Environment deicing control system and method based on coal mine hot air unit

Abstract

The present application relates to the technical field of coal mine hot air unit, and discloses an environment deicing control system and method based on a coal mine hot air unit, which comprises: a collection module that collects multidimensional environment data of a mine, the multidimensional environment data comprising shaft wall environment data, shaft mouth environment data and mine space layout data; an analysis module that analyzes icing risk factors of the mine according to the multidimensional environment data, the icing risk factors being used to represent icing risk factors of the mine; and a control module that generates environment deicing control parameters of the mine according to the icing risk factors, so as to control the coal mine hot air unit to perform a matching environment deicing control operation. It can be seen that, by collecting multidimensional environment heterogeneous data of the mine, the present application can improve the analysis accuracy and comprehensiveness of the icing risk factors of the mine, and thus can improve the accuracy and comprehensiveness of the environment deicing control of the coal mine hot air unit, and improve the environment deicing efficiency of the coal mine.

Description

Technical Field

[0001] This invention relates to the field of coal mine hot air handling unit technology, and in particular to an environmental de-icing control system and method based on coal mine hot air handling unit. Background Technology

[0002] In coal mine production operations in frigid regions, key areas such as the main and auxiliary shaft entrances, air intake shaft entrances, entrances and exits of open-pit storage and transportation systems, and entrances and exits of belt conveyor bridges often face severe low-temperature freezing problems in winter, posing a great safety hazard to coal mine production.

[0003] To address the aforementioned issues, hot air is supplied to the mine entrance and surrounding key areas using coal mine hot air units to raise the ambient temperature above freezing, thereby preventing icing or melting existing ice layers. This is a widely adopted and relatively effective active antifreezing and de-icing method.

[0004] However, in practice, it has been found that existing hot air handling unit systems typically rely on thermostats or manual experience to control the units on and off. This often results in: the unit operating at full capacity when outdoor temperatures rise or the target area's actual temperature has reached the target, causing significant energy waste; and in extremely cold weather or when humidity suddenly increases, the set power may be insufficient to maintain the target area's temperature above freezing, leading to de-icing failure. In summary, existing hot air handling unit systems suffer from relatively low control accuracy, resulting in relatively low de-icing efficiency.

[0005] Therefore, it is particularly important to propose a technical solution to improve the accuracy of environmental de-icing control in coal mine hot air units, thereby improving the efficiency of environmental de-icing. Summary of the Invention

[0006] This invention provides an environmental de-icing control system and method based on a coal mine hot air blower unit, which can improve the accuracy of environmental de-icing control of the coal mine hot air blower unit and thus improve the environmental de-icing efficiency.

[0007] To address the aforementioned technical problems, the first aspect of this invention discloses an environmental de-icing control system based on a coal mine hot air unit, the system comprising:

[0008] The acquisition module is used to collect multi-dimensional environmental data of the mine, including well wall environmental data, wellhead environmental data, and mine spatial layout data.

[0009] The analysis module is used to analyze the icing risk factors of the mine based on the multi-dimensional environmental data, wherein the icing risk factors are used to represent the icing risk factors of the mine.

[0010] The control module is used to generate environmental de-icing control parameters for the mine based on the icing risk factor, so as to control the coal mine hot air unit to perform matching environmental de-icing control operations.

[0011] As an optional implementation, in the first aspect of the present invention, the specific method by which the analysis module analyzes the icing risk factors of the mine based on the multi-dimensional environmental data includes:

[0012] Based on the well wall environment data, well wall heat dissipation characteristic data is generated, which is used to represent the heat exchange intensity between the well wall and its external environment.

[0013] Based on the wellhead environmental data, cold and humid intrusion characteristic data is generated. The cold and humid intrusion characteristic data is used to represent the movement trajectory and impact intensity of the target cold air entering the mine, as well as the migration vector and spatial distribution of the water vapor carried by the target cold air entering the mine. The target cold air is used to represent airflow that is lower than the preset temperature and / or higher than the preset humidity of the mine.

[0014] Based on the mine spatial layout data, the heat dissipation characteristic data of the mine wall and the cold and humid intrusion characteristic data are dynamically coupled and analyzed to generate first temperature distribution characteristic data and second temperature distribution characteristic data of the target location of the mine. The first temperature distribution characteristic data is used to represent the real-time surface temperature of the target location and its spatial variation. The second temperature distribution characteristic data is used to represent the water vapor saturation critical temperature of the air layer near the target location under the current air pressure and humidity conditions and its spatial variation.

[0015] Based on the spatiotemporal differences between the first temperature distribution characteristic data and the second temperature distribution characteristic data, the icing risk factor of the mine is analyzed.

[0016] As an optional implementation, in the first aspect of the present invention, the specific method by which the analysis module generates well wall heat dissipation characteristic data based on the well wall environment data includes:

[0017] Extract the wellbore material properties, wellbore geometry, and wellbore temperature gradient data from the wellbore environment data;

[0018] Based on the well wall material properties data and well wall geometric structure data, the equivalent thermal resistance distribution data of the well wall is determined. The equivalent thermal resistance distribution data of the well wall is used to represent the degree of obstruction to heat transfer at different locations of the well wall.

[0019] Based on the well wall temperature gradient data and the well wall equivalent thermal resistance distribution data, spatial weighted fusion calculation is used to generate well wall heat dissipation characteristic data.

[0020] As an optional implementation, in the first aspect of the present invention, the specific method by which the analysis module generates cold and wet intrusion characteristic data based on the wellhead environmental data includes:

[0021] Extract wellhead airflow dynamics data and ambient humidity spatiotemporal variation data from the wellhead environmental data. The wellhead airflow dynamics data includes at least one of the following: airflow direction data, airflow intensity data, airflow turbulence degree data, and airflow intensity sudden change prediction data. The ambient humidity spatiotemporal variation data includes at least one of the following: ambient water vapor mass distribution data, humidity variation data, and humidity variation direction data.

[0022] Based on the wellhead airflow dynamics data, cold air intrusion vector field data is generated. The cold air intrusion vector field data is used to determine the trajectory and impact intensity of the target cold air entering the mine.

[0023] Based on the spatiotemporal variation data of ambient humidity and the cold air intrusion vector field data, humidity migration intensity distribution data is calculated. The humidity migration intensity distribution data is used to represent the migration vector and spatial distribution of water vapor carried by the target cold air entering the mine.

[0024] By fusing the cold air intrusion vector field data with the humidity migration intensity distribution data, cold and humid intrusion characteristic data are generated.

[0025] As an optional implementation, in the first aspect of the present invention, the analysis module performs dynamic coupling analysis on the heat dissipation characteristic data of the mine wall and the cold and damp intrusion characteristic data based on the mine spatial layout data to generate first temperature distribution characteristic data and second temperature distribution characteristic data of the target location of the mine. The specific method for generating these features includes:

[0026] Based on the mine spatial layout data, a mine topology mapping table is constructed. The mine topology mapping table is used to define the spatial correlation between the heat dissipation source location of the mine, the intrusion point location of the target cold air entering the mine, and the target location.

[0027] Based on the mine topology mapping table, the heat dissipation characteristic data of the well wall is mapped to the spatial influence weight of the heat source. The spatial influence weight of the heat source is used to represent the degree of non-uniform thermal influence of the well wall heat dissipation on different locations of the mine.

[0028] Based on the mine topology mapping table, the cold and humid intrusion feature data is mapped to a cold and humid flux spatial transport path, which is used to represent the diffusion direction and intensity of the target cold air under the structural constraints of the mine.

[0029] The first temperature distribution characteristic data and the second temperature distribution characteristic data of the target location of the mine are generated by iterative calculation of the dynamic field of heat and moisture flux.

[0030] As an optional implementation, in the first aspect of the present invention, the specific method by which the analysis module analyzes the icing risk factor of the mine based on the spatiotemporal difference characteristics between the first temperature distribution characteristic data and the second temperature distribution characteristic data includes:

[0031] Calculate the real-time difference sequence between the first temperature distribution feature data and the second temperature distribution feature data;

[0032] The duration of negative values, the rate of change of negative value intensity, and the spatial coverage density features are extracted from the real-time difference sequence. The duration of negative values ​​is used to represent the continuous length of time that the wall temperature is lower than the dew point temperature. The rate of change of negative value intensity is used to represent the rate of change of the negative temperature difference per unit time. The spatial coverage density features are used to represent the degree of distribution and clustering of icing risk points in the mine. The icing risk points are used to represent points where the duration of negative values ​​is greater than or equal to a preset duration.

[0033] Based on the weighted fusion results of the duration of negative values, the rate of change of negative value intensity, and the spatial coverage density characteristics, the icing risk factors of the mine are analyzed.

[0034] As an optional implementation, in the first aspect of the present invention, the control module generates environmental de-icing control parameters for the mine based on the icing risk factor, and controls the coal mine hot air unit to perform matching environmental de-icing control operations in the following specific ways:

[0035] The spatial distribution characteristics and risk level transition trends of the aforementioned icing risk factors are analyzed. The risk level transition trends are used to represent the direction and probability of icing risk changes over time.

[0036] Based on the spatial distribution characteristics, the spatial deployment topology of coal mine hot air units is matched to generate a hot air intervention priority sequence;

[0037] Based on the risk level transition trend, the hot air output control gradient is determined. The hot air output control gradient is used to define the combination strategy of wind temperature-air volume-wind direction-wind frequency under different risk levels.

[0038] By integrating the hot air intervention priority sequence and the hot air output control gradient, environmental de-icing control parameters for the mine are generated to control the coal mine hot air unit to perform matching environmental de-icing control operations.

[0039] The second aspect of this invention discloses an environmental de-icing control method based on a coal mine hot air unit, the method comprising:

[0040] Collect multi-dimensional environmental data of the mine, including well wall environmental data, wellhead environmental data and mine spatial layout data;

[0041] Based on the multi-dimensional environmental data, the icing risk factors of the mine are analyzed, and the icing risk factors are used to represent the icing risk factors of the mine.

[0042] Based on the icing risk factor, environmental de-icing control parameters for the mine are generated to control the coal mine hot air unit to perform matching environmental de-icing control operations.

[0043] As an optional implementation, in a second aspect of the present invention, the step of analyzing the icing risk factors of the mine based on the multi-dimensional environmental data includes:

[0044] Based on the well wall environment data, well wall heat dissipation characteristic data is generated, which is used to represent the heat exchange intensity between the well wall and its external environment.

[0045] Based on the wellhead environmental data, cold and humid intrusion characteristic data is generated. The cold and humid intrusion characteristic data is used to represent the movement trajectory and impact intensity of the target cold air entering the mine, as well as the migration vector and spatial distribution of the water vapor carried by the target cold air entering the mine. The target cold air is used to represent airflow that is lower than the preset temperature and / or higher than the preset humidity of the mine.

[0046] Based on the mine spatial layout data, the heat dissipation characteristic data of the mine wall and the cold and humid intrusion characteristic data are dynamically coupled and analyzed to generate first temperature distribution characteristic data and second temperature distribution characteristic data of the target location of the mine. The first temperature distribution characteristic data is used to represent the real-time surface temperature of the target location and its spatial variation. The second temperature distribution characteristic data is used to represent the water vapor saturation critical temperature of the air layer near the target location under the current air pressure and humidity conditions and its spatial variation.

[0047] Based on the spatiotemporal differences between the first temperature distribution characteristic data and the second temperature distribution characteristic data, the icing risk factor of the mine is analyzed.

[0048] As an optional implementation, in a second aspect of the invention, generating wellbore heat dissipation characteristic data based on the wellbore environment data includes:

[0049] Extract the wellbore material properties, wellbore geometry, and wellbore temperature gradient data from the wellbore environment data;

[0050] Based on the well wall material properties data and well wall geometric structure data, the equivalent thermal resistance distribution data of the well wall is determined. The equivalent thermal resistance distribution data of the well wall is used to represent the degree of obstruction to heat transfer at different locations of the well wall.

[0051] Based on the well wall temperature gradient data and the well wall equivalent thermal resistance distribution data, spatial weighted fusion calculation is used to generate well wall heat dissipation characteristic data.

[0052] As an optional implementation, in a second aspect of the invention, generating cold and wet intrusion characteristic data based on the wellhead environmental data includes:

[0053] Extract wellhead airflow dynamics data and ambient humidity spatiotemporal variation data from the wellhead environmental data. The wellhead airflow dynamics data includes at least one of the following: airflow direction data, airflow intensity data, airflow turbulence degree data, and airflow intensity sudden change prediction data. The ambient humidity spatiotemporal variation data includes at least one of the following: ambient water vapor mass distribution data, humidity variation data, and humidity variation direction data.

[0054] Based on the wellhead airflow dynamics data, cold air intrusion vector field data is generated. The cold air intrusion vector field data is used to determine the trajectory and impact intensity of the target cold air entering the mine.

[0055] Based on the spatiotemporal variation data of ambient humidity and the cold air intrusion vector field data, humidity migration intensity distribution data is calculated. The humidity migration intensity distribution data is used to represent the migration vector and spatial distribution of water vapor carried by the target cold air entering the mine.

[0056] By fusing the cold air intrusion vector field data with the humidity migration intensity distribution data, cold and humid intrusion characteristic data are generated.

[0057] As an optional implementation, in a second aspect of the invention, the step of dynamically coupling and analyzing the heat dissipation characteristic data of the mine wall and the cold and damp intrusion characteristic data based on the mine spatial layout data to generate first temperature distribution characteristic data and second temperature distribution characteristic data for the target location of the mine includes:

[0058] Based on the mine spatial layout data, a mine topology mapping table is constructed. The mine topology mapping table is used to define the spatial correlation between the heat dissipation source location of the mine, the intrusion point location of the target cold air entering the mine, and the target location.

[0059] Based on the mine topology mapping table, the heat dissipation characteristic data of the well wall is mapped to the spatial influence weight of the heat source. The spatial influence weight of the heat source is used to represent the degree of non-uniform thermal influence of the well wall heat dissipation on different locations of the mine.

[0060] Based on the mine topology mapping table, the cold and humid intrusion feature data is mapped to a cold and humid flux spatial transport path, which is used to represent the diffusion direction and intensity of the target cold air under the structural constraints of the mine.

[0061] The first temperature distribution characteristic data and the second temperature distribution characteristic data of the target location of the mine are generated by iterative calculation of the dynamic field of heat and moisture flux.

[0062] As an optional implementation, in a second aspect of the present invention, the step of analyzing the icing risk factor of the mine based on the spatiotemporal difference characteristics between the first temperature distribution characteristic data and the second temperature distribution characteristic data includes:

[0063] Calculate the real-time difference sequence between the first temperature distribution feature data and the second temperature distribution feature data;

[0064] The duration of negative values, the rate of change of negative value intensity, and the spatial coverage density features are extracted from the real-time difference sequence. The duration of negative values ​​is used to represent the continuous length of time that the wall temperature is lower than the dew point temperature. The rate of change of negative value intensity is used to represent the rate of change of the negative temperature difference per unit time. The spatial coverage density features are used to represent the degree of distribution and clustering of icing risk points in the mine. The icing risk points are used to represent points where the duration of negative values ​​is greater than or equal to a preset duration.

[0065] Based on the weighted fusion results of the duration of negative values, the rate of change of negative value intensity, and the spatial coverage density characteristics, the icing risk factors of the mine are analyzed.

[0066] As an optional implementation, in a second aspect of the invention, generating environmental de-icing control parameters for the mine based on the icing risk factor to control the coal mine hot air unit to perform matching environmental de-icing control operations includes:

[0067] The spatial distribution characteristics and risk level transition trends of the aforementioned icing risk factors are analyzed. The risk level transition trends are used to represent the direction and probability of icing risk changes over time.

[0068] Based on the spatial distribution characteristics, the spatial deployment topology of coal mine hot air units is matched to generate a hot air intervention priority sequence;

[0069] Based on the risk level transition trend, the hot air output control gradient is determined. The hot air output control gradient is used to define the combination strategy of wind temperature-air volume-wind direction-wind frequency under different risk levels.

[0070] By integrating the hot air intervention priority sequence and the hot air output control gradient, environmental de-icing control parameters for the mine are generated to control the coal mine hot air unit to perform matching environmental de-icing control operations.

[0071] A third aspect of this invention discloses another environmental de-icing control system based on a coal mine hot air unit, the system comprising:

[0072] Memory containing executable program code;

[0073] A processor coupled to the memory;

[0074] The processor calls the executable program code stored in the memory to execute the environmental de-icing control method based on coal mine hot air unit disclosed in the second aspect of the present invention.

[0075] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the environmental de-icing control method based on a coal mine hot air unit disclosed in the second aspect of the present invention.

[0076] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0077] In this embodiment of the invention, the acquisition module collects multi-dimensional environmental data of the mine, including mine wall environmental data, mine entrance environmental data, and mine spatial layout data; the analysis module analyzes the icing risk factors of the mine based on the multi-dimensional environmental data, and the icing risk factors are used to represent the icing risk factors of the mine; the control module generates environmental de-icing control parameters of the mine based on the icing risk factors, so as to control the coal mine hot air unit to perform matching environmental de-icing control operations. It is evident that implementing this invention can overcome the limitations of traditional single-point monitoring by integrating multi-dimensional heterogeneous environmental data from the mine, including the mine wall environment, mine entrance environment, and mine spatial layout. This allows for the construction of a complete digital profile of the mine environment and a panoramic model of the mine's thermal and humidity field, improving the comprehensiveness of environmental perception, avoiding misjudgments of icing risks due to missing data dimensions, eliminating monitoring blind spots, and thus improving the accuracy and comprehensiveness of the analysis of icing risk factors in the mine. Consequently, it can improve the accuracy and comprehensiveness of environmental de-icing control for coal mine hot air blowers. While reducing the possibility of energy waste caused by excessive de-icing of coal mine hot air blowers, it also enhances the safety hazard protection capability against insufficient de-icing of coal mine hot air blowers, improves the efficiency of coal mine environmental de-icing, and achieves a balanced optimization of safe production and economic operation in coal mines. Attached Figure Description

[0078] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0079] Figure 1 This is a schematic diagram of the structure of an environmental de-icing control system based on a coal mine hot air unit disclosed in an embodiment of the present invention;

[0080] Figure 2 This is a schematic flowchart of an environmental de-icing control method based on a coal mine hot air unit disclosed in an embodiment of the present invention;

[0081] Figure 3 This is a schematic diagram of another environmental de-icing control system based on a coal mine hot air unit disclosed in an embodiment of the present invention. Detailed Implementation

[0082] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0083] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0084] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0085] This invention discloses an environmental de-icing control system and method based on coal mine hot air blower units. By integrating multi-dimensional heterogeneous environmental data collected from the mine, including the mine wall environment, mine entrance environment, and mine spatial layout, it breaks through the limitations of traditional single-point monitoring, constructs a complete digital profile of the mine environment and a panoramic model of the mine's thermal and humidity field, improves the comprehensiveness of environmental perception, avoids misjudgments of icing risks due to missing data dimensions, eliminates monitoring blind spots, and thus improves the accuracy and comprehensiveness of the analysis of icing risk factors in the mine. This, in turn, improves the accuracy and comprehensiveness of environmental de-icing control for coal mine hot air blower units. While reducing the possibility of energy waste caused by excessive de-icing of coal mine hot air blower units, it also improves the safety hazard protection capability against insufficient de-icing of coal mine hot air blower units, increases the efficiency of coal mine environmental de-icing, and achieves a balanced optimization of safe production and economic operation in coal mines. Detailed descriptions follow.

[0086] Example 1

[0087] Please see Figure 1 , Figure 1 This is a schematic diagram of an environmental de-icing control system based on a coal mine hot air unit, as disclosed in an embodiment of the present invention. Figure 1 The described environmental de-icing control system based on coal mine hot air units can be applied to coal mine hot air units, and also to intelligent devices associated with coal mine hot air units. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the scope of these applications. Figure 1 As shown, the environmental de-icing control system based on the coal mine hot air unit may include:

[0088] The acquisition module 101 is used to collect multi-dimensional environmental data of the mine, including well wall environmental data, wellhead environmental data and mine spatial layout data.

[0089] In this embodiment of the invention, optionally, the above-mentioned multi-dimensional environmental data can be obtained by a multi-dimensional environmental perception sensor, which may specifically include one or more of the following: temperature sensor, heat flow sensor, humidity sensor, light sensor, image sensor, radar, etc.

[0090] Further optionally, the aforementioned wellbore environmental data may include: wellbore surface temperature (measured using attached temperature sensors), the thermal conductivity of the wellbore material (such as the thermophysical parameters of concrete or rock walls), wellbore geometry (such as thickness, curvature, and surface area, obtained through laser scanning or design drawings), and wellbore temperature gradient (calculated using multi-point temperature sensors to determine the internal and external temperature differences). This serves to provide a basis for analyzing wellbore heat dissipation.

[0091] Further optionally, the wellhead environmental data mentioned above may include air temperature, humidity, wind speed, wind direction (obtained through meteorological station sensors), and the degree of airflow turbulence (such as turbulence intensity, calculated from wind speed fluctuation data). This serves to provide input for analyzing the intrusion of cold, moist air.

[0092] Optionally, the aforementioned mine spatial layout data, including the mine's three-dimensional model, tunnel distribution, depth, and width (imported from CAD drawings or BIM models), serves to provide a framework for spatial analysis.

[0093] The acquisition module sends data to the central processing unit via a sensor network (such as ZigBee or LoRa wireless transmission) and performs preliminary preprocessing (such as data filtering and normalization).

[0094] Analysis module 102 is used to analyze the icing risk factors of the mine based on multi-dimensional environmental data. The icing risk factors are used to represent the icing risk factors of the mine.

[0095] In this embodiment of the invention, optionally, the icing risk factor analysis stage can be implemented using the following logic:

[0096] Input the collected data into the icing risk assessment model:

[0097] Calculate the heat dissipation capacity of the wellbore (e.g., how the thermal conductivity of the rock affects the rate of heat loss).

[0098] Quantify the intensity of cold and humid air intrusion (e.g., the amount of water vapor brought in per unit time when the wind speed is 2 m / s).

[0099] Combine spatial topology to predict low-temperature regions (such as cold air stagnation caused by eddies at tunnel corners).

[0100] Output: Generate numerical risk values ​​(e.g., 0-100) and coordinates of high-risk locations.

[0101] In this embodiment of the invention, as an optional implementation, the specific method by which the analysis module 102 analyzes the icing risk factors of the mine based on multi-dimensional environmental data includes:

[0102] Based on well wall environmental data, well wall heat dissipation characteristic data is generated, which is used to represent the heat exchange intensity between the well wall and its external environment.

[0103] Based on wellhead environmental data, cold and humid intrusion characteristic data are generated. The cold and humid intrusion characteristic data are used to represent the movement trajectory and impact intensity of the target cold air entering the mine, as well as the migration vector and spatial distribution of the water vapor carried by the target cold air entering the mine. The target cold air is used to represent airflow that is lower than the preset temperature and / or higher than the preset humidity of the mine.

[0104] Based on the spatial layout data of the mine, dynamic coupling analysis is performed on the heat dissipation characteristic data of the mine wall and the cold and damp intrusion characteristic data to generate the first temperature distribution characteristic data and the second temperature distribution characteristic data of the target location of the mine. The first temperature distribution characteristic data is used to represent the real-time surface temperature of the target location and its spatial variation. The second temperature distribution characteristic data is used to represent the water vapor saturation critical temperature of the air layer near the target location under the current air pressure and humidity conditions and its spatial variation.

[0105] Based on the spatiotemporal differences between the first and second temperature distribution characteristic data, the icing risk factors of the mine are analyzed.

[0106] In this embodiment of the invention, optionally, the generation of well wall heat dissipation characteristic data can be implemented according to the following operation logic:

[0107] Calculate the equivalent thermal resistance (e.g., R = thickness / λ) based on the well wall material (e.g., sandstone with a thermal conductivity of λ = 1.5 W / m·K) and geometric structure (e.g., an arc-shaped surface to increase the heat dissipation area).

[0108] By combining temperature gradients (such as a 20°C temperature difference between the inside and outside of the well wall), a heat dissipation intensity distribution map (unit: W / m²) is output through weighted fusion to quantify the heat loss rate of different sections of the well wall, identify weak points in heat dissipation, and locate heat dissipation hotspot areas.

[0109] Alternatively, the generation of cold and damp intrusion feature data can be implemented according to the following operational logic:

[0110] Cold air intrusion vector field: Based on wellhead airflow data (e.g., wind direction northwest, turbulence intensity 0.3), the airflow trajectory and pressure distribution are generated by computational fluid dynamics (CFD) simulation.

[0111] Humidity migration intensity: Combine humidity data (e.g., inlet humidity 90%RH) and airflow field to calculate water vapor diffusion flux (g / m²·s).

[0112] Fusion output: Generates a cold and wet flux density contour map with directional arrows.

[0113] Alternatively, for dynamic coupling analysis, the following operational logic can be used:

[0114] A three-dimensional mesh model of the mine was established, with heat dissipation from the well wall as the negative heat source (heat absorption) and cold and damp intrusion as the cold source + moisture source.

[0115] Iterative calculations were performed using the finite element thermo-humidity coupling algorithm:

[0116] First temperature data: Surface temperature of grid nodes (e.g., -5℃).

[0117] Second temperature data: air layer dew point temperature (e.g., -3°C) to accurately predict the icing critical point (surface temperature < dew point temperature).

[0118] Alternatively, for the analysis of freezing risk factors, the following operational logic can be followed:

[0119] Calculate the temperature difference ΔT (surface temperature - dew point temperature) on a grid-by-grid basis.

[0120] Identify regions where ΔT < 0 and perform statistics:

[0121] Duration of negative values: For example, ΔT < -2℃ for 3 consecutive hours at point A.

[0122] Negative intensity change rate: e.g., ΔT decreases by 0.1℃ per minute.

[0123] Spatial coverage density: such as 5 high-risk grids within a 10m² area.

[0124] As can be seen, implementing this optional embodiment can reveal the heat transfer law between the well wall and the environment by quantifying the heat exchange intensity, accurately locate weak heat dissipation areas (such as fracture development sections), and provide a basis for identifying areas of rapid cooling caused by high heat dissipation; it can track the movement trajectory and intensity of cold and humid airflow, predict the path of cold air invasion (such as the vortex zone from the wellhead to the corner of the roadway), block the water vapor migration channel in advance, and inhibit the formation of icing core conditions; it can directly capture the critical state of icing (when the surface temperature ≤ the dew point temperature) by comparing the surface temperature and the dew point temperature, breaking through the limitations of traditional single temperature monitoring and improving the sensitivity of risk identification; it can simultaneously assess the time accumulation effect and spatial diffusion trend, identify high-risk areas of continuous icing (such as blind roadways with long-term low temperatures), and avoid false alarms caused by instantaneous temperature fluctuations.

[0125] In this optional embodiment, as an optional implementation method, the specific way in which the analysis module 102 generates well wall heat dissipation characteristic data based on well wall environmental data includes:

[0126] Extract wellbore material properties, wellbore geometry, and wellbore temperature gradient data from the wellbore environmental data;

[0127] Based on the well wall material properties and well wall geometry data, the equivalent thermal resistance distribution data of the well wall is determined. The equivalent thermal resistance distribution data of the well wall is used to represent the degree of obstruction to heat transfer at different locations of the well wall.

[0128] Based on wellbore temperature gradient data and wellbore equivalent thermal resistance distribution data, wellbore heat dissipation characteristic data are generated through spatial weighted fusion calculation.

[0129] In this embodiment of the invention, optionally, for material property processing: input the thermal conductivity database of materials such as sandstone and concrete, and automatically match the material at the sensor location.

[0130] For geometric modeling: the wellbore scan data is discretized into a triangular mesh, and the normal vector and area of ​​each mesh are calculated.

[0131] For equivalent thermal resistance calculation: For non-uniform wellbore (such as areas containing insulation layers), calculate the local thermal resistance value according to the parallel / series thermal resistance formula.

[0132] For spatially weighted fusion: using thermal resistance as the weight, interpolate the temperature gradient data to generate a wellbore heat dissipation thermal map (red high heat dissipation area).

[0133] As can be seen, implementing this optional embodiment can quantify the non-uniform heat dissipation characteristics of the well wall (such as concrete section vs. rock stratum section) by integrating material properties and geometric structure, overcome the errors of traditional homogenization modeling, and accurately reflect local heat loss; it can correlate temperature gradient and thermal resistance distribution, reveal the formation mechanism of heat dissipation hotspots (such as high thermal conductivity material + large temperature difference area), and provide target location support for targeted heat preservation measures.

[0134] In this optional embodiment, as another optional implementation, the specific method by which the analysis module 102 generates cold and wet intrusion characteristic data based on wellhead environmental data includes:

[0135] Extract wellhead airflow dynamics data and ambient humidity spatiotemporal variation data from the wellhead environmental data. The wellhead airflow dynamics data shall include at least one of the following: airflow direction data, airflow intensity data, airflow turbulence degree data, and airflow intensity change prediction data. The ambient humidity spatiotemporal variation data shall include at least one of the following: ambient water vapor mass distribution data, humidity variation data, and humidity variation direction data.

[0136] Based on the wellhead airflow dynamics data, cold air intrusion vector field data is generated. The cold air intrusion vector field data is used to determine the trajectory and impact intensity of the target cold air entering the mine.

[0137] Based on the spatiotemporal variation data of ambient humidity and the vector field data of cold air intrusion, the humidity migration intensity distribution data is calculated. The humidity migration intensity distribution data is used to represent the migration vector and spatial distribution of water vapor carried by the target cold air entering the mine.

[0138] By fusing cold air intrusion vector field data with humidity migration intensity distribution data, cold and humid intrusion characteristic data are generated.

[0139] In this embodiment of the invention, optionally, for the generation of cold air intrusion vector field: a vector field equation is constructed based on wind speed / direction data, and the airflow trajectory is simulated by Lagrange particle tracking.

[0140] Impact strength = air kinetic energy density (0.5 × density × wind speed²).

[0141] For humidity migration calculation: water vapor mass distribution data is mapped onto airflow particles, and the spatial distribution of humidity is updated according to the particle movement trajectory.

[0142] For data fusion: the airflow vector field is overlaid with the humidity cloud map to output a streamline map with humidity labels (e.g., the thickness of the arrow represents wind speed, and the shade of the color represents humidity).

[0143] As can be seen, implementing this optional embodiment can analyze the kinematic characteristics of airflow, predict the dynamics of cold front advancement (such as cold wave intrusion caused by sudden changes in wellhead airflow), and achieve visualized early warning of intrusion paths; it can correlate water vapor distribution with airflow movement, quantify moisture conduction flux (such as the number of grams of intruding water vapor per unit time), and accurately assess the maturity of icing humidity conditions; it can establish a temperature-humidity joint action model, identify high synergistic risk areas (such as low-temperature and high-humidity drainage tunnels), and solve the omission of synergistic effects caused by independent analysis of temperature and humidity.

[0144] In this optional embodiment, as another optional implementation, the analysis module 102, based on the mine spatial layout data, performs dynamic coupling analysis on the mine wall heat dissipation characteristic data and cold and damp intrusion characteristic data to generate the first temperature distribution characteristic data and the second temperature distribution characteristic data of the target location of the mine. The specific methods for generating these data include:

[0145] Based on the spatial layout data of the mine, a topological relationship mapping table of the mine is constructed. The topological relationship mapping table of the mine is used to define the spatial relationship between the location of the heat dissipation source of the mine, the intrusion point of the target cold air entering the mine, and the target location.

[0146] Based on the mine topology mapping table, the heat dissipation characteristic data of the well wall is mapped to the spatial influence weight of the heat source. The spatial influence weight of the heat source is used to represent the degree of non-uniform thermal influence of the well wall heat dissipation on different locations of the mine.

[0147] Based on the mine topology mapping table, the cold and humid intrusion feature data are mapped to the spatial transport path of cold and humid flux. The spatial transport path of cold and humid flux is used to represent the diffusion direction and intensity of target cold air under the structural constraints of the mine.

[0148] Through iterative calculation of the dynamic field of heat and moisture flux, the first temperature distribution characteristic data and the second temperature distribution characteristic data of the target location in the mine are generated.

[0149] In this embodiment of the invention, as an optional implementation, the above-described method for constructing a topological relationship mapping table is as follows:

[0150] Based on mine spatial layout data (such as a 3D model), the mine is abstracted into a graph structure: nodes represent locations (such as tunnel intersections), and edges represent connections (such as tunnel segments). The mapping table contains the coordinates of each node, a list of adjacent nodes, path length, etc. For example, an adjacency matrix or linked list can be used for storage.

[0151] Marking in the 3D model of the mine:

[0152] Heat source location marking: S, such as S1 (x=102.3m, y=-15.7m, z=3.4m), indicating sandstone fracture zone;

[0153] Intrusion point location marker: P, such as P0 (x=0m, y=0m, z=0m), for example, indicating the main wellhead;

[0154] Target location marker: T, such as T1 (x=85.6m, y=-22.1m, z=1.8m), indicating the corner of a transport lane.

[0155] Optionally, for the spatial influence weight of the mapped heat sources: this weight represents the degree of thermal impact (dimensionless or proportional coefficient) of well wall heat dissipation on different locations inside the mine, taking into account distance attenuation and obstacle shading. Based on the topology mapping table, for each target location, the shortest path distance to each heat source is calculated, and then the weight is calculated using an attenuation model (such as exponential attenuation). For example, the closer the distance, the higher the weight, and the weight of paths with curves decreases. Finally, a heat source influence weight distribution map is generated.

[0156] Alternatively, the following formula can be used: W=Q / d^n; where Q represents the heat dissipation intensity, d represents the distance between the target location and the heat source, and n represents the preset parameter, which can be 2.

[0157] Further, optionally, the spatial transport path of the cold and moist flux can be mapped: this path can represent the diffusion direction and intensity of cold and moist air under the constraints of the mine structure (e.g., flux vector field). Based on the topological mapping table, the aforementioned cold and moist intrusion characteristic data are mapped to the mine grid. Considering the tunnel geometry (e.g., increased flow velocity in narrow sections), the path integral method is used to calculate the attenuation of the cold and moist flux along the topological path (e.g., due to friction). A flux transport path map is then generated.

[0158] Further optional, for iterative calculation of the dynamic field of heat and moisture flux: a first temperature distribution and a second temperature distribution are generated by iteratively simulating the heat and moisture transfer process. This can be achieved by initializing the temperature and humidity at various points in the mine (e.g., from sensor data).

[0159] Further optional, for time-step iterations:

[0160] For heat flux calculation: Based on the influence weight of heat sources, the heat dissipation from the well wall is distributed to each point, and the temperature is updated (e.g., heat input causes the temperature to rise).

[0161] For calculating cold and humidity flux: Based on the cold and humidity transfer path, the cold and humidity flux is superimposed on each point to update the temperature and humidity (e.g., cold air lowers the temperature and humidity increases the humidity).

[0162] For calculating dew point temperature: a second temperature distribution is generated using a dew point formula (such as an approximation of the Magnus formula) based on the updated humidity, air pressure, and temperature.

[0163] Iterate until the change is less than the threshold or a steady state is reached. Output the first and second temperature distribution characteristic data.

[0164] Example: The topology mapping table shows that the distance between the wellhead and roadway A is short, so the cold and wet flux transfer is fast; after iterative calculation, the surface temperature of roadway A drops to 2℃ and the dew point temperature to 3℃, and the negative difference indicates risk.

[0165] As can be seen, implementing this optional embodiment can define spatial association rules, quantify the attenuation effect of heat / cold sources on target locations (such as the inverse square law of distance), and overcome the analysis blind spots caused by physical isolation; it can convert heat dissipation data into spatial thermal influence coefficients, identify thermal shielding effect areas (such as low-temperature areas on the leeward side of equipment), and reveal abnormal temperature distribution caused by tunnel layout; it can combine structural constraints to modify diffusion models, predict airflow-restricted accumulation points (such as areas with sudden increases in wind speed in narrow sections), and locate high-risk locations for sudden icing; it can couple heat conduction and mass transfer equations to simulate the evolution process of unsteady temperature fields and realize spatiotemporal extrapolation and prediction of icing risks.

[0166] In an optional embodiment, the analysis module 102 analyzes the mine's icing risk factors based on the spatiotemporal differences between the first temperature distribution characteristic data and the second temperature distribution characteristic data in the following specific ways:

[0167] Calculate the real-time difference sequence between the first temperature distribution characteristic data and the second temperature distribution characteristic data;

[0168] The duration of negative values, the rate of change of negative value intensity, and the spatial coverage density features are extracted from the real-time difference sequence. The duration of negative values ​​is used to indicate the continuous length of time that the wall temperature is lower than the dew point temperature. The rate of change of negative value intensity is used to indicate the rate of change of the negative temperature difference per unit time. The spatial coverage density features are used to indicate the degree of distribution and aggregation of icing risk points in the mine. Icing risk points are used to indicate points where the duration of negative values ​​is greater than or equal to the preset duration.

[0169] Based on the weighted fusion results of the duration of negative values, the rate of change of negative value intensity, and the spatial cover density characteristics, the icing risk factors of the mine are analyzed.

[0170] In this embodiment of the invention, optionally, for calculating the real-time difference sequence: the difference sequence is a sequence (unit: °C) showing the change of the difference between surface temperature and dew point temperature over time, with negative values ​​indicating icing conditions. For each target location, the difference (difference = surface temperature - dew point temperature) can be calculated at fixed time intervals (e.g., per second) to form a time series. The sequence length can cover one period (e.g., 24 hours).

[0171] Further optional, for extracting spatiotemporal difference features:

[0172] For the duration of negative values: This represents the continuous time (in seconds) during which the surface temperature is below the dew point temperature. The longer the duration, the higher the risk of icing. The difference sequence can be scanned to identify consecutive negative value segments and calculate the duration of each segment. The maximum or average value is taken as the feature value. For example, if consecutive negative values ​​exceed 5 minutes, the duration is recorded.

[0173] For negative intensity change rate: This represents the rate at which the negative difference value changes per unit time (unit: °C / s). A large change rate indicates a rapid temperature drop and increased risk. The slope of the negative value segment in the difference sequence can be calculated (e.g., through linear regression), or the change rate of the difference between adjacent time points can be calculated.

[0174] For spatial cover density characteristics: this represents the degree of clustering of icing risk points (such as points with negative values ​​lasting longer than a preset duration) in the mine (e.g., number of points / area). High density indicates widespread risk. It can identify all risk points and calculate their spatial density (e.g., using kernel density estimation), or calculate the number of risk points per unit area.

[0175] For weighted fusion analysis of the icing risk factor: The icing risk factor is a scalar value (e.g., a value between 0 and 1) combining three features to quantify overall risk. The three features can be normalized (e.g., scaled to the 0-1 range), and then weighted and summed: Risk Factor = w1 × Negative Duration Score + w2 × Negative Intensity Change Rate Score + w3 × Spatial Coverage Density Score. The weights w1, w2, and w3 are set empirically. The risk factor can be mapped to levels (e.g., low, medium, high).

[0176] Example: A point has a negative difference value that lasts for 10 minutes with a change rate of -0.5℃ / min. It has a high spatial density and a weighted risk factor of 0.8, which corresponds to high risk.

[0177] As can be seen, implementing this optional embodiment can continuously monitor temperature difference changes, capture the critical state transition point of icing (such as the moment when ΔT first < 0), and provide an early warning time window; it can quantify the duration of low temperature, distinguish between instantaneous cooling and persistent risks (such as short-term ventilation vs. cold wave retention), and avoid wasting resources in self-recoverable areas; it can monitor the rate of temperature difference deterioration, identify accelerated icing processes (such as sudden cooling zones of -0.5℃ / min), and trigger emergency de-icing response mechanisms; it can assess the degree of risk point aggregation, locate systemic high-risk icing zones (such as continuous freezing points in main transport tunnels), and guide regionalized centralized de-icing operations; and it can integrate time / space / intensity multi-dimensional factors to generate a risk level spectrum map, supporting the formulation of graded response strategies.

[0178] The control module 103 is used to generate environmental de-icing control parameters for the mine based on the icing risk factor, so as to control the coal mine hot air unit to perform matching environmental de-icing control operations.

[0179] In this embodiment of the invention, optionally, the matching control strategy is as follows:

[0180] Low-risk areas (e.g., risk value <30): Turn off the hot air blower.

[0181] Medium risk (e.g., 30-70): Activate hot air at 50% airflow and 40℃.

[0182] High risk (e.g., >70): Directional spray of 60℃ hot air to coordinates (x,y,z) at a wind speed of 8m / s.

[0183] The parameters are sent to the PLC controller of the hot air unit via an industrial bus.

[0184] It is evident that implementing the embodiments of the present invention can break through the limitations of traditional single-point monitoring by integrating multi-dimensional heterogeneous environmental data collected from the mine, including the mine wall environment, the mine entrance environment, and the mine spatial layout. This allows for the construction of a complete digital profile of the mine environment and a panoramic model of the mine's thermal and humidity field, improving the comprehensiveness of environmental perception, avoiding misjudgments of icing risks due to missing data dimensions, eliminating monitoring blind spots, and thereby improving the accuracy and comprehensiveness of the analysis of icing risk factors in the mine. Consequently, it can improve the accuracy and comprehensiveness of environmental de-icing control of coal mine hot air blowers. While reducing the possibility of energy waste caused by excessive de-icing of coal mine hot air blowers, it also improves the safety hazard protection capability of insufficient de-icing of coal mine hot air blowers, improves the efficiency of coal mine environmental de-icing, and achieves a balanced optimization of safe production and economic operation in coal mines.

[0185] In another optional embodiment, the control module 103 generates environmental de-icing control parameters for the mine based on the icing risk factor, and controls the coal mine hot air unit to perform matching environmental de-icing control operations in the following specific ways:

[0186] The spatial distribution characteristics of icing risk factors and the trend of risk level transitions are analyzed. The trend of risk level transitions is used to represent the direction and probability of icing risk changes over time.

[0187] Based on spatial distribution characteristics, the spatial deployment topology of coal mine hot air units is matched to generate a hot air intervention priority sequence;

[0188] Based on the risk level transition trend, the hot air output control gradient is determined. The hot air output control gradient is used to define the combination strategy of wind temperature-air volume-wind direction-wind frequency under different risk levels.

[0189] By integrating the hot air intervention priority sequence and the hot air output regulation gradient, environmental de-icing control parameters for the mine are generated to control the coal mine hot air unit to perform matching environmental de-icing control operations.

[0190] In this embodiment of the invention, optionally, for risk spatial distribution analysis: the mine is divided into 1m³ grids, and the risk level (level 1-5) of each grid is marked.

[0191] For hot air intervention priority: Path planning algorithm: Calculate the shortest path from the hot air unit to each high-risk grid, and prioritize the processing of areas with short paths and high risk levels.

[0192] For hot air output control gradient: different risk levels are matched with different air temperatures, and thus different air volume and direction strategies.

[0193] Regarding parameter generation and execution:

[0194] JSON generation command: {"coordinates":"(x,y,z)", "temperature":65, "airflow":100, "mode":"rotation"};

[0195] The data is sent to the corresponding hot air blower via the Industrial Internet of Things (IIoT).

[0196] As can be seen, implementing this optional embodiment can visualize the geographical distribution of risks, associate the deployment location of hot air blowers (such as units within 200m of high-risk areas), and optimize equipment dispatch logic; it can integrate risk levels and path costs to generate the optimal handling sequence (handling high-risk + low-arrival-cost areas first), shorten emergency response delays, and reduce equipment idling energy consumption, thereby optimizing hot air blower scheduling paths and further shortening the handling delay in high-risk areas, achieving optimal spatiotemporal allocation of de-icing resources; it can define combined control parameter sets to avoid excessive energy consumption caused by "one-size-fits-all" control, improve the suitability of parameter sets, thereby facilitating the matching of different chemical conditions and avoiding the abuse of high-temperature and strong winds, achieving a dynamic balance between energy consumption intensity and safety thresholds; it can coordinate spatial priority and intensity strategies, outputting customized instructions with coordinates (such as "coordinates (x,y,z): 65℃ wind + rotating jet"), improving the intelligence level of instruction generation, and enhancing the accuracy, comprehensiveness, and flexibility of de-icing control.

[0197] Example 2

[0198] Please see Figure 2 , Figure 2 This is a schematic flowchart of an environmental de-icing control method based on a coal mine hot air unit, as disclosed in an embodiment of the present invention. Figure 2 The described environmental de-icing control method based on coal mine hot air units can be applied to coal mine hot air units, and also to intelligent devices associated with coal mine hot air units. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the scope of the application. Figure 2 As shown, the environmental de-icing control method based on coal mine hot air units may include the following operations:

[0199] 201. Collect multi-dimensional environmental data of the mine, including well wall environmental data, wellhead environmental data, and mine spatial layout data;

[0200] 202. Based on multi-dimensional environmental data, analyze the icing risk factors of the mine. The icing risk factors are used to represent the icing risk factors of the mine.

[0201] 203. Based on the icing risk factors, generate environmental de-icing control parameters for the mine to control the coal mine hot air unit to perform matching environmental de-icing control operations.

[0202] It is evident that implementing the embodiments of the present invention can break through the limitations of traditional single-point monitoring by integrating multi-dimensional heterogeneous environmental data collected from the mine, including the mine wall environment, the mine entrance environment, and the mine spatial layout. This allows for the construction of a complete digital profile of the mine environment and a panoramic model of the mine's thermal and humidity field, improving the comprehensiveness of environmental perception, avoiding misjudgments of icing risks due to missing data dimensions, eliminating monitoring blind spots, and thereby improving the accuracy and comprehensiveness of the analysis of icing risk factors in the mine. Consequently, it can improve the accuracy and comprehensiveness of environmental de-icing control of coal mine hot air blowers. While reducing the possibility of energy waste caused by excessive de-icing of coal mine hot air blowers, it also improves the safety hazard protection capability of insufficient de-icing of coal mine hot air blowers, improves the efficiency of coal mine environmental de-icing, and achieves a balanced optimization of safe production and economic operation in coal mines.

[0203] In this embodiment of the invention, as an optional implementation, the above-mentioned analysis of mine icing risk factors based on multi-dimensional environmental data includes:

[0204] Based on well wall environmental data, well wall heat dissipation characteristic data is generated, which is used to represent the heat exchange intensity between the well wall and its external environment.

[0205] Based on wellhead environmental data, cold and humid intrusion characteristic data are generated. The cold and humid intrusion characteristic data are used to represent the movement trajectory and impact intensity of the target cold air entering the mine, as well as the migration vector and spatial distribution of the water vapor carried by the target cold air entering the mine. The target cold air is used to represent airflow that is lower than the preset temperature and / or higher than the preset humidity of the mine.

[0206] Based on the spatial layout data of the mine, dynamic coupling analysis is performed on the heat dissipation characteristic data of the mine wall and the cold and damp intrusion characteristic data to generate the first temperature distribution characteristic data and the second temperature distribution characteristic data of the target location of the mine. The first temperature distribution characteristic data is used to represent the real-time surface temperature of the target location and its spatial variation. The second temperature distribution characteristic data is used to represent the water vapor saturation critical temperature of the air layer near the target location under the current air pressure and humidity conditions and its spatial variation.

[0207] Based on the spatiotemporal differences between the first and second temperature distribution characteristic data, the icing risk factors of the mine are analyzed.

[0208] As can be seen, implementing this optional embodiment can reveal the heat transfer law between the well wall and the environment by quantifying the heat exchange intensity, accurately locate weak heat dissipation areas (such as fracture development sections), and provide a basis for identifying areas of rapid cooling caused by high heat dissipation; it can track the movement trajectory and intensity of cold and humid airflow, predict the path of cold air invasion (such as the vortex zone from the wellhead to the corner of the roadway), block the water vapor migration channel in advance, and inhibit the formation of icing core conditions; it can directly capture the critical state of icing (when the surface temperature ≤ the dew point temperature) by comparing the surface temperature and the dew point temperature, breaking through the limitations of traditional single temperature monitoring and improving the sensitivity of risk identification; it can simultaneously assess the time accumulation effect and spatial diffusion trend, identify high-risk areas of continuous icing (such as blind roadways with long-term low temperatures), and avoid false alarms caused by instantaneous temperature fluctuations.

[0209] In this optional embodiment, as an optional implementation, the above-mentioned generation of well wall heat dissipation characteristic data based on well wall environmental data includes:

[0210] Extract wellbore material properties, wellbore geometry, and wellbore temperature gradient data from the wellbore environmental data;

[0211] Based on the well wall material properties and well wall geometry data, the equivalent thermal resistance distribution data of the well wall is determined. The equivalent thermal resistance distribution data of the well wall is used to represent the degree of obstruction to heat transfer at different locations of the well wall.

[0212] Based on wellbore temperature gradient data and wellbore equivalent thermal resistance distribution data, wellbore heat dissipation characteristic data are generated through spatial weighted fusion calculation.

[0213] As can be seen, implementing this optional embodiment can quantify the non-uniform heat dissipation characteristics of the well wall (such as concrete section vs. rock stratum section) by integrating material properties and geometric structure, overcome the errors of traditional homogenization modeling, and accurately reflect local heat loss; it can correlate temperature gradient and thermal resistance distribution, reveal the formation mechanism of heat dissipation hotspots (such as high thermal conductivity material + large temperature difference area), and provide target location support for targeted heat preservation measures.

[0214] In this optional embodiment, as another optional implementation, the above-mentioned generation of cold and wet intrusion characteristic data based on wellhead environmental data includes:

[0215] Extract wellhead airflow dynamics data and ambient humidity spatiotemporal variation data from the wellhead environmental data. The wellhead airflow dynamics data shall include at least one of the following: airflow direction data, airflow intensity data, airflow turbulence degree data, and airflow intensity change prediction data. The ambient humidity spatiotemporal variation data shall include at least one of the following: ambient water vapor mass distribution data, humidity variation data, and humidity variation direction data.

[0216] Based on the wellhead airflow dynamics data, cold air intrusion vector field data is generated. The cold air intrusion vector field data is used to determine the trajectory and impact intensity of the target cold air entering the mine.

[0217] Based on the spatiotemporal variation data of ambient humidity and the vector field data of cold air intrusion, the humidity migration intensity distribution data is calculated. The humidity migration intensity distribution data is used to represent the migration vector and spatial distribution of water vapor carried by the target cold air entering the mine.

[0218] By fusing cold air intrusion vector field data with humidity migration intensity distribution data, cold and humid intrusion characteristic data are generated.

[0219] As can be seen, implementing this optional embodiment can analyze the kinematic characteristics of airflow, predict the dynamics of cold front advancement (such as cold wave intrusion caused by sudden changes in wellhead airflow), and achieve visualized early warning of intrusion paths; it can correlate water vapor distribution with airflow movement, quantify moisture conduction flux (such as the number of grams of intruding water vapor per unit time), and accurately assess the maturity of icing humidity conditions; it can establish a temperature-humidity joint action model, identify high-risk areas of synergy (such as low-temperature and high-humidity drainage tunnels), and solve the omission of synergistic effects caused by independent analysis of temperature and humidity.

[0220] In this optional embodiment, as another optional implementation, the above-mentioned dynamic coupling analysis of mine wall heat dissipation characteristic data and cold and damp intrusion characteristic data based on mine spatial layout data to generate first temperature distribution characteristic data and second temperature distribution characteristic data of the target location of the mine includes:

[0221] Based on the spatial layout data of the mine, a topological relationship mapping table of the mine is constructed. The topological relationship mapping table of the mine is used to define the spatial correlation between the location of the heat dissipation source of the mine, the intrusion point of the target cold air entering the mine, and the target location.

[0222] Based on the mine topology mapping table, the heat dissipation characteristic data of the well wall is mapped to the spatial influence weight of the heat source. The spatial influence weight of the heat source is used to represent the degree of non-uniform thermal influence of the well wall heat dissipation on different locations of the mine.

[0223] Based on the mine topology mapping table, the cold and humid intrusion feature data are mapped into the spatial transport path of cold and humid flux. The spatial transport path of cold and humid flux is used to represent the diffusion direction and intensity of target cold air under the structural constraints of the mine.

[0224] Through iterative calculation of the dynamic field of heat and moisture flux, the first temperature distribution characteristic data and the second temperature distribution characteristic data of the target location in the mine are generated.

[0225] As can be seen, implementing this optional embodiment can define spatial association rules, quantify the attenuation effect of heat / cold sources on target locations (such as the inverse square law of distance), and overcome the analysis blind spots caused by physical isolation; it can convert heat dissipation data into spatial thermal influence coefficients, identify thermal shielding effect areas (such as low-temperature areas on the leeward side of equipment), and reveal abnormal temperature distribution caused by tunnel layout; it can combine structural constraints to modify diffusion models, predict airflow-restricted accumulation points (such as areas with sudden increases in wind speed in narrow sections), and locate high-risk locations for sudden icing; it can couple heat conduction and mass transfer equations to simulate the evolution process of unsteady temperature fields and realize spatiotemporal extrapolation and prediction of icing risks.

[0226] In an optional embodiment, the above-mentioned analysis of the mine's icing risk factors based on the spatiotemporal difference characteristics between the first temperature distribution characteristic data and the second temperature distribution characteristic data includes:

[0227] Calculate the real-time difference sequence between the first temperature distribution characteristic data and the second temperature distribution characteristic data;

[0228] The duration of negative values, the rate of change of negative value intensity, and the spatial coverage density features are extracted from the real-time difference sequence. The duration of negative values ​​is used to indicate the continuous length of time that the wall temperature is lower than the dew point temperature. The rate of change of negative value intensity is used to indicate the rate of change of the negative temperature difference per unit time. The spatial coverage density features are used to indicate the degree of distribution and aggregation of icing risk points in the mine. Icing risk points are used to indicate points where the duration of negative values ​​is greater than or equal to the preset duration.

[0229] Based on the weighted fusion results of the duration of negative values, the rate of change of negative value intensity, and the spatial cover density characteristics, the icing risk factors of the mine are analyzed.

[0230] As can be seen, implementing this optional embodiment can continuously monitor temperature difference changes, capture the critical state transition point of icing (such as the moment when ΔT first < 0), and provide an early warning time window; it can quantify the duration of low temperature, distinguish between instantaneous cooling and persistent risks (such as short-term ventilation vs. cold wave retention), and avoid wasting resources in self-recoverable areas; it can monitor the rate of temperature difference deterioration, identify accelerated icing processes (such as sudden cooling zones of -0.5℃ / min), and trigger emergency de-icing response mechanisms; it can assess the degree of risk point aggregation, locate systemic high-risk icing zones (such as continuous freezing points in main transport tunnels), and guide regionalized centralized de-icing operations; and it can integrate time / space / intensity multi-dimensional factors to generate a risk level spectrum map, supporting the formulation of graded response strategies.

[0231] In another optional embodiment, the above-mentioned generation of environmental de-icing control parameters for the mine based on the icing risk factor, to control the coal mine hot air unit to perform matching environmental de-icing control operations, includes:

[0232] The spatial distribution characteristics of icing risk factors and the trend of risk level transitions are analyzed. The trend of risk level transitions is used to represent the direction and probability of icing risk changes over time.

[0233] Based on spatial distribution characteristics, the spatial deployment topology of coal mine hot air units is matched to generate a hot air intervention priority sequence;

[0234] Based on the risk level transition trend, the hot air output control gradient is determined. The hot air output control gradient is used to define the combination strategy of wind temperature-air volume-wind direction-wind frequency under different risk levels.

[0235] By integrating the hot air intervention priority sequence and the hot air output regulation gradient, environmental de-icing control parameters for the mine are generated to control the coal mine hot air unit to perform matching environmental de-icing control operations.

[0236] As can be seen, implementing this optional embodiment can visualize the geographical distribution of risks, associate the deployment location of hot air blowers (such as units within 200m of high-risk areas), and optimize equipment dispatch logic; it can integrate risk levels and path costs to generate the optimal handling sequence (handling high-risk + low-arrival-cost areas first), shorten emergency response delays, and reduce equipment idling energy consumption, thereby optimizing hot air blower scheduling paths and further shortening the handling delay in high-risk areas, achieving optimal spatiotemporal allocation of de-icing resources; it can define combined control parameter sets to avoid excessive energy consumption caused by "one-size-fits-all" control, improve the suitability of parameter sets, thereby facilitating the matching of different chemical conditions and avoiding the abuse of high-temperature and strong winds, achieving a dynamic balance between energy consumption intensity and safety thresholds; it can coordinate spatial priority and intensity strategies, outputting customized instructions with coordinates (such as "coordinates (x,y,z): 65℃ wind + rotating jet"), improving the intelligence level of instruction generation, and enhancing the accuracy, comprehensiveness, and flexibility of de-icing control.

[0237] Example 3

[0238] Please see Figure 3 , Figure 3 This is a schematic diagram of another environmental de-icing control system based on a coal mine hot air unit, disclosed in an embodiment of the present invention. This environmental de-icing control system based on a coal mine hot air unit can be applied to the coal mine hot air unit, and can also be applied to intelligent devices associated with the coal mine hot air unit. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. The embodiments of the present invention do not limit the scope of these devices. Figure 3 As shown, the environmental de-icing control system based on the coal mine hot air unit may include:

[0239] Memory 301 that stores executable program code.

[0240] Processor 302 coupled to memory 301.

[0241] The processor 302 calls the executable program code stored in the memory 401 to execute the steps in the environmental de-icing control method based on the coal mine hot air unit described in Embodiment 2 of the present invention.

[0242] Example 4

[0243] This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the environmental de-icing control method based on a coal mine hot air unit described in Embodiment 2 of this invention.

[0244] Example 5

[0245] This invention discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the environmental de-icing control method based on a coal mine hot air unit described in Embodiment 2.

[0246] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0247] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0248] Finally, it should be noted that the environmental de-icing control system and method based on a coal mine hot air unit disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An environmental de-icing control system based on a coal mine hot air unit, characterized in that, The system includes: The acquisition module is used to collect multi-dimensional environmental data of the mine, including well wall environmental data, wellhead environmental data, and mine spatial layout data. The analysis module is used to analyze the icing risk factors of the mine based on the multi-dimensional environmental data, wherein the icing risk factors are used to represent the icing risk factors of the mine. The control module is used to generate environmental de-icing control parameters for the mine based on the icing risk factors, so as to control the coal mine hot air unit to perform matching environmental de-icing control operations. Furthermore, the specific methods by which the analysis module analyzes the icing risk factors of the mine based on the multi-dimensional environmental data include: Based on the well wall environment data, well wall heat dissipation characteristic data is generated, which is used to represent the heat exchange intensity between the well wall and its external environment. Based on the wellhead environmental data, cold and humid intrusion characteristic data is generated. The cold and humid intrusion characteristic data is used to represent the movement trajectory and impact intensity of the target cold air entering the mine, as well as the migration vector and spatial distribution of the water vapor carried by the target cold air entering the mine. The target cold air is used to represent airflow that is lower than the preset temperature and / or higher than the preset humidity of the mine. Based on the mine spatial layout data, the heat dissipation characteristic data of the mine wall and the cold and humid intrusion characteristic data are dynamically coupled and analyzed to generate first temperature distribution characteristic data and second temperature distribution characteristic data of the target location of the mine. The first temperature distribution characteristic data is used to represent the real-time surface temperature of the target location and its spatial variation. The second temperature distribution characteristic data is used to represent the water vapor saturation critical temperature of the air layer near the target location under the current air pressure and humidity conditions and its spatial variation. Based on the spatiotemporal differences between the first temperature distribution characteristic data and the second temperature distribution characteristic data, the icing risk factor of the mine is analyzed.

2. The environmental de-icing control system based on a coal mine hot air unit according to claim 1, characterized in that, The specific methods by which the analysis module generates well wall heat dissipation characteristic data based on the well wall environment data include: Extract the wellbore material properties, wellbore geometry, and wellbore temperature gradient data from the wellbore environment data; Based on the well wall material properties data and well wall geometric structure data, the equivalent thermal resistance distribution data of the well wall is determined. The equivalent thermal resistance distribution data of the well wall is used to represent the degree of obstruction to heat transfer at different locations of the well wall. Based on the well wall temperature gradient data and the well wall equivalent thermal resistance distribution data, spatial weighted fusion calculation is used to generate well wall heat dissipation characteristic data.

3. The environmental de-icing control system based on a coal mine hot air unit according to claim 1, characterized in that, The specific methods by which the analysis module generates cold and wet intrusion characteristic data based on the wellhead environmental data include: Extract wellhead airflow dynamics data and ambient humidity spatiotemporal variation data from the wellhead environmental data. The wellhead airflow dynamics data includes at least one of the following: airflow direction data, airflow intensity data, airflow turbulence degree data, and airflow intensity sudden change prediction data. The ambient humidity spatiotemporal variation data includes at least one of the following: ambient water vapor mass distribution data, humidity variation data, and humidity variation direction data. Based on the wellhead airflow dynamics data, cold air intrusion vector field data is generated. The cold air intrusion vector field data is used to determine the trajectory and impact intensity of the target cold air entering the mine. Based on the spatiotemporal variation data of ambient humidity and the cold air intrusion vector field data, humidity migration intensity distribution data is calculated. The humidity migration intensity distribution data is used to represent the migration vector and spatial distribution of water vapor carried by the target cold air entering the mine. By fusing the cold air intrusion vector field data with the humidity migration intensity distribution data, cold and humid intrusion characteristic data are generated.

4. The environmental de-icing control system based on a coal mine hot air unit according to claim 1, characterized in that, The analysis module performs dynamic coupling analysis on the heat dissipation characteristic data of the well wall and the cold and damp intrusion characteristic data based on the mine spatial layout data, and generates the first temperature distribution characteristic data and the second temperature distribution characteristic data of the target location of the mine in the following specific ways: Based on the mine spatial layout data, a mine topology mapping table is constructed. The mine topology mapping table is used to define the spatial correlation between the heat dissipation source location of the mine, the intrusion point location of the target cold air entering the mine, and the target location. Based on the mine topology mapping table, the heat dissipation characteristic data of the well wall is mapped to the spatial influence weight of the heat source. The spatial influence weight of the heat source is used to represent the degree of non-uniform thermal influence of the well wall heat dissipation on different locations of the mine. Based on the mine topology mapping table, the cold and humid intrusion feature data is mapped to a cold and humid flux spatial transport path, which is used to represent the diffusion direction and intensity of the target cold air under the structural constraints of the mine. The first temperature distribution characteristic data and the second temperature distribution characteristic data of the target location of the mine are generated by iterative calculation of the dynamic field of heat and moisture flux.

5. The environmental de-icing control system based on a coal mine hot air unit according to claim 1, characterized in that, The analysis module analyzes the icing risk factors of the mine based on the spatiotemporal differences between the first temperature distribution characteristic data and the second temperature distribution characteristic data. The specific methods include: Calculate the real-time difference sequence between the first temperature distribution feature data and the second temperature distribution feature data; The duration of negative values, the rate of change of negative value intensity, and the spatial coverage density features are extracted from the real-time difference sequence. The duration of negative values ​​is used to represent the continuous length of time that the wall temperature is lower than the dew point temperature. The rate of change of negative value intensity is used to represent the rate of change of the negative temperature difference per unit time. The spatial coverage density features are used to represent the degree of distribution and clustering of icing risk points in the mine. The icing risk points are used to represent points where the duration of negative values ​​is greater than or equal to a preset duration. Based on the weighted fusion results of the duration of negative values, the rate of change of negative value intensity, and the spatial coverage density characteristics, the icing risk factors of the mine are analyzed.

6. The environmental de-icing control system based on a coal mine hot air unit according to any one of claims 1-5, characterized in that, The control module generates environmental de-icing control parameters for the mine based on the icing risk factor, and the specific methods for controlling the coal mine hot air unit to perform matching environmental de-icing control operations include: The spatial distribution characteristics and risk level transition trends of the aforementioned icing risk factors are analyzed. The risk level transition trends are used to represent the direction and probability of icing risk changes over time. Based on the spatial distribution characteristics, the spatial deployment topology of coal mine hot air units is matched to generate a hot air intervention priority sequence; Based on the risk level transition trend, the hot air output control gradient is determined. The hot air output control gradient is used to define the combination strategy of wind temperature-air volume-wind direction-wind frequency under different risk levels. By integrating the hot air intervention priority sequence and the hot air output control gradient, environmental de-icing control parameters for the mine are generated to control the coal mine hot air unit to perform matching environmental de-icing control operations.

7. A method for environmental de-icing control based on a coal mine hot air unit, characterized in that, The method includes: Collect multi-dimensional environmental data of the mine, including well wall environmental data, wellhead environmental data and mine spatial layout data; Based on the multi-dimensional environmental data, the icing risk factors of the mine are analyzed, and the icing risk factors are used to represent the icing risk factors of the mine. Based on the icing risk factor, environmental de-icing control parameters for the mine are generated to control the coal mine hot air unit to perform matching environmental de-icing control operations. And, the analysis of the mine's icing risk factors based on the multi-dimensional environmental data includes: Based on the well wall environment data, well wall heat dissipation characteristic data is generated, which is used to represent the heat exchange intensity between the well wall and its external environment. Based on the wellhead environmental data, cold and humid intrusion characteristic data is generated. The cold and humid intrusion characteristic data is used to represent the movement trajectory and impact intensity of the target cold air entering the mine, as well as the migration vector and spatial distribution of the water vapor carried by the target cold air entering the mine. The target cold air is used to represent airflow that is lower than the preset temperature and / or higher than the preset humidity of the mine. Based on the mine spatial layout data, the heat dissipation characteristic data of the mine wall and the cold and humid intrusion characteristic data are dynamically coupled and analyzed to generate first temperature distribution characteristic data and second temperature distribution characteristic data of the target location of the mine. The first temperature distribution characteristic data is used to represent the real-time surface temperature of the target location and its spatial variation. The second temperature distribution characteristic data is used to represent the water vapor saturation critical temperature of the air layer near the target location under the current air pressure and humidity conditions and its spatial variation. Based on the spatiotemporal differences between the first temperature distribution characteristic data and the second temperature distribution characteristic data, the icing risk factor of the mine is analyzed.

8. An environmental de-icing control system based on a coal mine hot air unit, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the environmental de-icing control method based on the coal mine hot air unit as described in claim 7.

9. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the environmental de-icing control method based on a coal mine hot air unit as described in claim 7.

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