Intelligent efficient comprehensive mechanized coal mining system and method

Abstract

The invention relates to the field of mechanical coal mining, and discloses an intelligent efficient comprehensive mechanical coal mining system and method.The method comprises the steps that the crack distribution condition of a coal mine collection area is recognized, the hidden fault position and the stress abnormal degree of the coal mine collection area are analyzed, and a three-dimensional risk distribution diagram of the coal mine collection area is constructed; performing collection path planning on a coal face corresponding to the coal mine collection area to obtain a collection path, identifying spatial constraint features of the coal mine collection area, and constructing a mining optimization instruction of the coal mine collection area; acquiring acquisition data generated during coal mine acquisition, identifying mining mechanical characteristics of acquisition equipment, analyzing a damage change rule of a coal mine acquisition area, and calculating energy transfer efficiency of the acquisition equipment; and performing coal mine acquisition process optimization on the acquisition equipment to obtain a coal mine acquisition optimization process, and performing coal mine acquisition on the coal mine acquisition area. According to the invention, the energy consumption of intelligent efficient comprehensive mechanized coal mining can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of mechanized coal mining, and particularly to an intelligent and efficient fully mechanized coal mining system and method. Background Art

[0002] In the modern coal mining industry, intelligent and efficient fully mechanized coal mining plays a crucial role. From ensuring stable energy supply to meeting the large demand for coal in industrial production and social life, to enhancing the economic benefits and market competitiveness of coal enterprises, the development level of intelligent and efficient fully mechanized coal mining technology directly affects the development trend of the industry and profoundly impacts the national energy security pattern and the stable operation of the economic society. Achieving intelligent and efficient coal mining can greatly improve production efficiency, reduce labor costs, reduce safety accidents, and promote the coal industry towards modernization and intelligentization.

[0003] Currently, coal mining mainly relies on traditional fully mechanized coal mining methods. This method conducts coal mining through the coordinated operation of equipment such as coal shearers, scraper conveyors, and hydraulic supports. In actual operation, the coal shearer cuts coal according to a predefined program, the scraper conveyor transports the coal out, and the hydraulic support ensures the safety of the working space. However, the coal mining environment is extremely complex. The underground geological conditions vary widely, the thickness and hardness of coal seams are different, and geological structures such as faults and folds frequently appear. The mining methods and techniques for different geological terrains are also different. Since the operation procedures of traditional fully mechanized coal mining methods are relatively fixed, it is difficult to adjust the coal mining strategy in a timely manner according to the real-time changing geological conditions and mining environment, resulting in low mining efficiency and serious resource waste. Summary of the Invention

[0004] The present invention provides an intelligent and efficient fully mechanized coal mining system and method, and its main purpose is to reduce the energy consumption of intelligent and efficient fully mechanized coal mining.

[0005] To achieve the above object, an intelligent and efficient fully mechanized coal mining system provided by the present invention includes: a risk distribution map construction module, a mining instruction construction module, a transfer efficiency calculation module, and a coal mine acquisition module;

[0006] The risk distribution map construction module is used to obtain geological exploration data of the coal mine acquisition area, identify the crack distribution status of the coal mine acquisition area by using the geological exploration data, analyze the location of hidden faults and the degree of stress anomaly in the coal mine acquisition area based on the crack distribution status, and construct a three-dimensional risk distribution map of the coal mine acquisition area based on the location of the hidden faults and the degree of stress anomaly;

[0007] The mining instruction construction module is used to utilize the three-dimensional risk distribution map to plan the collection path for the coal mining face corresponding to the coal mine collection area, obtain the collection path, identify the spatial constraint characteristics of the coal mine collection area, and construct the mining optimization instruction for the coal mine collection area based on the spatial constraint characteristics and the collection path;

[0008] The transfer efficiency calculation module is used to, based on the mining optimization instruction, utilize the collection equipment to collect coal in the coal mine collection area, obtain the collection data generated during coal collection, utilize the collection data to identify the mining mechanical characteristics of the collection equipment, analyze the damage change law of the coal mine collection area based on the mining mechanical characteristics, and calculate the energy transfer efficiency of the collection equipment based on the damage change law;

[0009] The coal mine collection module is used to utilize the collection data to analyze the energy consumption distribution characteristics of the collection equipment during coal mine collection, calculate the vibration matching coefficient of the collection equipment according to the energy consumption distribution characteristics, optimize the coal mine collection process of the collection equipment based on the vibration matching coefficient and the energy transfer efficiency to obtain the optimized coal mine collection process, and utilize the collection equipment to collect coal in the coal mine collection area based on the optimized coal mine collection process.

[0010] Optionally, the identification of the crack distribution condition of the coal mine collection area by using the geological exploration data includes:

[0011] Utilize the geological exploration data to identify the lithology information of different strata in the coal mine collection area;

[0012] Query the historical geological structure data and crack development data of the coal mine collection area to obtain the historical geological data;

[0013] Perform grid processing on the lithology information and the historical geological data to obtain the grid geological data;

[0014] Based on the grid geological data, construct the crack contour image of the coal mine collection area;

[0015] Based on the crack contour image, identify the crack characteristics of the coal mine collection area, and analyze the crack distribution condition of the coal mine collection area based on the crack characteristics.

[0016] Optionally, the analysis of the concealed fault position and stress anomaly degree of the coal mine collection area based on the crack distribution condition includes:

[0017] Query the geological model and borehole exploration information of the coal mine collection area;

[0018] Based on the geological model and the borehole exploration information, perform microseismic signal scanning on the coal mine collection area to obtain a stress field evolution map;

[0019] Analyze the spatial correlation characteristics between the fracture distribution and the stress field evolution map;

[0020] Based on the spatial correlation characteristics, analyze the location of hidden faults and the degree of stress anomaly in the coal mine collection area.

[0021] Optionally, based on the location of the hidden faults and the degree of stress anomaly, construct a three-dimensional risk distribution map of the coal mine collection area, including:

[0022] Use the location of the hidden faults and the degree of stress anomaly to construct a risk characteristic matrix of the coal mine collection area;

[0023] Assign risk weight coefficients to the matrix elements in the risk characteristic matrix to obtain a weighted risk coefficient matrix;

[0024] Use the weighted risk coefficient matrix to construct a dynamic risk field model of the coal mine collection area;

[0025] Perform three-dimensional visualization processing on the dynamic risk field model to obtain a three-dimensional risk distribution map.

[0026] Optionally, use the three-dimensional risk distribution map to plan the collection path for the coal mining face corresponding to the coal mine collection area, including:

[0027] Perform grid division on the three-dimensional risk distribution map to obtain a grid map;

[0028] Use the grid map to construct an initial path for the coal mining face corresponding to the coal mine collection area;

[0029] Perform path risk assessment on the initial path, and perform multi-objective optimization on the initial collection path according to the assessment results of the path risk assessment to obtain a collection path.

[0030] Optionally, based on the spatial constraint characteristics and the collection path, construct an extraction optimization instruction for the coal mine collection area, including:

[0031] Based on the spatial constraint characteristics, construct an extraction cutting parameter instruction for the coal mine collection area;

[0032] Identify the spatial conditions and roof stability at different positions in the collection path to construct an extraction support parameter instruction for the coal mine collection area;

[0033] Identify the path direction and spatial constraints in the acquisition path to construct the mining operation direction and attitude instructions for the coal mine acquisition area;

[0034] Based on the spatial constraint features and the acquisition path, construct the mining speed and acceleration instructions for the coal mine acquisition area;

[0035] Based on the mining cutting parameter instructions, the mining support parameter instructions, the mining operation direction and attitude instructions, and the mining speed and acceleration instructions, determine the mining optimization instructions for the coal mine acquisition area.

[0036] Optionally, the identifying the mining mechanical characteristics of the acquisition equipment by using the acquisition data includes:

[0037] Perform multi-dimensional feature partitioning on the acquisition data to obtain a partitioned feature data set;

[0038] Use the partitioned feature data set to identify the cutting force and traction resistance of the acquisition equipment to determine the first mechanical characteristics of the acquisition equipment;

[0039] Use the partitioned feature data set to identify the transportation resistance and chain tension of the acquisition equipment to determine the second mechanical characteristics in the acquisition equipment;

[0040] Use the partitioned feature data set to identify the supporting force and pushing force of the acquisition equipment to determine the third mechanical characteristics in the acquisition equipment;

[0041] Based on the first mechanical characteristics, the second mechanical characteristics, and the third mechanical characteristics, determine the mining mechanical characteristics of the acquisition equipment.

[0042] Optionally, the analyzing the damage change law of the coal mine acquisition area based on the mining mechanical characteristics includes:

[0043] Perform wavelet fusion on the mining mechanical characteristics to obtain fusion data;

[0044] Use the fusion data to perform damage field modeling on the coal mine acquisition area to obtain a dynamic damage evolution field;

[0045] Use the dynamic damage evolution field to identify the damage hot spots in the coal mine acquisition area;

[0046] Based on the damage hot spots, analyze the damage change law of the coal mine acquisition area.

[0047] Optionally, the calculating the energy transfer efficiency of the acquisition equipment based on the damage change law includes:

[0048] Identify the cutting resistance and traction speed of the acquisition device based on the damage variation law;

[0049] Calculate the effective crushing energy of the acquisition device based on the cutting resistance and the traction speed;

[0050] Calculate the total input energy of the device of the acquisition device;

[0051] Calculate the energy transfer efficiency of the acquisition device based on the effective crushing energy and the total input energy of the device.

[0052] An intelligent and efficient fully mechanized coal mining method, characterized in that the method includes:

[0053] Obtain the geological exploration data of the coal mine acquisition area, use the geological exploration data to identify the crack distribution condition of the coal mine acquisition area, based on the crack distribution condition, analyze the location of the hidden fault and the stress anomaly degree in the coal mine acquisition area, and based on the location of the hidden fault and the stress anomaly degree, construct a three-dimensional risk distribution map of the coal mine acquisition area;

[0054] Use the three-dimensional risk distribution map to plan the acquisition path for the coal mining face corresponding to the coal mine acquisition area to obtain the acquisition path, identify the spatial constraint characteristics of the coal mine acquisition area, and based on the spatial constraint characteristics and the acquisition path, construct the mining optimization instruction for the coal mine acquisition area;

[0055] Based on the mining optimization instruction, use the acquisition device to carry out coal mine acquisition in the coal mine acquisition area, and obtain the acquisition data generated during coal mine acquisition. Use the acquisition data to identify the mining mechanical characteristics of the acquisition device. Based on the mining mechanical characteristics, analyze the damage variation law of the coal mine acquisition area. Based on the damage variation law, calculate the energy transfer efficiency of the acquisition device;

[0056] Use the acquisition data to analyze the energy consumption distribution characteristics of the acquisition device during coal mine acquisition. According to the energy consumption distribution characteristics, calculate the vibration matching coefficient of the acquisition device. Based on the vibration matching coefficient and the energy transfer efficiency, optimize the coal mine acquisition process of the acquisition device to obtain the optimized coal mine acquisition process. Based on the optimized coal mine acquisition process, use the acquisition device to carry out coal mine acquisition in the coal mine acquisition area.

[0057] In the embodiments of the present invention, based on the application requirements, the present invention first obtains the geological exploration data of the coal mine collection area, which is collected by means of a radar detector array and physical sensors, covering information such as rock chemical composition and lithology, providing basic support for coal mining operations, helping the mining team initially master the geological conditions, and using the geological exploration data to identify the crack distribution conditions to help users anticipate mining problems in advance and avoid potential safety hazards that may be brought by areas with dense cracks; further, the present invention queries the geological model and borehole exploration information, and performs microseismic signal scanning to obtain a stress field evolution map, so as to analyze the spatial correlation characteristics between it and the crack distribution, determine the location of hidden faults and the degree of stress anomalies, provide a basis for avoiding potential hazard areas and improving the safety factor when planning the collection plan, and then constructs a three-dimensional risk distribution map based on the location of hidden faults and the degree of stress anomalies to present a panoramic view of risks for the mining team, so as to quickly judge the risk areas and optimize the mining plan; further, the present invention obtains a collection path through steps such as grid division, construction of an initial collection path, path risk assessment, and multi-objective optimization. The obtained collection path can enable the coal mining equipment to avoid high-risk areas, provide standardized and scientific guidance for coal mining operations, improve the mining efficiency and reduce energy consumption, and when executing the mining optimization instruction for coal mine collection, obtain collection data and use these data to identify the mining mechanical characteristics, determine the cutting force, traction resistance, etc. through multi-dimensional feature division, provide a basis for analyzing energy consumption, and analyze the damage change law of the coal mine collection area based on the mining mechanical characteristics. Through steps such as wavelet fusion, damage field modeling, identification of damage hotspots, and use of a fuzzy inference system, understand the change of collection efficiency; furthermore, the present invention uses the collection data to analyze the energy consumption distribution characteristics, classifies and statistically analyzes the energy consumption data according to equipment systems, operation links, and time periods, finds out the peak and trough areas of energy consumption, and provides a direction for energy-saving optimization. Therefore, the present invention can reduce the energy consumption of intelligent and efficient fully mechanized coal mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 FIG. is a functional module diagram of an intelligent and efficient fully mechanized coal mining system provided by an embodiment of the present invention;

[0059] Figure 2 FIG. is a schematic flowchart of an intelligent and efficient fully mechanized coal mining method provided by an embodiment of the present invention;

[0060] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] In addition, the sequence of steps in the following method embodiments is only an example and is not strictly limited.

[0063] In fact, the server devices deployed in the intelligent and efficient fully mechanized coal mining system may be composed of one or more devices. The above intelligent and efficient fully mechanized coal mining system can be implemented as: a service instance, a virtual machine, or a hardware device. For example, the intelligent and efficient fully mechanized coal mining system can be implemented as a service instance deployed on one or more devices in a cloud node. Briefly, the intelligent and efficient fully mechanized coal mining system can be understood as a software deployed on a cloud node for providing intelligent and efficient fully mechanized coal mining services to each client. Or, the intelligent and efficient fully mechanized coal mining system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. An application software for managing each client is installed in the virtual machine. Or, the intelligent and efficient fully mechanized coal mining system can also be implemented as a server composed of many identical or different types of hardware devices, and one or more hardware devices are set to provide intelligent and efficient fully mechanized coal mining services to each client.

[0064] In terms of implementation form, the intelligent and efficient fully mechanized coal mining system and the client adapt to each other. That is, if the intelligent and efficient fully mechanized coal mining system is an application installed on a cloud service platform, then the client is a client that establishes a communication connection with the application; or if the intelligent and efficient fully mechanized coal mining system is implemented as a website, then the client is implemented as a web page; or if the intelligent and efficient fully mechanized coal mining system is implemented as a cloud service platform, then the client is implemented as a small program in an instant messaging application.

[0065] Refer to Figure 1 As shown, it is a functional module diagram of the intelligent and efficient fully mechanized coal mining system provided by an embodiment of the present invention.

[0066] The intelligent and efficient fully mechanized coal mining system 100 described in the present invention can be set in a cloud server. In terms of implementation form, it can be used as one or more service devices, or can be installed as an application on the cloud (such as a server or server cluster for intelligent and efficient fully mechanized coal mining), or can also be developed into a website. According to the functions achieved, the intelligent and efficient fully mechanized coal mining system 100 includes a risk distribution map construction module 101, a mining instruction construction module 102, a transmission efficiency calculation module 103, and a coal mine acquisition module 104.

[0067] In the embodiments of the present invention, in the tracking based on intelligent and efficient fully mechanized coal mining, each of the above modules can be independently implemented and called with other modules. Here, the call can be understood as that a certain module can be connected to multiple modules of another type and provide corresponding services for the multiple modules it is connected to. In the intelligent and efficient fully mechanized coal mining system provided by the embodiments of the present invention, without modifying the program code, the applicable range of the intelligent and efficient fully mechanized coal mining architecture can be adjusted by adding modules and directly calling them, so as to achieve cluster-level horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the intelligent and efficient fully mechanized coal mining system. In practical applications, the above modules can be set in the same device or different devices, or can also be set in virtual devices, such as service instances in a cloud server.

[0068] Next, specific embodiments will be used to respectively illustrate the various components and specific working processes of the intelligent and efficient fully mechanized coal mining system.

[0069] The risk distribution map construction module 201 is used to obtain the geological exploration data of the coal mine acquisition area, use the geological exploration data to identify the crack distribution status of the coal mine acquisition area, and based on the crack distribution status, analyze the hidden fault position and stress anomaly degree of the coal mine acquisition area, and construct a three-dimensional risk distribution map of the coal mine acquisition area based on the hidden fault position and the stress anomaly degree.

[0070] In the embodiments of the present invention, obtaining the geological exploration data of the coal mine acquisition area can provide basic data support for coal mining operations to help the mining team initially understand the geological conditions of the mining area.

[0071] Among them, the geological exploration data refers to the data used to describe the geology and geomorphic structure of the coal mine acquisition area, including information such as the chemical composition and element content of underground rocks, soil, and water samples, and information such as the lithology, structure, and tectonics of the rocks.

[0072] Optionally, the geological exploration data can be obtained by deploying a radar detector array and physical sensors in the coal mine acquisition area.

[0073] Furthermore, by using the geological exploration data to identify the fracture distribution in the coal mine collection area, the embodiments of the present invention can help users anticipate potential problems during the mining process in advance. For example, in areas with dense fractures, coal may be more easily broken, resulting in coal dust flying during mining, increasing safety hazards, and thus avoiding danger.

[0074] Among them, the fracture distribution refers to various characteristics of fractures in the coal mine collection area and their spatial distribution, such as the length, width, density distribution, and connectivity of fractures.

[0075] As an embodiment of the present invention, using the geological exploration data to identify the fracture distribution in the coal mine collection area includes: using the geological exploration data to identify the lithological information of different strata in the coal mine collection area, querying the historical geological structure data and fracture development data of the coal mine collection area to obtain historical geological data, performing grid processing on the lithological information and the historical geological data to obtain grid geological data, constructing a fracture contour image of the coal mine collection area based on the grid geological data, identifying the fracture characteristics of the coal mine collection area based on the fracture contour image, and analyzing the fracture distribution in the coal mine collection area based on the fracture characteristics.

[0076] Among them, the lithological information refers to various attributes of rocks in different strata of the coal mine collection area, such as the composition of rocks, and the fracture contour image is a visual image generated based on the grid geological data.

[0077] Optionally, the lithological information can be obtained by using geological exploration data in combination with spectral analysis technology to analyze the absorption and reflection characteristics of different strata rocks to electromagnetic waves and light and the microscopic structure of rocks. The historical geological data can be obtained through the coal mine historical geological database. The grid geological data can be obtained by using the Kriging interpolation method in geostatistics to perform interpolation calculations on the lithological information and the historical geological data according to a certain spatial grid scale, and then using geological modeling software to construct a grid geological data model. The fracture contour image can be generated by using digital image processing technology to perform edge enhancement and threshold segmentation on the grid geological data. The fracture characteristics can be obtained by using an edge detection algorithm to process the fracture contour image and extract geometric characteristics such as the length, width, and direction of the fractures. The fracture distribution can be identified based on the extracted fracture characteristic data by using a spatial analysis tool, such as the spatial analysis module of ArcGIS software, to analyze the distribution density and trend of fractures in different strata and different regions.

[0078] In an embodiment of the present invention, by analyzing the location of hidden faults and the degree of stress anomaly in the coal mine collection area based on the crack distribution condition, the specific locations with potential hazards in the coal mine collection area and the degree of hazards in some areas can be determined. Furthermore, when planning the collection scheme, these hazards can be avoided as much as possible to improve the safety factor of coal mine collection.

[0079] Among them, the location of the hidden fault refers to a fault that is not easily directly observed or identified on the ground surface or in existing geological exploration data, and the degree of stress anomaly refers to the internal force per unit area borne within an object.

[0080] As an embodiment of the present invention, analyzing the location of hidden faults and the degree of stress anomaly in the coal mine collection area based on the crack distribution condition includes: querying the geological model and borehole exploration information of the coal mine collection area, performing microseismic signal scanning on the coal mine collection area based on the geological model and the borehole exploration information to obtain a stress field evolution map, analyzing the spatial correlation characteristics between the crack distribution condition and the stress field evolution map, and analyzing the location of hidden faults and the degree of stress anomaly in the coal mine collection area based on the spatial correlation characteristics.

[0081] Among them, the geological model refers to a digital or physical simulation and abstract representation of the geological conditions in the coal mine collection area, and the borehole exploration information refers to relevant data obtained through borehole operations in the coal mine collection area, such as the position coordinates of the boreholes and the core sample information at different depths.

[0082] Optionally, querying the geological model and borehole exploration information of the coal mine collection area can be achieved by querying the database management system of the coal mine collection area. Performing microseismic signal scanning on the coal mine collection area to obtain a stress field evolution map can be achieved by reasonably arranging multiple microseismic sensors in the coal mine collection area, using microseismic monitoring technology to capture microseismic signals generated by rock fractures in real time, and then using the microseismic signals to draw a stress field evolution map. Analyzing the spatial correlation characteristics between the crack distribution condition and the stress field evolution map can be achieved by using a spatial analysis tool (GIS) to calculate indicators such as the distance and overlap degree between the crack location and the stress concentration area. Analyzing the location of hidden faults and the degree of stress anomaly in the coal mine collection area based on the spatial correlation characteristics can be achieved by analyzing the spatial correlation characteristics. For example, when it is found that the crack distribution shows regular linear characteristics and highly coincides with the stress concentration area, it indicates that there is a hidden fault here. Then, by comparing the stress value in the stress field evolution map with the normal stress range in this area and calculating the stress deviation rate, the degree of stress anomaly can be determined.

[0083] Furthermore, in the embodiment of the present invention, by constructing the three-dimensional risk distribution map of the coal mine collection area based on the concealed fault position and the stress anomaly degree, a clear risk panorama can be provided for the mining team, so as to quickly judge which areas have high risks and which areas are relatively safe, thereby optimizing the mining plan.

[0084] Among them, the three-dimensional risk distribution map refers to a visualization tool that intuitively displays the risk status of the coal mine collection area.

[0085] As an embodiment of the present invention, constructing the three-dimensional risk distribution map of the coal mine collection area based on the concealed fault position and the stress anomaly degree includes: using the concealed fault position and the stress anomaly degree to construct a risk feature matrix of the coal mine collection area, assigning risk weight coefficients to the matrix elements in the risk feature matrix to obtain a weighted risk coefficient matrix, using the weighted risk coefficient matrix to construct a dynamic risk field model of the coal mine collection area, and performing three-dimensional visualization processing on the dynamic risk field model to obtain a three-dimensional risk distribution map.

[0086] Among them, the risk feature matrix refers to a data structure that organizes and represents various factors related to risks in the coal mine collection area in the form of a matrix. The weighted risk coefficient matrix refers to a matrix obtained by considering the influence degree of different risk factors on the overall risk based on the risk feature matrix. The dynamic risk field model refers to a dynamic mathematical description and three-dimensional space modeling of the risk status of the coal mine collection area.

[0087] Optionally, for the risk feature matrix, data fusion algorithms such as Kalman filtering or Bayesian fusion method can be used to perform spatio-temporal alignment processing on the concealed fault position data and the stress anomaly degree data, and then fuse them to obtain fusion data, and then use the fusion data to construct it. The weighted risk coefficient matrix can be determined by using the analytic hierarchy process to assign weights to each risk factor in the risk feature matrix, and then perform weighted calculation on the weighted risk coefficient matrix with the determined risk factor assignment weights. For the dynamic risk field model, three-dimensional geological modeling software such as Surpac can be used to perform three-dimensional grid processing on the weighted risk coefficient matrix. The three-dimensional risk distribution map can be obtained by using computer graphics technology and spatio-temporal analysis algorithms to perform spatio-temporal coupling rendering on the dynamic risk field model.

[0088] The mining instruction construction module 202 is used to use the three-dimensional risk distribution map to plan the collection path for the coal mining face corresponding to the coal mine collection area to obtain a collection path, identify the spatial constraint features of the coal mine collection area, and construct the mining optimization instruction of the coal mine collection area based on the spatial constraint features and the collection path.

[0089] In an embodiment of the present invention, by using the three-dimensional risk distribution map, a collection path planning is performed on the coal mining face corresponding to the coal mine collection area, and the obtained collection path can enable the coal mining equipment to avoid high-risk areas as much as possible, reducing potential safety hazards during the mining process.

[0090] As an embodiment of the present invention, the using the three-dimensional risk distribution map to perform collection path planning on the coal mining face corresponding to the coal mine collection area to obtain a collection path includes: performing grid division on the three-dimensional risk distribution map to obtain a grid map, using the grid map to construct an initial path of the coal mining face corresponding to the coal mine collection area, and performing path risk assessment on the initial path, so as to perform multi-objective optimization on the initial collection path according to the evaluation result of the path risk assessment to obtain a collection path.

[0091] Among them, the grid map refers to a map representation form based on a grid data structure, and the fuzzy judgment matrix refers to a matrix constructed on the basis of fuzzy mathematics theory for dealing with decision-making problems with fuzziness and uncertainty.

[0092] Optionally, the performing grid division on the three-dimensional risk distribution map to obtain a grid map can be implemented by using OpenCV tools. The initial path can be obtained by using a heuristic search algorithm, such as the A* algorithm, based on the risk values and spatial position relationships of each grid unit in the grid map, with the starting point and ending point of the coal mining face as the constraint conditions for path search, and then a series of obtained path sets are used as the initialized path. The performing path risk assessment on the initial path can first determine multiple factors affecting path risk, such as risk level, path length, and slope, etc., then assign weights to each factor, and then construct a fuzzy judgment matrix according to the influence degree of each factor on different sections of the initial collection path, and then use the fuzzy judgment matrix for evaluation. The performing multi-objective optimization on the initial collection path according to the evaluation result of the path risk assessment to obtain a collection path can use the fuzzy analytic hierarchy process, combine the fuzzy judgment matrix to calculate the risk scores of each section on the initial path, accumulate the risk scores of each section to obtain the comprehensive risk assessment value of the entire initial path. For example, for an initial path, the weights of the three factors of risk level, path length, and slope are set to 0.5, 0.3, and 0.2 respectively. At a certain section, according to the three-dimensional risk distribution map, the risk level is judged to be high, corresponding to a risk score of 8 points; the path length is relatively long, with a score of 6 points; the slope is relatively steep, with a score of 7 points. Then the risk score of this section is 7.2 points. Path screening is performed according to the specific calculated scores, and the path with the highest score or the top three scores is selected. Specifically, the selection needs to be combined with the actual application.

[0093] Furthermore, by identifying the spatial constraint features of the coal mine collection area, the embodiments of the present invention can further optimize the collection path, enabling the path planning to not only consider risk avoidance but also fully adapt to geological conditions and the spatial requirements of equipment operation, thereby improving the feasibility and safety of the mining plan.

[0094] Among them, the spatial constraint features refer to the spatial form of the coal seam, the spatial limitations caused by geological structures such as faults, and the spatial operation range limitations of mining equipment.

[0095] Optionally, the spatial constraint features can be obtained by collecting geological and mining data of the coal mine collection area through geological exploration and monitoring systems, analyzing the constraints of geological structures, coal seam occurrence, equipment, and technology on space using GIS, geological modeling software, etc., and then using data fusion to determine the spatial constraints at different positions and stages.

[0096] Furthermore, by constructing the mining optimization instructions for the coal mine collection area based on the spatial constraint features and the collection path, the embodiments of the present invention can provide clear operation guidance for subsequent coal mining operations, making the coal mining process more standardized and scientific, which helps to improve the mining efficiency and reduce energy consumption.

[0097] Among them, the mining optimization instructions refer to a set of instructions based on the spatial constraint features and the collection path of the coal mine collection area, which are used to guide the coal mine collection equipment to perform efficient and safe mining operations, and include cutting parameter instructions, mining support parameter instructions, mining operation direction and attitude instructions, and mining speed and acceleration instructions.

[0098] As an embodiment of the present invention, constructing the mining optimization instructions for the coal mine collection area based on the spatial constraint features and the collection path includes: constructing the mining cutting parameter instructions for the coal mine collection area based on the spatial constraint features, identifying the spatial conditions and roof stability at different positions in the collection path to construct the mining support parameter instructions for the coal mine collection area, identifying the path trend and spatial limitations in the collection path to construct the mining operation direction and attitude instructions for the coal mine collection area, constructing the mining speed and acceleration instructions for the coal mine collection area based on the spatial constraint features and the collection path, and determining the mining optimization instructions for the coal mine collection area based on the mining cutting parameter instructions, the mining support parameter instructions, the mining operation direction and attitude instructions, and the mining speed and acceleration instructions.

[0099] Among them, the mining cutting parameter instruction refers to a series of working parameter instructions set for the cutting mechanism of the coal mining equipment according to the spatial constraint characteristics of the coal mine collection area, such as factors like the thickness, hardness, and geological structure of the coal seam. The spatial conditions refer to various situations related to the space aspect in the coal mine collection area during mining operations, including the size, shape, height, and width of the mining space, as well as the restrictions on the mining space caused by geological structures (such as faults and folds) existing in the space, and factors such as the operating space and passage space of the equipment in this space. The roof stability refers to the ability of the roof rock stratum above the coal seam to maintain its own stability without caving or deformation damage during the coal mine mining process. The mining support parameter instruction refers to the parameter instruction for the support operation formulated to ensure the safety of the mining operation based on the spatial conditions and roof stability conditions at different positions in the collection path. The mining operation direction and attitude instruction refers to the instruction for determining the operation direction and attitude adjustment of the coal mining equipment according to the trend of the collection path, spatial restrictions, and the characteristics of the equipment itself. The mining speed and acceleration instruction refers to the instruction for setting the operation speed and acceleration of the coal mining equipment by comprehensively considering factors such as spatial constraint characteristics, the complexity of the collection path, equipment performance, coal production requirements, and safety factors.

[0100] Optionally, the mining cutting parameter instruction can be obtained by combining information such as the coal seam thickness, hardness, and equipment operating space in the spatial constraint characteristics, using numerical simulation software to analyze the mining effects under different cutting parameters, and then determining parameters such as the pick arrangement, cutting depth, and cutting speed according to the simulation results. The mining support parameter instruction can be obtained by evaluating the roof bearing capacity using the roof mechanics analysis model based on the spatial conditions at different positions in the collection path and the roof stability monitoring data, and thus constructing parameters such as the support method (such as bolt support, hydraulic support), support spacing, and strength. The mining operation direction and attitude instruction can be generated by identifying the trend of the collection path, turning radius, and spatial restrictions, using the path planning algorithm and three-dimensional geographic information system to determine the operation trajectory of the equipment, and then calculating the operation direction and attitude adjustment amount of the equipment according to the operation trajectory. The mining speed and acceleration instruction can be obtained by using the dynamics model to simulate the operation state of the equipment at different speeds and accelerations, and comprehensively considering equipment performance, safety requirements, and mining efficiency to determine the appropriate mining speed and acceleration parameters, and thus constructing the corresponding instruction.

[0101] The transfer efficiency calculation module 203 is used to perform coal mine collection on the coal mine collection area using the collection equipment based on the mining optimization instruction, obtain the collection data generated during coal mine collection, use the collection data to identify the mining mechanical characteristics of the collection equipment, analyze the damage change law of the coal mine collection area based on the mining mechanical characteristics, and calculate the energy transfer efficiency of the collection equipment based on the damage change law.

[0102] In an embodiment of the present invention, based on the mining optimization instruction, a coal mine collection area is collected by a collection device, and collection data generated during the coal mine collection is obtained, so as to understand the effect during the specific implementation of the standardized collection method obtained after a detailed geological analysis of the coal mine collection area, and it is also convenient for users to further optimize the coal mine collection.

[0103] Among them, the collection data includes data related to the operation of the collection device, such as mechanical parameter data, operation status data, geological feature data, fracture distribution data, and spatial condition data, etc.

[0104] In an embodiment of the present invention, by using the collection data to identify the mining mechanical characteristics of the collection device, the working state and force conditions of the collection device under different working conditions can be obtained, so as to facilitate the analysis of the energy consumption of the device.

[0105] Among them, the mining mechanical characteristics refer to the characteristics and laws shown in aspects such as rock mechanics, mine pressure and its control involved in the process of mineral resource mining.

[0106] As an embodiment of the present invention, using the collection data to identify the mining mechanical characteristics of the collection device includes: performing multi-dimensional feature partitioning on the collection data to obtain a partitioned feature data set, using the partitioned feature data set to identify the cutting force and traction resistance of the collection device to determine the first mechanical characteristics of the collection device, using the partitioned feature data set to identify the transportation resistance and chain tension of the collection device to determine the second mechanical characteristics in the collection device, using the partitioned feature data set to identify the support force and pushing force of the collection device to determine the third mechanical characteristics in the collection device, and based on the first mechanical characteristics, the second mechanical characteristics, and the third mechanical characteristics, determining the mining mechanical characteristics of the collection device.

[0107] Among them, the cutting force refers to the force exerted by the cutting mechanism of the coal mine collection device when cutting coal and rock, the traction resistance refers to the resistance encountered when the collection device moves under the action of the traction mechanism during the coal mine mining process, the transportation resistance refers to the resistance received by transportation devices (such as conveyor belts, scraper conveyors, etc.) during the coal transportation process, the chain tension refers to the tension borne by the chain used to drive the scraper conveyor or other transmission mechanisms in the coal mine collection device, the support force refers to the force exerted by the support mechanism (such as hydraulic supports, etc.) of the coal mine collection device on the coal seam roof or other support surfaces, and the pushing force refers to the force used to push the collection device or other related devices forward during the coal mine mining process.

[0108] Optionally, the divided characteristic data set can be obtained by disassembling the collected complex data including equipment operation parameters, geological conditions, etc. from dimensions such as time, space, and physical quantity with the help of data mining algorithms. The first mechanical characteristic can be extracted from the divided characteristic data set by using sensor measurement data and combining with a mechanical model to obtain parameters related to cutting and traction, such as motor current, rotational speed, etc. The second mechanical characteristic can be obtained by screening data related to the transportation system, such as conveyor belt speed, material weight, from the divided characteristic data set, and then estimating the transportation resistance by applying the principles of dynamics and empirical formulas. The third mechanical characteristic can be calculated from the divided characteristic data set based on the hydraulic system pressure data and support structure parameters by using the conversion relationship between pressure and force to obtain the magnitude of the support force, and then analyzing data such as the stroke and pressure of the push cylinder, and combining with the principle of mechanical transmission to determine the push force.

[0109] In an embodiment of the present invention, by analyzing the damage change law of the coal mine collection area based on the mining mechanical characteristics, the collection efficiency under the current equipment collection state can be understood. For example, in a certain collection state, if the damage change in the coal mine collection area becomes smaller and smaller, it means that the equipment's coal collection efficiency is getting lower and lower.

[0110] As an embodiment of the present invention, analyzing the damage change law of the coal mine collection area based on the mining mechanical characteristics includes: performing wavelet fusion on the mining mechanical characteristics to obtain fusion data, using the fusion data to model the damage field of the coal mine collection area to obtain a dynamic damage evolution field, using the dynamic damage evolution field to identify the damage hotspots in the coal mine collection area, and analyzing the damage change law of the coal mine collection area based on the damage hotspots.

[0111] Among them, the dynamic damage evolution field refers to a spatial field established based on the coal mine collection area, which reflects the change of the damage state of this area over time and during the mining process. The damage hotspots refer to specific areas identified in the dynamic damage evolution field, and these areas have the characteristics of relatively high damage degree or significantly higher damage change rate than other areas.

[0112] Optionally, for the fused data, the wavelet transform algorithm can be used to decompose the mining mechanical characteristic data such as cutting force and traction resistance into sub-signals of different scales and frequencies, and then the weighted average method can be used to perform fusion processing on the decomposed sub-signals to obtain the fused data. The dynamic damage evolution field can be obtained by using numerical simulation software (such as FLAC3D, ANSYS, etc.) in combination with the fused data to construct a mathematical model reflecting the damage situation in the coal mining area. The damage hotspots can be obtained by using the clustering analysis algorithm to identify the areas with high damage degree and drastic changes in the dynamic damage evolution field. The damage change law can be obtained by inputting the damage hotspots into the fuzzy inference system, reasoning according to the preset fuzzy rules and membership functions, and analyzing the damage change trends and laws at different mining stages and different positions in the coal mining area through the output results of the fuzzy inference system.

[0113] Furthermore, in the embodiment of the present invention, calculating the energy transfer efficiency of the collection device based on the damage change law can help users understand the performance of the collection device and the energy utilization efficiency in the mining process, and further provide a basis for optimizing the device operation parameters and improving the mining process.

[0114] Wherein, the energy transfer efficiency refers to the ratio of the effective output energy to the total input energy during the energy conversion and transfer process.

[0115] As an embodiment of the present invention, calculating the energy transfer efficiency of the collection device based on the damage change law includes: based on the damage change law, identifying the cutting resistance and traction speed of the collection device, and based on the cutting resistance and the traction speed, using the following formula to calculate the effective crushing energy of the collection device:

[0116]

[0117] Wherein, represents the effective crushing energy, n represents, represents the cutting resistance of the collection device at the i-th moment, represents the traction speed of the collection device at the i-th moment, represents the crushing contribution factor, which can be 0.5, represents the degree of fragmentation, represents the data sampling time interval of the collection device;

[0118] Using the following formula to calculate the total input energy of the collection device:

[0119]

[0120] Wherein, represents the total input energy of the device, T represents the number of data sampling periods of the collection device, represents the voltage of the cutting motor in the acquisition device at time t, represents the current of the cutting motor in the acquisition device at time t, represents the voltage of the transportation system of the acquisition device at time t, represents the current of the transportation system of the acquisition device at time t, represents the voltage of the support system of the acquisition device at time t, represents the current of the support system of the acquisition device at time t, represents the data sampling time interval of the acquisition device,

[0121] Based on the effective crushing energy and the total input energy of the device, use the following formula to calculate the energy transfer efficiency of the acquisition device:

[0122]

[0123] where, represents the energy transfer efficiency, represents the effective crushing energy, represents the total input energy of the acquisition device.

[0124] Among them, the crushing contribution factor refers to the coefficient used to measure the contribution degree of the cutting action of the acquisition device at a certain moment to the coal crushing effect. A coal cutting model can be established by using computer numerical simulation software (such as discrete element software PFC, etc.), the cutting process under different working conditions can be simulated, and the relationship between factors such as cutting force and cutting speed and the coal crushing degree can be analyzed to obtain it. The degree of crushing refers to the index describing the degree of coal being crushed at a certain moment. It can be obtained by collecting images of the coal crushing situation during the cutting process, and then using image processing and computer vision technology to analyze the captured images and identify the size, shape and distribution of coal particles.

[0125] It should be further noted that the above formula for calculating the energy transfer efficiency first identifies the cutting resistance and traction speed according to the damage change law, and calculates the effective crushing energy based on this. This is based on the principles of energy conservation and work, considering the contribution of the cutting action to coal crushing and the degree of coal crushing. Then, by measuring the voltages and currents of the cutting motor, transportation system, and support system, the total input energy of the device is calculated according to the electric energy calculation formula. Finally, according to the definition that the energy transfer efficiency is equal to the ratio of the effective output energy to the total input energy, the effective crushing energy is divided by the total input energy of the device to obtain the energy transfer efficiency, providing a basis for evaluating the device performance and optimizing the mining process. The calculation formula of the effective crushing energy can effectively reflect the energy conversion process, which not only conforms to the actual physical process but also considers the influence of multiple factors on the crushing effect; it has operability and practicability, the parameters are easy to obtain, and it can provide a quantitative evaluation index.

[0126] The coal mine acquisition module 204 is used to analyze the energy consumption distribution characteristics of the acquisition equipment during coal mine acquisition by using the acquisition data. According to the energy consumption distribution characteristics, calculate the vibration matching coefficient of the acquisition equipment. Based on the vibration matching coefficient and the energy transfer efficiency, optimize the coal mine acquisition process of the acquisition equipment to obtain an optimized coal mine acquisition process. Based on the optimized coal mine acquisition process, use the acquisition equipment to acquire coal in the coal mine acquisition area.

[0127] In the embodiment of the present invention, by using the acquisition data to analyze the energy consumption distribution characteristics of the acquisition equipment during coal mine acquisition, it helps to discover the peak and trough areas of energy consumption, find out the links or components with large energy consumption, and provide a basis for subsequent targeted energy-saving optimization, so as to take corresponding measures to reduce energy consumption and improve energy utilization efficiency.

[0128] Among them, the energy consumption distribution characteristics refer to the energy consumption distribution of the coal mine acquisition equipment during the coal mine acquisition operation in different systems, different operation links, and different time periods.

[0129] Optionally, the energy consumption distribution characteristics can be obtained by classifying the acquisition data according to the equipment system (cutting, transportation, support, etc.), operation links (cutting, loading, transportation, etc.), and time periods, statistically analyzing the energy consumption data under each classification, and analyzing its proportion, change trend, and correlation factors.

[0130] Furthermore, in the embodiment of the present invention, by calculating the vibration matching coefficient of the acquisition equipment according to the energy consumption distribution characteristics, the influence of the vibration state of the equipment on energy consumption can be evaluated, thereby providing a reference basis for adjusting the equipment operation parameters and optimizing the equipment structure.

[0131] As an embodiment of the present invention, calculating the vibration matching coefficient of the acquisition equipment according to the energy consumption distribution characteristics includes: identifying the vibration characteristics of the acquisition equipment, performing quantization processing on the vibration characteristics and the energy consumption distribution characteristics to obtain an energy consumption distribution vector and a vibration vector, and based on the energy consumption distribution vector and the vibration vector, calculating the vibration matching coefficient of the acquisition equipment by using the following formula:

[0132]

[0133] Among them, represents the vibration matching coefficient, m represents the total number of vectors of the energy consumption distribution vector (which is also the total number of vectors of the vibration vector), represents the i-th energy consumption distribution vector, represents the i-th vibration vector, represents the vector mean of the energy consumption distribution vector, represents the vector mean of the vibration vector.

[0134] Among them, the vibration characteristics refer to the vibration-related characteristics exhibited by the acquisition device during operation, mainly including the frequency, amplitude, phase of vibration, as well as the direction and waveform of vibration, etc.

[0135] Optionally, the vibration characteristics can be obtained by installing vibration sensors at key parts of the acquisition device (such as the cutting head, fuselage, connection of transportation components, etc.), continuously collecting vibration data during equipment operation, and then using signal processing techniques (such as Fourier transform) to analyze the vibration data and extract characteristic parameters such as vibration frequency, amplitude, and phase. The quantization processing of the vibration characteristics and the energy consumption distribution characteristics can be realized by means of data standardization technology.

[0136] It should be further noted that the above vibration matching coefficient calculation formula measures the correlation between the energy consumption distribution vector and the vibration vector based on the covariance of vectors. By calculating the sum of the products of the deviations of each vector from its respective mean and dividing by the total number of vectors, the obtained vibration matching coefficient can reflect the degree of linear relationship between the energy consumption distribution characteristics and the vibration characteristics. The calculation formula of the vibration matching coefficient can quantitatively represent the matching degree between the energy consumption distribution and vibration of the acquisition device with a numerical value. The larger the coefficient, the stronger the correlation between the two, that is, there is a relatively close connection between the vibration state of the device and the energy consumption distribution; on the contrary, the smaller the coefficient, the weaker the correlation between the two. This helps to further understand the operating characteristics of the acquisition device by analyzing the relationship between vibration and energy consumption, and provides a basis for the optimization of the device.

[0137] Furthermore, in the embodiment of the present invention, by using the vibration matching coefficient and the energy transfer efficiency, the coal mine acquisition process of the acquisition device is optimized, and the obtained coal mine acquisition optimization process can make the acquisition device operate under more reasonable working parameters, improve the acquisition efficiency, reduce energy consumption, and reduce equipment wear.

[0138] Among them, the coal mine acquisition optimization process refers to a set of optimization schemes for adjusting and improving the operation processes and technical parameters such as cutting parameters, operating speed, and support timing of the acquisition device according to the vibration matching coefficient and the energy transfer efficiency to achieve more efficient, energy-saving, and stable mining operations.

[0139] Optionally, the coal mine acquisition optimization process can be obtained by judging the correlation degree between the equipment vibration and energy consumption according to the vibration matching coefficient, and combining the energy transfer efficiency to adjust and optimize the process parameters such as the cutting speed, traction speed, and support strength of the acquisition device.

[0140] In the embodiments of the present invention, by using the coal mine collection optimization process to collect coal mines in the coal mine collection area with the collection equipment, the optimized collection process can be applied to the actual coal mine collection process, enabling the collection equipment to operate according to the new process requirements, and achieving efficient and energy-saving mining of the coal mine collection area.

[0141] As Figure 2 shown, it is a schematic flowchart of an intelligent and efficient fully mechanized coal mining method provided by an embodiment of the present invention. In this embodiment, the intelligent and efficient fully mechanized coal mining method includes:

[0142] Obtain geological exploration data of the coal mine collection area, use the geological exploration data to identify the crack distribution in the coal mine collection area, based on the crack distribution, analyze the location of hidden faults and the degree of stress anomaly in the coal mine collection area, and based on the location of the hidden faults and the degree of stress anomaly, construct a three-dimensional risk distribution map of the coal mine collection area;

[0143] Use the three-dimensional risk distribution map to plan the collection path for the coal mining face corresponding to the coal mine collection area to obtain the collection path, identify the spatial constraint characteristics of the coal mine collection area, and based on the spatial constraint characteristics and the collection path, construct the mining optimization instruction for the coal mine collection area;

[0144] Based on the mining optimization instruction, use the collection equipment to collect coal mines in the coal mine collection area, and obtain the collection data generated during coal mine collection. Use the collection data to identify the mining mechanical characteristics of the collection equipment, based on the mining mechanical characteristics, analyze the damage change law of the coal mine collection area, and based on the damage change law, calculate the energy transfer efficiency of the collection equipment;

[0145] Use the collection data to analyze the energy consumption distribution characteristics of the collection equipment during coal mine collection. According to the energy consumption distribution characteristics, calculate the vibration matching coefficient of the collection equipment. Based on the vibration matching coefficient and the energy transfer efficiency, optimize the coal mine collection process of the collection equipment to obtain the coal mine collection optimization process, and use the collection equipment to collect coal mines in the coal mine collection area based on the coal mine collection optimization process.

[0146] In several embodiments provided by the present invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0147] In addition, in each embodiment of the present invention, each functional module can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent and efficient fully mechanized coal mining system, characterized in that, The described intelligent and efficient fully mechanized coal mining system includes: a risk distribution map construction module, a mining instruction construction module, a transfer efficiency calculation module, and a coal mine collection module; The risk distribution map construction module is used to obtain geological exploration data of the coal mine collection area, identify the crack distribution status of the coal mine collection area using the geological exploration data, analyze the concealed fault position and stress anomaly degree of the coal mine collection area based on the crack distribution status, and construct a three-dimensional risk distribution map of the coal mine collection area based on the concealed fault position and the stress anomaly degree; The mining instruction construction module is used to use the three-dimensional risk distribution map to plan the collection path for the coal mining face corresponding to the coal mine collection area, obtain the collection path, identify the spatial constraint characteristics of the coal mine collection area, and construct the mining optimization instruction for the coal mine collection area based on the spatial constraint characteristics and the collection path; The transfer efficiency calculation module is used to, based on the mining optimization instruction, use the collection equipment to collect coal in the coal mine collection area and obtain the collection data generated during coal collection. Use the collection data to identify the mining mechanical characteristics of the collection equipment, analyze the damage change law of the coal mine collection area based on the mining mechanical characteristics, and calculate the energy transfer efficiency of the collection equipment based on the damage change law; The coal mine collection module is used to analyze the energy consumption distribution characteristics of the collection equipment during coal collection using the collection data, calculate the vibration matching coefficient of the collection equipment according to the energy consumption distribution characteristics, optimize the coal collection process of the collection equipment based on the vibration matching coefficient and the energy transfer efficiency to obtain the optimized coal collection process, and use the collection equipment to collect coal in the coal mine collection area based on the optimized coal collection process.

2. The intelligent and efficient fully mechanized coal mining system according to claim 1, characterized in that The step of identifying the crack distribution status of the coal mine collection area using the geological exploration data includes: Using the geological exploration data to identify the lithology information of different strata in the coal mine collection area; Querying the historical geological structure data and crack development data of the coal mine collection area to obtain historical geological data; Performing grid processing on the lithology information and the historical geological data to obtain grid geological data; Constructing a crack contour image of the coal mine collection area based on the grid geological data; Identifying the crack characteristics of the coal mine collection area based on the crack contour image, and analyzing the crack distribution status of the coal mine collection area based on the crack characteristics.

3. An intelligent and efficient fully mechanized coal mining system according to claim 1, characterized in that, The step of analyzing the concealed fault position and stress anomaly degree of the coal mine collection area based on the crack distribution status includes: Querying the geological model and borehole exploration information of the coal mine collection area; Performing microseismic signal scanning on the coal mine collection area based on the geological model and the borehole exploration information to obtain a stress field evolution map; Analyzing the spatial correlation characteristics between the crack distribution status and the stress field evolution map; Analyzing the concealed fault position and stress anomaly degree of the coal mine collection area based on the spatial correlation characteristics.

4. An intelligent and efficient fully mechanized coal mining system according to claim 1, characterized in that, Constructing a three-dimensional risk distribution map of the coal mine collection area based on the concealed fault position and the stress anomaly degree, including: Constructing a risk characteristic matrix of the coal mine collection area by using the concealed fault position and the stress anomaly degree; Allocating risk weight coefficients to the matrix elements in the risk characteristic matrix to obtain a weighted risk coefficient matrix; Constructing a dynamic risk field model of the coal mine collection area by using the weighted risk coefficient matrix; Performing three-dimensional visualization processing on the dynamic risk field model to obtain a three-dimensional risk distribution map.

5. An intelligent and efficient fully mechanized coal mining system according to claim 1, characterized in that, Using the three-dimensional risk distribution map to plan a collection path for the coal mining face corresponding to the coal mine collection area to obtain a collection path, including: Performing grid division on the three-dimensional risk distribution map to obtain a grid map; Constructing an initial path of the coal mining face corresponding to the coal mine collection area by using the grid map; Performing path risk assessment on the initial path, and performing multi-objective optimization on the initial collection path according to the evaluation result of the path risk assessment to obtain a collection path.

6. The intelligent and efficient fully mechanized coal mining system according to claim 1, characterized in that, Constructing a mining optimization instruction for the coal mine collection area based on the space constraint feature and the collection path, including: Constructing a mining cutting parameter instruction for the coal mine collection area based on the space constraint feature; Identifying the space conditions and roof stability at different positions in the collection path to construct a mining support parameter instruction for the coal mine collection area; Identifying the path trend and space limitation in the collection path to construct a mining operation direction and attitude instruction for the coal mine collection area; Constructing a mining speed and acceleration instruction for the coal mine collection area based on the space constraint feature and the collection path; Determining a mining optimization instruction for the coal mine collection area based on the mining cutting parameter instruction, the mining support parameter instruction, the mining operation direction and attitude instruction, and the mining speed and acceleration instruction.

7. An intelligent and efficient fully mechanized coal mining system according to claim 1, characterized in that, Using the collection data to identify the mining mechanical characteristics of the collection equipment, including: Performing multi-dimensional feature division on the collection data to obtain a divided feature data set; Identifying the cutting force and traction resistance of the collection equipment by using the divided feature data set to determine the first mechanical characteristic of the collection equipment; Identifying the transportation resistance and chain tension of the collection equipment by using the divided feature data set to determine the second mechanical characteristic in the collection equipment; Identifying the supporting force and pushing force of the collection equipment by using the divided feature data set to determine the third mechanical characteristic in the collection equipment; Determining the mining mechanical characteristics of the collection equipment based on the first mechanical characteristic, the second mechanical characteristic, and the third mechanical characteristic.

8. An intelligent and efficient fully mechanized coal mining system according to claim 1, characterized in that, Analyzing the damage change law of the coal mine collection area based on the mining mechanical characteristics, including: Performing wavelet fusion on the mining mechanical characteristics to obtain fusion data; Using the fusion data to perform damage field modeling on the coal mine collection area to obtain a dynamic damage evolution field; Identifying the damage hot spots of the coal mine collection area by using the dynamic damage evolution field; Analyze the damage variation law of the coal mine collection area based on the damage hotspots.

9. An intelligent and efficient fully mechanized coal mining system as claimed in claim 1, wherein, Calculating the energy transfer efficiency of the collection device based on the damage variation law includes: Identifying the cutting resistance and traction speed of the collection device based on the damage variation law; Calculating the effective crushing energy of the collection device based on the cutting resistance and the traction speed; Calculating the total input energy of the collection device, Calculating the energy transfer efficiency of the collection device based on the effective crushing energy and the total input energy of the device.

10. An intelligent and efficient fully mechanized coal mining method, characterized in that, The method includes: Obtaining the geological exploration data of the coal mine collection area, using the geological exploration data to identify the fracture distribution condition of the coal mine collection area, based on the fracture distribution condition, analyzing the location of hidden faults and the degree of stress anomaly in the coal mine collection area, and constructing a three-dimensional risk distribution map of the coal mine collection area based on the location of hidden faults and the degree of stress anomaly; Using the three-dimensional risk distribution map to plan the collection path for the coal mining face corresponding to the coal mine collection area to obtain the collection path, identifying the spatial constraint characteristics of the coal mine collection area, and constructing an extraction optimization instruction for the coal mine collection area based on the spatial constraint characteristics and the collection path; Based on the extraction optimization instruction, using the collection device to collect coal in the coal mine collection area, and obtaining the collection data generated during coal collection, using the collection data to identify the mining mechanical characteristics of the collection device, based on the mining mechanical characteristics, analyzing the damage variation law of the coal mine collection area, and based on the damage variation law, calculating the energy transfer efficiency of the collection device; Analyzing the energy consumption distribution characteristics of the collection device during coal collection using the collection data, calculating the vibration matching coefficient of the collection device according to the energy consumption distribution characteristics, optimizing the coal collection process of the collection device based on the vibration matching coefficient and the energy transfer efficiency to obtain an optimized coal collection process, and using the collection device to collect coal in the coal mine collection area based on the optimized coal collection process.

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