Goaf geological model generation method and device and related product

Abstract

This application relates to the field of mine geological exploration and remediation technology, and discloses a method, apparatus, and related products for generating geological models of goaf areas. The method includes: acquiring structured geological data of the target goaf area; the structured geological data includes directional drilling data, geophysical data, and geochemical data; performing data cleaning and spatiotemporal alignment on the directional drilling data, geophysical data, and geochemical data to obtain processed structured geological data; processing the processed structured geological data based on D-S evidence theory to generate geological feature labels for each grid; and generating a comprehensive geological model of the target goaf area based on the processed structured geological data using the geological feature labels. This application can generate a comprehensive geological model of small-scale goaf areas that closely resembles real-world conditions by fusing multi-source heterogeneous geological data.

Description

Technical Field

[0001] This application relates to the field of mine geological exploration and remediation technology, and more specifically, to a method, apparatus and related products for generating geological models of goaf areas. Background Technology

[0002] In mining operations, the existence of small-scale goaf areas poses a significant threat to safe production. Due to the long history of mining and the lack of standardized data, the distribution range and geological structure of small-scale goaf areas exhibit complex and variable characteristics. This lack of clear data support and unclear geological conditions makes it difficult to accurately implement risk assessment, dynamic monitoring, and early prevention and control work for small-scale goaf areas, seriously threatening the lives of mine workers and the property safety of mine production.

[0003] To clarify the actual geological conditions of small mine goaf areas from the root, break through the current dilemma of safety production management caused by lack of data and vague geological characteristics, and provide accurate and comprehensive geological references for mine goaf risk management and production operation arrangements, it is particularly important to generate goaf geological models that fit the actual situation of the mine. Summary of the Invention

[0004] In view of the above situation, this application provides a method, apparatus and related products for generating geological models of goaf areas, which aims to solve the above problems or at least partially solve the above problems.

[0005] In a first aspect, embodiments of this application provide a method for generating a geological model of a goaf, the method comprising: Obtain structured geological data of the target goaf; the structured geological data includes directional drilling data, geophysical data, and geochemical data; The directional drilling data, geophysical data, and geochemical data are cleaned and spatiotemporally aligned to obtain processed structured geological data. Based on the DS evidence theory, the processed structured geological data is processed to generate geological feature labels for each grid. Based on the geological feature labels, the processed structured geological data generates a comprehensive geological model of the target goaf.

[0006] Secondly, this application also provides a geological model generation device for goaf areas, the device comprising: The acquisition module is used to acquire structured geological data of the target goaf area; the structured geological data includes directional drilling data, geophysical data, and geochemical data. The preprocessing module is used to perform data cleaning and spatiotemporal alignment on the directional drilling data, geophysical data, and geochemical data to obtain processed structured geological data. The grid classification module is used to process the processed structured geological data based on the DS evidence theory to generate geological feature labels for each grid. The modeling module is used to generate a comprehensive geological model of the target goaf based on the geological feature labels and the processed structured geological data.

[0007] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the above-described method for generating a geological model of a goaf.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium storing one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform the steps of the above-described method for generating a geological model of a goaf.

[0009] Fifthly, an optional implementation of this application also provides a computer program product, the computer program product carrying program code, the program code including instructions that can be used to execute the steps of the goaf geological model generation method as described in the first aspect.

[0010] Using the above technical solutions, the method, apparatus, and related products for generating geological models of goaf areas provided in this application embodiment can first acquire multi-source structured geological data such as directional drilling, geophysical exploration, and geochemical exploration to comprehensively obtain geological information of the target goaf area; then, perform data cleaning and spatiotemporal alignment processing on the drilling, geophysical, and geochemical data to remove data noise and unify the spatiotemporal reference of the data; based on DS evidence theory, process the processed structured geological data to generate geological feature labels for each grid; finally, based on the geological feature labels of each area and the processed structured geological data, generate a comprehensive geological model of the target goaf area, that is, by integrating multi-dimensional geological information to form a visualized geological representation result. Ultimately, this embodiment can generate a comprehensive geological model of a small kiln goaf area that is closer to the real situation through the effective fusion and accurate representation of multi-source heterogeneous geological data.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating the method for generating a geological model of a goaf area provided in an embodiment of this application is shown. Figure 2 A schematic diagram of the structure of the geological model generation device for goaf areas provided in an embodiment of this application is shown; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0016] As mentioned earlier, the existence of small-scale goaf areas in mining operations poses a significant threat to safety. Due to the long history of mining and the lack of standardized data, the distribution and geological structure of these goaf areas are complex and varied. This lack of clear data and unclear geological conditions makes it difficult to accurately implement risk assessment, dynamic monitoring, and early prevention measures, seriously threatening the lives of miners and the property of mine workers. To clarify the actual geological conditions of small-scale goaf areas from the root, overcome the current predicament of safety management caused by data gaps and ambiguous geological characteristics, and provide accurate and comprehensive geological references for goaf risk management and production operation arrangements, generating a goaf geological model that fits the actual conditions of the mine is particularly important. Based on this, this invention proposes a method, apparatus, and related products for generating goaf geological models. The following detailed description of specific embodiments further illustrates this application.

[0017] To facilitate understanding of this embodiment, a detailed description of the method for generating a geological model of a goaf area disclosed in this application embodiment will be provided first. The execution entity of the goaf area geological model generation method provided in this application embodiment is generally a computer device with certain computing capabilities. This computer device may include, for example, a terminal device, a server, or other processing devices. The terminal device may be a user equipment (UE), a mobile device, a user terminal, or a terminal, etc. In some possible implementations, this goaf area geological model generation method can be implemented by a processor calling computer-readable instructions stored in memory.

[0018] Figure 1 This document illustrates a flowchart of the method for generating geological models of goaf areas provided in an embodiment of this application. Figure 1 It can be seen that the embodiments of this application include at least steps S101-S104: S101: Acquire structured geological data of the target goaf; the structured geological data includes directional drilling data, geophysical data, and geochemical data; S102: Perform data cleaning and spatiotemporal alignment processing on the directional drilling data, geophysical data, and geochemical data to obtain processed structured geological data. S103: Based on the DS evidence theory, the processed structured geological data is processed to generate geological feature labels for each grid. S104: Based on the geological feature labels, the processed structured geological data generates a comprehensive geological model of the target goaf.

[0019] As can be seen, this embodiment first acquires multi-source structured geological data such as directional drilling, geophysical exploration, and geochemical exploration, which can comprehensively obtain geological information of the target goaf. Then, the drilling, geophysical exploration, and geochemical exploration data are cleaned and spatiotemporally aligned to remove data noise and unify the spatiotemporal benchmark. Based on the DS evidence theory, the processed structured geological data is processed to generate geological feature labels for each grid. Finally, based on the geological feature labels and the processed structured geological data, a comprehensive geological model of the target goaf is generated. That is, by integrating multi-dimensional geological information, a visualized geological representation result is formed. Ultimately, this embodiment can generate a comprehensive geological model of the small kiln goaf that is closer to the real situation through the effective fusion and accurate representation of multi-source heterogeneous geological data.

[0020] The following provides a detailed explanation of S101-S104.

[0021] Regarding the above S101: In this embodiment, the directional drilling data includes borehole coordinates, trajectory parameters, and core records. The trajectory parameters include azimuth and dip angles, and the core records include lithology, recovery rate, and information on missing sections. The geophysical data includes transient electromagnetic resistivity data and sonic logging wave velocity data. The geochemical data includes formation chemical composition and soil geochemical measurement data, where the formation chemical composition includes carbon dioxide concentration and heavy metal element content.

[0022] In practice, the raw unstructured / semi-structured directional drilling data, geophysical data, and geochemical data can be acquired first, and then a classification directory can be established according to data type, acquisition time, and spatial coordinates. Further, the directory can be subdivided, and key fields of the raw data can be extracted. For all data under each category, it can be stored in the form of "field + value," ultimately yielding the structured geological data of the target goaf. In practice, the structured geological data can be stored in an Excel / MySQL spreadsheet.

[0023] Key fields for directional drilling data include: borehole ID, depth (m), lithology (e.g., "sandstone mudstone" or "limestone"), core recovery rate (%), and length of missing section (m); key fields for geophysical data include: survey line ID, survey point coordinates (x / y), depth (m), and resistivity value (Ω). Key fields in geochemical data include: sampling point ID, coordinates (x / y), CO2 concentration (ppm), and indicator element (e.g., As / Pb) content (mg / kg); key fields in geological survey data include: fault strike / dip, stratum thickness, and coordinates of surface subsidence areas.

[0024] For example, the raw data obtained is: electromagnetic signal records obtained from a mining area on January 8, 2026, using the transient electromagnetic method. First, this raw data is categorized under "Geophysical Data". Then, a directory is created named "Geophysical Data - 20260108 - Line L05" based on "Acquisition Time (20260108) + Spatial Identifier (Line L05)". Finally, from the electromagnetic signal records in this directory, the following information is extracted: "Line ID, Measurement Point Coordinates (x / y), Depth (m), Resistivity Value (Ω)". Fields such as "m)" are used; finally, all data in this directory is stored in a table in the form of "field + value", and each data item is associated with "spatial coordinates + timestamp".

[0025] In terms of drilling accuracy and trajectory control, complex geological formations and differences in rock properties are key factors leading to borehole deviations. Different strata have varying hardness, lithology, and dip angles. For example, in some mining areas, the upper layer is soft sandy mudstone, while the lower layer is hard limestone. During drilling, uneven stress on the drill bit easily causes the actual borehole trajectory to deviate from the designed trajectory. In mining areas like Wangjialing Mine, due to limitations in equipment performance and construction conditions, borehole trajectory deviations can reach several meters or even greater, making it impossible for the borehole to accurately reach the target location and creating blind spots in the detection.

[0026] Based on this, in some embodiments of this application, the directional drilling data is generated according to the following method: Real-time acquisition of attitude data, hydraulic data, and formation anomaly data, wherein the attitude data includes the current azimuth angle, dip angle, and tool face angle of the borehole; the hydraulic data includes the pressure of the propulsion cylinder, the pressure of the pressurization cylinder, and the speed and torque of the power head hydraulic motor; and the formation anomaly data includes the drill pipe vibration value and the drive motor current. The attitude data, hydraulic data, and formation anomaly data are preprocessed to obtain preprocessed data; Based on the DS evidence theory, the preprocessed data is processed to obtain a comprehensive working condition judgment result; the comprehensive working condition judgment result includes the working condition type and deviation level. Based on the operating condition type and deviation level, calculate the adjustment parameters of each actuator of the drilling equipment; Based on the aforementioned adjustment parameters, the drilling equipment is controlled to obtain the directional drilling data.

[0027] First, it should be noted that this embodiment is based on a borehole trajectory measurement and control system, which includes a sensor module, a data processing unit, and a control execution mechanism.

[0028] The sensor module includes attitude sensors, hydraulic status sensors, and formation anomaly sensors. The attitude sensors include a gyroscope, accelerometer, and magnetometer, used to acquire real-time borehole azimuth, dip angle, and tool face angle. The hydraulic status sensors include: a pressure sensor to monitor cylinder inlet and outlet pressure; a flow sensor to monitor hydraulic oil flow; and a torque sensor to monitor the power head output torque. The formation anomaly sensors include: a vibration sensor, fixed to the drill pipe support, to monitor drilling vibration; and a motor current sensor to monitor the hydraulic drive motor current.

[0029] The data processing unit is equipped with the trajectory correction method provided in the embodiments of this application.

[0030] The control actuator, implemented by a hydraulic compensation system, includes hydraulic actuators such as propulsion / retreat cylinders, power head hydraulic motors, pressurization cylinders, electromagnetic proportional valve groups, variable pumps, and directional control valves. These actuators receive control commands from the data processing unit and precisely adjust drilling direction, speed, and pressure parameters. The specific workflow is as follows: Command Reception: Receives target parameter commands (such as cylinder pressure difference, power head speed, and target drilling pressure) from the data processing unit and converts them into current control signals (0-24V) for the electromagnetic proportional valve, with a response time ≤20ms; Precise Execution: Adjusts the inlet pressure of each cylinder (error ±0.1MPa) through the proportional relief valve, matches the power head speed and output flow with the variable pump, and switches the cylinder oil circuit direction through the directional control valve to achieve dynamic adjustment of "left / right / up / down" yaw and drilling parameters; Execution Feedback: During the execution of the action, the hydraulic status sensor and attitude sensor in the sensor module collect data such as "actual cylinder pressure, actual azimuth angle, and vibration value" in real time (sampling frequency 10Hz) and transmits them back to the data processing unit for verification of the compensation effect, forming a closed-loop control of "perception-decision-execution-feedback".

[0031] In specific implementation, firstly, the sensor modules of the above system are used to collect the following data in real time and send them to the data processing unit: attitude data: current azimuth angle, inclination angle, and tool face angle of the borehole; hydraulic data: pressure of the propulsion cylinder, pressure of the pressurization cylinder, speed and torque of the power head hydraulic motor; formation anomaly data: drill rod vibration value and drive motor current.

[0032] Next, the data processing unit standardizes and removes outliers from the collected raw data, and fuses the preprocessed sensor data by improving the DS evidence theory algorithm to obtain a comprehensive working condition judgment result, such as trajectory left deviation with a confidence level ≥ 0.9. Specifically, firstly, an identification framework for directional drilling conditions is constructed, encompassing all pre-defined possible condition types and their corresponding deviation level ranges. Then, for three data sources—pre-processed attitude data, hydraulic data, and formation anomaly data—features characterizing the drilling equipment's operating status are extracted. Based on the correlation between these features and condition types and deviation levels, a basic probability assignment function is constructed for each data source using a pre-defined membership function or a trained mapping model. This quantifies the support of a single data source for each condition type and deviation level within the identification framework. Next, using the synthesis rules of DS evidence theory, the basic probability assignment functions corresponding to multiple data sources are fused to eliminate information conflicts between different data sources, resulting in a fused comprehensive basic probability assignment function. Finally, according to pre-defined decision rules, the program selects the condition type and corresponding deviation level range with the highest support from the comprehensive basic probability assignment function, determining it as the final comprehensive condition judgment result.

[0033] Specifically, in some embodiments, the operating conditions include trajectory direction deviation, pressure, and excessive ground vibration. The deviation level of the trajectory direction deviation is divided into slight deviation, moderate deviation, and severe deviation, and the deviation level of excessive ground vibration is divided into slight vibration and severe vibration.

[0034] During implementation, a framework for identifying directional drilling conditions can be first constructed, encompassing three types of working conditions: trajectory direction deviation, pressure buildup, and excessive ground vibration. Trajectory direction deviation is further categorized into three levels: slight deviation, moderate deviation, and severe deviation; and excessive ground vibration is categorized into two levels: slight vibration and severe vibration. Then, feature quantities are extracted from the three data sources: pre-processed attitude data, hydraulic data, and ground anomaly data. For example, features related to trajectory direction deviation, such as borehole azimuth fluctuation amplitude, dip angle change rate, and tool face angle offset, can be extracted from the attitude data. Similarly, features related to propulsion cylinder pressure mutation, pressurization cylinder pressure maintenance duration, and power head hydraulic motor torque variation can be extracted from the hydraulic data. Features related to the pressure-bearing condition, such as the effective value of drill pipe vibration and the peak value of drive motor current, are extracted from formation anomaly data. Based on a preset correlation threshold model between these features and the type of working condition and deviation level, a corresponding basic probability assignment function is constructed for each data source type. For example, when the azimuth fluctuation amplitude in the attitude data is in the range of 0.5°-1° and the dip angle change rate is 0.2° / min-0.5° / min, this data source is assigned a support of 0.6 for a slight deviation level and 0.2 for a moderate deviation level. A support of 0.1 is assigned to the pressure-bearing condition, and a support of 0.1 is assigned to the slight vibration level of the formation vibration exceeding the standard. A support score of 0.05 is assigned to both the severe vibration level and the data source. When the sudden change in propulsion cylinder pressure in the hydraulic data is less than 1 MPa, or the duration of pressure maintenance in the pressurizing cylinder exceeds a preset threshold, a support score of 0.1 is assigned to the data source for the pressure-bearing condition. A support score of 0.7 is assigned to the minor deviation level of the trajectory direction, and a support score of 0.1 is assigned to the moderate deviation level. A support score of 0.05 is assigned to the minor vibration level and the severe vibration level of the formation vibration exceeding the standard. When the effective value of drill pipe vibration in the abnormal formation data is in the range of 10 mm / s-15 mm / s, and the peak value of the drive motor current does not exceed 1.1 times the rated value, a support score of 0.05 is assigned to the data source for the formation vibration level. The support scores for the mild vibration level (0.5) and severe vibration level (0.1) are assigned to the mild deviation level of the trajectory direction deviation (0.3) and the support score for the pressure condition (0.1). The program then calls the synthesis rules of the DS evidence theory to perform pairwise fusion calculations on the basic probability allocation functions corresponding to the three data sources, eliminating information conflicts between different data sources and obtaining the fused comprehensive basic probability allocation function. Finally, based on the preset decision rule of "selecting the condition type and corresponding deviation level with the highest support in the comprehensive basic probability allocation function as the final result", the comprehensive condition judgment result of this drilling is determined to be the mild deviation level of the trajectory direction deviation.

[0035] Next, based on the working condition type and deviation level, the adjustment parameters of each actuator of the drilling equipment are calculated; based on these adjustment parameters, the drilling equipment is controlled to obtain the directional drilling data. In specific implementation, firstly, for example, in trajectory direction deviation, if it is determined that the trajectory is deviated to the left (i.e., the actual azimuth angle is less than the design value), the deviation Δα can be calculated as the design azimuth angle minus the actual azimuth angle. When the deviation is minor (Δα ≤ 0.5°), the pressure of the left propulsion cylinder is reduced by 10%, while the pressure of the right propulsion cylinder is increased by 10%. This creates a right-push, left-pull force difference through the proportional overflow valve, causing the drill rod to deflect to the right to correct the direction. For moderate or severe deviations (Δα > 0.5°), in addition to increasing the pressure difference adjustment range of the propulsion cylinders to 15% to 20%, the power head speed n also needs to be reduced simultaneously by 5% to 10% to extend the direction correction time and avoid the deviation from expanding due to high-speed drilling. If the deviation is determined to be a trajectory inclination angle deviation, i.e., actual tilting, the deviation Δβ is calculated as the actual inclination angle minus the design inclination angle. The adjustment steps are to control the pressure of the lower propulsion cylinder to increase by 12% to 18% and the pressure of the upper propulsion cylinder to decrease by 8% to 15%, forming a push-pull mechanism to correct the inclination angle. At the same time, the pressure of the pressurizing cylinder P2 is kept stable within ±0.5MPa to avoid drilling pressure fluctuations affecting accuracy.

[0036] In pressure-boosting operation, the goal is to eliminate friction between the drill string and the wellbore, ensuring effective transmission of drilling pressure. The specific steps involve activating the hydraulic oscillator integrated into the pressurizing cylinder circuit, controlling the high-frequency solenoid valve to periodically open and close at a frequency of 5 to 10 Hz, causing the pressurizing cylinder to generate longitudinal micro-vibrations with an amplitude of 0.5 to 1 mm to reduce static friction. The pressurizing cylinder pressure is then adjusted synchronously: based on the current drop ΔI, the target pressure is calculated using the formula P2 target = P2 current + ΔI × k, where the empirical coefficient k is calibrated through field testing to be 0.8 to 1.2 MPa / A. This ensures that the actual drilling pressure returns to the design value with an error not exceeding 1 MPa. If the torque T is still too low, the power head torque is adjusted as a supplement, controlling the hydraulic motor output torque to increase by 5% to 8%, achieved by increasing the flow rate using a variable pump, thus preventing pressure buildup without drilling progress.

[0037] In situations where ground vibration exceeds the standard, the goal is to reduce the impact caused by hard rock or fractures. First, calculate the vibration deviation ΔV as the actual vibration value V minus a preset threshold of 1.5 m / s². For mild vibration (ΔV ≤ 0.5 m / s²), the adjustment steps are to reduce the power head speed n by 8% to 12, while simultaneously increasing the pressure of the pressurizing cylinder P2 by 5% to 10% to enhance rock-breaking ability and reduce vibration. For severe vibration (ΔV > 0.5 m / s²), in addition to reducing speed and increasing pressure, the drilling operation must be paused, the pressure of the propulsion cylinder reduced to zero, and the drill bit kept rotating at a low speed of 10 to 20 rpm for 3 to 5 seconds. Once the vibration value drops to no more than 1.5 m / s², the drilling can resume, but the propulsion speed must be reduced by 20% to avoid drill pipe jamming caused by fractures.

[0038] This embodiment acquires real-time borehole attitude data, hydraulic data, and formation anomaly data, and combines this with the DS evidence theory algorithm to achieve multi-source data fusion and judgment. It can accurately output a comprehensive working condition judgment result that includes the working condition type and deviation level, effectively avoiding the misjudgment problem caused by single data judgment. Based on the working condition type and deviation level, the adjustment parameters of each actuator of the drilling equipment are calculated in a targeted manner. Differentiated and precise control strategies can be adopted for different working conditions and corresponding deviation levels, such as trajectory direction deviation, pressure, and excessive formation vibration. This enables parameterized and precise control of actuators such as propulsion cylinders, pressurization cylinders, power head hydraulic motors, and high-frequency solenoid valves. Finally, by adjusting the parameters to control the drilling equipment, the borehole trajectory deviation can be effectively corrected, the pressure problem caused by the friction between the drill string and the well wall can be eliminated, the impact of excessive formation vibration caused by hard rock or fractures can be alleviated, and the borehole trajectory can be prevented from deviating due to vibration and the drill pipe can be stuck. This ensures that the actual borehole trajectory accurately returns to the design path, greatly improving the accuracy and reliability of directional drilling trajectory control in mines and obtaining accurate directional drilling data.

[0039] In practical applications, the geological conditions in small-scale mining areas are complex and diverse, and some directional drilling equipment lacks adaptability. In soft strata, drill rods are prone to bending and breakage, significantly reducing drilling efficiency; in high-water-pressure strata, the sealing performance of the equipment faces challenges and is prone to failure. Furthermore, limited downhole space makes it difficult to deploy large equipment, while small equipment often cannot meet drilling requirements in terms of power and torque. Therefore, in some embodiments of this application, the drill rod material of the drilling equipment is carbon fiber reinforced epoxy resin matrix composite material or titanium alloy material; The cutting tooth parameters of the drill bit in the drilling equipment are determined according to the following method: The system acquires real-time data on wellhead drilling pressure, drill bit torque, power unit speed, mechanical drilling speed, and drill bit diameter, and then uses this data to calculate the energy required to break a unit volume of rock using the mechanical energy formula. Based on the prefitted mechanical specific energy-formation hardness coefficient relationship model, the current formation hardness coefficient is determined according to the energy required to break up a unit volume of rock. Based on a preset mapping relationship between formation hardness coefficient and cutting tooth parameters, the cutting tooth parameters of the drill bit are determined according to the current formation hardness coefficient; the cutting tooth parameters include tooth width, rotational speed, and torque.

[0040] In this embodiment, the drill rod of the drilling equipment is made of a high-strength composite material, such as carbon fiber reinforced epoxy resin matrix composite material or titanium alloy material, with a tensile strength ≥1200MPa, an elongation at break ≤1.5%, and the flexibility coefficient of the drill rod satisfies the following formula.

[0041] in, Indicates the application of force. Indicates the length of the drill pipe. This represents the elastic modulus of the drill pipe material. This represents the moment of inertia of the drill pipe section.

[0042] In addition, in this embodiment, the steps for determining the cutting tooth parameters of the drill bit are as follows: 1. Obtain the drill bit diameter and use sensors to acquire real-time data on wellhead drilling pressure, drill bit torque, power unit speed, and mechanical drilling speed. The sampling frequency is adapted to the real-time requirements of drilling operations. 2. Substitute the wellhead drilling pressure, drill bit torque, power unit speed, mechanical drilling speed and drill bit diameter into the mechanical specific energy formula to calculate the energy required to break a unit volume of rock; 3. Input the energy required to break a unit volume of rock into the mechanical specific energy-formation hardness coefficient relationship model based on the laboratory prefit, and calculate the current formation hardness coefficient. 4. Based on the current real-time formation hardness coefficient and the preset mapping relationship between the formation hardness coefficient and cutting tooth parameters, adjust the output power and torque of the drill bit cutting tooth assembly and hydraulic power unit to achieve dynamic matching of drilling parameters. The output power P can be adjusted in real-time according to the following formula to meet the needs of different working conditions.

[0043]

[0044] Where n is the rotational speed.

[0045] This embodiment proposes a formation-adaptive drilling device. By using advanced materials such as carbon fiber or titanium alloy to manufacture the drill pipe, the device significantly improves the drill pipe's lightweight, bending resistance, and corrosion resistance. This effectively reduces the risk of drill pipe deformation and breakage in complex formations such as soft or high-water-pressure formations, enhancing the device's adaptability. Simultaneously, the solution dynamically calculates formation hardness based on real-time drilling parameters and intelligently matches parameters such as the tooth width, rotational speed, and torque of the drill bit's cutting teeth, achieving precise adaptation of drill bit performance to formation conditions. This not only overcomes the problem of insufficient equipment power under downhole space constraints but also significantly improves drilling efficiency and reliability, comprehensively addressing the issues of poor adaptability and high failure rate of directional drilling equipment in complex geological environments such as small-scale goaf areas.

[0046] Regarding S102 above: First, the drilling, geophysical, and geochemical data are cleaned. Specifically, for drilling data, the 3σ principle can be used to remove abnormal trajectory parameters such as sudden fluctuations in azimuth angle (±5°) and abnormal core recovery rates (e.g., a single segment recovery rate dropping sharply from 80% to 10% without geological interpretation). For missing core records, stratigraphic information is supplemented using the lithological mean of two adjacent segments. Finally, clean drilling trajectory data and core stratification data are obtained. For geophysical resistivity data, wavelet transform denoising can be used to eliminate resistivity spikes caused by electromagnetic interference. For unmeasured sections, such as depths below 100m, inverse distance weighting is used for preliminary interpolation to complete the data. Finally, a smooth resistivity profile is obtained, with no data gaps ≤5m. For geochemical element content data, Z-score standardization can be used to convert different element contents into the [-1,1] interval. Outliers caused by sampling errors are removed, resulting in a standardized geochemical data matrix (mean = 0, standard deviation = 1).

[0047] After obtaining the cleaned drilling, geophysical, and geochemical data, spatiotemporal alignment is performed to obtain processed structured geological data. Specifically, spatial alignment: using the drilling borehole coordinates (WGS84 coordinate system) as a reference, ArcGIS Pro software is used to convert the coordinates of the geophysical survey lines and geochemical sampling points to the same coordinate system; for the geophysical / geochemical planar data (x / y), the z-coordinate is assigned according to the depth of the nearest borehole. For example, if a geochemical point is closest to borehole ID001, the z-value is taken as the depth corresponding to the horizontal distance of that borehole. Temporal alignment: data from the same exploration period, such as data within one month, is retained, while older data with a time span greater than three months are removed to avoid the influence of stratigraphic changes. Standardized Formatting: All data is converted to a grid cell format, such as dividing into 2m×2m×2m three-dimensional grids. Each grid corresponds to a unique ID, storing the average values ​​of drilling / geophysical / geochemical exploration within that grid. Finally, a spatiotemporally consistent "3D grid-multi-source data" association table is obtained, such as grid ID 001-001-001: Lithology = sandy mudstone, resistivity = 8Ω m, CO2 = 600ppm.

[0048] Regarding the above S103: Based on the DS evidence theory, the processed structured geological data is processed to generate geological feature labels for each grid.

[0049] Specifically, in some embodiments, the process of processing the structured geological data based on DS evidence theory to generate geological feature labels for each grid includes: Acquire multiple geological feature types and their corresponding geological feature data; For each grid, based on the geological feature data corresponding to the multiple geological feature types, the drilling data, geophysical data and geochemical data of that grid in the processed structured geological data, a basic probability allocation matrix is ​​generated; Based on the basic probability assignment matrix, geological feature labels corresponding to the grid are generated.

[0050] In this embodiment, the geological feature types include four categories: A1: Goaf exists within the grid (missing cores + low resistivity + abnormal CO2); A2: Intact strata exist within the grid (no missing cores + high resistivity + normal CO2); A3: Goaf boundary exists within the grid (discontinuous cores + abrupt changes in resistivity + fluctuations in CO2); A4: Goaf filling material exists within the grid (loose cores + medium resistivity + moderate CO2).

[0051] Geological feature data consists of drilling, geophysical, and geochemical data matched to various geological feature types. Drilling data includes the length of the missing core section, core recovery rate, and core condition. Geophysical data mainly consists of resistivity values, and geochemical data mainly consists of CO2 concentration. For example, the geological feature data corresponding to A1 are a core missing section length L ≥ 0.5m in the drilling data and a resistivity ρ < 10Ω in the geophysical data. m (low resistivity), CO2 concentration C > 500 ppm in geochemical data.

[0052] During implementation, for each grid, based on local geological feature data, the drilling data, geophysical data, and geochemical data of that grid in the processed structured geological data can be used to generate a basic probability allocation matrix. Specifically, Based on the evidence body rules of each data source, the basic probability allocation is calculated, which is the degree of confidence of a single data source in that grid belonging to a certain geological feature type (BPA value ∈ [0,1], the sum of all hypothesis BPA is 1). Drilling data corresponds to BPA1, geophysical data corresponds to BPA2, and geochemical data corresponds to BPA3. The three together form the basic probability allocation matrix.

[0053] For example, grid drilling data shows that the core is loose and the recovery rate is 65%. According to the rules, BPA1(A4)=0.6, BPA1(A3)=0.3, BPA1(Θ)=0.1; geophysical data shows a resistivity of 30Ω. m, according to the rules BPA2(A4)=0.5, BPA2(A3)=0.4, BPA2(Θ)=0.1; the geochemical data shows a CO2 concentration of 400ppm, according to the rules BPA3(A4)=0.4, BPA3(A3)=0.3, BPA3(Θ)=0.3, the basic probability distribution matrix of this grid is: [BPA1(A1)=0,BPA1(A2)=0,BPA1(A3)=0.3,BPA1(A4)=0.6,BPA1(Θ)=0.1; BPA2(A1)=0,BPA2(A2)=0,BPA2(A3)=0.4,BPA2(A4)=0.5,BPA2(Θ)=0.1; BPA3(A1)=0, BPA3(A2)=0, BPA3(A3)=0.3, BPA3(A4)=0.4, BPA3(Θ)=0.3].

[0054] Finally, based on the basic probability assignment matrix, geological feature labels corresponding to the grid are generated. For example, the geological feature label of a grid can be determined based on the highest confidence value in the matrix.

[0055] In order to accurately obtain the geological feature labels of the grid during implementation, the conflict degree can be calculated first using a formula. For example, if a certain grid has BPA1(A1)=0.7 and BPA2(A2)=0.7, traditional DS cannot handle it.

[0056] k=1 ∑ Ai∩Aj∩Ak≠ BPA1(A i BPA2(A) j BPA3(A) k ) If k > 0.5, it is considered a high-conflict hypothesis. Data source weights are introduced, for example, drilling w1 = 0.4, geophysical exploration w2 = 0.3, and geochemical exploration w3 = 0.3. The conflict amount k is then distributed to each hypothesis according to its weight, instead of being entirely distributed to the uncertainty Θ in the traditional approach. That is, the fused BPA is calculated according to the improved DS combination rule. fusion The formula is: BPA fusion (A)=∑ Ai∩Aj∩Ak=A BPA1(A i BPA2(A) j )BPA3(Ak)+w1k·BPA1(A)+w2k·BPA2(A)+w3k·BPA3(A) / 1 k For example, high-collision meshes are processed into BPA. fusion (A1) = 0.5, BPA fusion (A2) = 0.3, BPA fusion (Θ)=0.2, eliminating the conflict.

[0057] Finally, the confidence level of each hypothesis is calculated using the following formula: Bel(Ai)=∑ A Ai BPA fusion (A) If the maximum Bel(A)i )>0.5, and with Bel(A j If the difference between the values ​​of the two values ​​is greater than 0.2, then the geological feature label of the grid is determined to be A. i Otherwise, mark it as "to be verified".

[0058] In this embodiment, the conflict quantity k is weighted and distributed to each hypothesis, rather than being entirely allocated to the uncertainty Θ in the traditional way. This fully extracts the effective information from the conflict data, avoids the waste of decision-making value caused by simply discarding conflict information, highlights the leading role of more reliable data sources such as drilling, makes the fusion results more consistent with the actual logic of goaf detection, effectively compresses the proportion of meaningless uncertainty, makes the differences in the confidence of various geological feature hypotheses more significant, reduces the number of "to be verified" grids, avoids the expansion of detection blind spots, and ultimately improves the accuracy of goaf geological feature identification and the practical value of the three-dimensional geological model, making the fusion results more consistent with the actual geological conditions under complex strata.

[0059] For example, taking grid ID002-002-002 as an example, the drilling data in its processed structured geological data shows a core missing section with a length of 0.8m, and the geophysical data shows a resistivity of 7Ω. m, geochemical data shows a CO2 concentration of 650ppm. According to the Basic Probability Allocation (BPA) rule for each data source, drilling data has a missing core (L=0.8m≥0.5m), corresponding to BPA1(A1)=0.7, BPA1(A2)=0 (no missing core, not meeting condition A2), BPA1(A3)=0.2, BPA1(A4)=0 (not meeting condition A4 due to loose core), and BPA1(Θ)=0.1. Geophysical data has a resistivity ρ=7Ω. m<10Ω m (low resistivity) corresponds to BPA2(A1)=0.6, BPA2(A2)=0 (not high resistivity, not meeting condition A2), BPA2(A3)=0.3, BPA2(A4)=0 (not medium resistivity, not meeting condition A4), and BPA2(Θ)=0.1. Geochemical data shows CO2 concentration C=650ppm>500ppm (abnormal), corresponding to BPA3(A1)=0.5, BPA3(A2)=0 (not normal CO2, not meeting condition A2), BPA3(A3)=0.4, BPA3(A4)=0 (not medium CO2, not meeting condition A4), and BPA3(Θ)=0.1. This generates a complete basic probability allocation matrix containing the four hypotheses A1, A2, A3, and A4. Then, the conflict degree k=0.2 (less than 0.5) is calculated. (Non-high conflict) Drilling weights w1=0.4, geophysical weights w2=0.3, and geochemical weights w3=0.3 are introduced. After fusion, BPAfusion(A1)=0.62, BPAfusion(A2)=0, BPAfusion(A3)=0.28, BPAfusion(A4)=0, and BPAfusion(Θ)=0.1 are calculated by improving the DS combination rule. Then, the confidence levels of each hypothesis Bel(A1)=0.62, Bel(A2)=0, Bel(A3)=0.28, and Bel(A4)=0 are calculated. Since the maximum Bel(A1)=0.62>0.5 and the difference between it and the second largest Bel(A3) is 0.34>0.2, the geological feature label corresponding to this grid is finally generated as A1 (there is a goaf in the grid).

[0060] Regarding S104 above: Finally, based on the geological feature labels of each region, the processed structured geological data is used to generate a comprehensive geological model of the target goaf area.

[0061] In practical implementation, based on the geological feature labels assigned to each 3D grid unit, these labels are first bound to the corresponding grid spatial locations as core geological attributes to construct a discretized initial framework of geological features. Subsequently, multi-source information from the processed structured geological data is integrated, including rock strata depths revealed by drilling, wave velocity anomalies reflected by geophysical exploration, elemental concentrations detected by geochemical exploration, and geotechnical parameters. Through spatial analysis techniques such as Kriging interpolation or co-simulation, continuous attribute data is filled into the grid model to quantitatively characterize the physicochemical properties of the geological body. Furthermore, based on geological laws and spatial correlations, 3D modeling algorithms are used to connect similar labels of adjacent grids and optimize boundaries to generate continuous and smooth geological interfaces and entities, clearly depicting the spatial morphology, distribution range, and surrounding rock structure of the goaf. Finally, through cross-validation and uncertainty analysis, the consistency between the model and the measured data is ensured, forming a comprehensive geological model of the goaf that integrates geometric morphology, attribute distribution, and risk factors.

[0062] In some embodiments, the method further includes: Based on the comprehensive geological model, two-dimensional output data is generated; the two-dimensional output data includes at least one of the following: a planar distribution map of the goaf, a resistivity-depth profile map, and a carbon dioxide concentration contour map; Based on the comprehensive geological model, three-dimensional output data is generated; the three-dimensional output data includes at least one of the following: a three-dimensional rendering of the goaf, and a cross-sectional view of the borehole trajectory and the goaf. The volume, depth range, filling material type, and recommended drilling targets of each goaf are marked in the integrated geological model, and the integrated geological model is exported.

[0063] In practice, for example, Surfer is used to draw a planar distribution map of the goaf, a resistivity-depth profile map, and a CO2 concentration contour map; K-MINE is used to generate a 3D rendering of the goaf, in which the goaf is displayed transparently in red, the strata are distinguished by different colors, and a cross-section map of the borehole trajectory and the goaf is created.

[0064] The model labels the volume, depth range, and filling material type of each goaf, as well as recommended drilling targets (such as goaf boundaries). A comprehensive geological model of the goaf is visualized (supporting rotation, sectioning, and attribute querying) and can be exported to CAD / KML format to guide subsequent drilling parameter adjustments (such as avoiding goafs and optimizing borehole trajectories).

[0065] This embodiment generates two-dimensional and three-dimensional multi-type output data based on a comprehensive geological model of the goaf, enabling multi-dimensional visualization of the spatial distribution, physicochemical parameters, and borehole interactions of the goaf, thus enhancing the intuitiveness and comprehensiveness of geological feature expression. By labeling the goaf volume, depth range, filling material type, and recommended drilling targets and exporting the model, it can provide accurate data support for drilling engineering planning and implementation, enhance the practicality and operability of the scheme, facilitate cross-scenario application and subsequent geological analysis work, and improve the overall efficiency and scientific nature of goaf geological exploration.

[0066] Those skilled in the art will understand that in the above-described method of the specific embodiments, the order in which the steps are written does not imply a strict execution order, but constitutes no limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0067] It should be noted that in practical applications, all the above-described possible implementation methods can be combined in any way to form possible embodiments of this application, and will not be described in detail here. The information (including but not limited to device information, user information, etc.) and data (including but not limited to data used for analysis, storage, and display) involved in this application are all information and data authorized by the user or fully authorized by all parties. The software tools or components appearing in the embodiments of this application are merely illustrative examples and do not represent actual use.

[0068] Based on the same concept, this application also provides a goaf geological model generation device, which corresponds one-to-one with the goaf geological model generation method in the above embodiments. Figure 2 A schematic diagram of the geological model generation device for goaf areas provided in this application embodiment is shown. See also: Figure 2 As shown, the geological model generation device 200 for goaf areas provided in this application embodiment includes: The acquisition module 201 is used to acquire structured geological data of the target goaf area; the structured geological data includes directional drilling data, geophysical data, and geochemical data. Preprocessing module 202 is used to perform data cleaning and spatiotemporal alignment processing on the directional drilling data, geophysical data, and geochemical data to obtain processed structured geological data. The grid classification module 203 is used to process the processed structured geological data based on the DS evidence theory to generate geological feature labels for each grid. Modeling module 204 is used to generate a comprehensive geological model of the target goaf based on the geological feature labels and the processed structured geological data.

[0069] In some embodiments, in the above-described apparatus, the acquisition module is further configured to: Real-time acquisition of attitude data, hydraulic data, and formation anomaly data, wherein the attitude data includes the current azimuth angle, dip angle, and tool face angle of the borehole; the hydraulic data includes the pressure of the propulsion cylinder, the pressure of the pressurization cylinder, and the speed and torque of the power head hydraulic motor; and the formation anomaly data includes the drill pipe vibration value and the drive motor current. The attitude data, hydraulic data, and formation anomaly data are preprocessed to obtain preprocessed data; Based on the DS evidence theory, the preprocessed data is processed to obtain a comprehensive working condition judgment result; the comprehensive working condition judgment result includes the working condition type and deviation level; Based on the operating condition type and deviation level, calculate the adjustment parameters of each actuator of the drilling equipment; Based on the aforementioned adjustment parameters, the drilling equipment is controlled to obtain the directional drilling data.

[0070] In some embodiments, in the above-described device, the operating conditions include trajectory direction deviation, pressure, and excessive ground vibration. The deviation level of the trajectory direction deviation is divided into slight deviation, moderate deviation, and severe deviation, and the deviation level of excessive ground vibration is divided into slight vibration and severe vibration.

[0071] In some embodiments, the drill rod of the drilling equipment is made of carbon fiber reinforced epoxy resin matrix composite material or titanium alloy material; the device further includes a determining module for: The system acquires real-time data on wellhead drilling pressure, drill bit torque, power unit speed, mechanical drilling speed, and drill bit diameter, and then uses this data to calculate the energy required to break a unit volume of rock using the mechanical energy formula. Based on the prefitted mechanical specific energy-formation hardness coefficient relationship model, the current formation hardness coefficient is determined according to the energy required to break up a unit volume of rock. Based on a preset mapping relationship between formation hardness coefficient and cutting tooth parameters, the cutting tooth parameters of the drill bit are determined according to the current formation hardness coefficient; the cutting tooth parameters include tooth width, rotational speed, and torque.

[0072] In some embodiments, in the above-described apparatus, the grid classification module 203 is specifically used for: Acquire multiple geological feature types and their corresponding geological feature data; For each grid, based on the geological feature data corresponding to the multiple geological feature types, the drilling data, geophysical data and geochemical data of that grid in the processed structured geological data, a basic probability allocation matrix is ​​generated; Based on the basic probability assignment matrix, geological feature labels corresponding to the grid are generated.

[0073] In some embodiments, the apparatus further includes a visualization module for: Based on the comprehensive geological model, two-dimensional output data is generated; the two-dimensional output data includes at least one of the following: a planar distribution map of the goaf, a resistivity-depth profile map, and a carbon dioxide concentration contour map; Based on the comprehensive geological model, three-dimensional output data is generated; the three-dimensional output data includes at least one of the following: a three-dimensional rendering of the goaf, and a cross-sectional view of the borehole trajectory and the goaf. The volume, depth range, filling material type, and recommended drilling targets of each goaf are marked in the integrated geological model, and the integrated geological model is exported.

[0074] This invention provides a device for generating a geological model of a goaf. First, it acquires geological information from directional drilling, geophysical exploration, and geochemical exploration to comprehensively obtain geological information about the target goaf. Then, it performs data cleaning and spatiotemporal alignment on the drilling, geophysical, and geochemical data to remove data noise and unify the spatiotemporal reference. Based on the DS evidence theory, it processes the structured geological data to generate geological feature labels for each grid. Finally, based on the geological feature labels and the processed structured geological data, it generates a comprehensive geological model of the target goaf. This means that by integrating multi-dimensional geological information, a visualized geological representation is formed. Ultimately, this embodiment can generate a comprehensive geological model of a small-scale goaf that more closely resembles the actual situation through the effective fusion and accurate representation of multi-source heterogeneous geological data.

[0075] Specific limitations regarding the goaf geological model generation device can be found in the limitations of the goaf geological model generation method described above, and will not be repeated here. Each module in the aforementioned goaf geological model generation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0076] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Figure 3 As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0077] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0078] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0079] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a geological model generation device for the goaf at the logical level. The processor executes the program stored in memory and specifically performs the aforementioned methods.

[0080] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0081] This electronic device can execute the goaf geological model generation method provided in several embodiments of this application, and realize the goaf geological model generation device in Figure 2 The functions of the embodiments shown are not described in detail here.

[0082] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform the goaf geological model generation method provided in several embodiments of this application.

[0083] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0088] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0089] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0090] It should also be noted that 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. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0091] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for generating a geological model of a goaf, characterized in that, The method includes: Obtain structured geological data of the target goaf; the structured geological data includes directional drilling data, geophysical data, and geochemical data; The directional drilling data, geophysical data, and geochemical data are cleaned and spatiotemporally aligned to obtain processed structured geological data. Based on the DS evidence theory, the processed structured geological data is processed to generate geological feature labels for each grid. Based on the geological feature labels, the processed structured geological data generates a comprehensive geological model of the target goaf.

2. The method according to claim 1, characterized in that, The directional drilling data was generated according to the following method: Real-time acquisition of attitude data, hydraulic data, and formation anomaly data, wherein the attitude data includes the current azimuth angle, dip angle, and tool face angle of the borehole; the hydraulic data includes the pressure of the propulsion cylinder, the pressure of the pressurization cylinder, and the speed and torque of the power head hydraulic motor; and the formation anomaly data includes the drill pipe vibration value and the drive motor current. The attitude data, hydraulic data, and formation anomaly data are preprocessed to obtain preprocessed data; Based on the DS evidence theory, the preprocessed data is processed to obtain a comprehensive working condition judgment result; the comprehensive working condition judgment result includes the working condition type and deviation level. Based on the operating condition type and deviation level, calculate the adjustment parameters of each actuator of the drilling equipment; Based on the aforementioned adjustment parameters, the drilling equipment is controlled to obtain the directional drilling data.

3. The method according to claim 2, characterized in that, The operating conditions include trajectory direction deviation, pressure, and excessive ground vibration. The deviation levels of the trajectory direction deviation are divided into slight deviation, moderate deviation, and severe deviation, and the deviation levels of excessive ground vibration are divided into slight vibration and severe vibration.

4. The method according to claim 2, characterized in that, The drill rod of the drilling equipment is made of carbon fiber reinforced epoxy resin matrix composite material or titanium alloy material; The cutting tooth parameters of the drill bit in the drilling equipment are determined according to the following method: The system acquires real-time data on wellhead drilling pressure, drill bit torque, power unit speed, mechanical drilling speed, and drill bit diameter, and then uses this data to calculate the energy required to break a unit volume of rock using the mechanical energy formula. Based on the prefitted mechanical specific energy-formation hardness coefficient relationship model, the current formation hardness coefficient is determined according to the energy required to break up a unit volume of rock. Based on the preset mapping relationship between the formation hardness coefficient and the cutting tooth parameters, the cutting tooth parameters of the drill bit are determined according to the current formation hardness coefficient. The cutting tooth parameters include tooth width, rotational speed, and torque.

5. The method according to claim 1, characterized in that, The process, based on DS evidence theory, involves processing the structured geological data to generate geological feature labels for each grid, including: Acquire multiple geological feature types and their corresponding geological feature data; For each grid, based on the geological feature data corresponding to the multiple geological feature types, the drilling data, geophysical data and geochemical data of that grid in the processed structured geological data, a basic probability allocation matrix is ​​generated; Based on the basic probability assignment matrix, geological feature labels corresponding to the grid are generated.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Based on the comprehensive geological model, two-dimensional output data is generated; the two-dimensional output data includes at least one of the following: a planar distribution map of the goaf, a resistivity-depth profile map, and a carbon dioxide concentration contour map; Based on the comprehensive geological model, three-dimensional output data is generated; the three-dimensional output data includes at least one of the following: a three-dimensional rendering of the goaf, and a cross-sectional view of the borehole trajectory and the goaf. The volume, depth range, filling material type, and recommended drilling targets of each goaf are marked in the integrated geological model, and the integrated geological model is exported.

7. A device for generating a geological model of a goaf, characterized in that, The device includes: The acquisition module is used to acquire structured geological data of the target goaf area; the structured geological data includes directional drilling data, geophysical data, and geochemical data. The preprocessing module is used to perform data cleaning and spatiotemporal alignment on the directional drilling data, geophysical data, and geochemical data to obtain processed structured geological data. The grid classification module is used to process the processed structured geological data based on the DS evidence theory to generate geological feature labels for each grid. The modeling module is used to generate a comprehensive geological model of the target goaf based on the geological feature labels and the processed structured geological data.

8. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, characterized in that, when executed, the executable instructions cause the processor to perform the steps of the goaf geological model generation method as described in any one of claims 1-6.

9. A computer-readable storage medium storing one or more programs, characterized in that, When the one or more programs are executed by an electronic device comprising multiple applications, the electronic device performs the steps of the goaf geological model generation method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product carries program code, and the instructions included in the program code can be used to execute the steps of the method for generating a geological model of a goaf area as described in any one of claims 1-6.

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