A method and system for optimizing a mining path in combination with geological data

Abstract

This invention discloses a mining path optimization method and system incorporating geological data, belonging to the field of path optimization technology. A mining path optimization system incorporating geological data includes a geological data modeling module and a mining path optimization module. This invention introduces the mining heat hazard mechanism into the path planning process, and by constructing mining environmental load data and an environmental saturation model, it achieves a quantitative assessment of the risks of high temperature, high humidity, and pollutant accumulation, avoiding the heat hazard hazards caused by traditional path planning neglecting environmental safety factors. Based on real-time geological monitoring data and roadway topology, it dynamically calculates the heat and moisture exchange capacity and pollutant dilution capacity of roadway nodes, enabling the path planning results to accurately reflect changes in underground ventilation and environmental conditions, thus improving the accuracy of planning decisions.

Description

Technical Field

[0001] This invention relates to the field of path optimization technology, and in particular to a mining path optimization method and system that incorporates geological data. Background Technology

[0002] As mines develop towards deeper, more intensive, and multi-equipment collaborative mining, mining roadways generally face mining heat hazards such as high geothermal activity, high temperature and humidity, and pollutant accumulation. Existing mining path planning technologies mainly focus on spatial and efficiency factors, typically considering roadway topology, travel distance, transportation time, equipment collision avoidance, work scheduling sequence, and local safety constraints. However, existing technologies generally neglect the mining heat hazards generated during the mining process, including equipment heat dissipation, geothermal superposition, heat and humidity accumulation, and the dynamic enrichment process of pollutants under limited ventilation conditions. This results in insufficient safety margins in path planning results under high temperature, high humidity, or multi-equipment parallel operation scenarios.

[0003] Therefore, a dynamic path planning method that combines geological data and takes into account the control of mining heat hazards is needed to make up for the shortcomings of existing technologies in adaptability to high heat hazard environments. Summary of the Invention

[0004] This invention aims to provide a mining path optimization method and system that combines geological data. It deeply integrates geological monitoring data, ventilation thermodynamic models and path planning, and realizes dynamic path optimization under the constraints of mining heat hazards, providing a new technical path for safe, efficient and sustainable mining in deep mines.

[0005] A mining route optimization method incorporating geological data includes the following steps: The current mining area contains at least two mining machines; a mining roadway topology map of the mining area is obtained by combining geological data; the mining roadway topology map contains several roadway nodes; geological monitoring data of the mining area is acquired in real time; the geological monitoring data package contains wind force data, air pressure data and mining geothermal gradient data of each roadway node; based on the mining roadway topology map and geological monitoring data, the mining environmental load data corresponding to each roadway node is calculated; the mining environmental load data package contains mining heat and moisture exchange capacity and mining pollutant dilution capacity; In the mining roadway topology map, the mining environmental load feature vector corresponding to the mining equipment of the mining path to be planned is obtained; based on the mining environmental load feature vector, the fluctuation of mining environmental load data when the mining equipment passes through the roadway node is simulated and calculated to obtain simulated mining environmental load data; Based on the simulated mining environment load data, perform path search with a mining environment recovery time window, and output the optimized mining path corresponding to all mining equipment to be planned.

[0006] As a preferred embodiment of the present invention, the specific steps for calculation based on the mining roadway topology map and geological monitoring data include: The roadway nodes in the mining roadway topology map are considered as independent mining thermal control bodies. For any mining thermal control body, the roadway physical geometry data is acquired. The roadway physical geometry data package contains the roadway cross-sectional perimeter, roadway cross-sectional area, and roadway wall roughness data. Based on the wind force data, air pressure data, and roadway physical geometry data corresponding to the mining thermal control body, the mining convective heat transfer coefficient between the current airflow and the roadway wall is calculated. Based on the wet-bulb temperature limit, mining convective heat transfer coefficient, and mining geothermal gradient data, the mining heat and moisture exchange capacity of the mining thermal control body is calculated. A dynamic mining ventilation model for the mining thermal control body is constructed based on wind data; the dilution capacity of mining pollutants in the mining thermal control body is calculated based on the dynamic mining ventilation model.

[0007] As a preferred technical solution of the present invention, in the dynamic mining ventilation model, the theoretical air volume is calculated based on wind force data and roadway cross-sectional area; the roadway leakage coefficient is matched based on roadway cross-sectional area and roadway perimeter; the theoretical air volume is corrected using the roadway leakage coefficient to obtain the effective sewage discharge air volume of the mining thermal control body. The longitudinal dispersion coefficient within the mining thermal control body is calculated based on the roughness data and wind data of the roadway wall. The maximum pollutant mass flow rate that the mining thermal control body can accommodate and transport per unit time is calculated based on the effective sewage discharge air volume and the longitudinal dispersion coefficient, which is the mining pollutant dilution capacity.

[0008] As a preferred embodiment of the present invention, the specific steps for obtaining the mining environmental load feature vector corresponding to the mining equipment for the planned mining path include: The physical model of the current roadway node is reconstructed based on the roadway physical geometry data; the mining equipment of the mining path to be planned is fused with the roadway physical model using digital 3D technology to obtain a 3D model of the mining roadway; the radiation angle coefficient between the mining equipment and the roadway node is obtained. Using the wind force data of the current roadway nodes as boundary conditions, the streamline distribution of the three-dimensional model of the mining roadway is pre-simulated to obtain the thermal hazard intensity coefficient of the mining equipment; the thermal inertia state equation of the mining equipment is established; the unsteady-state heat flux of the mining equipment is obtained based on the radiation angle coefficient and the thermal inertia state equation; the thermal hazard intensity coefficient of the mining equipment is used as the pollution diffusion source term, and the unsteady-state heat flux is used as the heat source term. After feature fusion, the mining environmental load feature vector is obtained.

[0009] As a preferred embodiment of the present invention, the specific steps for simulating and calculating the fluctuation of mining environmental load data when mining equipment passes through roadway nodes based on the mining environmental load characteristic vector include: Based on the wind force data of the roadway nodes, the corresponding wind speed vector is obtained; at the same time, the moving speed vector of the mining equipment when passing through the roadway nodes is simulated; the dot product of the wind speed vector and the moving speed vector is calculated to obtain the relative motion state of the mining equipment; the relative airflow velocity is calculated using the wind speed vector and the moving speed vector. When the relative motion state is headwind, the additional mining power required by the mining equipment is calculated based on the relative airflow velocity and the physical geometry data of the roadway. The additional mining power is then superimposed on the unsteady heat flux in the mining environmental load feature vector to obtain the corrected mining environmental load feature vector. When the relative motion state is downwind, the absolute value of the difference between the magnitude of the moving velocity vector and the magnitude of the wind speed vector is calculated to obtain the relative velocity deficit value; based on the relative velocity deficit value, the mining pollutant dilution capacity of the roadway node is reverse-weighted and compressed to obtain the corrected mining environmental load characteristic vector. Using the corrected mining environment load feature vector as the numerator and the mining environment load feature vector as the denominator, the mining environment saturation index at each time step is calculated; the longitudinal dispersion coefficient is used as the time constant to construct a mining environment saturation decay model; the mining environment saturation index is input into the mining environment saturation decay model to generate simulated mining environment load data that fluctuates with time.

[0010] As a preferred embodiment of the present invention, the specific steps of performing path search with a mining environment recovery time window based on simulated mining environment load data include: Set a safe threshold for mining environment saturation; extract the duration of values ​​exceeding the safe threshold from simulated mining environment load data, and define it as the mining environment recovery time window; construct a spatiotemporal dynamic road network model, and map the mining environment recovery time window into the inaccessibility constraints of mining equipment on the planned mining path within a specific time period in the spatiotemporal dynamic road network model; perform a global path search in the spatiotemporal dynamic road network model, and output the optimized mining path corresponding to all mining equipment on the planned mining path.

[0011] A mining route optimization system incorporating geological data includes: The geological data modeling module includes a geological data processing unit and a mining environment modeling unit. The geological data processing unit is used when the current mining area contains at least two mining machines. It combines geological data to obtain a mining roadway topology map of the mining area, which includes several roadway nodes. It acquires real-time geological monitoring data for the mining area; the geological monitoring data package contains wind force data, air pressure data, and mining geothermal gradient data for each roadway node; and calculates the mining environmental load data corresponding to each roadway node based on the mining roadway topology map and geological monitoring data. The mining environmental load data package contains mining heat and moisture exchange capacity and mining pollutant dilution capacity. The mining environment modeling unit is used to obtain the mining environmental load feature vector corresponding to the mining machine for the planned mining path in the mining roadway topology map; and simulates the fluctuation of the mining environmental load data when the mining machine passes through roadway nodes based on the mining environmental load feature vector to obtain simulated mining environmental load data. The mining path optimization module includes a path output optimization unit. The path output optimization unit is used to perform path search with a mining environment recovery time window based on simulated mining environment load data, and output the optimized mining path corresponding to all mining equipment to be planned.

[0012] The present invention has the following advantages: 1. This invention incorporates the mechanism of mining heat hazards into the route planning process. By constructing mining environmental load data and an environmental saturation model, it achieves a quantitative assessment of the risks of high temperature, high humidity, and pollutant accumulation, avoiding the heat hazard hazards caused by neglecting environmental safety factors in traditional route planning. Based on real-time geological monitoring data and roadway topology, it dynamically calculates the heat and moisture exchange capacity and pollutant dilution capacity of roadway nodes, enabling the route planning results to truly reflect changes in underground ventilation and environmental conditions, thereby improving the accuracy of planning decisions.

[0013] 2. This invention establishes a mining environment recovery time window and introduces a spatiotemporal dynamic road network model to transform the recovery process after environmental overload into a passability constraint, effectively preventing multiple mining equipment from continuously passing through the same roadway node in a short period of time, thus preventing environmental overload. It constructs mining environment load feature vectors for different mining equipment, comprehensively considers the coupling effects of equipment operating conditions, movement direction and airflow conditions, and realizes differentiated path optimization under the condition of multi-equipment collaborative operation. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a mining path optimization system that incorporates geological data, as used in an embodiment of the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this invention.

[0016] Example 1: A mining route optimization method combining geological data, comprising the following steps: The current mining area contains at least two mining machines; a mining roadway topology map of the mining area is obtained by combining geological data; the mining roadway topology map contains several roadway nodes; geological monitoring data of the mining area is acquired in real time; the geological monitoring data package contains wind force data, air pressure data and mining geothermal gradient data of each roadway node; based on the mining roadway topology map and geological monitoring data, the mining environmental load data corresponding to each roadway node is calculated; the mining environmental load data package contains mining heat and moisture exchange capacity and mining pollutant dilution capacity; In practical applications, mining equipment generally refers to mobile or fixed equipment that undertakes mining, loading, support, or inspection operations within roadways. Common examples include tunneling machines, coal mining machines, loader jacks, underground transport vehicles, belt conveyors and their drive units, drilling rigs and bolt trolleys, shotcrete support equipment, and robots or monitoring trolleys used for inspection and measurement. In this embodiment, the mining equipment includes at least two pieces of working equipment that can move within the roadway. This is to demonstrate the cumulative impact of simultaneous operation of multiple pieces of equipment on the heat, humidity, and pollutant load of the roadway, thereby ensuring that path planning not only meets accessibility and efficiency requirements but also meets the constraints of heat hazards and ventilation safety.

[0017] A mining roadway topology map is a data format that abstracts the underground roadway network into a node-edge structure. Nodes correspond to roadway intersections, chambers, working face entrances, connecting roadway endpoints, or monitoring points, while edges correspond to traversable roadway segments between two nodes. Attributes such as roadway length, slope, cross-sectional dimensions, passage restrictions, and ventilation direction can be attached to edges or nodes. Mining roadway topology maps are typically stored in graph databases, adjacency lists, adjacency matrices, or attributed vector network files. Their function is to provide a computable road network foundation for path search and to serve as a spatial carrier for environmental load calculation, enabling monitoring values ​​such as wind force, air pressure, and geothermal energy to be mapped to specific nodes and roadway segments. This supports subsequent time-updated dynamic traversability assessments and global optimal path solutions.

[0018] In geological monitoring data, wind force data at each roadway node represents the driving intensity and direction of airflow on equipment and the environment at that location. In engineering, this corresponds to wind speed, wind direction, or equivalent wind speed converted from ventilation volume, and is used to characterize convective heat transfer capacity and pollutant transport capacity. Air pressure data represents the static pressure or differential pressure level at the roadway node, which reflects ventilation resistance and airflow distribution status, and helps to judge the actual air supply capacity of the ventilation network and unfavorable conditions such as local return air and eddies. Mining geothermal gradient data represents the temperature rise trend of the surrounding rock with depth or along the roadway direction. It is a root cause indicator of heat hazards and is used to estimate the continuous heat release intensity from the surrounding rock to the roadway air. The above data are usually acquired in real time through underground monitoring systems, such as deploying wind speed and direction sensors, differential pressure gauges, or air pressure sensors at key nodes, and are verified in conjunction with ventilation station data. The geothermal gradient can be calculated from geothermal monitoring boreholes, surrounding rock temperature sensors, or geological exploration temperature data and updated by interpolation according to nodes, thereby forming a continuous monitoring sequence corresponding to nodes and time.

[0019] The specific steps for calculations based on mining roadway topology maps and geological monitoring data include: Treating each roadway node in the mining roadway topology map as an independent mining thermal control body means treating each monitoring point or key location as a computational unit capable of independently calculating energy and ventilation costs. Within this unit, air conditions, wall conditions, and heat source input are assumed to be approximately uniform over a short period, allowing for quantitative calculation of heat exchange, humidity changes, and pollutant transport capacity at that node. The above steps discretize the complex continuous spatial problem of roadways into multiple computable nodes and correspond them to the edge relationships in the topology map, facilitating the subsequent direct embedding of node environmental capabilities into the costs and constraints of path planning.

[0020] For any mining thermal control body, the physical geometry data of the roadway is acquired. The roadway physical geometry data package contains the roadway cross-sectional perimeter, roadway cross-sectional area, and roadway wall roughness data. The roadway physical geometry data provides boundary and resistance conditions for subsequent heat exchange and ventilation calculations. Among them, the roadway cross-sectional area represents the effective flow area that the roadway can accommodate, affecting the air volume and average wind speed. The roadway cross-sectional perimeter represents the wetted perimeter of the airflow in contact with the roadway wall, which determines the size of the contact boundary for convective heat exchange and moisture transfer between the air and the surrounding rock. The roadway wall roughness represents the degree of wall unevenness and friction characteristics. The greater the roughness, the stronger the airflow resistance and near-wall turbulence, which will change the heat exchange intensity and ventilation loss. The above-mentioned roadway physical geometry data usually comes from roadway design drawings, 3D laser scanning modeling, or on-site measurements, and can be collected into a geometric parameter set for the control body by nodes.

[0021] Calculate the mining convective heat transfer coefficient between the current airflow and the roadway wall based on the wind force data, air pressure data, and roadway physical geometry data corresponding to the mining thermal control body; calculate the mining heat and moisture exchange capacity of the mining thermal control body based on the wet-bulb temperature limit, mining convective heat transfer coefficient, and mining geothermal gradient data; The mining convective heat transfer coefficient between airflow and tunnel walls is calculated based on wind force data, air pressure data, and tunnel physical geometry data. This is to obtain the heat intensity that can be exchanged per unit area between the wall and the airflow under a unit temperature difference. The mining convective heat transfer coefficient reflects the ability of the airflow to carry away heat from the surrounding rock. In the calculation, the average wind speed or equivalent wind speed at the node is first obtained from the wind force data and cross-sectional area. Then, the airflow conditions are corrected by combining the ventilation pressure difference and resistance state reflected by the air pressure data. The near-wall flow state and friction effects are determined by using the cross-sectional perimeter and roughness, thereby obtaining a convective heat transfer coefficient that matches the current ventilation conditions. Among them, convection emphasizes the removal of heat from the wall through the flowing air. Unlike simple conduction, it is very sensitive to wind speed, roughness, and cross-sectional scale.

[0022] The mining heat and moisture exchange capacity is calculated based on wet-bulb temperature limits, convective heat transfer coefficients, and mining geothermal gradient data. This is used to assess how much heat and moisture load a roadway node can still bear under acceptable heat and moisture conditions for personnel and equipment. The wet-bulb temperature limit is a control indicator used to characterize the decrease in human body heat dissipation capacity under high temperature and humidity conditions; exceeding this limit will significantly increase the risk of heat hazards. The mining geothermal gradient data is used to characterize the intensity source of continuous heat release from the surrounding rock into the roadway. The larger the geothermal gradient, the higher the background temperature of the surrounding rock, and the greater the heat input to the airflow. By combining the surrounding rock heat flux corresponding to the geothermal gradient with the convective heat transfer capacity, and using the wet-bulb temperature limit as an upper limit constraint, the maximum heat that the control body can exchange and remove under the current ventilation conditions, as well as the accompanying moisture exchange capacity, can be obtained. This forms the mining heat and moisture exchange capacity, which is used to determine whether the node is close to heat saturation and whether frequent passage should be restricted or recovery time should be set in the path planning.

[0023] A dynamic mining ventilation model for the mining thermal control body is constructed based on wind data; the dilution capacity of mining pollutants in the mining thermal control body is calculated based on the dynamic mining ventilation model. The dynamic mining ventilation model based on wind data refers to the establishment of a ventilation calculation framework that reflects the change of ventilation capacity over time by using the real-time wind conditions collected at the roadway node as the main input. The wind data describes the intensity and direction of air flow in the roadway, while the dynamic aspect emphasizes that the model is not a fixed parameter model, but is continuously updated with the adjustment of the ventilation system, equipment operation disturbances, and changes in local roadway conditions. This is used to characterize the actual available ventilation and sewage discharge capacity of a certain control body in different time periods, providing a basis for subsequent calculations of pollutant transport and dilution.

[0024] In the dynamic mining ventilation model, the theoretical air volume is calculated based on wind data and roadway cross-sectional area. It is obtained by combining the monitored wind speed with the effective flow area of ​​the roadway corresponding to the node to obtain the volume of air passing through the cross-section per unit time. This theoretical air volume reflects the amount of air that can be transported under ideal airflow conditions without considering air leakage and local losses. It is the benchmark value for subsequent correction calculations, and its physical significance lies in characterizing the upper limit of the nominal ventilation capacity of the roadway under the current wind conditions.

[0025] Matching the roadway leakage coefficient based on the roadway cross-sectional area and roadway perimeter is to reflect the air leakage phenomenon caused by cracks in the surrounding rock, support gaps, and structural complexity of the roadway. The cross-sectional area represents the scale of the main ventilation channel, while the cross-sectional perimeter represents the boundary length of the contact between air and the surrounding rock or support structure. The combination of the two can characterize the geometric conditions under which air leakage occurs. The roadway leakage coefficient is a dimensionless parameter used to quantify the proportion of air leakage that may occur when passing through a roadway node. The larger the value, the more serious the air leakage, and the smaller the actual air volume that can be used for sewage discharge and cooling.

[0026] By correcting the theoretical air volume using the roadway leakage coefficient, the effective exhaust air volume of the mining thermal control body is obtained. This refers to the actual amount of air involved in the carrying and transport of pollutants, obtained by subtracting the air volume lost due to factors such as leakage, short circuits, or local backflow from the theoretical air volume. This effective exhaust air volume reflects the ventilation capacity of the node under actual working conditions for removing dust, harmful gases, and heat and humidity loads. It is a key parameter for measuring the exhaust and cooling potential of the mining thermal control body.

[0027] The longitudinal dispersion coefficient within the mining thermal control body is calculated based on roughness data and wind data of the tunnel wall. This coefficient describes the diffusion and mixing capacity of pollutants in the axial direction of the tunnel. The longitudinal dispersion coefficient characterizes the axial diffusion effect of pollutants driven by airflow due to factors such as wall roughness and enhanced turbulence. The rougher the tunnel wall and the stronger the wind, the more obvious the flow field disturbance, and the easier it is for pollutants to be stretched, mixed, and diffused downstream. The larger the coefficient, the less likely the pollutants are to accumulate at local nodes, and the easier they are to be carried away by the airflow.

[0028] The maximum pollutant mass flow rate that the mining thermal control body can accommodate and transport per unit time is calculated based on the effective exhaust air volume and the longitudinal dispersion coefficient, which is the mining pollutant dilution capacity. The calculation of the mining pollutant dilution capacity based on the effective exhaust air volume and the longitudinal dispersion coefficient refers to the calculation of the maximum pollutant mass flow rate that the mining thermal control body can safely accommodate and transport per unit time, taking into account both air transport capacity and pollutant diffusion capacity. Among them, the effective exhaust air volume determines the upper limit of pollutant transport that can be carried away as a whole, and the longitudinal dispersion coefficient determines the diffusion efficiency of pollutants that are spread and mixed in a local area. The two together constrain the exhaust capacity of the node, and the calculation result is defined as the mining pollutant dilution capacity, which serves as an important basis for determining whether the node is suitable for continuous passage of multiple devices or whether an environmental recovery time window needs to be set in path planning.

[0029] In the mining roadway topology map, the mining environmental load feature vector corresponding to the mining equipment for the planned mining path is obtained. Obtaining the mining environmental load feature vector corresponding to the mining equipment for the planned mining path in the mining roadway topology map means that before path planning, a set of parameters that can describe the comprehensive impact of each mining equipment participating in the planning is constructed. This feature vector is used to characterize the heat, pollution disturbance and its coupling relationship with ventilation conditions introduced by the equipment when passing through a certain roadway node. Thus, the path search not only focuses on whether the equipment can pass, but also quantifies the degree of impact of the equipment on mining heat hazards and environmental load after passing through.

[0030] The specific steps for obtaining the mining environmental load feature vector corresponding to the mining equipment for the planned mining path include: Reconstructing a roadway physical model based on roadway physical geometry data refers to using the cross-sectional shape, dimensions, length, and wall conditions of the roadway corresponding to the node to recreate the actual spatial form of the roadway in a computer environment. This physical model is not a simple linear channel, but a three-dimensional geometric entity that can reflect the real ventilation and heat exchange boundaries, providing basic spatial constraints for subsequent analysis of airflow distribution, heat transfer, and pollution diffusion.

[0031] The mining equipment for the planned mining path is integrated with the roadway physical model using digital 3D technology to obtain a 3D model of the mining roadway. This involves introducing the actual or equivalent geometric shape and scale information of the mining equipment into the roadway physical model, so that the equipment appears in the roadway as a spatial entity. Through this integration, the relative positional relationship between the equipment and the roadway wall, the shielding effect, and the compression effect on the airflow channel can be accurately described, thus providing a more realistic 3D scene for analyzing the local ventilation, heat dissipation, and pollution diffusion behavior under the condition of equipment presence.

[0032] Obtaining the radiation angle coefficient between mining equipment and roadway nodes is to quantify the geometric relationship of energy exchange between the surface of mining equipment and the surrounding rock of the roadway through thermal radiation. The radiation angle coefficient is used to represent the proportion of heat that can be seen and received by the other side when the equipment surface radiates heat to the roadway wall. The radiation angle coefficient is closely related to the shape and orientation of the equipment, its position in the roadway, and the cross-sectional shape of the roadway. It is an important parameter for expanding the equipment heat dissipation from simple convection to consider radiation effects at the same time.

[0033] Using the wind force data of the current roadway node as boundary conditions, a streamline distribution simulation is performed on the three-dimensional model of the mining roadway to obtain the thermal hazard intensity coefficient of the mining equipment. This refers to introducing measured or real-time updated wind speed and direction conditions into the three-dimensional model of the mining roadway to simulate the flow path and distribution state of air around the equipment and inside the roadway. Through the streamline distribution simulation, it is possible to identify whether a low-speed zone, backflow zone, or local stagnation zone is formed around the equipment, and to evaluate the equipment's blocking effect on the ventilation channel. Thus, a thermal hazard intensity coefficient characterizing the tendency of the equipment to cause mining thermal hazards and pollution accumulation at that node is obtained. The larger the thermal hazard intensity coefficient of the mining equipment, the more likely the equipment is to exacerbate local high temperature and humidity and pollution accumulation at that location.

[0034] A thermal inertia state equation is established for the mining equipment. Based on the radiation angle coefficient and the thermal inertia state equation, the unsteady-state heat flux of the mining equipment is obtained. The above steps describe the dynamic process of heat absorption, storage, and release during the operation of the mining equipment from the perspective of energy conservation. Thermal inertia is used to characterize the equipment's hysteresis response to heat changes in a short period of time. This state equation comprehensively considers the equipment's own heat generation power, material heat capacity, heat dissipation conditions, and changes in operating conditions, so that the equipment's heat release is no longer regarded as instantaneously constant, but as a dynamic quantity that changes with time, providing a mathematical basis for subsequent calculation of unsteady-state heat flux.

[0035] Unsteady-state heat flux is calculated based on the characteristics of equipment heat change over time, representing the instantaneous heat density released by the equipment to the surrounding roadway environment at different time stages. The unsteady-state aspect emphasizes that the heat flux will continuously change with the equipment's operating status, environmental conditions, and previous heat storage. The radiation angle coefficient is used to reasonably distribute the radiative heat transfer portion to the roadway surrounding rock and air environment, thereby obtaining a heat source expression form that better reflects the actual working conditions.

[0036] Using the thermal hazard intensity coefficient of mining equipment as the pollution diffusion source term and the unsteady heat flux as the heat source term, the mining environmental load feature vector is obtained after feature fusion. This means that the ventilation disturbance and pollution accumulation trend caused by equipment are uniformly represented as pollution source intensity, and the dynamic heat dissipation behavior of equipment is uniformly represented as heat source intensity. The two are combined with the original environmental capacity index of the node into a vectorized expression. This mining environmental load feature vector comprehensively reflects the superimposed impact of equipment on thermal hazards and pollution at a specific roadway node. It is the core input for subsequent simulation of environmental load fluctuations, calculation of environmental saturation, and execution of constraint path search.

[0037] The simulation of mining environmental load data is obtained by simulating the fluctuation of mining environmental load data when mining equipment passes through roadway nodes based on the characteristic vector of mining environmental load. This means that in the path planning stage, instead of assuming that the environmental capacity of the node is constant, the dynamic process of the change in the heat and pollution carrying capacity of the node before and after the equipment passes through is combined with the equipment movement state and ventilation conditions. This results in simulated mining environmental load data that changes over time, which is used to depict the real evolution behavior of the environment from being able to carry the load to being saturated and then recovering.

[0038] The specific steps for simulating and calculating the fluctuation of mining environmental load data when mining equipment passes through roadway nodes based on the mining environmental load characteristic vector include: The wind speed vector, derived from wind data at roadway nodes, represents the monitored wind force and direction as a unified physical quantity with direction and amplitude, describing the actual airflow direction and speed within the roadway. Simultaneously, the moving speed vector of mining equipment passing through roadway nodes is simulated. This vector is set based on equipment type, operating conditions, and path planning speed, determining the equipment's direction and speed within the roadway. By calculating the dot product of the wind speed vector and the moving speed vector, their directional relationship can be determined, thus identifying whether the equipment movement and airflow are mutually antagonistic or superimposed. This result is defined as the relative motion state of the mining equipment. Furthermore, the difference between the two vectors is used to calculate the relative airflow velocity, characterizing the actual airflow intensity felt on the equipment surface.

[0039] Simultaneously, the moving velocity vector of the mining equipment when passing through the roadway node is simulated; the dot product of the wind speed vector and the moving velocity vector is calculated to obtain the relative motion state of the mining equipment; and the relative airflow velocity is calculated using the wind speed vector and the moving velocity vector. When the relative motion state is headwind, the additional mining power required by the mining equipment is calculated based on the relative airflow velocity and the physical geometry data of the roadway. The additional mining power is then superimposed on the unsteady heat flux in the mining environmental load feature vector to obtain the corrected mining environmental load feature vector. When the relative motion state is headwind, it means that the direction of the mining equipment is opposite to the direction of the airflow. The equipment needs to overcome greater air resistance and cause stronger airflow disturbance. At this time, based on the relative airflow velocity and the physical geometry data of the roadway, the additional mining power required for the equipment to maintain a given speed at this node is calculated. The additional power reflects the increased energy consumption and heat generation due to headwind conditions. The additional mining power is equivalently converted into additional heat release and superimposed on the unsteady heat flux in the mining environmental load characteristic vector to obtain the corrected mining environmental load characteristic vector, thereby reflecting the amplification effect of headwind on mining heat hazards.

[0040] When the relative motion state is downwind, the absolute value of the difference between the magnitude of the moving velocity vector and the magnitude of the wind speed vector is calculated to obtain the relative velocity deficit value; based on the relative velocity deficit value, the mining pollutant dilution capacity of the roadway node is reverse-weighted and compressed to obtain the corrected mining environmental load characteristic vector. When the relative motion is with the wind, the direction of movement of the mining equipment is consistent with the direction of the airflow. Although the driving force required by the equipment may be reduced, the equipment will drag and occupy the original airflow, resulting in a decrease in the effective airflow passing through the node per unit time. At this time, by calculating the absolute value of the difference between the magnitude of the moving speed vector of the equipment and the magnitude of the wind speed vector, a relative speed deficit value is obtained, which is used to measure the degree of weakening of the airflow transport efficiency by the equipment. Based on this relative speed deficit value, the original mining pollutant dilution capacity of the roadway node is reverse-weighted and compressed. That is, the larger the speed deficit, the more obvious the attenuation of dilution capacity, thus obtaining the corrected mining environmental load characteristic vector after considering the with-wind disturbance.

[0041] Using the corrected mining environment load feature vector as the numerator and the mining environment load feature vector as the denominator, the mining environment saturation index at each time step is calculated; the longitudinal dispersion coefficient is used as the time constant to construct a mining environment saturation decay model; the mining environment saturation index is input into the mining environment saturation decay model to generate simulated mining environment load data that fluctuates with time.

[0042] By using the modified mining environmental load feature vector as the numerator and the original mining environmental load feature vector as the denominator, the mining environmental saturation index at each time step is calculated to obtain a dimensionless index representing the degree of occupancy of the node's environmental load relative to its baseline carrying capacity at the current moment. The larger the mining environmental saturation index, the closer the heat and pollution load is to or exceeds the node's upper limit. Furthermore, the longitudinal dispersion coefficient is used as a time constant to construct a mining environmental saturation decay model, which describes the recovery process of heat and pollutants being gradually carried away by ventilation and diffusion after the equipment leaves. By inputting the mining environmental saturation index into this decay model, simulated mining environmental load data that fluctuates over time can be generated, providing a dynamic basis for subsequently determining the environmental recovery time window and performing constrained path searches.

[0043] Based on simulated mining environmental load data, a path search with a mining environment recovery time window is performed, and the optimized mining path corresponding to all mining equipment to be planned is output. The above steps mean that in the path planning process, instead of relying solely on whether the roadway is connected or the distance is shortest, the environmental carrying capacity of the roadway nodes in different time periods is taken as the core constraint. The dynamic impact of mining heat hazards and pollution accumulation is directly introduced into the path decision, thereby outputting an optimized mining path that meets both operational efficiency requirements and environmental safety constraints.

[0044] The specific steps for performing a path search with a mining environment recovery time window based on simulated mining environment load data include: Setting a mining environment saturation safety threshold is to provide a definite safety boundary for the risks of heat hazards and pollution at roadway nodes. The mining environment saturation safety threshold is used to characterize the maximum proportion of environmental load that can be achieved within the acceptable range of personnel, equipment, and ventilation systems. When the mining environment saturation index exceeds this threshold, it means that the node is in a high-risk state of high temperature and humidity or rich in pollutants. Continuing to pass through may cause mining heat hazards or safety accidents. Therefore, it needs to be regarded as a temporarily unusable area in the route planning.

[0045] The duration during which the value of the simulated mining environmental load exceeds the safe threshold of environmental saturation is defined as the mining environment recovery time window. The mining environment recovery time window refers to analyzing the change curve of the environmental load over time for each roadway node, and finding the time interval required for the environmental load to exceed the safe threshold and gradually decay back to the safe range. The mining environment recovery time window reflects the minimum time required for a node to recover to a safe state through ventilation and diffusion after experiencing equipment passage and environmental disturbances. It is a key indicator describing the cooling and purification capacity of the roadway environment.

[0046] Constructing a spatiotemporal dynamic road network model refers to introducing a time dimension into the traditional roadway topology map that only includes spatial connectivity, so that each roadway and each node has different traversable states at different time periods. In this model, the mining environment recovery time window is mapped as the inaccessibility constraint of mining equipment on the corresponding node or roadway within a specific time period. That is, within the recovery time window, even if it is spatially accessible, it is regarded as temporarily closed in the time dimension, thus forming a dynamic road network that is simultaneously affected by spatial structure and time constraints.

[0047] In the spatiotemporal dynamic road network model, the mining environment recovery time window is mapped to the inaccessibility constraint of mining equipment on the mining path to be planned within a specific time period; global path search is performed in the spatiotemporal dynamic road network model to output the optimized mining path corresponding to all mining equipment on the mining path to be planned.

[0048] Performing global path search in a spatiotemporal dynamic road network model refers to uniformly optimizing and solving all possible paths under the comprehensive conditions of considering roadway connectivity, equipment travel time, and environmental restoration constraints. Through this search, a travel route that always meets the environmental safety threshold and has the best overall cost can be found for each mining equipment with a planned mining path, thereby outputting the corresponding optimized mining path and effectively avoiding the risks of mining heat hazards and pollution under multi-equipment collaboration.

[0049] In this embodiment, all calculations and simulations are based on a physical-data fusion dynamic simulation model. The core idea is to take the tunnel topology, geological monitoring data, and equipment operating status as inputs, and perform joint calculations using a physical model of airflow and heat and moisture transport, a pollutant diffusion model, and a path search algorithm. The physical model ensures the engineering interpretability of the results, and the data-driven update mechanism adapts to real-time changes in operating conditions, ultimately forming a dynamic environmental load sequence that can be used for path optimization. When mapping the mining tunnel topology to a thermal control volume and calculating the mining environmental load data, a heat transfer and ventilation equation based on the control volume is used. The convective heat transfer coefficient can be calculated from wind speed, pressure, tunnel cross-sectional characteristics, and wall roughness using empirical correlations or turbulent heat transfer theory. The heat and moisture exchange capacity is calculated using wet-bulb temperature as a boundary condition and combined with the geothermal gradient to determine the maximum heat transfer capacity of the control volume under the current airflow conditions. The pollutant dilution capacity is calculated based on a dynamic ventilation model, effective exhaust air volume, and longitudinal dispersion coefficient. The entire process can be solved discretically for heat, moisture, and pollutant transport within the control volume using finite difference or finite volume methods.

[0050] When obtaining the characteristic vector of mining environmental load, digital 3D modeling and streamline pre-simulation technology is adopted. Specifically, a 3D geometric model is first constructed based on the cross-sectional geometry and length information of the roadway, and then the equipment geometric model is embedded in it to form a composite scene. Subsequently, flow field pre-simulation is performed using wind data as boundary conditions to obtain the local wind speed distribution and turbulence intensity, thereby obtaining the heat hazard intensity coefficient. The unsteady heat flux of the equipment is dynamically updated through the thermal inertia state equation, and the radiative heat transfer component is calculated by combining the radiation angle coefficient. Finally, the pollution diffusion source term and the heat source term are fused to form the characteristic vector. This process can be achieved through a simplified model of 3D computational fluid dynamics or an engineering equivalent model to ensure computational efficiency and engineering usability.

[0051] When simulating fluctuations in mining environmental load, a vector operation and state update algorithm is used to convert wind speed and equipment speed into wind speed vector and moving speed vector, respectively. The relative motion state is determined by the dot product, and the relative airflow speed is calculated. In the case of headwind, the additional power is calculated by wind resistance and roadway geometry and converted into additional heat flux. In the case of tailwind, the relative velocity deficit is obtained by the velocity difference and the pollutant dilution capacity is weighted and compressed to obtain the corrected environmental load characteristic vector. Then, the environmental saturation index is calculated by the ratio of this vector to the original vector, and an exponential decay model is constructed using the longitudinal dispersion coefficient as the time constant to complete the dynamic simulation of environmental load over time.

[0052] When performing path search with a recovery time window, a spatiotemporal dynamic road network modeling and global search algorithm are adopted. The mining environment recovery time window is mapped as an inaccessible constraint in the time dimension to the road network, forming a dynamic road network structure that is constrained in both space and time. When performing global path search on this structure, a heuristic search or a time-extended version of the shortest path algorithm can be used to output the optimal path while ensuring the environmental safety threshold. Furthermore, by coordinating the paths of all devices to be planned, environmental overload caused by multiple devices occupying the same node at the same time can be avoided.

[0053] When data units differ, this embodiment addresses this issue through a unified unit system and data preprocessing mechanism. Specifically, a unified standard unit system is established during the data input stage. For example, wind speed is standardized to meters per second, pressure to Pascals, temperature to degrees Celsius or Kelvin, distance to meters, and mass to kilograms. Unit conversion and verification are performed on data from different sensors or systems. Simultaneously, dimensional analysis is conducted on physical quantities involving different units during model calculation to ensure dimensional consistency between the two sides of the equation. If unit inconsistencies are found, automatic conversion or data anomaly alerts are performed, thereby ensuring the comparability of calculation results and the correctness of their physical meaning.

[0054] In practical applications, taking the parallel operation of multiple working faces in a deep mine as an example, there are two or more tunneling and transportation equipment underground at the same time. They need to complete tunneling, muck removal, and support operations through multiple interconnected roadways within the same time period. First, a mining roadway topology map is constructed based on real-time geological monitoring data, and the heat and moisture exchange capacity and pollutant dilution capacity of each roadway node are calculated. Then, for different equipment, combined with its operating conditions and the three-dimensional model of the roadway, a mining environmental load feature vector is constructed to simulate the dynamic disturbance of the environmental load when the equipment passes through each node under downwind or upwind conditions, obtaining simulated mining environmental load data that changes over time. On this basis, the recovery time after environmental saturation exceeds the safety threshold is mapped to the impassable constraint in the spatiotemporal road network. Finally, through global path search, the optimal travel path that meets the environmental safety conditions in both time and space is output for each piece of equipment.

[0055] Adopting this technical solution can significantly improve the safety and scientific nature of path planning: on the one hand, by introducing the accumulation of mining heat hazards and pollution into the path search process, it avoids the continuous passage of multiple devices leading to local high temperature and humidity and excessive levels of harmful gases, effectively reducing the risk of heat hazards and ventilation accidents; on the other hand, by introducing an environmental recovery time window and a spatiotemporal dynamic road network model, it enables refined scheduling of the roadway's environmental carrying capacity, improves the efficiency of ventilation resource utilization, and reduces unnecessary waiting and detours; overall, this solution realizes the transformation from traditional path planning that only considers distance and accessibility to intelligent path optimization that takes into account both efficiency and mining heat hazard safety, significantly improving the safety level and operational efficiency of multi-equipment collaborative operations underground.

[0056] Example 2, a mining route optimization system combining geological data, see [link / reference] Figure 1 As shown, it includes: The geological data modeling module includes a geological data processing unit and a mining environment modeling unit. The geological data processing unit is used when the current mining area contains at least two mining machines. It combines geological data to obtain a mining roadway topology map of the mining area, which includes several roadway nodes. It acquires real-time geological monitoring data for the mining area; the geological monitoring data package contains wind force data, air pressure data, and mining geothermal gradient data for each roadway node; and calculates the mining environmental load data corresponding to each roadway node based on the mining roadway topology map and geological monitoring data. The mining environmental load data package contains mining heat and moisture exchange capacity and mining pollutant dilution capacity. The mining environment modeling unit is used to obtain the mining environmental load feature vector corresponding to the mining machine for the planned mining path in the mining roadway topology map; and simulates the fluctuation of the mining environmental load data when the mining machine passes through roadway nodes based on the mining environmental load feature vector to obtain simulated mining environmental load data. The mining path optimization module includes a path output optimization unit. The path output optimization unit is used to perform path search with a mining environment recovery time window based on simulated mining environment load data, and output the optimized mining path corresponding to all mining equipment to be planned.

[0057] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Parts not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A mining path optimization method combining geological data, characterized in that, Includes the following steps: The current mining area contains at least two mining rigs; a topological map of the mining tunnels in the mining area is obtained by combining geological data; The mining roadway topology map contains several roadway nodes; real-time geological monitoring data of the mining area is acquired; the geological monitoring data package contains wind force data, air pressure data and mining geothermal gradient data of each roadway node; based on the mining roadway topology map and geological monitoring data, the mining environmental load data corresponding to each roadway node is calculated; the mining environmental load data package contains mining heat and moisture exchange capacity and mining pollutant dilution capacity. In the mining roadway topology map, the mining environmental load feature vector corresponding to the mining equipment of the mining path to be planned is obtained; based on the mining environmental load feature vector, the fluctuation of mining environmental load data when the mining equipment passes through the roadway node is simulated and calculated to obtain simulated mining environmental load data; Based on the simulated mining environment load data, perform a path search with a mining environment recovery time window, and output the optimized mining path corresponding to all mining equipment to be planned.

2. The mining path optimization method combining geological data according to claim 1, characterized in that, The specific steps for calculations based on mining roadway topology maps and geological monitoring data include: The roadway nodes in the mining roadway topology map are considered as independent mining thermal control bodies. For any mining thermal control body, the roadway physical geometry data is acquired. The roadway physical geometry data package contains the roadway cross-sectional perimeter, roadway cross-sectional area, and roadway wall roughness data. Based on the wind force data, air pressure data, and roadway physical geometry data corresponding to the mining thermal control body, the mining convective heat transfer coefficient between the current airflow and the roadway wall is calculated. Based on the wet-bulb temperature limit, mining convective heat transfer coefficient, and mining geothermal gradient data, the mining heat and moisture exchange capacity of the mining thermal control body is calculated. A dynamic mining ventilation model for the mining thermal control body is constructed based on wind data; the dilution capacity of mining pollutants in the mining thermal control body is calculated based on the dynamic mining ventilation model.

3. The mining path optimization method combining geological data according to claim 2, characterized in that, In the dynamic mining ventilation model, the theoretical air volume is calculated based on wind data and roadway cross-sectional area; the roadway leakage coefficient is matched based on roadway cross-sectional area and roadway perimeter; the theoretical air volume is corrected using the roadway leakage coefficient to obtain the effective sewage discharge air volume of the mining thermal control body. The longitudinal dispersion coefficient within the mining thermal control body is calculated based on the roughness data and wind data of the roadway wall. The maximum pollutant mass flow rate that the mining thermal control body can accommodate and transport per unit time is calculated based on the effective sewage discharge air volume and the longitudinal dispersion coefficient, which is the mining pollutant dilution capacity.

4. The mining path optimization method combining geological data according to claim 3, characterized in that, The specific steps for obtaining the mining environmental load feature vector corresponding to the mining equipment for the planned mining path include: The physical model of the current roadway node is reconstructed based on the roadway physical geometry data; the mining equipment of the mining path to be planned is fused with the roadway physical model using digital 3D technology to obtain a 3D model of the mining roadway; the radiation angle coefficient between the mining equipment and the roadway node is obtained. Using the wind force data of the current roadway nodes as boundary conditions, the streamline distribution of the three-dimensional model of the mining roadway is pre-simulated to obtain the thermal hazard intensity coefficient of the mining equipment; the thermal inertia state equation of the mining equipment is established; the unsteady-state heat flux of the mining equipment is obtained based on the radiation angle coefficient and the thermal inertia state equation; the thermal hazard intensity coefficient of the mining equipment is used as the pollution diffusion source term, and the unsteady-state heat flux is used as the heat source term. After feature fusion, the mining environmental load feature vector is obtained.

5. The mining path optimization method combining geological data according to claim 4, characterized in that, The specific steps for simulating and calculating the fluctuation of mining environmental load data when mining equipment passes through roadway nodes based on the mining environmental load characteristic vector include: Based on the wind force data of the roadway nodes, the corresponding wind speed vector is obtained; at the same time, the moving speed vector of the mining equipment when passing through the roadway nodes is simulated; the dot product of the wind speed vector and the moving speed vector is calculated to obtain the relative motion state of the mining equipment; the relative airflow velocity is calculated using the wind speed vector and the moving speed vector. When the relative motion state is headwind, the additional mining power required by the mining equipment is calculated based on the relative airflow velocity and the physical geometry data of the roadway. The additional mining power is then superimposed on the unsteady heat flux in the mining environmental load feature vector to obtain the corrected mining environmental load feature vector. When the relative motion state is downwind, the absolute value of the difference between the magnitude of the moving velocity vector and the magnitude of the wind speed vector is calculated to obtain the relative velocity deficit value; based on the relative velocity deficit value, the mining pollutant dilution capacity of the roadway node is reverse-weighted and compressed to obtain the corrected mining environmental load characteristic vector. Using the corrected mining environment load feature vector as the numerator and the mining environment load feature vector as the denominator, the mining environment saturation index at each time step is calculated; the longitudinal dispersion coefficient is used as the time constant to construct a mining environment saturation decay model; the mining environment saturation index is input into the mining environment saturation decay model to generate simulated mining environment load data that fluctuates with time.

6. The mining path optimization method combining geological data according to claim 5, characterized in that, The specific steps for performing a path search with a mining environment recovery time window based on simulated mining environment load data include: Set a safe threshold for mining environment saturation; extract the duration of values ​​exceeding the safe threshold from simulated mining environment load data, and define it as the mining environment recovery time window; construct a spatiotemporal dynamic road network model, and map the mining environment recovery time window into the inaccessibility constraints of mining equipment on the planned mining path within a specific time period in the spatiotemporal dynamic road network model; perform a global path search in the spatiotemporal dynamic road network model, and output the optimized mining path corresponding to all mining equipment on the planned mining path.

7. A mining route optimization system incorporating geological data, characterized in that, The system employs a mining path optimization method combining geological data as described in any one of claims 1-6, comprising: The geological data modeling module includes a geological data processing unit and a mining environment modeling unit. The geological data processing unit is used when the current mining area contains at least two mining machines. It combines geological data to obtain a mining roadway topology map of the mining area, which includes several roadway nodes. It acquires real-time geological monitoring data for the mining area; the geological monitoring data package contains wind force data, air pressure data, and mining geothermal gradient data for each roadway node; and calculates the mining environmental load data corresponding to each roadway node based on the mining roadway topology map and geological monitoring data. The mining environmental load data package contains mining heat and moisture exchange capacity and mining pollutant dilution capacity. The mining environment modeling unit is used to obtain the mining environmental load feature vector corresponding to the mining machine for the planned mining path in the mining roadway topology map; and simulates the fluctuation of the mining environmental load data when the mining machine passes through roadway nodes based on the mining environmental load feature vector to obtain simulated mining environmental load data. The mining path optimization module includes a path output optimization unit. The path output optimization unit is used to perform path search with a mining environment recovery time window based on simulated mining environment load data, and output the optimized mining path corresponding to all mining equipment to be planned.

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