A method, system, device and medium for modeling ground temperature and automatically processing heat damage

Abstract

The application discloses a kind of ground temperature modeling and heat damage automation processing method, system, equipment and medium, and specific steps include borehole ground temperature data acquisition and preprocessing, three-dimensional ground temperature field data volume construction, working face ground temperature automatic extraction based on spatial analytic geometry, hierarchical quantitative heat damage automatic regulation calculation, automation closed-loop feedback regulation.The application has the advantages that: abandon the mode of traditional manual reading map estimation working face temperature, automatically match working face range by three-dimensional grid and spatial plane geometry operation, eliminate subjective error caused by artificial visual;Construct "prediction-decision-execution-real-time feedback iteration" complete automation closed loop, avoid the insufficient cooling or energy waste caused by fixed parameter regulation;Solve the industry pain points of existing technology modeling, data extraction, equipment regulation mutual fragmentation, realize the leapfrog upgrade of heat damage from qualitative analysis to data-driven automatic control in deep mine.

Description

Technical Field

[0001] This invention belongs to the field of mine heat hazard prevention and control technology, specifically relating to a method, system, equipment and medium for geothermal modeling and automated heat hazard treatment. Background Technology

[0002] As coal mining depths continue to increase, the geothermal temperature of the original rock in deep mines has risen significantly, making mine heat hazards a core bottleneck restricting safe production and efficient continuous mining in deep mines. High-temperature working environments not only drastically reduce the working comfort of underground workers, inducing occupational health risks such as heatstroke and heat stress, but can also lead to secondary safety hazards such as overheating of underground equipment and abnormal gas release in severe cases. Furthermore, the spatial distribution characteristics of the geothermal field are core basic parameters for optimizing mine ventilation systems, laying out underground cooling equipment, and planning mining sequence. Therefore, accurately obtaining the geothermal distribution across any mining face and achieving automated heat hazard control based on geothermal prediction results is a key technological requirement for intelligent heat hazard prevention and control in deep mines.

[0003] Currently, the technical means for obtaining underground rock mass geothermal fields and carrying out heat hazard control in the industry are mainly divided into two major sections: geothermal field modeling and analysis, and underground temperature control and regulation. Existing technologies have obvious technical fragmentation and inherent defects, and can be specifically divided into three categories: borehole temperature measurement two-dimensional analysis technology, three-dimensional geothermal visualization simulation technology, and traditional mine heat hazard control technology.

[0004] The first type of two-dimensional geothermal inversion mapping technology, based on borehole temperature measurement data, uses single-point temperature measurement data from underground exploration boreholes as a foundation. It generates two-dimensional profiles and planar contour maps through interpolation algorithms such as Kriging and inverse distance weighting, thereby qualitatively analyzing the geothermal distribution patterns in mining areas. However, this technology relies solely on borehole temperature measurement data for numerical inversion calculations, ultimately outputting two-dimensional profile charts. It can only achieve a macroscopic trend judgment of geothermal temperature across the entire area and cannot perform refined data extraction for mining faces. Moreover, due to the limitations of two-dimensional planar projection, the details of three-dimensional geothermal changes in areas with complex geological structures are obscured, easily leading to misjudgments of localized thermal hazards at the working face.

[0005] The second category is 3D geothermal visualization processing solutions relying on commercial geological modeling software such as Voxler, Surfer, and Petrel. This type of software can import discrete borehole temperature measurement data and generate 3D geothermal data volumes and isosurface cloud maps through grid interpolation. Compared to 2D drawing, it offers stronger spatial intuitiveness. However, the overall application process is merely a post-processing visualization process involving data import, parameter adjustment, and graphic output. Furthermore, it suffers from the following technical problems: There is no standardized, automated algorithm for extracting geothermal temperature from any working face. In engineering applications, technicians must manually locate the approximate spatial position of the working face in the 3D view and visually estimate the temperature value based on isosurface colors. This manual estimation introduces a subjective error of ±2~3℃, making it difficult to meet the precision requirements for refined thermal hazard prevention in deep mines. Moreover, adjustments to the mining plan require manual rereading of the maps, with each data extraction taking several hours, resulting in extremely low work efficiency. Additionally, it cannot output structured quantitative data that can be directly read by automated control systems.

[0006] The third category is traditional manual control technology for heat hazards in mines. Current methods for cooling and controlling heat hazards in deep mines all follow a pattern of "first manually interpreting geothermal maps, then developing cooling plans based on engineering experience, and finally manually adjusting the operating parameters of ventilation fans and refrigeration units," without establishing a quantitative mapping relationship between temperature values ​​and control equipment parameters. Faced with high geothermal conditions at different working faces, the adjustment range of ventilation fan frequency, the power output of refrigeration units, and the location of local refrigeration equipment all rely on the subjective experience of technicians. On the one hand, this easily leads to insufficient ventilation and low cooling power, resulting in inadequate heat hazard control; on the other hand, it easily leads to excessive ventilation and full-load cooling, resulting in a large waste of electricity.

[0007] Patent publication number CN114036806A discloses a three-dimensional geothermal field numerical simulation method based on anisotropic thermal conductivity media. This patent fully considers the anisotropic characteristics of rock mass thermal conductivity by constructing a three-dimensional prism model, assigning thermal conductivity in tensor form, and iteratively solving in a spatial wavenumber hybrid domain. It adopts a Fourier transform hybrid domain algorithm to significantly reduce the memory usage and computation time of large-scale grid computing, and achieves high-precision numerical modeling output of the entire three-dimensional geothermal field, improving the computational efficiency and simulation accuracy of geothermal field simulation under complex geological media. However, it only focuses on the numerical iterative simulation of the entire geothermal field and only outputs the unified temperature field results of the overall region. It cannot extract the exclusive geothermal distribution structured dataset of any inclined working face. It has the defects of relying on manual map reading for engineering-oriented data extraction, poor temperature quantization output accuracy, and low extraction efficiency, making it difficult to adapt to the engineering requirements of integrated management and control under intelligent mining in deep mines.

[0008] In summary, existing geothermal modeling and visualization technologies can only display temperature field graphics and lack a means to automatically and accurately extract geothermal temperature from any working face; traditional heat hazard control methods rely on manual experience-based decision-making and lack standardized hierarchical and quantitative control models. There is an urgent need to propose a technical solution that can extract geothermal temperature from any working face and achieve on-demand temperature control. Summary of the Invention

[0009] The problem this invention aims to solve is that the current field of coal mine heat hazard control lacks a technical method that can extract the ground temperature of any working face and achieve on-demand temperature control.

[0010] The present invention solves the above-mentioned technical problems through the following technical means: A method for modeling geothermal fields and automating the handling of thermal hazards includes the following steps: S1. Borehole geothermal data acquisition and preprocessing: Collect three-dimensional spatial coordinates and measured geothermal raw point data of multiple exploration boreholes in the mining area, remove abnormal temperature gradient points, unify the depth reference surface, and standardize and format the measurement point dataset for storage. S2. Construction of 3D geothermal field data volume: The spatial interpolation algorithm is used to perform 3D grid subdivision and interpolation on the preprocessed discrete measurement point data to generate a global 3D geothermal field digitization matrix containing the coordinates of each grid node and the corresponding geothermal value. S3. Automatic extraction of geothermal temperature from working face based on spatial analytical geometry: Obtain the spatial geometric parameters of the target mining working face, construct the point-normal equation of the three-dimensional spatial plane where the working face is located, calculate the spatial distance from each grid node of the three-dimensional geothermal field to the plane and filter the nodes through the distance threshold, and output the structured geothermal distribution data of the entire working face with coordinate information after working face boundary clipping and gap interpolation filling. S4. Graded and quantitative automatic control calculation of heat hazards: Establish a mapping model between predicted temperature and required air supply volume of the working face, set multi-level temperature threshold matching differential control strategy, automatically calculate the frequency adjustment of the main ventilation fan and the operating power of the refrigeration unit based on the predicted ground temperature of the working face, and locate the high temperature area as the coordinates for the layout of local refrigeration equipment. S5. Automated closed-loop feedback regulation: The calculated equipment control parameters are sent to the mine PLC controller to regulate the ventilation and cooling equipment. The ambient temperature is continuously collected by the real-time temperature sensor of the working face. If the measured temperature is still higher than the preset threshold, S4-S5 are executed iteratively until the working face temperature drops to a safe range.

[0011] Abandoning the traditional method of manually estimating working face temperature by reading maps, this method automatically matches the working face range through three-dimensional mesh and spatial planar geometric calculations, eliminating subjective errors caused by manual observation; it constructs a complete automated closed loop of "prediction-decision-execution-real-time feedback iteration", avoiding insufficient cooling or energy waste caused by fixed parameter control; it solves the industry pain point of the disconnect between existing technology modeling, data extraction and equipment control, and realizes a leapfrog upgrade from qualitative analysis to data-driven automatic control of thermal hazards in deep mines.

[0012] Preferably, the data preprocessing in step S1 specifically includes: calculating the geothermal gradient of each borehole, removing abnormal measurement points that deviate from the average geothermal gradient of the region by ±3 standard deviations; uniformly converting all temperature measurement data to the same reference surface; and standardizing the storage fields to include borehole number, latitude and longitude coordinates, elevation, and measured geothermal temperature.

[0013] This claim defines a standardized preprocessing logic for borehole geothermal data. By eliminating gradient anomalies and uniform reference surface correction, it removes systematic errors and discrete outliers from the original temperature measurement data, ensuring the consistency and reliability of the basic data input for 3D modeling, and improving the accuracy of subsequent geothermal field simulation and working face temperature extraction results from the source.

[0014] Preferably, in step S2, Kriging interpolation or co-Kriging interpolation is used to perform three-dimensional mesh interpolation; the size of the three-dimensional mesh element is set to orientation × dip × vertical; and each three-dimensional mesh node in the study area is set to... Spatial coordinates are set as The mathematical expression for the digitized matrix of the three-dimensional geothermal field corresponding to the coordinate point is set as follows: In the formula, These represent the total number of grid nodes in the X, Y, and Z directions, respectively.

[0015] This claim defines the construction method and digital matrix representation of the three-dimensional geothermal field data volume. It uses a Kriging-type interpolation algorithm to achieve full-domain grid filling of discrete measuring points. The standardized matrix form facilitates batch analytical calculations by computers, providing a directly computable digital data foundation for subsequent automatic geometric extraction of working face temperature.

[0016] Preferably, the spatial geometric parameters of the target working surface in step S3 include the three-dimensional coordinates of the center point of the working surface. , directional length , tendency length The working face inclination angle α and the clockwise azimuth angle β (north); the working face point-normal plane equation is: In the formula Let be the coordinates of any point on the plane. Normal vector N The three components.

[0017] This claim defines a three-dimensional planar mathematical modeling method for inclined working faces. By using the inclination angle and azimuth angle of the working face, the plane normal vector and point normal equation are derived. This method can accurately mathematically represent the spatial boundary of mining working faces with any inclination angle and any azimuth, and is suitable for various inclined, gently inclined and steeply inclined working face conditions in underground mines.

[0018] Preferably, the filtering nodes in step S3 filter nodes that meet the following criteria: The grid nodes, the The calculation formula is: In the formula, This is the distance threshold from the grid node to the working plane.

[0019] This claim defines a mesh node distance filtering mechanism and a distance threshold determination rule to accurately filter temperature meshes that fit the working surface plane. Combined with gap interpolation to complete the output of temperature across the entire working surface without dead angles, it avoids the problem of local data loss caused by mesh discretization.

[0020] Preferably, the temperature-airflow mapping model in step S4 is as follows: In the formula To meet the basic air supply volume required for the minimum downhole wind speed, Predicting the temperature of the working face, For safe temperature threshold, This is the temperature-to-airflow conversion factor.

[0021] This claim defines a temperature-airflow quantitative mapping model, establishes a standardized quantitative conversion relationship between predicted temperature and working face airflow demand, replaces the traditional empirical estimation method, realizes automatic and accurate calculation of airflow supply, and provides a quantitative calculation basis for graded control.

[0022] Preferably, the formula for calculating the fan frequency adjustment in step S4 is as follows: The set power of the refrigeration unit is: In the formula To measure the air volume at the working face in real time. Supply the target air volume required for the working face. This refers to the calibration coefficient for the fan's air volume-frequency characteristic. Set the power for the refrigeration unit. The rated power of the unit, This is the load factor.

[0023] This claim defines the formula for quantitative calculation of fan frequency and cooling power, which can directly output equipment adjustment parameters that can be recognized by PLC. At the same time, it dynamically matches the cooling load based on the area ratio of high-temperature areas, taking into account both the cooling effect and the energy consumption of equipment operation, and significantly improving the accuracy of control.

[0024] This invention also discloses a system employing any one of the above-described methods for geothermal field modeling and automated heat hazard treatment, comprising: The data acquisition module is used to collect raw borehole temperature data, real-time working face temperature data, and spatial geometric parameters of the mining working face. A three-dimensional geothermal field modeling module, connected to the data acquisition module, is used to preprocess the raw temperature measurement data and construct a global three-dimensional geothermal field digital matrix. The working face geothermal extraction module is connected to the three-dimensional geothermal field modeling module and is used to automatically filter, trim, and interpolate the working face structured geothermal data based on spatial analytical geometry. A graded control calculation module is connected to the working face ground temperature extraction module and is used to calculate the control parameters of ventilation and cooling equipment in a graded manner based on the temperature-airflow mapping model. The equipment execution and feedback module is connected to the hierarchical control calculation module and the data acquisition module, respectively. It includes a PLC controller, a ventilation fan, a refrigeration unit, and an underground temperature sensor. It is used to issue control commands to adjust the equipment and collect real-time temperature to form a closed-loop iterative adjustment.

[0025] The present invention also discloses a computer device employing the geothermal field modeling and automated heat hazard treatment method described in any of the above claims, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the geothermal field modeling and automated heat hazard treatment method described in any of the above claims.

[0026] The present invention also discloses a computer-readable storage medium employing the geothermal field modeling and automated heat hazard treatment method described in any of the above claims, wherein a computer program is stored thereon, and when the computer program is executed by a processor, it implements the steps of the geothermal field modeling and automated heat hazard treatment method described in any of the above claims.

[0027] The advantages of this invention are: (1) Fully automatic and high-precision extraction of geothermal temperature at the working face: The three-dimensional geothermal field grid is automatically matched with the spatial plane of the mining working face using a spatial analytical geometric mathematical model. Through distance threshold filtering, boundary clipping, and interpolation completion, the structured output of the temperature of the entire area of ​​any inclined working face is realized, completely abandoning the traditional manual map reading and estimation mode and eliminating the subjective error of ±2~3℃. After the mining scheme is adjusted, only the geometric parameters of the working face need to be updated to quickly generate data, improving the extraction efficiency by more than 90%, solving the defects of poor accuracy, long time consumption, and inability to output machine-readable data in traditional methods. (2) Construct a temperature-equipment parameter quantification model: Establish a standardized temperature-air volume mapping formula, fan frequency regulation formula, and cooling power calculation model to replace the traditional mode of relying on the experience of technical personnel to formulate cooling plans. Based on the predicted ground temperature value, automatically output the control parameters of ventilation and cooling equipment that can be directly identified by PLC, open up the data link between ground temperature prediction and underground control hardware, eliminate the problem of lag in manual decision-making, and adapt to the continuous mining rhythm of deep mines. (3) Graded control + real-time feedback to form a complete automated closed-loop management: Set up multi-level temperature thresholds to match differentiated heat hazard control strategies, and start ventilation and cooling equipment in four categories of working conditions: normal, early warning, alarm, and emergency; Combined with real-time temperature sensors in the well to continuously collect environmental data, and automatically iterate and recalculate control parameters if the temperature does not meet the standard, forming a "prediction-decision-execution-feedback" closed loop, which avoids the risk of heat hazard caused by insufficient cooling and prevents energy waste caused by excessive cooling. (4) Full-process standardization: The whole set of technologies forms a standardized complete link of “drilling data preprocessing → three-dimensional geothermal field modeling → working face temperature extraction → graded automatic temperature control”. It is not bound to specific commercial modeling software and is not limited by the geological conditions of a single mining area. It is not only applicable to various deep coal mines, but can also be extended to metal and non-metal mines, high geothermal tunnels, geothermal exploration, underground space environmental temperature control and other types of underground engineering. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the overall process of a geothermal field modeling and automated heat hazard treatment method according to Embodiment 1 of the present invention; Figure 2 This is a three-dimensional geothermal field data volume of a geothermal field modeling and automated heat hazard processing method according to Embodiment 1 of the present invention; Figure 3 This is a flowchart of the working face ground temperature data extraction method for a ground temperature field modeling and automated heat hazard treatment method according to Embodiment 1 of the present invention. Figure 4 This invention relates to a graded automatic thermal hazard control method based on predicted ground temperature, which is a method for modeling a geothermal field and automating thermal hazard treatment according to Embodiment 1 of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Example 1: The geothermal field modeling and automated heat hazard treatment method of the present invention are described in the following reference. Figure 1 Specifically, it includes the following steps: S1. Borehole geothermal data acquisition and preprocessing: Collect three-dimensional spatial coordinates and measured geothermal raw point data of multiple exploration boreholes in the mining area, remove abnormal temperature gradient points, unify the depth reference surface, and standardize and format the measurement point dataset for storage.

[0031] The specific process of step S1 includes: S11. Borehole Geothermal Raw Data Acquisition. Complete raw temperature data is collected from all surface geological exploration boreholes, underground horizontal temperature measurement boreholes, and deep roadway temperature measurement boreholes within the target mining area. Using a dedicated high-precision borehole temperature measurement instrument, raw temperature values ​​are continuously collected at different depths along the complete borehole depth. During the collection process, the unified three-dimensional spatial coordinates of the mining area corresponding to each temperature measurement point are simultaneously recorded. The coordinate system includes three types of parameters: longitude, latitude, and vertical elevation. After the on-site collection is completed, the measurement point information corresponding to all boreholes is uniformly summarized to form a complete raw measurement point dataset. The dataset retains all basic record information, including the unique borehole number, on-site temperature measurement date, segmented measurement depth, instrument-measured raw temperature, and three-dimensional raw coordinates. This example uses the -610m to -920m deep mining area of ​​the Huainan Xieqiao Mine as an example, collecting over sixty sets of complete exploration borehole data. After segmented collection from each borehole, over one thousand valid raw measurement point data with complete three-dimensional coordinate information are obtained, fully covering the entire planned deep mining area.

[0032] S12. Preprocessing for Quality Control of Measurement Point Data. First, the data is grouped according to borehole number. The geothermal gradient values ​​corresponding to each measurement point inside each borehole are calculated sequentially. The average value and standard deviation of the geothermal gradient of all valid measurement points in the entire mining area are statistically analyzed. Abnormal measurement points whose geothermal gradient deviates from the regional average geothermal gradient by ±3 times the standard deviation are screened and directly removed. This eliminates distorted temperature data caused by borehole fissure seepage, poor contact of temperature measuring instruments, and errors in manual readings on site, thus avoiding interference from outliers in the accuracy of subsequent 3D geothermal field modeling. Then, the official unified elevation conversion and correction parameters of the mining area are retrieved to uniformly convert the original elevation values ​​of all measurement points. The elevation benchmark of all measurement points is uniformly converted to the Yellow Sea sea level standard benchmark (or other elevation benchmarks), eliminating systematic measurement deviations caused by inconsistent elevation starting benchmarks in different exploration stages and construction batches of boreholes. Finally, all measurement point records are traversed, and invalid measurement point records with missing 3D coordinates, blank measured temperature readings, and repeated acquisition of the same coordinates are deleted in batches, completing the entire set of measurement point data cleaning and noise reduction operations.

[0033] S13. Standardized and formatted storage of measuring point data. All valid measuring points, after outlier removal, elevation benchmark correction, and cleaning of duplicate and invalid data, undergo unified field standardization processing to generate a standardized structured data table. Each measuring point record in the table has five fixed standardized storage fields: borehole unique number, measuring point longitude coordinates, measuring point latitude coordinates, measuring point vertical elevation, and corrected measured ground temperature. The standardized structured data requires no manual secondary conversion or processing and can be directly recognized and read by spatial interpolation programs and 3D geological modeling software, enabling seamless and automatic data transfer to the subsequent S2 3D geothermal field data volume construction process.

[0034] S2. Construction of 3D geothermal field data volume: The spatial interpolation algorithm is used to perform 3D grid subdivision and interpolation on the preprocessed discrete measurement point data to generate a global 3D geothermal field digitization matrix containing the coordinates of each grid node and the corresponding geothermal value.

[0035] The specific process of step S2 includes: S21. Mesh generation parameter configuration. See [link / reference] Figure 2 Based on the mining area's planned excavation scope, geological structure complexity, and working face temperature extraction accuracy requirements, a three-dimensional grid size is set. The grid is divided into units along three dimensions: strike, dip, and vertical. In this embodiment, the grid unit size is configured as strike × dip × vertical = 50m × 50m × 10m. Workers can flexibly adjust the grid unit size in each dimension according to the density of exploration boreholes in the target area and the accuracy requirements of deep mining design. The overall boundary range of the three-dimensional grid is determined based on the mining area's planar boundary and the upper and lower limits of vertical mining. The total number of grid nodes along the X, Y, and Z coordinate axes is counted. Complete the construction of the full-domain three-dimensional mesh spatial framework.

[0036] S22. Spatial Interpolation and Global Temperature Assignment. Read the standardized structured measurement point dataset output from S1, and perform three-dimensional spatial interpolation calculations for discrete measurement points using Kriging interpolation or co-Kriging interpolation algorithms. During the interpolation process, based on the three-dimensional coordinates of the discrete measurement points and the measured ground temperature values, assign values ​​to each set of grid nodes within the three-dimensional grid framework. Perform temperature numerical interpolation calculations and match corresponding spatial coordinates for each grid node. Interpolation predicted geothermal values This fully utilizes the spatial distribution patterns of borehole-measured geothermal temperatures to reconstruct the continuous temperature variation characteristics of the formation, thus compensating for the problem of sparse distribution of borehole measuring points and data gaps between measuring points.

[0037] S23. Generation of a 3D geothermal field digital matrix: This involves integrating the coordinates of all grid nodes with their corresponding geothermal values ​​to form a digital 3D geothermal field data matrix that can be directly read and processed by a computer program. The mathematical expression of this matrix is ​​as follows: Each position within the matrix uniquely corresponds to a set of three-dimensional spatial coordinates and a matched predicted geothermal temperature, forming a continuous digital thermal field model covering the entire research area of ​​the mining area. This standardized matrix data does not require manual graphic conversion and can be directly input into the subsequent S3 working face geothermal automatic extraction and calculation module, supporting automated data extraction and calculation based on spatial analytical geometry.

[0038] S3. Automatic extraction of geothermal temperature from working face based on spatial analytical geometry: Obtain the spatial geometric parameters of the target mining working face, construct the point-normal equation of the three-dimensional spatial plane where the working face is located, calculate the spatial distance from each grid node of the three-dimensional geothermal field to the plane and filter the nodes by distance threshold, and output the structured geothermal distribution data of the entire working face with coordinate information after working face boundary clipping and gap interpolation filling.

[0039] The specific process of step S3 includes: S31. Reading spatial geometric parameters of the mining face. (See also...) Figure 3 The complete spatial geometric parameters of the target mining face to be analyzed are retrieved from the mine mining engineering design drawings and mining planning database, specifically including the three-dimensional coordinates of the center point of the working face. Working face length Working face dip length The system includes the working face dip angle α and the working face azimuth angle β, measured clockwise with true north as 0°. It fully records all basic parameters of the working face's spatial positioning, spatial extension, and tilt attitude, adapting to various mining working faces with different attitudes such as gentle tilt, tilt, and steep tilt, without the need for manual simplification of the working face geometry.

[0040] S32. Construction of the three-dimensional spatial planar mathematical model of the working face. Based on the obtained working face inclination angle. α With azimuth β Calculate the normal vector of the spatial plane containing the working surface. Combined with the coordinates of the center point of the working face The point-normal equation of the working surface space plane is obtained. By using standardized mathematical equations, the complete planar boundary of the inclined working face in the three-dimensional geological space is accurately represented, and the geometric mapping relationship between the three-dimensional mesh data volume and the mining working face is established, replacing the traditional operation mode of manually selecting the working face in the three-dimensional view.

[0041] S33, Grid Node Distance Threshold Filtering. Traverse all grid nodes within the 3D geothermal field data constructed in S2. Calculate the vertical spatial distance from each grid node to the aforementioned working surface plane point by point. Preset distance threshold , The value is taken as half of the vertical dimension of the 3D mesh element (the specific value may be adjusted appropriately according to the actual situation), and the elements that meet the requirements are selected and retained. All grid nodes were initially extracted to obtain the temperature grid point set close to the working surface. Irrelevant grid data that are far from the working surface were filtered out to reduce the subsequent calculation range and reduce redundant data interference.

[0042] S34. Working surface boundary trimming and gap data interpolation filling. First, based on the working surface direction length... , tendency length The selected grid point set is trimmed to remove redundant grid nodes that exceed the actual mining outline of the working face, resulting in a basic temperature point set that is only located within the plane projection range of the working face. For the local data gaps in the working face caused by grid discretization, the inverse distance weighted interpolation method is used to fill in the temperature values ​​in the gaps, eliminating the problem of local data loss caused by grid division, and finally generating a global geothermal distribution data matrix that completely covers the mining outline of the working face and has no data gaps.

[0043] S35. Standardized Output of Structured Geothermal Data from the Working Face. The filtered, cropped, and interpolated geothermal data from the working face is uniformly organized into a standardized structured data table. The table simultaneously outputs three core fields: local relative coordinates within the working face, absolute three-dimensional spatial coordinates of the measuring points, and corresponding predicted geothermal values. The output format can be directly recognized and read by the mine's environmental automation control system and PLC control program, eliminating the need for manual secondary map reading and temperature estimation. Quantified temperature data is directly transmitted to the downstream S4 graded and quantitative thermal hazard automatic control calculation process.

[0044] S4. Graded and quantitative automatic control calculation of heat hazards: Establish a mapping model between predicted temperature and the required air supply volume of the working face, set multi-level temperature threshold matching differential control strategies, automatically calculate the frequency adjustment of the main ventilation fan and the operating power of the refrigeration unit based on the predicted ground temperature of the working face, and locate the high-temperature area as the coordinates for the layout of local refrigeration equipment.

[0045] The specific process of step S4 includes: S41, Temperature-Demand Airflow Quantification Mapping Solution, see [link / reference] Figure 4 Read the structured geothermal distribution data of the entire working face output by S3, and extract the average predicted temperature of the working face. As a control input indicator, 26℃ (the specific temperature will be set according to actual needs) is used as the downhole safe temperature threshold. Then based on and Call the preset mapping model Complete the solution for the target air supply volume; among which To meet the minimum safe wind speed of 0.25 m / s for the fixed foundation air supply in the well, The temperature-to-airflow conversion factor, calibrated based on the working space volume and ventilation efficiency, is 0.5~2.0 m³ / (s) in this embodiment. Within the temperature range of ℃. When the predicted working face temperature is below or equal to 26℃, the basic air supply volume remains unchanged. Only when the predicted temperature exceeds the safety threshold will the incremental air supply volume corresponding to the temperature rise be automatically added to obtain the target air supply volume for the working face that matches the current degree of heat hazard. .

[0046] S42, Multi-level temperature control strategy matching. Pre-set four-level temperature control threshold ranges, automatically matching according to... Determine the corresponding control level and match the specific handling logic: When When the condition is determined to be Level 0 normal operating condition, the original operating parameters of the underground ventilation and cooling equipment are kept unchanged, and no equipment adjustment actions are triggered; when When the condition is determined to be a Level 1 warning condition, only the main ventilation fan airflow adjustment program is activated; when When the alarm condition is determined to be Level 2, the ventilation fan will be increased and the local refrigeration unit at the working face will be started simultaneously; when When the emergency condition is determined to be Level 3, the ventilation system is controlled to operate at full load and the centralized refrigeration unit is operated at full power. At the same time, a high temperature heat hazard audible and visual alarm signal is sent to the mine dispatch center (the thresholds for each stage of Level 4 temperature control can be adjusted appropriately according to the actual situation).

[0047] S43. Quantitative calculation of ventilation equipment control parameters, and retrieval of the current actual air supply volume obtained from real-time monitoring of the working face ventilation system. Combined with the air volume-frequency characteristic coefficient specified by the fan manufacturer Then through the formula Automatically calculates the frequency adjustment of the main ventilation fan inverter. This adjustment amount is a precise digital control parameter that can be directly sent to the PLC, enabling stepless and precise adjustment of the fan's air volume.

[0048] S44. Calculation of Refrigeration Unit Power and Layout Location. For Level 2 and Level 3 control conditions, the refrigeration power calculation process is initiated. The ground temperature distribution data matrix of the working face is traversed, and the area percentage of the working face with a temperature exceeding 28℃ is used as the load factor. Combined with the rated power of the refrigeration unit Through formula Calculate the target operating power of the chiller unit; simultaneously retrieve the three-dimensional coordinates of the grid nodes corresponding to the maximum temperature within the geothermal matrix of the working face. This coordinate system is used as the optimal location for deploying mobile localized cooling equipment, providing quantitative spatial guidance for on-site deployment of cooling equipment.

[0049] S45. Standardized and packaged output of control parameters. The calculated fan frequency adjustment amount... Refrigeration unit set power The recommended layout coordinates for localized cooling equipment and the unified encapsulation of heat hazard level alarm signals into standardized control command data packets are transmitted in their entirety to the subsequent S5 automated closed-loop feedback adjustment process, providing a complete quantitative control basis for the automatic execution of downhole equipment.

[0050] S5. Automated closed-loop feedback regulation: The calculated equipment control parameters are sent to the mine PLC controller to regulate the ventilation and cooling equipment. The ambient temperature is continuously collected by the real-time temperature sensor of the working face. If the measured temperature is still higher than the preset threshold, S4-S5 are executed iteratively until the working face temperature drops to a safe range.

[0051] The specific process of step S5 includes: S51. Issuance of Control Commands and Execution by Underground Equipment. The system receives standardized control command data packets output from S4. These packets contain the main ventilation fan frequency adjustment, target operating power of the cooling units, recommended deployment coordinates of local cooling equipment, and heat hazard classification alarm information. The system then sends the quantified control parameters to the mine's underground PLC programmable logic controller via industrial Ethernet or fieldbus. The PLC controller executes corresponding equipment control actions sequentially based on the received parameters, including adjusting the main ventilation fan inverter output frequency, regulating the opening of underground branch ventilation dampers, and starting, stopping, and adjusting the operating power of the local and centralized cooling units at the working face. If a Level 3 emergency condition is detected, a high-temperature heat hazard early warning message is simultaneously pushed to the mine's surface dispatch center, along with the recommended deployment coordinates of cooling equipment in the high-temperature area for on-site deployment by maintenance personnel.

[0052] S52. Real-time acquisition and feedback of working face ambient temperature: High-precision temperature sensors are deployed at three key locations at the head, middle and tail of the mining face. After the equipment adjustment action is completed, the real-time ambient temperature data of the working face is continuously collected, and the measured temperature value is simultaneously transmitted back to the hierarchical control calculation module in real time, forming a temperature data feedback link to realize the real-time transmission of the underground field environmental status.

[0053] S53, Iterative Judgment and Cyclic Adjustment of Control Effect: The measured ambient temperature returned by the sensor is compared with the 26℃ safe temperature threshold. If the measured temperature is still higher than the safe threshold, it automatically jumps to S4 to re-execute the entire process of temperature-airflow mapping calculation, graded control matching, and quantitative solution of equipment control parameters, generating a new round of equipment control instructions and issuing them for execution again. If the measured temperature falls back to the safe range of 26℃ or below, the iterative adjustment is terminated, the current equipment operating parameters are kept stable, and temperature monitoring data is continuously collected in a cyclic manner. Once the temperature exceeds the standard again, the control process is automatically restarted, constructing a fully automatic closed-loop control link of "ground temperature prediction - graded decision-making - equipment execution - real-time temperature measurement feedback".

[0054] In summary, the present invention has the following advantages: First, relying on spatial analytical geometry algorithms, the system automatically extracts the ground temperature at the working face, abandoning the traditional manual estimation method based on maps and eliminating subjective errors of ±2~3℃. Structured temperature data can be output within minutes after the mining plan is adjusted, significantly improving extraction efficiency. The data can be directly connected to the control system. Second, a quantitative mapping model of temperature and ventilation and cooling equipment parameters is established, coupled with a four-level graded temperature control strategy. Differentiated control schemes are automatically matched according to the degree of ground temperature exceedance, completely eliminating human experience-based decision-making. The system accurately calculates the frequency adjustment of fans and the power of cooling units, balancing cooling effect and energy consumption. Third, a complete automated closed loop of "prediction-decision-execution-feedback" is constructed. Relying on real-time monitoring and iterative adjustment by multiple underground temperature sensors, the system continuously ensures that the working face temperature remains stable within a safe range, solving the problem of lag in traditional control. Fourth, the entire technical process is standardized, not dependent on specific commercial software or geological conditions of mining areas. It can be adapted to various underground engineering scenarios such as coal mines, metal mines, high-temperature tunnels, and geothermal development. It has strong versatility and is easy to implement and replicate, solving the industry pain point of the current disconnect between ground temperature modeling, data extraction, and thermal hazard control from the source.

[0055] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Terms such as "upper," "lower," "left," "right," "front," and "rear" used in the invention are merely for clarity of description and are not intended to limit the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0056] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for modeling geothermal fields and automating heat hazard treatment, characterized in that, Includes the following steps: S1. Borehole geothermal data acquisition and preprocessing: Collect three-dimensional spatial coordinates and measured geothermal raw point data of multiple exploration boreholes in the mining area, remove abnormal temperature gradient points, unify the depth reference surface, and standardize and format the measurement point dataset for storage. S2. Construction of 3D geothermal field data volume: The spatial interpolation algorithm is used to perform 3D grid subdivision and interpolation on the preprocessed discrete measurement point data to generate a global 3D geothermal field digitization matrix containing the coordinates of each grid node and the corresponding geothermal value. S3. Automatic extraction of geothermal temperature from working face based on spatial analytical geometry: Obtain the spatial geometric parameters of the target mining working face, construct the point-normal equation of the three-dimensional spatial plane where the working face is located, calculate the spatial distance from each grid node of the three-dimensional geothermal field to the plane and filter the nodes through the distance threshold, and output the structured geothermal distribution data of the entire working face with coordinate information after working face boundary clipping and gap interpolation filling. S4. Graded and quantitative automatic control calculation of heat hazards: Establish a mapping model between predicted temperature and required air supply volume of the working face, set multi-level temperature threshold matching differential control strategy, automatically calculate the frequency adjustment of the main ventilation fan and the operating power of the refrigeration unit based on the predicted ground temperature of the working face, and locate the high temperature area as the coordinates for the layout of local refrigeration equipment. S5. Automated closed-loop feedback regulation: The calculated equipment control parameters are sent to the mine PLC controller to regulate the ventilation and cooling equipment. The ambient temperature is continuously collected by the real-time temperature sensor of the working face. If the measured temperature is still higher than the preset threshold, S4-S5 are executed iteratively until the working face temperature drops to a safe range.

2. The method for geothermal field modeling and automated heat hazard treatment according to claim 1, characterized in that, The data preprocessing in step S1 specifically includes: calculating the geothermal gradient of each borehole, removing outlier measurement points that deviate from the average geothermal gradient of the region by ±3 standard deviations; uniformly converting all temperature measurement data to the same reference surface; and standardizing the storage fields to include borehole number, latitude and longitude coordinates, elevation, and measured geothermal temperature.

3. The method for geothermal field modeling and automated heat hazard treatment according to claim 1, characterized in that, In step S2, Kriging interpolation or co-Kriging interpolation is used to perform three-dimensional mesh interpolation; the three-dimensional mesh cell size is set to orientation × dip × vertical; each three-dimensional mesh node in the study area is set to... Spatial coordinates are set as The mathematical expression for the digitized matrix of the three-dimensional geothermal field corresponding to the coordinate point is set as follows: In the formula, These represent the total number of grid nodes in the X, Y, and Z directions, respectively.

4. The method for geothermal field modeling and automated heat hazard treatment according to claim 1, characterized in that, The spatial geometric parameters of the target working surface in step S3 include the three-dimensional coordinates of the center point of the working surface. , directional length , tendency length Working face inclination angle α North-clockwise azimuth β ; The working surface point normal plane equation is: In the formula Let be the coordinates of any point on the plane. Normal vector N The three components.

5. The method for geothermal field modeling and automated heat hazard treatment according to claim 1, characterized in that, The filtering nodes described in step S3 are those that meet the following criteria: The grid nodes, the The calculation formula is: In the formula, This is the distance threshold from the grid node to the working plane.

6. The method for geothermal field modeling and automated heat hazard treatment according to claim 1, characterized in that, The temperature-airflow mapping model in step S4 is as follows: In the formula To meet the basic air supply volume required for the minimum downhole wind speed, Predicting the temperature of the working face, For safe temperature threshold, This is the temperature-to-airflow conversion factor.

7. The method for geothermal field modeling and automated heat hazard treatment according to claim 1, characterized in that, The formula for calculating the fan frequency adjustment in step S4 is as follows: The set power of the refrigeration unit is: In the formula To measure the air volume at the working face in real time. Supply the target air volume required for the working face. This refers to the calibration coefficient for the fan's air volume-frequency characteristic. Set the power for the refrigeration unit. The rated power of the unit, This is the load factor.

8. A geothermal field modeling and automated thermal hazard treatment system, characterized in that, The method for performing geothermal field modeling and automated heat hazard treatment as described in any one of claims 1 to 7 includes: The data acquisition module is used to collect raw borehole temperature data, real-time working face temperature data, and spatial geometric parameters of the mining working face. A three-dimensional geothermal field modeling module, connected to the data acquisition module, is used to preprocess the raw temperature measurement data and construct a global three-dimensional geothermal field digital matrix. The working face geothermal extraction module is connected to the three-dimensional geothermal field modeling module and is used to automatically filter, trim, and interpolate the working face structured geothermal data based on spatial analytical geometry. A graded control calculation module is connected to the working face ground temperature extraction module and is used to calculate the control parameters of ventilation and cooling equipment in a graded manner based on the temperature-airflow mapping model. The equipment execution and feedback module is connected to the hierarchical control calculation module and the data acquisition module, respectively. It includes a PLC controller, a ventilation fan, a refrigeration unit, and an underground temperature sensor. It is used to issue control commands to adjust the equipment and collect real-time temperature to form a closed-loop iterative adjustment.

9. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the geothermal field modeling and automated heat hazard treatment method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the geothermal field modeling and automated heat hazard treatment method according to any one of claims 1 to 7.

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