead to local collapse" is 90%.

[0176] Output warning: Preliminary result: "Cracks are expanding rapidly, risk level: medium, real-time monitoring is recommended."

[0177] The second hidden danger data (increased settlement):

[0178] Input: The settlement of the rock and soil monitoring point increases from 0 to 15 cm, with a change rate of 3 cm / d.

[0179] Correlation analysis: Combined with the previous result (crack expansion), the model infers that settlement and crack expansion may be manifestations of the same hidden danger process. Output correlation warning: "Intensified settlement, crack expansion may cause local instability, risk level: high".

[0180] Integration result: Comprehensive warning information: "The expansion of cracks in the mine rock and soil (5mm / d) is accompanied by intensified settlement (15cm). The instability risk level is high. It is recommended to stop operations and implement reinforcement measures."

[0181] The beneficial effects of the above technical solution are as follows: by gradually analyzing the hidden danger data, the model can dynamically track the development trend of hidden dangers. Correlating and analyzing multiple hidden danger indicators (such as crack expansion, settlement changes, and inclination angle changes) can discover more complex safety issues. Based on similarity matching and correlation analysis, the early warning information generated by the model is more accurate. Comprehensive early warning information provides clear suggestions (such as reinforcement, suspension of operations, evacuation of personnel, etc.) to help managers make quick decisions and avoid disasters.

[0182] In another embodiment, hierarchical warning information is generated according to the type and severity level of the potential safety hazard, including:

[0183] When the hidden danger type is rock and soil cracking and the severity level is low, the primary warning information is triggered;

[0184] When the hidden danger type is rock and soil cracking and the severity level is high, the intermediate warning information is triggered;

[0185] When the hidden danger type is local collapse and the severity level is low, the intermediate warning information is triggered;

[0186] When the hidden danger type is local collapse and the severity level is high, an advanced warning message is triggered.

[0187] The working principle of the above technical solution is as follows: the early warning system first identifies the type of hidden danger based on the input data, such as "rock cracking" or "local collapse". Rock cracking: cracks appear on the surface or inside the rock, which is usually an early sign of instability. Local collapse: local collapse of the rock, which is usually the subsequent development result of cracking, settlement, etc.

[0188] Severity level: The system evaluates the severity of the hidden dangers based on real-time data, such as crack width, settlement, collapse volume, etc. The severity is usually divided into low (no immediate danger) and high (immediate danger exists).

[0189] The triggering conditions of the early warning information, the system sets different early warning levels according to the type and severity of hidden dangers: Rock and soil cracking (low level): triggers the primary early warning, indicating potential hidden dangers. Rock and soil cracking (high level): triggers the intermediate early warning, indicating the risk of accelerated deterioration. Partial collapse (low level): triggers the intermediate early warning, indicating the possibility of danger. Partial collapse (high level): triggers the advanced early warning, prompting immediate disposal.

[0190] Data processing and evaluation process, monitoring data input: sensors or equipment collect real-time monitoring data, such as crack width, settlement or collapse volume. The model identifies the type of hidden danger based on the feature library, such as "cracks extend to 3mm, identified as rock cracking". Severity is assessed based on preset thresholds: Rock cracking: crack width <5mm: low level. Crack width ≥5mm: high level. Local collapse: collapse volume <2 cubic meters: low level. Collapse volume ≥2 cubic meters: high level. Warning trigger: The system triggers the corresponding warning information according to the rules.

[0191] For example: cracking of rock mass, with low severity:

[0192] Input data: Crack width is 2mm.

[0193] Hidden danger identification: determined to be "rock and soil cracking".

[0194] Severity assessment: width <5mm, low level.

[0195] Trigger warning: Primary warning, prompt message: "Risk of crack expansion, regular monitoring is required."

[0196] The rock and soil mass is cracked, with a high degree of severity:

[0197] Input data: Crack width is 8mm.

[0198] Hidden danger identification: determined to be "rock and soil cracking".

[0199] Severity assessment: width ≥5mm, the level is high.

[0200] Trigger warning: Intermediate warning, prompt message: "The cracks are expanding significantly and there is a risk of local instability."

[0201] Local collapse, less severe:

[0202] Input data: The collapsed volume is 1 cubic meter.

[0203] Hazard identification: determined to be “partial collapse”.

[0204] Severity assessment: Volume < 2 cubic meters, the level is low.

[0205] Trigger warning: Intermediate warning, prompt message: "Local collapse phenomenon, protective measures need to be strengthened."

[0206] Local collapse, high severity:

[0207] Input data: The collapsed volume is 3 cubic meters.

[0208] Hazard identification: determined to be “partial collapse”.

[0209] Severity assessment: Volume ≥ 2 cubic meters, the level is high.

[0210] Trigger warning: Advanced warning, prompt message: "Local collapse is serious, it is recommended to stop work and carry out emergency reinforcement."

[0211] The beneficial effects of the above technical solution are: combining the type of hidden danger with the severity, triggering different levels of early warning information, and helping to clarify the management level. The classification of severity reduces overreaction to minor hidden dangers and avoids neglect of major hidden dangers. Different warning levels correspond to different disposal measures. Through the classification of early warning information, decision makers can quickly match the appropriate response strategy. Through the classification of hidden danger types and severity, the system can more accurately identify the development stage of hidden dangers.

[0212] In another embodiment, the initially formulated governance plan is optimized, including:

[0213] Identify a number of potential safety hazards, including cracking and local collapse of the mine rock and soil;

[0214] At each warning moment, identify the types of potential safety hazards that exist and analyze the relationships between them;

[0215] Based on the mutual impact of hidden dangers, optimize the preliminary treatment plan and adjust the details of the treatment measures;

[0216] In the case of multiple potential safety hazards, comprehensive governance measures are formulated to improve the overall governance effect, including:

[0217] When there is a risk of cracking and local collapse of the rock mass, the optimization of the treatment plan includes taking anchor support measures in priority according to the risk of cracking, and implementing concrete reinforcement plans according to the risk of collapse to ensure the treatment effect;

[0218] Based on the relationship between different hidden dangers, combined with the severity of hidden dangers, risk assessment results and governance technical requirements, a joint governance plan is formulated to avoid duplication of governance and improve resource utilization efficiency;

[0219] Adjust the treatment plan in real time according to the actual monitoring data and treatment effect of each hidden danger to ensure the coordination and effectiveness of various measures;

[0220] In the comprehensive treatment measures, according to the temporal and spatial characteristics of different hidden dangers, the corresponding support structure, reinforcement technology and protective materials are adjusted to ensure the feasibility and economy of the treatment plan;

[0221] Among them, the relationship between different hidden dangers is quantitatively analyzed by the following formula:

[0222]

[0223] R represents the comprehensive hidden danger risk assessment value, ω i represents the weight of hidden danger i (e.g., the weight of collapse caused by cracking is higher than that of simple settlement), R i represents the single risk assessment value of hidden danger i (such as crack expansion rate, collapse impact range and other specific indicators), and n represents the total number of hidden danger types; different hidden dangers are weighted by this formula to determine the priority of each hidden danger in the comprehensive treatment plan;

[0224] The optimized governance solution is based on the following steps:

[0225] Obtain the monitoring data of each current hidden danger, and combine the interactions between hidden dangers (such as the risk of collapse caused by crack expansion), and use the preset dynamic model to calculate the comprehensive treatment effect of the current hidden dangers;

[0226] Based on the calculated comprehensive treatment effect, adjust the specific parameters of the treatment measures of anchor support and reinforcement scheme to form the final optimized treatment plan;

[0227] If there is a deviation between the actual effect of the optimization plan and the expected effect, the plan will be re-optimized based on dynamic monitoring data and real-time feedback to form a closed-loop feedback mechanism to improve the governance effect.

[0228] The working principle of the above technical solution is to use monitoring equipment (such as surface deformation sensors, inclinometers, etc.) to collect real-time data of mining rock and soil bodies and identify potential safety hazards. For example, if the width of cracks on the surface of the rock and soil body reaches a critical value, or the rock layer tilts beyond the stable range, the potential dangers of cracking or collapse can be identified.

[0229] Based on the monitoring data and the interaction between hidden dangers, the preset dynamic models (such as crack propagation model and rock mass stability model) are used to evaluate the impact of control measures on the overall risk. Preliminary control measures are designed for different hidden dangers, for example: Cracking risk: anchor support is preferred to enhance the integrity of the structure. Local collapse risk: concrete reinforcement is used to stabilize high-risk areas.

[0230] After implementing the governance, monitor the governance effect and adjust the governance plan through real-time data. If the effect of the plan deviates from expectations, for example, the cracking speed has not been significantly reduced, analyze the influencing factors, adjust parameters such as the number of anchor rods or the amount of concrete, and form a closed-loop feedback mechanism. For multiple hidden dangers, formulate a joint governance plan to avoid repeated governance. For example, when there are cracking and collapse risks at the same time, unify support and reinforcement measures to ensure safety and improve resource utilization efficiency.

[0231] Optimize support and reinforcement plans based on the dynamic changes of hidden dangers in time and space. For example, when cracking is mainly concentrated in a certain mining section, strengthen the anchoring density in that area. As the risk of collapse increases over time, a phased governance strategy can be adopted to gradually expand the scope of governance.

[0232] The beneficial effects of the above technical solutions are: systematic analysis and identification of hidden dangers, and determination of governance priorities through weighted calculations, ensuring timely and effective governance of high-risk areas, and reducing the possibility of mine accidents. Through joint governance solutions, duplicate construction and waste of resources are avoided. For example, the optimized bolt support and concrete reinforcement solutions take into account the hidden dangers of cracking and collapse. The closed-loop feedback mechanism ensures that the governance plan is adjusted in real time with the monitoring data, adapting to the complex changes in the mine environment, and ensuring that the governance measures remain efficient at all times.

[0233] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A monitoring system for cracking and local collapse of rock and soil in mines based on satellite remote sensing, characterized in that: include: Satellite remote sensing unit, used to conduct full-range stereoscopic monitoring of the mining area and obtain real-life three-dimensional data of the mining area; A data analysis unit is used to analyze the real-scene three-dimensional data and obtain analysis results, which include illegal production and construction behaviors of the mine, local potential risk points and types of safety hazards; A monitoring and early warning unit is used to generate early warning information for the current types of safety hazards based on the analysis results. The types of safety hazards include cracking of rock and soil and local collapse; The governance management unit is used to formulate corresponding mine governance plans based on the current early warning information, combined with mine design data and development specifications.

2. The satellite remote sensing-based mining rock and soil cracking and local collapse monitoring system according to claim 1 is characterized in that: Satellite remote sensing unit includes: The orbit planning module is used to plan the optimal observation orbit of the satellite according to the geographical characteristics and monitoring requirements of the mining area to ensure comprehensive coverage of the mining area; The data collection trigger module is used to synchronously trigger the data collection process when the satellite passes through the mining area based on the orbit planning results, and record the collected time and location information in real time; A data acquisition module, used to acquire lidar data and multispectral data after triggering the data acquisition process; The stereo image synthesis module is used to fuse lidar data with multispectral data based on three-dimensional modeling technology to generate real-life three-dimensional image data of the mining area.

3. The satellite remote sensing-based mining rock and soil cracking and local collapse monitoring system according to claim 1 is characterized in that: The data analysis unit includes: A data receiving module, used for receiving real-scene three-dimensional data and extracting an initial analysis object from the real-scene three-dimensional data; The initial analysis determination module is used to determine the analysis results of the initial analysis object, including the illegal production and construction behaviors of the mine, local potential risk points and corresponding safety hazard types; A second analysis and determination module is used to identify and classify multiple new potential analysis objects based on the analysis results, and determine the analysis results for each new potential analysis object until the continuous number of local potential risk points identified in the new analysis results reaches a preset number threshold, or the current analysis object is the last analysis object; The analysis result report generation module is used to generate a comprehensive analysis result report based on each analysis result and the corresponding safety hazard type, and output the report content. The report includes a list of illegal production and construction behaviors in the mine, details of local potential risk points, and a classification description of safety hazard types.

4. The satellite remote sensing-based mining rock and soil cracking and local collapse control monitoring system according to claim 1 is characterized in that: The monitoring and early warning unit includes: The first early warning module is used to judge the potential safety hazard based on the analysis result and obtain the potential safety hazard judgment result; The early warning information generation module generates early warning information for the current safety hazard type based on the safety hazard judgment result, wherein the safety hazard type includes rock and soil cracking and local collapse.

5. The satellite remote sensing-based mining rock and soil cracking and local collapse management monitoring system according to claim 1 is characterized in that: The governance management units include: The early warning assessment module is used to assess the severity of potential safety hazards based on the current early warning information; The treatment plan formulation module is used to determine the reinforcement or support measures to be taken in combination with the mine design data when there is a risk of cracking of the rock and soil mass in the early warning information; When there is a risk of local collapse in the early warning information, formulate corresponding protection or repair plans according to the development specifications; The governance plan optimization module is used to comprehensively consider the requirements of mine design data and development specifications, and optimize the preliminary governance plan. If multiple safety hazards in the early warning information exist at the same time, the governance plan optimization module will comprehensively formulate comprehensive governance measures to improve the overall governance effect; The governance plan generation module generates the corresponding mine governance plan based on the optimized governance measures. The governance plan includes specific implementation steps, required materials, time schedule and expected results.

6. The satellite remote sensing-based mining rock and soil cracking and local collapse monitoring system according to claim 4 is characterized in that: Safety hazard assessment, including: If the illegal production and construction behavior of the mine exceeds the illegal production threshold and is less than the preset illegal production limit, obtain the local potential risk points; If the local potential risk point exceeds the potential risk point threshold and is less than the preset risk point limit, a safety hazard judgment result is generated.

7. The satellite remote sensing-based mining rock and soil cracking and local collapse management monitoring system according to claim 4 is characterized in that: Generate early warning information for current safety hazard types, including: Obtain the severity level associated with each hidden danger type as a data group to be analyzed; After sorting each data group to be analyzed according to the hidden danger type, the data corresponding to each hidden danger type and severity level is stored in the corresponding data group to be analyzed, thereby obtaining the hidden danger data to be analyzed; Obtain safety hazard warning specification data, and obtain warning rule data from the safety hazard warning specification data; After noise elimination processing is performed on the warning rule data, the rule feature vector is constructed, and the specification-related data is obtained from the safety hazard warning specification data; After clustering the rule feature vectors according to the normative associated data, a data group to be trained is obtained. After question-answering training is performed on the initial model using the data group to be trained, an early warning analysis model is obtained. Input the hidden danger data to be analyzed into the preset early warning analysis model one by one to generate early warning information of the current safety hidden dangers; Generate graded warning information based on the type and severity of potential safety hazards; The graded warning information is input into the warning analysis model and integrated to generate the final monitoring and warning information data.

8. The satellite remote sensing-based monitoring system for cracking and local collapse of rock and soil in mines according to claim 7 is characterized in that: Generate early warning information of current safety hazards, including: Inputting the first hidden danger data to be analyzed into the early warning analysis model, performing similarity matching on the first hidden danger data to be analyzed in the early warning analysis model, and outputting a first early warning result according to the matching result; Starting from the second hidden danger data to be analyzed, the previous warning result is input into the warning analysis model together to obtain the corresponding associated warning result, and the first warning result and all associated warning results are integrated to generate warning information for the current safety hazard type.

9. The satellite remote sensing-based mining rock and soil cracking and local collapse control monitoring system according to claim 8, characterized in that: Generate graded warning information based on the type and severity of potential safety hazards, including: When the hidden danger type is rock and soil cracking and the severity level is low, the primary warning information is triggered; When the hidden danger type is rock and soil cracking and the severity level is high, the intermediate warning information is triggered; When the hidden danger type is local collapse and the severity level is low, the intermediate warning information is triggered; When the hidden danger type is local collapse and the severity level is high, an advanced warning message is triggered.

10. The satellite remote sensing-based monitoring system for cracking and local collapse of rock and soil in mines according to claim 5, characterized in that: Optimize the initially formulated governance plan, including: At each warning moment, identify the types of potential safety hazards that exist and analyze the relationships between them; Based on the mutual impact of hidden dangers, optimize the preliminary treatment plan and adjust the details of the treatment measures; In the case of multiple potential safety hazards, comprehensive governance measures are formulated to improve the overall governance effect, including: When there is a risk of cracking and local collapse of the rock mass, the optimization of the treatment plan includes taking anchor support measures in priority according to the risk of cracking, and implementing concrete reinforcement plans according to the risk of collapse to ensure the treatment effect; Based on the relationship between different hidden dangers, combined with the severity of hidden dangers, risk assessment results and governance technical requirements, a joint governance plan is formulated to avoid duplication of governance and improve resource utilization efficiency; Adjust the treatment plan in real time according to the actual monitoring data and treatment effect of each hidden danger to ensure the coordination and effectiveness of various measures; In the comprehensive treatment measures, according to the temporal and spatial characteristics of different hidden dangers, the corresponding support structure, reinforcement technology and protective materials are adjusted to ensure the feasibility and economy of the treatment plan; The optimized governance solution is based on the following steps: Obtain the monitoring data of each current hidden danger, and combine the interactions between hidden dangers to calculate the comprehensive treatment effect of the current hidden dangers using the preset dynamic model; Based on the calculated comprehensive treatment effect, adjust the specific parameters of the treatment measures of anchor support and reinforcement scheme to form the final optimized treatment plan; If there is a deviation between the actual effect of the optimization plan and the expected effect, the plan will be re-optimized based on dynamic monitoring data and real-time feedback to form a closed-loop feedback mechanism to improve the governance effect.

Images