Intelligent Control System and Method for Gas Management Based on Hot Air Handling Unit

Abstract

This invention relates to the field of hot air handling unit technology, and discloses an intelligent control system and method for gas management based on hot air handling units. The system comprises: a data acquisition module that collects real-time gas concentration data from multiple monitoring points in the mine and the corresponding operating status data of the hot air handling units; an analysis module that analyzes the spatial distribution characteristics and diffusion trends of the mine's gas concentration based on the real-time gas concentration data, generating dynamic trend data of the mine's current gas concentration; a prediction module that predicts the evolution of the mine's gas concentration within a preset first time period based on the dynamic trend data; and a control module that generates target control parameters for the hot air handling units based on the gas concentration evolution data and the operating status data, thereby dynamically controlling the operating status of the hot air handling units. Therefore, implementing this invention can improve the accuracy of gas management control using hot air handling units.

Description

Technical Field

[0001] This invention relates to the field of hot air handling unit technology, and in particular to an intelligent control system and method for gas management based on hot air handling units. Background Technology

[0002] In the field of coal mine safety production, the control of methane gas (mainly composed of methane) is a core aspect concerning the life safety of underground workers and the stable operation of the mine. The coal mine ventilation system is the main means of diluting and removing methane gas, and the hot air unit, as an important component of the mine ventilation system, especially in cold regions or deep mines, undertakes the key tasks of delivering fresh air at a suitable temperature underground, preventing the shaft from freezing, and ensuring a comfortable working environment.

[0003] Currently, the operation and control of coal mine hot air units mainly rely on the experience and judgment of operators. However, practice has shown that the occurrence and emission of gas in mines have significant non-uniformity, dynamism, and uncertainty. This manual experience is insufficient to capture and respond to the instantaneous changes in gas underground, resulting in certain blind spots in gas control, such as low-concentration areas and corners, which pose certain risks and hidden dangers to safe production in mines.

[0004] Therefore, it is particularly important to propose a technical solution to improve the accuracy of gas control and regulation in hot air units. Summary of the Invention

[0005] This invention provides an intelligent control system and method for gas management based on hot air units, which can help improve the accuracy of gas management control of hot air units.

[0006] To address the aforementioned technical problems, the first aspect of this invention discloses an intelligent control system for gas management based on a hot air unit, the system comprising:

[0007] The data acquisition module is used to collect real-time gas concentration data from multiple detection points in the mine and the operating status data of the corresponding hot air unit in the mine.

[0008] The analysis module is used to analyze the spatial distribution characteristics and diffusion trend of the gas concentration in the mine based on the real-time gas concentration data, and generate the current dynamic trend data of the gas concentration in the mine.

[0009] The prediction module is used to predict the evolution trend of gas concentration in the mine within a preset first time period based on the dynamic trend data of gas concentration.

[0010] The control module is used to generate target control parameters for the hot air unit based on the gas concentration evolution trend data and the operating status data, so as to dynamically regulate the operating status of the hot air unit.

[0011] As an optional implementation, in the first aspect of the present invention, the specific method by which the analysis module analyzes the spatial distribution characteristics and diffusion trend of the gas concentration in the mine based on the real-time gas concentration data, and generates the current dynamic trend data of the gas concentration in the mine, includes:

[0012] Based on the real-time gas concentration data of each detection point and the perceived spatial relationship between the target space corresponding to each detection point, the concentration gradient region of the mine is divided; the concentration gradient region is used to represent blocks in which the spatial relationship is adjacent and the real-time gas concentration data are all within the same preset concentration threshold range;

[0013] Based on the concentration change data of each concentration gradient region within a preset second time period, the gas migration path and migration rate between adjacent gradient regions are calculated; the gas migration path is used to describe the diffusion trajectory and direction of gas between the concentration gradient regions, and the migration rate is used to quantify the diffusion rate.

[0014] Based on the spatial layout data of each concentration gradient region and the gas migration path between adjacent gradient regions, a gas spatial distribution feature is generated; the gas spatial distribution feature is used to characterize the spatial correlation structure and concentration gradient distribution pattern between the concentration gradient regions.

[0015] Based on the coupling relationship between the gas migration path and the migration rate, a gas diffusion trend is generated; the gas diffusion trend is used to predict the conduction path and intensity changes of gas concentration in the mine within a preset third time period;

[0016] Based on the spatial distribution characteristics of the gas and the gas diffusion trend, the current dynamic trend data of the gas concentration in the mine is generated.

[0017] As an optional implementation, in the first aspect of the present invention, the specific method by which the prediction module predicts the evolution trend data of the gas concentration in the mine within a preset first time period based on the dynamic trend data of gas concentration includes:

[0018] Analyze the spatial distribution characteristics and diffusion trend of methane in the dynamic trend data of methane concentration;

[0019] Based on the gas migration path, a sequence of conduction paths between adjacent gradient regions is determined; the sequence of conduction paths is used to define the spatial conduction order of gas diffusion.

[0020] Based on the migration rate, the conduction delay of gas reaching each of the concentration gradient regions along the conduction path sequence is calculated; the conduction delay is used to quantify the time interval between gas arrivals in each of the concentration gradient regions.

[0021] Based on the conduction path sequence and the conduction delay, and superimposed with the real-time gas concentration data of the current concentration gradient region, the gas concentration evolution trend data of the mine within a preset first time period is predicted; the gas concentration evolution trend data is used to describe the spatial distribution change process of gas concentration within the preset first time period.

[0022] As an optional implementation, in the first aspect of the present invention, the control module generates target control parameters for the hot air unit based on the gas concentration evolution trend data and the operating status data, and dynamically regulates the operating status of the hot air unit in the following specific ways:

[0023] The gas concentration evolution data is analyzed to determine the conduction path sequence, conduction delay, and spatial distribution characteristics of the gas. The conduction path sequence defines the spatial conduction order of gas diffusion, the conduction delay quantifies the time interval between gas conduction to the target area, and the spatial distribution characteristics characterize the spatial correlation structure and concentration gradient distribution pattern between concentration gradient regions. The concentration gradient region represents a block where the spatial relationship is adjacent and the real-time gas concentration data are all within the same preset concentration threshold range.

[0024] Based on the operating status data, the adjustable parameter range of the hot air unit is determined; the adjustable parameter range is used to limit the adjustment boundary of at least one of the following: air volume, air temperature, air direction, air frequency, and air volume.

[0025] Based on the conduction path sequence and the conduction delay, a hot air supply direction control strategy is matched for the hot air unit; the hot air supply direction control strategy is used to direct the hot air flow towards the upstream area of ​​the gas diffusion path.

[0026] Based on the spatial distribution characteristics of the gas and the adjustable parameter range, zonal control parameters are generated; the zonal control parameters are used to perform matching ventilation dilution and / or stable air supply operations for different concentration gradient regions;

[0027] Based on the air supply direction control strategy and the zone control parameters, target control parameters for the hot air unit are generated to dynamically control the operating status of the hot air unit.

[0028] As an optional implementation, in the first aspect of the present invention, the control module generates target control parameters for the hot air unit based on the air supply direction control strategy and the zone control parameters, and dynamically controls the operating state of the hot air unit in the following specific ways:

[0029] Based on the conduction delay, the control triggering timing is calculated, and a dynamic control timing instruction is generated; the control triggering timing is used to determine the control start time point for each concentration gradient region, and the control triggering timing is obtained by comparing the conduction delay with a preset response time threshold.

[0030] Based on the zonal control parameters and the adjustable parameter range of the hot air unit, the matching degree between the required control resources and the available equipment resources for each concentration gradient region is calculated to generate a dynamic priority coefficient; the dynamic priority coefficient is used to characterize the control urgency of different concentration gradient regions.

[0031] By integrating the air supply direction control strategy, the dynamic control timing command, and the dynamic priority coefficient, a set of control parameters for the hot air unit is generated; the set of control parameters includes at least one of the following: air supply direction angle value, zone air supply volume gradient value, and air supply temperature gradient value, and also includes a corresponding execution timing flag.

[0032] Based on the equipment operation constraints in the parsed operating status data of the hot air unit, conflict resolution verification is performed on the control parameter set to generate target control parameters for the hot air unit, so as to dynamically regulate the operating status of the hot air unit; the conflict resolution verification is used to control the switching of air supply direction and the adjustment of air volume and temperature to be executed in a coordinated manner within the mechanical response limit of the equipment.

[0033] As an optional implementation, in the first aspect of the invention, the system further includes:

[0034] The acquisition module is used to acquire the latest detected actual gas concentration data at each detection point and the operation feedback data of the hot air unit after the control module performs the operation of dynamically adjusting the operating status of the hot air unit; the operation feedback data is used to record the actual operating status of the hot air unit in executing the target control parameters.

[0035] The calculation module is used to calculate the concentration control deviation based on the actual gas concentration data and the predicted gas concentration evolution trend data; the concentration control deviation is used to quantify the degree of difference between the actual gas concentration change and the predicted trend, and the concentration control deviation is calculated by comparing the predicted concentration value and the measured concentration value at the same time point.

[0036] The calculation module is also used to calculate the equipment execution deviation based on the operation feedback data and the target control parameters; the equipment execution deviation is used to characterize the degree of deviation between the actual operating state of the hot air unit and the target control parameters, and the equipment execution deviation is calculated by the set value of the target control parameters and the actual output value of the equipment;

[0037] The correction module is used to dynamically correct the preset response time threshold of the conduction delay in the gas concentration evolution trend data, the adjustable parameter range of the hot air unit, and the division rules of the concentration gradient region of the mine based on the concentration control deviation and the equipment execution deviation.

[0038] The second aspect of this invention discloses an intelligent control method for gas management based on a hot air unit, the method comprising:

[0039] Real-time gas concentration data from multiple monitoring points in the mine and operating status data of the corresponding hot air unit in the mine are collected.

[0040] Based on the real-time gas concentration data, the spatial distribution characteristics and diffusion trend of the gas concentration in the mine are analyzed, and the current dynamic trend data of the gas concentration in the mine is generated.

[0041] Based on the dynamic trend data of gas concentration, predict the evolution trend of gas concentration in the mine within a preset first time period;

[0042] Based on the gas concentration evolution data and the operating status data, target control parameters for the hot air unit are generated to dynamically regulate the operating status of the hot air unit.

[0043] As an optional implementation, in a second aspect of the present invention, the step of analyzing the spatial distribution characteristics and diffusion trend of the gas concentration in the mine based on the real-time gas concentration data, and generating current dynamic trend data of the gas concentration in the mine, includes:

[0044] Based on the real-time gas concentration data of each detection point and the perceived spatial relationship between the target space corresponding to each detection point, the concentration gradient region of the mine is divided; the concentration gradient region is used to represent blocks in which the spatial relationship is adjacent and the real-time gas concentration data are all within the same preset concentration threshold range;

[0045] Based on the concentration change data of each concentration gradient region within a preset second time period, the gas migration path and migration rate between adjacent gradient regions are calculated; the gas migration path is used to describe the diffusion trajectory and direction of gas between the concentration gradient regions, and the migration rate is used to quantify the diffusion rate.

[0046] Based on the spatial layout data of each concentration gradient region and the gas migration path between adjacent gradient regions, a gas spatial distribution feature is generated; the gas spatial distribution feature is used to characterize the spatial correlation structure and concentration gradient distribution pattern between the concentration gradient regions.

[0047] Based on the coupling relationship between the gas migration path and the migration rate, a gas diffusion trend is generated; the gas diffusion trend is used to predict the conduction path and intensity changes of gas concentration in the mine within a preset third time period;

[0048] Based on the spatial distribution characteristics of the gas and the gas diffusion trend, the current dynamic trend data of the gas concentration in the mine is generated.

[0049] As an optional implementation, in a second aspect of the present invention, the step of predicting the evolution trend of the mine's gas concentration within a preset first time period based on the dynamic trend data of gas concentration includes:

[0050] Analyze the spatial distribution characteristics and diffusion trend of methane in the dynamic trend data of methane concentration;

[0051] Based on the gas migration path, a sequence of conduction paths between adjacent gradient regions is determined; the sequence of conduction paths is used to define the spatial conduction order of gas diffusion.

[0052] Based on the migration rate, the conduction delay of gas reaching each of the concentration gradient regions along the conduction path sequence is calculated; the conduction delay is used to quantify the time interval between gas arrivals in each of the concentration gradient regions.

[0053] Based on the conduction path sequence and the conduction delay, and superimposed with the real-time gas concentration data of the current concentration gradient region, the gas concentration evolution trend data of the mine within a preset first time period is predicted; the gas concentration evolution trend data is used to describe the spatial distribution change process of gas concentration within the preset first time period.

[0054] As an optional implementation, in a second aspect of the present invention, generating target control parameters for the hot air unit based on the gas concentration evolution trend data and the operating status data to dynamically regulate the operating status of the hot air unit includes:

[0055] The gas concentration evolution data is analyzed to determine the conduction path sequence, conduction delay, and spatial distribution characteristics of the gas. The conduction path sequence defines the spatial conduction order of gas diffusion, the conduction delay quantifies the time interval between gas conduction to the target area, and the spatial distribution characteristics characterize the spatial correlation structure and concentration gradient distribution pattern between concentration gradient regions. The concentration gradient region represents a block where the spatial relationship is adjacent and the real-time gas concentration data are all within the same preset concentration threshold range.

[0056] Based on the operating status data, the adjustable parameter range of the hot air unit is determined; the adjustable parameter range is used to limit the adjustment boundary of at least one of the following: air volume, air temperature, air direction, air frequency, and air volume.

[0057] Based on the conduction path sequence and the conduction delay, a hot air supply direction control strategy is matched for the hot air unit; the hot air supply direction control strategy is used to direct the hot air flow towards the upstream area of ​​the gas diffusion path.

[0058] Based on the spatial distribution characteristics of the gas and the adjustable parameter range, zonal control parameters are generated; the zonal control parameters are used to perform matching ventilation dilution and / or stable air supply operations for different concentration gradient regions;

[0059] Based on the air supply direction control strategy and the zone control parameters, target control parameters for the hot air unit are generated to dynamically control the operating status of the hot air unit.

[0060] As an optional implementation, in a second aspect of the present invention, generating target control parameters for the hot air handling unit based on the air supply direction control strategy and the zone control parameters to dynamically control the operating state of the hot air handling unit includes:

[0061] Based on the conduction delay, the control triggering timing is calculated, and a dynamic control timing instruction is generated; the control triggering timing is used to determine the control start time point for each concentration gradient region, and the control triggering timing is obtained by comparing the conduction delay with a preset response time threshold.

[0062] Based on the zonal control parameters and the adjustable parameter range of the hot air unit, the matching degree between the required control resources and the available equipment resources for each concentration gradient region is calculated to generate a dynamic priority coefficient; the dynamic priority coefficient is used to characterize the control urgency of different concentration gradient regions.

[0063] By integrating the air supply direction control strategy, the dynamic control timing command, and the dynamic priority coefficient, a set of control parameters for the hot air unit is generated; the set of control parameters includes at least one of the following: air supply direction angle value, zone air supply volume gradient value, and air supply temperature gradient value, and also includes a corresponding execution timing flag.

[0064] Based on the equipment operation constraints in the parsed operating status data of the hot air unit, conflict resolution verification is performed on the control parameter set to generate target control parameters for the hot air unit, so as to dynamically regulate the operating status of the hot air unit; the conflict resolution verification is used to control the switching of air supply direction and the adjustment of air volume and temperature to be executed in a coordinated manner within the mechanical response limit of the equipment.

[0065] As an optional implementation, in a second aspect of the invention, the method further includes:

[0066] After performing the operation of dynamically adjusting the operating status of the hot air unit, the latest detected actual gas concentration data at each detection point and the operating feedback data of the hot air unit are obtained; the operating feedback data is used to record the actual operating status of the hot air unit in executing the target control parameters.

[0067] Based on the actual gas concentration data and the predicted gas concentration evolution trend data, the concentration control deviation is calculated; the concentration control deviation is used to quantify the degree of difference between the actual gas concentration change and the predicted trend, and the concentration control deviation is calculated by comparing the predicted concentration value and the measured concentration value at the same time point.

[0068] Based on the operational feedback data and the target control parameters, the equipment execution deviation is calculated; the equipment execution deviation is used to characterize the degree of deviation between the actual operating state of the hot air unit and the target control parameters, and the equipment execution deviation is calculated by the set value of the target control parameters and the actual output value of the equipment;

[0069] Based on the concentration control deviation and the equipment execution deviation, the preset response time threshold of the conduction delay in the gas concentration evolution trend data, the adjustable parameter range of the hot air unit, and the division rules of the concentration gradient region of the mine are dynamically corrected.

[0070] A third aspect of this invention discloses another intelligent control system for gas management based on a hot air unit, the system comprising:

[0071] Memory containing executable program code;

[0072] A processor coupled to the memory;

[0073] The processor calls the executable program code stored in the memory to execute the intelligent control method for gas management based on hot air units disclosed in the second aspect of the present invention.

[0074] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the intelligent control method for gas management based on hot air units disclosed in the second aspect of the present invention.

[0075] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0076] In this embodiment of the invention, the acquisition module collects real-time gas concentration data from multiple detection points in the mine and the operating status data of the corresponding hot air blower unit; the analysis module analyzes the spatial distribution characteristics and diffusion trend of the gas concentration in the mine based on the real-time gas concentration data, and generates the current dynamic trend data of the gas concentration in the mine; the prediction module predicts the evolution trend data of the gas concentration in the mine within a preset first time period based on the dynamic trend data of the gas concentration; and the control module generates target control parameters for the hot air blower unit based on the evolution trend data of the gas concentration and the operating status data, so as to dynamically regulate the operating status of the hot air blower unit. It is evident that implementing this invention can improve the real-time and comprehensiveness of environmental monitoring by collecting real-time data on gas concentrations and hot air blower unit operating status at multiple monitoring points in the mine. This facilitates the construction of a dynamic panoramic view of mine gas distribution, providing a data foundation for precise control. Furthermore, it can generate dynamic trend data by analyzing the spatial distribution characteristics and diffusion trends of gas, improving the analytical accuracy of gas evolution patterns. This helps identify potential risk areas and achieve early warning of hidden dangers. Predicting future gas concentration evolution trends enhances the foresight of control strategies, enabling advance planning of control actions and avoiding safety risks caused by sudden increases in gas concentration. Finally, generating target control parameters based on evolution trends and equipment status improves the targeting of hot air blower unit control and the accuracy of gas control, facilitating dynamic optimization of ventilation efficiency and ultimately achieving proactive suppression of gas concentration and an overall improvement in mine safety. Attached Figure Description

[0077] 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.

[0078] Figure 1 This is a schematic diagram of the structure of an intelligent control system for gas management based on a hot air unit, as disclosed in an embodiment of the present invention.

[0079] Figure 2 This is a schematic diagram of another intelligent control system for gas management based on a hot air unit disclosed in an embodiment of the present invention;

[0080] Figure 3 This is a schematic flowchart of an intelligent control method for gas management based on a hot air unit disclosed in an embodiment of the present invention;

[0081] Figure 4 This is a schematic diagram of another intelligent control system for gas management based on a 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 intelligent control system and method for gas management based on hot air blower units. It improves the real-time and comprehensiveness of environmental monitoring by collecting real-time data on gas concentrations at multiple monitoring points in the mine and the operating status of the hot air blower units. This facilitates the construction of a dynamic panoramic view of mine gas distribution, providing a data foundation for precise control. It generates dynamic trend data by analyzing the spatial distribution characteristics and diffusion trends of gas, improving the accuracy of gas evolution analysis and facilitating the identification of potential risk areas, thus enabling early warning of hidden dangers. It enhances the foresight of control strategies by predicting future gas concentration evolution trends, allowing for advance planning of control actions and avoiding safety risks caused by sudden increases in gas concentration. Furthermore, it improves the targeting and accuracy of hot air blower unit control by generating target control parameters based on evolution trends and equipment status, thereby dynamically optimizing ventilation efficiency and achieving proactive suppression of gas concentration and an overall improvement in mine safety. These are described in detail below.

[0086] Example 1

[0087] Please see Figure 1 , Figure 1 This is a schematic diagram of a gas control intelligent regulation system based on a hot air unit, as disclosed in an embodiment of the present invention. Figure 1 The described intelligent control system for gas management based on hot air handling units can be applied to mine hot air handling units, specifically coal mine hot air handling units. It can also be applied to intelligent devices associated with coal mine hot air handling units, including but 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 1 As shown, the intelligent control system for gas management based on hot air units can include the following operations:

[0088] The acquisition module 101 is used to acquire real-time gas concentration data from multiple detection points in the mine and the operating status data of the corresponding hot air blower units in the mine;

[0089] In this embodiment of the invention, optionally, for the above-mentioned detection point arrangement, gas sensors can be deployed in key / non-critical areas such as mining faces, return airways, and transport roadways to monitor gas concentration (%) in real time; at the same time, operating status data such as air volume (m³ / min), air temperature (°C), fan speed (rpm), wind direction, and wind frequency can be collected through the PLC interface / other interfaces of the hot air unit to provide dynamic environmental input and equipment operating condition baseline for the system.

[0090] Analysis module 102 is used to analyze the spatial distribution characteristics and diffusion trend of gas concentration in the mine based on real-time gas concentration data, and generate dynamic trend data of the current gas concentration in the mine.

[0091] In this embodiment of the invention, optionally, the input to the analysis module described above can be set to: real-time gas concentration data (such as time series + spatial coordinates).

[0092] The output is set to: dynamic trend data of gas concentration (including gradient region division, migration path, etc.).

[0093] Further, specifically, the three-dimensional space of the mine can be discretized into grid cells, and a full-mine concentration field can be generated by interpolation based on the detection point data.

[0094] Clustering algorithms (such as DBSCAN) can be used to merge spatially adjacent cells with similar concentrations into a concentration gradient region (e.g., grouping consecutive tunnels with concentrations of 0.5%-0.8% into the same region).

[0095] In this embodiment of the invention, as an optional implementation, the analysis module 102, based on real-time gas concentration data, analyzes the spatial distribution characteristics and diffusion trend of gas concentration in the mine, and generates current dynamic trend data of gas concentration in the mine in the following specific ways:

[0096] Based on the real-time gas concentration data of each detection point and the spatial relationship between the target spaces corresponding to each detection point, the concentration gradient region of the mine is divided; the concentration gradient region is used to represent blocks that are spatially adjacent and whose real-time gas concentration data are all within the same preset concentration threshold range.

[0097] Based on the concentration change data of each concentration gradient region within the preset second time period, the gas migration path and migration rate between adjacent gradient regions are calculated; the gas migration path is used to describe the diffusion trajectory and direction of gas between concentration gradient regions, and the migration rate is used to quantify the diffusion rate.

[0098] Based on the spatial layout data of each concentration gradient region and the gas migration path between adjacent gradient regions, a gas spatial distribution feature is generated; the gas spatial distribution feature is used to characterize the spatial correlation structure between concentration gradient regions and the concentration gradient distribution pattern.

[0099] Based on the coupling relationship between gas migration path and migration rate, a gas diffusion trend is generated; the gas diffusion trend is used to predict the conduction path and intensity changes of gas concentration in the mine within a preset third time period.

[0100] Based on the spatial distribution characteristics and diffusion trends of gas, dynamic trend data of current gas concentration in the mine is generated.

[0101] In this embodiment of the invention, optionally, the aforementioned preset second time period is used to represent a historical time period, and the concentration change data of each concentration gradient region within the preset second time period can be obtained by retrieving the memory;

[0102] Optionally, the aforementioned preset third time period can be used to represent a preset range of time periods when the operation of generating gas diffusion trends based on the coupling relationship between gas migration path and migration rate is performed, such as within 5 minutes. The specific time period can be adjusted according to the actual scenario. It is shorter than the preset first time period described later, which is more inclined to be based on the current long-term prediction.

[0103] Further, optionally, for the above-mentioned concentration gradient region division:

[0104] A roadway topology network can be constructed, with detection points as nodes, connecting adjacent nodes to form a spatial relationship diagram; nodes can be merged based on concentration difference thresholds (e.g., concentration difference between adjacent points ≤ 0.2%) to form continuous gradient blocks (e.g., high concentration area in the middle of the coal mining face) to transform discrete point data into structured regions, which facilitates diffusion analysis.

[0105] Further, optionally, for the above calculation of gas migration path and rate:

[0106] Migration path:

[0107] Calculate the concentration gradient vector at the boundary of the gradient region, with the direction pointing towards the low concentration area (e.g., from the working face to the return airway).

[0108] The diffusion trajectory is determined by the minimum resistance path algorithm (preferably selecting the roadway with low ventilation resistance).

[0109] Migration rate calculation logic: Rate = Diffusion coefficient × Modulus of concentration gradient vector / Air density;

[0110] Further optionally, the diffusion coefficient can be corrected in real time by one or more correction factors, including but not limited to roadway roughness, roadway material, roadway curvature, roadway leaks, roadway-related obstacles, roadway effects, and wind speed, to correct the migration rate; for example: D correction = D baseline × K;

[0111] In addition, D correction is used to represent the corrected gas diffusion coefficient; D reference is used to represent the gas diffusion coefficient under standard operating conditions (which can be determined by the laboratory); K is used to represent the cumulative coefficient of the above-mentioned correction factors that are actually selected, so as to represent the combined effect of the above correction factors.

[0112] Further, optionally, the quantification criteria for each of the above-mentioned correction factors may include:

[0113] Tunnel roughness can be expressed using the Manning coefficient or Darcy friction factor;

[0114] Tunnel material: can be quantified by material permeability (k, m²);

[0115] Tunnel curvature: can be represented by the ratio of radius of curvature (R) to tunnel width (W);

[0116] Leakage points in tunnels: the leakage rate can be calculated based on the air pressure gradient;

[0117] Obstacles can be identified by calculating the obstruction rate based on the cross-sectional area.

[0118] Roadway function: Different preset priority values ​​are matched to roadways with different functions and roles.

[0119] Further, optionally, the above-mentioned spatial distribution characteristics of gas can be generated as follows:

[0120] The gradient region is divided into layers according to concentration values ​​(e.g., 0-0.5% is the safety layer, 0.5%-1.0% is the warning layer; the specific stratification criteria can also be adapted to the actual application scenario, such as multi-level stratification, irregular range stratification, etc.), and the spatial topology relationship of each layer is output to reveal the spatial correlation between high / low concentration areas (e.g., the concentration change in the return airway lags behind the working face by 10 minutes, etc.).

[0121] Further, optionally, regarding the generation of the above-mentioned gas diffusion trend:

[0122] Conduction path and intensity prediction:

[0123] A directed diffusion graph is constructed based on the migration path, and the edge weights are weighted by the migration rate.

[0124] Predict the gas flux (concentration × rate) of each path in the future time period (e.g., 10 minutes).

[0125] It is evident that implementing this optional embodiment can improve the spatial structuring characterization of gas distribution by dividing concentration gradient regions according to the spatial relationship of detection points, thereby facilitating the transformation of discrete data into continuous regional models and accurately locating high-concentration accumulation areas; it can improve the quantitative analysis capability of diffusion dynamics by calculating the gas migration path and rate between adjacent regions, thereby facilitating the revelation of the spatiotemporal laws of gas conduction and predicting the diffusion direction and speed; it can improve the depth of investigation of concentration gradient correlation by generating gas spatial distribution characteristics, thereby facilitating the identification of spatial conduction topology and providing a basis for zoned governance; and it can improve the predictive reliability of gas conduction path and intensity by coupling migration path and rate to generate diffusion trends, thereby facilitating the prediction of future risk transmission trajectories and enabling targeted intervention for critical paths.

[0126] Prediction module 103 is used to predict the evolution trend of methane concentration in the mine within a preset first time period based on dynamic trend data of methane concentration.

[0127] In this embodiment of the invention, optionally, the input to the prediction module can be set to: dynamic trend data of gas concentration.

[0128] The output is set as follows: the evolution of gas concentration (spatiotemporal distribution prediction) over the next 30 minutes (example).

[0129] Further, specifically, a gas migration model can be established based on the diffusion equation, and the gas transmission process in the gradient region can be deduced by combining the conduction path sequence and time delay parameters.

[0130] Example: If the migration rate from region A to region B is 0.1 m / s and the distance is 10 m, then the conduction delay is 100 s. The current concentration is superimposed to predict the future concentration in region B.

[0131] In this embodiment of the invention, as another optional implementation, the specific method by which the prediction module 103 predicts the evolution trend of the mine's gas concentration within a preset first time period based on the dynamic trend data of gas concentration includes:

[0132] Analyze the spatial distribution characteristics and gas diffusion trends in dynamic trend data of gas concentration;

[0133] Based on the gas migration path, the sequence of conduction paths between adjacent gradient regions is determined; the sequence of conduction paths is used to define the spatial conduction order of gas diffusion.

[0134] Based on the migration rate, the conduction delay of gas along the conduction path sequence to each concentration gradient region is calculated; the conduction delay is used to quantify the time interval between gas arrivals in each concentration gradient region.

[0135] Based on the conduction path sequence and conduction delay, and superimposed with real-time gas concentration data of the current concentration gradient region, the gas concentration evolution trend data of the mine within the preset first time period is predicted; the gas concentration evolution trend data is used to describe the spatial distribution change process of gas concentration within the preset first time period.

[0136] In this embodiment of the invention, optionally, the above-mentioned conduction path sequence and delay calculation are performed as follows:

[0137] Sequence generation:

[0138] You can start from the high-concentration area and sort them according to the migration path direction (example: working face → transport roadway → return airway).

[0139] Propagation delay calculation:

[0140] Single-path latency: Latency = Path length / Migration rate;

[0141] When multiple paths are superimposed, the path with the longest latency can be taken as the critical path (bottleneck path).

[0142] Further, optionally, the following can be predicted regarding the above concentration evolution trend:

[0143] The concentration changes can be sequentially superimposed along the conduction path, taking the current concentration field as the initial state:

[0144] Example: At t+100s, the concentration in region B = current concentration + concentration increment from region A × attenuation coefficient, where the attenuation coefficient is related to the real-time environmental conditions of the conduction path.

[0145] Output the spatiotemporal matrix (e.g., time × location × predicted concentration).

[0146] It is evident that implementing this optional embodiment can improve the spatial sequence characterization of the diffusion process by analyzing the transmission path sequence, thereby helping to clarify the stage characteristics of gas migration and accurately locate key control points upstream of the diffusion; it can improve the precision of time dimension prediction by calculating the transmission delay, thereby helping to quantify the delay time of gas reaching each region and providing a time window reference for the timing of regulation; and it can improve the dynamic extrapolation capability of the spatiotemporal evolution model by superimposing real-time concentration and transmission sequence to predict the evolution trend, thereby helping to simulate the spatial transmission process of concentration and generate a visualized risk spread map to support decision-making.

[0147] The control module 104 is used to generate target control parameters for the hot air unit based on the gas concentration evolution trend data and operating status data, so as to dynamically regulate the operating status of the hot air unit.

[0148] In this embodiment of the invention, optionally, the input to the control module described above can be set as: gas evolution status data + hot air unit operating status.

[0149] The output is set to the target control parameters (such as increasing the air volume by 20% or adjusting the direction by 30°).

[0150] Further, specifically, strategy matching can be used: if the gas is predicted to spread to region C, then an instruction to "supplier air upstream of C" is generated.

[0151] Parameter generation: Calculate the required dilution air volume based on the concentration distribution, and generate feasible parameters by combining the adjustable range of the equipment (such as the maximum air volume of the fan of 2000m³ / min).

[0152] It is evident that implementing this invention can improve the real-time and comprehensiveness of environmental monitoring by collecting real-time data on gas concentrations and hot air blower unit operating status at multiple monitoring points in the mine. This facilitates the construction of a dynamic panoramic view of mine gas distribution, providing a data foundation for precise control. Furthermore, it can generate dynamic trend data by analyzing the spatial distribution characteristics and diffusion trends of gas, improving the analytical accuracy of gas evolution patterns. This helps identify potential risk areas and achieve early warning of hidden dangers. Predicting future gas concentration evolution trends enhances the foresight of control strategies, enabling advance planning of control actions and avoiding safety risks caused by sudden increases in gas concentration. Finally, generating target control parameters based on evolution trends and equipment status improves the targeting of hot air blower unit control and the accuracy of gas control, facilitating dynamic optimization of ventilation efficiency and ultimately achieving proactive suppression of gas concentration and an overall improvement in mine safety.

[0153] In another optional implementation embodiment of the present invention, the control module 104 generates target control parameters for the hot air unit based on gas concentration evolution data and operating status data, and the specific methods for dynamically regulating the operating status of the hot air unit include:

[0154] This study analyzes the conduction path sequence, conduction delay, and spatial distribution characteristics of methane in the data on the evolution of methane concentration. The conduction path sequence is used to define the spatial conduction order of methane diffusion, the conduction delay is used to quantify the time interval between methane conduction to the target area, and the spatial distribution characteristics of methane are used to characterize the spatial correlation structure and concentration gradient distribution pattern between concentration gradient regions. Concentration gradient regions are used to represent blocks that are spatially adjacent and whose real-time methane concentration data are all within the same preset concentration threshold range.

[0155] Based on the operating status data, determine the adjustable parameter range of the hot air unit; the adjustable parameter range is used to limit the adjustment boundary of at least one of the following: air volume, air temperature, air direction, air frequency, and air volume.

[0156] Based on the conduction path sequence and conduction delay, a matching air supply direction control strategy is implemented for the hot air unit; the air supply direction control strategy is used to direct the hot air flow towards the upstream area of ​​the gas diffusion path.

[0157] Based on the spatial distribution characteristics of gas and the adjustable parameter range, zone control parameters are generated; the zone control parameters are used to perform matching ventilation dilution and / or stable air supply operations for different concentration gradient areas.

[0158] Based on the air supply direction control strategy and zone control parameters, target control parameters for the hot air handling unit are generated to dynamically regulate the operating status of the hot air handling unit.

[0159] In this embodiment of the invention, optionally, the above-mentioned air supply direction control strategy can be reversed to the gas diffusion direction: if the gas diffuses from east to west, the air supply direction is adjusted to the east (forming a blocking airflow) to suppress the diffusion rate and buy time for dilution.

[0160] Alternatively, the aforementioned "directional air supply" refers to the hot air flow being directed towards the upstream area of ​​the gas diffusion path. However, in this process, it is possible to use interference airflow to adjust the angle and frequency at the air supply source to conduct non-directional air supply. That is, in order to achieve the purpose of directional air supply of the hot air flow towards the upstream area of ​​the gas diffusion path, non-directional air supply can be carried out at the air supply source to adapt to the complex site environment of the mine.

[0161] Further, optionally, the above-mentioned zonal control parameters are generated as follows:

[0162] Concentration gradient regions can be categorized as follows: high-risk zones (e.g., >1.0%), transition zones (e.g., 0.5%-1.0%), and safe zones (e.g., <0.5%).

[0163] The corresponding control strategies include: emergency dilution, stable air supply, and energy-saving mode; specifically, they can be: increase air volume by 50% and decrease temperature by 5℃; maintain air volume fluctuation of ±10%; reduce air volume by 30%, etc. The specific limited operations mentioned above can be adjusted according to the actual application scenario in practical applications. The main point is the complete implementation logic of the method defined in the embodiments of this invention.

[0164] It is evident that implementing this optional embodiment can enhance the reverse resistance between airflow and gas diffusion by matching the air supply direction control strategy, thereby helping to block gas migration paths and weaken diffusion dynamics; it can improve the adaptability of differentiated treatment in different concentration areas by generating zonal control parameters, thereby facilitating enhanced dilution in high-risk areas and energy-saving operation in safe areas, thus achieving precise resource allocation and energy consumption optimization; and it can improve the synergy of multi-dimensional control by integrating directional strategies and zonal parameters to generate target control parameters, thereby facilitating the simultaneous achievement of diffusion suppression and zonal treatment, and thus improving the overall gas treatment efficiency and safety.

[0165] In this optional embodiment, as an optional implementation method, the control module 104 generates target control parameters for the hot air unit based on the air supply direction control strategy and zone control parameters, and the specific methods for dynamically controlling the operating state of the hot air unit include:

[0166] Based on the conduction delay, the control trigger timing is calculated to generate dynamic control timing instructions. The control trigger timing is used to determine the start time point of control for each concentration gradient region. The control trigger timing is obtained by comparing the conduction delay with a preset response time threshold.

[0167] Based on the zone control parameters and the adjustable parameter range of the hot air unit, the matching degree between the required control resources and the available equipment resources for each concentration gradient region is calculated, and a dynamic priority coefficient is generated. The dynamic priority coefficient is used to characterize the urgency of control in different concentration gradient regions.

[0168] By integrating the air supply direction control strategy, dynamic control timing instructions and dynamic priority coefficients, a set of control parameters for the hot air unit is generated. The set of control parameters includes at least one of the following: air supply direction angle value, zone air supply volume gradient value, and air supply temperature gradient value, as well as the corresponding execution timing flag.

[0169] Based on the equipment operation constraints in the parsed operating status data of the hot air unit, conflict resolution verification is performed on the set of control parameters to generate target control parameters for the hot air unit, so as to dynamically regulate the operating status of the hot air unit; conflict resolution verification is used to control the switching of air supply direction and the coordinated execution of air volume and temperature adjustment within the mechanical response limits of the equipment.

[0170] In this embodiment of the invention, optionally, the calculation of the dynamic priority coefficient described above is as follows:

[0171] Factor composition:

[0172] Urgency level = Current concentration / Safety threshold;

[0173] Trend degree = Concentration prediction change rate = [(predicted concentration - current concentration) / current concentration] × time weight, such as the concentration prediction change rate in the next 5 minutes. The time weight is used to amplify short-term drastic changes (e.g., weight = 1.5), and the steady-state change weight = 1.0.

[0174] Priority coefficient = Urgency × Trend;

[0175] Further optional, for the above conflict resolution verification:

[0176] For example, area A requires an air volume of 1000 m³ / min, and area B requires 800 m³ / min, but the upper limit of the fan is 1500 m³ / min;

[0177] It can be allocated according to priority coefficients: for example, zone A has a coefficient of 0.9, so it is allocated 900 m³ / min; zone B has a coefficient of 0.7, so it is allocated 600 m³ / min.

[0178] Timing staggered: Prioritize area A, and adjust area B after a 10-second delay.

[0179] As can be seen, implementing this optional embodiment can improve the timing accuracy of control actions by generating dynamic control timing instructions based on transmission delay, thereby facilitating early triggering of operations during critical windows and avoiding control failures caused by response lag; it can improve the intelligence of limited resource allocation by calculating dynamic priority coefficients, thereby facilitating priority protection of high-risk areas and reducing the risk of local gas over-limits; it can improve the systematic nature of multi-objective optimization by integrating directional strategies, timing instructions, and priority to generate control parameter sets, thereby facilitating the coordination of spatial, temporal, and resource constraints; and it can improve the guarantee capability of safe equipment operation through conflict resolution verification, thereby preventing mechanical over-limit operations and ensuring that control instructions are stably implemented within the physically executable range.

[0180] Example 2

[0181] Please see Figure 2 , Figure 2 This is a schematic diagram of another intelligent control system for gas management based on a hot air unit, as disclosed in an embodiment of the present invention. Figure 2 The described intelligent control system for gas management based on hot air handling units can be applied to mine hot air handling units, specifically coal mine hot air handling units. It can also be applied to intelligent devices associated with coal mine hot air handling units, including but 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 intelligent control system for gas management based on hot air units may include:

[0182] The acquisition module 201 is used to acquire real-time gas concentration data from multiple detection points in the mine and the operating status data of the corresponding hot air blower units in the mine;

[0183] Analysis module 202 is used to analyze the spatial distribution characteristics and diffusion trend of gas concentration in the mine based on real-time gas concentration data, and generate dynamic trend data of the current gas concentration in the mine.

[0184] Prediction module 203 is used to predict the evolution trend of methane concentration in the mine within a preset first time period based on dynamic trend data of methane concentration.

[0185] The control module 204 is used to generate target control parameters for the hot air unit based on the gas concentration evolution trend data and operating status data, so as to dynamically regulate the operating status of the hot air unit.

[0186] In this embodiment of the invention, for the supplementary explanation of 201-204, please refer to the supplementary explanation of 101-104 in Embodiment 1. This embodiment of the invention will not repeat the explanation.

[0187] The acquisition module 205 is used to acquire the latest detected actual gas concentration data and the operation feedback data of the hot air unit at each detection point after the operation of dynamically adjusting the operating status of the hot air unit is performed by the control module 204; the operation feedback data is used to record the actual operating status of the hot air unit in executing the target control parameters;

[0188] The calculation module 206 is used to calculate the concentration control deviation based on the actual gas concentration data and the predicted gas concentration evolution trend data. The concentration control deviation is used to quantify the degree of difference between the actual gas concentration change and the predicted trend. The concentration control deviation is calculated by comparing the predicted concentration value and the measured concentration value at the same time point.

[0189] The calculation module 206 is also used to calculate the equipment execution deviation based on the operation feedback data and the target control parameters. The equipment execution deviation is used to characterize the degree of deviation between the actual operating state of the hot air unit and the target control parameters. The equipment execution deviation is calculated by the set value of the target control parameters and the actual output value of the equipment.

[0190] The correction module 207 is used to dynamically correct the preset response time threshold of the transmission delay, the adjustable parameter range of the hot air unit, and the division rules of the concentration gradient region in the gas concentration evolution trend data based on the concentration control deviation and equipment execution deviation.

[0191] In this embodiment of the invention, optionally, the above-mentioned deviation calculation is performed as follows:

[0192] Concentration treatment deviation:

[0193] At the predicted time point T, the average value of |predicted concentration - measured concentration| is taken from all detection points.

[0194] Equipment execution deviation:

[0195] Compare the target parameters with the actual output (e.g., target air volume of 1000 m³ / min with actual air volume of 950 m³ / min).

[0196] Further, optionally, for the above dynamic correction rules:

[0197] Deviation types: high concentration treatment deviation, high equipment execution deviation, and failure of persistent space prediction, etc.

[0198] Targets for correction: response time threshold of propagation delay, adjustable parameter range, gradient region partitioning rules, etc.

[0199] Examples of corrective logic: shorten the preset response time from 5 minutes to 3 minutes, lower the maximum airflow threshold of the fan from 2000 m³ / min to 1800 m³ / min, increase the density of detection points, or adjust the clustering algorithm parameters, etc.

[0200] The deviation types and correction objects mentioned above correspond one-to-one with the correction logic examples mentioned above.

[0201] It is evident that implementing this optional embodiment can improve the integrity of closed-loop monitoring of the system status by acquiring actual concentration and equipment operation feedback data, thereby facilitating the comparison between expected and actual effects; it can improve the self-diagnostic capability of the prediction model by calculating the concentration treatment deviation, thereby locating the source of prediction error and driving model parameter correction; it can improve the quantitative evaluation accuracy of command implementation by calculating the equipment execution deviation, thereby identifying equipment execution shortcomings and optimizing control parameter boundaries; and it can improve the system's adaptive evolution capability by dynamically correcting response thresholds, parameter ranges, and regional division rules, thereby continuously improving prediction accuracy and control reliability, and ultimately achieving spiral optimization of treatment effectiveness.

[0202] Example 3

[0203] Please see Figure 3 , Figure 3 This is a flowchart illustrating an intelligent control method for gas management based on a hot air handling unit, as disclosed in an embodiment of the present invention. This intelligent control method for gas management based on a hot air handling unit can be applied to mine hot air handling units, specifically coal mine hot air handling units, and can also be applied to intelligent devices associated with such coal mine hot air handling 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. The embodiments of the present invention do not impose limitations on these devices. Figure 3 As shown, the intelligent control method for gas management based on hot air units may include:

[0204] 301. Collect real-time gas concentration data from multiple monitoring points in the mine and the operating status data of the corresponding hot air blower units in the mine;

[0205] 302. Based on real-time gas concentration data, analyze the spatial distribution characteristics and diffusion trend of gas concentration in the mine, and generate dynamic trend data of current gas concentration in the mine.

[0206] 303. Based on the dynamic trend data of gas concentration, predict the evolution of gas concentration in the mine within the preset first time period;

[0207] 304. Based on the gas concentration evolution trend data and operating status data, generate target control parameters for the hot air unit to dynamically regulate the operating status of the hot air unit.

[0208] It is evident that implementing this invention can improve the real-time and comprehensiveness of environmental monitoring by collecting real-time data on gas concentrations and hot air blower unit operating status at multiple monitoring points in the mine. This facilitates the construction of a dynamic panoramic view of mine gas distribution, providing a data foundation for precise control. Furthermore, it can generate dynamic trend data by analyzing the spatial distribution characteristics and diffusion trends of gas, improving the analytical accuracy of gas evolution patterns. This helps identify potential risk areas and achieve early warning of hidden dangers. Predicting future gas concentration evolution trends enhances the foresight of control strategies, enabling advance planning of control actions and avoiding safety risks caused by sudden increases in gas concentration. Finally, generating target control parameters based on evolution trends and equipment status improves the targeting of hot air blower unit control and the accuracy of gas control, facilitating dynamic optimization of ventilation efficiency and ultimately achieving proactive suppression of gas concentration and an overall improvement in mine safety.

[0209] In this embodiment of the invention, as an optional implementation, the above-mentioned method of analyzing the spatial distribution characteristics and diffusion trend of gas concentration in a mine based on real-time gas concentration data to generate current dynamic trend data of gas concentration in the mine includes:

[0210] Based on the real-time gas concentration data of each detection point and the spatial relationship between the target spaces corresponding to each detection point, the concentration gradient region of the mine is divided; the concentration gradient region is used to represent blocks that are spatially adjacent and whose real-time gas concentration data are all within the same preset concentration threshold range.

[0211] Based on the concentration change data of each concentration gradient region within the preset second time period, the gas migration path and migration rate between adjacent gradient regions are calculated; the gas migration path is used to describe the diffusion trajectory and direction of gas between concentration gradient regions, and the migration rate is used to quantify the diffusion rate.

[0212] Based on the spatial layout data of each concentration gradient region and the gas migration path between adjacent gradient regions, a gas spatial distribution feature is generated; the gas spatial distribution feature is used to characterize the spatial correlation structure between concentration gradient regions and the concentration gradient distribution pattern.

[0213] Based on the coupling relationship between gas migration path and migration rate, a gas diffusion trend is generated; the gas diffusion trend is used to predict the conduction path and intensity changes of gas concentration in the mine within a preset third time period.

[0214] Based on the spatial distribution characteristics and diffusion trends of gas, dynamic trend data of current gas concentration in the mine is generated.

[0215] It is evident that implementing this optional embodiment can improve the spatial structuring characterization of gas distribution by dividing concentration gradient regions according to the spatial relationship of detection points, thereby facilitating the transformation of discrete data into continuous regional models and accurately locating high-concentration accumulation areas; it can improve the quantitative analysis capability of diffusion dynamics by calculating the gas migration path and rate between adjacent regions, thereby facilitating the revelation of the spatiotemporal laws of gas conduction and predicting the diffusion direction and speed; it can improve the depth of investigation of concentration gradient correlation by generating gas spatial distribution characteristics, thereby facilitating the identification of spatial conduction topology and providing a basis for zoned governance; and it can improve the predictive reliability of gas conduction path and intensity by coupling migration path and rate to generate diffusion trends, thereby facilitating the prediction of future risk transmission trajectories and enabling targeted intervention for critical paths.

[0216] In this embodiment of the invention, as another optional implementation, the above-mentioned prediction of the evolution trend of mine gas concentration within a preset first time period based on dynamic trend data of gas concentration includes:

[0217] Analyze the spatial distribution characteristics and gas diffusion trends in dynamic trend data of gas concentration;

[0218] Based on the gas migration path, the sequence of conduction paths between adjacent gradient regions is determined; the sequence of conduction paths is used to define the spatial conduction order of gas diffusion.

[0219] Based on the migration rate, the conduction delay of gas along the conduction path sequence to each concentration gradient region is calculated; the conduction delay is used to quantify the time interval between gas arrivals in each concentration gradient region.

[0220] Based on the conduction path sequence and conduction delay, and superimposed with real-time gas concentration data of the current concentration gradient region, the gas concentration evolution trend data of the mine within the preset first time period is predicted; the gas concentration evolution trend data is used to describe the spatial distribution change process of gas concentration within the preset first time period.

[0221] It is evident that implementing this optional embodiment can improve the spatial sequence characterization of the diffusion process by analyzing the transmission path sequence, thereby helping to clarify the stage characteristics of gas migration and accurately locate key control points upstream of the diffusion; it can improve the precision of time dimension prediction by calculating the transmission delay, thereby helping to quantify the delay time of gas reaching each region and providing a time window reference for the timing of regulation; and it can improve the dynamic extrapolation capability of the spatiotemporal evolution model by superimposing real-time concentration and transmission sequence to predict the evolution trend, thereby helping to simulate the spatial transmission process of concentration and generate a visualized risk spread map to support decision-making.

[0222] In another optional implementation embodiment of the present invention, the above-mentioned generation of target control parameters for the hot air unit based on gas concentration evolution trend data and operating status data, in order to dynamically regulate the operating status of the hot air unit, includes:

[0223] This study analyzes the conduction path sequence, conduction delay, and spatial distribution characteristics of methane in the data on the evolution of methane concentration. The conduction path sequence is used to define the spatial conduction order of methane diffusion, the conduction delay is used to quantify the time interval between methane conduction to the target area, and the spatial distribution characteristics of methane are used to characterize the spatial correlation structure and concentration gradient distribution pattern between concentration gradient regions. Concentration gradient regions are used to represent blocks that are spatially adjacent and whose real-time methane concentration data are all within the same preset concentration threshold range.

[0224] Based on the operating status data, determine the adjustable parameter range of the hot air unit; the adjustable parameter range is used to limit the adjustment boundary of at least one of the following: air volume, air temperature, air direction, air frequency, and air volume.

[0225] Based on the conduction path sequence and conduction delay, a matching air supply direction control strategy is implemented for the hot air unit; the air supply direction control strategy is used to direct the hot air flow towards the upstream area of ​​the gas diffusion path.

[0226] Based on the spatial distribution characteristics of gas and the adjustable parameter range, zone control parameters are generated; the zone control parameters are used to perform matching ventilation dilution and / or stable air supply operations for different concentration gradient areas.

[0227] Based on the air supply direction control strategy and zone control parameters, target control parameters for the hot air handling unit are generated to dynamically regulate the operating status of the hot air handling unit.

[0228] It is evident that implementing this optional embodiment can enhance the reverse resistance between airflow and gas diffusion by matching the air supply direction control strategy, thereby helping to block gas migration paths and weaken diffusion dynamics; it can improve the adaptability of differentiated treatment in different concentration areas by generating zonal control parameters, thereby facilitating enhanced dilution in high-risk areas and energy-saving operation in safe areas, thus achieving precise resource allocation and energy consumption optimization; and it can improve the synergy of multi-dimensional control by integrating directional strategies and zonal parameters to generate target control parameters, thereby facilitating the simultaneous achievement of diffusion suppression and zonal treatment, and thus improving the overall gas treatment efficiency and safety.

[0229] In this optional embodiment, as an optional implementation method, the above-mentioned generation of target control parameters for the hot air unit based on the air supply direction control strategy and zoning control parameters to dynamically control the operating state of the hot air unit includes:

[0230] Based on the conduction delay, the control trigger timing is calculated to generate dynamic control timing instructions. The control trigger timing is used to determine the start time point of control for each concentration gradient region. The control trigger timing is obtained by comparing the conduction delay with a preset response time threshold.

[0231] Based on the zone control parameters and the adjustable parameter range of the hot air unit, the matching degree between the required control resources and the available equipment resources for each concentration gradient region is calculated, and a dynamic priority coefficient is generated. The dynamic priority coefficient is used to characterize the urgency of control in different concentration gradient regions.

[0232] By integrating the air supply direction control strategy, dynamic control timing instructions and dynamic priority coefficients, a set of control parameters for the hot air unit is generated. The set of control parameters includes at least one of the following: air supply direction angle value, zone air supply volume gradient value, and air supply temperature gradient value, as well as the corresponding execution timing flag.

[0233] Based on the equipment operation constraints in the parsed operating status data of the hot air unit, conflict resolution verification is performed on the set of control parameters to generate target control parameters for the hot air unit, so as to dynamically regulate the operating status of the hot air unit; conflict resolution verification is used to control the switching of air supply direction and the coordinated execution of air volume and temperature adjustment within the mechanical response limits of the equipment.

[0234] As can be seen, implementing this optional embodiment can improve the timing accuracy of control actions by generating dynamic control timing instructions based on transmission delay, thereby facilitating early triggering of operations during critical windows and avoiding control failures caused by response lag; it can improve the intelligence of limited resource allocation by calculating dynamic priority coefficients, thereby facilitating priority protection of high-risk areas and reducing the risk of local gas over-limits; it can improve the systematic nature of multi-objective optimization by integrating directional strategies, timing instructions, and priority to generate control parameter sets, thereby facilitating the coordination of spatial, temporal, and resource constraints; and it can improve the guarantee capability of safe equipment operation through conflict resolution verification, thereby preventing mechanical over-limit operations and ensuring that control instructions are stably implemented within the physically executable range.

[0235] In an optional embodiment, the method further includes:

[0236] After performing the operation of dynamically adjusting the operating status of the hot air unit, the latest actual gas concentration data detected at each detection point and the operating feedback data of the hot air unit are obtained; the operating feedback data is used to record the actual operating status of the hot air unit in executing the target control parameters;

[0237] Based on actual gas concentration data and predicted gas concentration evolution data, the concentration control deviation is calculated. The concentration control deviation is used to quantify the degree of difference between the actual gas concentration change and the predicted trend. The concentration control deviation is calculated by comparing the predicted concentration value and the measured concentration value at the same time point.

[0238] Based on operational feedback data and target control parameters, the equipment execution deviation is calculated. The equipment execution deviation is used to characterize the degree of deviation between the actual operating state of the hot air unit and the target control parameters. The equipment execution deviation is calculated by the set value of the target control parameters and the actual output value of the equipment.

[0239] Based on the concentration control deviation and equipment execution deviation, the preset response time threshold of the transmission delay in the gas concentration evolution trend data, the adjustable parameter range of the hot air unit, and the division rules of the concentration gradient region in the mine are dynamically corrected.

[0240] It is evident that implementing this optional embodiment can improve the integrity of closed-loop monitoring of the system status by acquiring actual concentration and equipment operation feedback data, thereby facilitating the comparison between expected and actual effects; it can improve the self-diagnostic capability of the prediction model by calculating the concentration treatment deviation, thereby locating the source of prediction error and driving model parameter correction; it can improve the quantitative evaluation accuracy of command implementation by calculating the equipment execution deviation, thereby identifying equipment execution shortcomings and optimizing control parameter boundaries; and it can improve the system's adaptive evolution capability by dynamically correcting response thresholds, parameter ranges, and regional division rules, thereby continuously improving prediction accuracy and control reliability, and ultimately achieving spiral optimization of treatment effectiveness.

[0241] Example 4

[0242] Please see Figure 4 , Figure 4 This is a schematic diagram of another intelligent control system for gas management based on a hot air handling unit disclosed in this invention. This intelligent control system for gas management based on a hot air handling unit can be applied to mine hot air handling units, specifically coal mine hot air handling units, and can also be applied to intelligent devices associated with such coal mine hot air handling 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 devices. Figure 4 As shown, the intelligent control system for gas management based on hot air units may include:

[0243] Memory 401 that stores executable program code.

[0244] Processor 402 coupled to memory 401.

[0245] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the intelligent control method for gas management based on hot air units described in Embodiment 3 of the present invention.

[0246] Example 5

[0247] 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 intelligent control method for gas management based on a hot air unit described in Embodiment 3 of this invention.

[0248] Example 6

[0249] 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 intelligent control method for gas management based on a hot air unit described in Embodiment 3.

[0250] 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.

[0251] 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.

[0252] Finally, it should be noted that the intelligent control system and method for gas management based on hot air units 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. A gas management intelligent regulation system based on a hot air unit, characterized in that, The system comprises: a collection module, configured to collect real-time gas concentration data of a plurality of detection points of a mine and operation state data of a corresponding hot air unit of the mine; an analysis module, configured to analyze spatial distribution characteristics and diffusion trends of gas concentration of the mine based on the real-time gas concentration data, and generate current gas concentration dynamic trend data of the mine; a prediction module, configured to predict gas concentration evolution trend data of the mine within a preset first period according to the gas concentration dynamic trend data; a control module, configured to generate target control parameters of the hot air unit based on the gas concentration evolution trend data and the operation state data, so as to dynamically regulate the operation state of the hot air unit; and a specific manner in which the analysis module analyzes spatial distribution characteristics and diffusion trends of gas concentration of the mine based on the real-time gas concentration data, and generates current gas concentration dynamic trend data of the mine comprises: dividing concentration gradient areas of the mine according to the real-time gas concentration data of each detection point and a perceived spatial position relationship between each target space corresponding to each detection point; the concentration gradient areas are used to represent blocks in which the spatial position relationship is adjacent and the real-time gas concentration data are all within a same preset concentration threshold range; calculating gas migration paths and migration rates between adjacent gradient areas based on concentration change data of each concentration gradient area within a preset second period; the gas migration paths are used to describe diffusion trajectories and directions of gas between the concentration gradient areas, and the migration rates are used to quantify diffusion rates; generating gas spatial distribution characteristics according to perceived spatial layout data of each concentration gradient area and the gas migration paths between the adjacent gradient areas; the gas spatial distribution characteristics are used to represent spatial correlation structures and concentration gradient distribution rules between the concentration gradient areas; generating a gas diffusion trend based on a coupling relationship between the gas migration paths and the migration rates; the gas diffusion trend is used to predict changes in gas concentration in a conduction path and intensity of the mine within a preset third period; generating the current gas concentration dynamic trend data of the mine based on the gas spatial distribution characteristics and the gas diffusion trend.

2. The gas management intelligent regulation system based on the hot air machine group according to claim 1, characterized in that, A specific manner in which the prediction module predicts gas concentration evolution trend data of the mine within a preset first period according to the gas concentration dynamic trend data comprises: analyzing the gas spatial distribution characteristics and the gas diffusion trend in the gas concentration dynamic trend data; determining a conduction path sequence between the adjacent gradient areas based on the gas migration paths; the conduction path sequence is used to define a spatial conduction order of gas diffusion; calculating a conduction time delay of gas along the conduction path sequence to each concentration gradient area according to the migration rates; the conduction time delay is used to quantify a time interval of gas reaching each concentration gradient area; and Superimpose real-time gas concentration data of the current concentration gradient region based on the conduction path sequence and the conduction time delay, and predict gas concentration evolution trend data of the mine within a preset first period; the gas concentration evolution trend data is used to describe the distribution change process of gas concentration in the spatial dimension within the preset first period.

3. The gas management intelligent regulation system based on the hot air machine group according to claim 1 or 2, characterized in that, The control module generates target control parameters of the hot air unit based on the gas concentration evolution trend data and the operation state data, so as to dynamically regulate the specific mode of the operation state of the hot air unit, which includes: Analyzing the conduction path sequence, the conduction time delay and the gas spatial distribution characteristics in the gas concentration evolution trend data; the conduction path sequence is used to define the spatial conduction sequence of gas diffusion, the conduction time delay is used to quantify the time interval of gas conduction to the target region, and the gas spatial distribution characteristics are used to represent the spatial correlation structure and concentration gradient distribution law between the concentration gradient regions; the concentration gradient region is used to represent the block with adjacent spatial position relationship and the real-time gas concentration data within the same preset concentration threshold range; Based on the operation state data, determine the adjustable parameter range of the hot air unit; the adjustable parameter range is used to limit at least one of the regulation boundaries of the air supply amount, the air supply temperature, the air supply direction, the air supply frequency and the air supply volume; According to the conduction path sequence and the conduction time delay, match the air supply direction regulation strategy; the air supply direction regulation strategy is used to direct the hot air flow to the upstream region of the gas diffusion path; Based on the gas spatial distribution characteristics and the adjustable parameter range, generate partition regulation parameters; the partition regulation parameters are used to perform matching ventilation dilution and / or stable air supply operation for different concentration gradient regions; According to the air supply direction regulation strategy and the partition regulation parameters, generate target control parameters of the hot air unit to dynamically regulate the operation state of the hot air unit.

4. The gas management intelligent regulation system based on the hot air machine group according to claim 3, characterized in that, The control module generates target control parameters of the hot air unit according to the air supply direction regulation strategy and the partition regulation parameters, so as to dynamically regulate the operation state of the hot air unit, which includes: Based on the conduction time delay, calculate the regulation trigger time, and generate dynamic regulation time sequence instruction; the regulation trigger time is used to determine the regulation starting time point for each concentration gradient region, and the regulation trigger time is obtained by comparing the conduction time delay with a preset response time threshold; According to the partition regulation parameters and the adjustable parameter range of the hot air unit, calculate the matching degree between the required regulation resources and the available resources of the equipment of each concentration gradient region, and generate dynamic priority coefficients; the dynamic priority coefficients are used to represent the regulation urgency of different concentration gradient regions; Fuse the air supply direction regulation strategy, the dynamic regulation time sequence instruction and the dynamic priority coefficient, and generate a control parameter set of the hot air unit; the control parameter set includes at least one of the air supply direction angle value, the partition air supply amount gradient value and the air supply temperature gradient value, and also includes the corresponding execution time sequence mark. Based on the equipment operation constraint condition in the running state data of the hot air unit, conflict resolution verification is performed on the control parameter set to generate a target control parameter of the hot air unit to dynamically regulate the running state of the hot air unit; the conflict resolution verification is used to control the switching of air supply direction, and the adjustment of air volume and temperature within the mechanical response limit of the equipment.

5. The gas management intelligent control system based on the hot air machine group according to any one of claims 1, 2, 4, characterized in that, The system further comprises: An acquisition module is configured to acquire actual gas concentration data newly detected by each detection point and running feedback data of the hot air unit after the control module performs the operation of dynamically regulating the running state of the hot air unit; the running feedback data is used to record the actual running state of the hot air unit in executing the target control parameter; A calculation module is configured to calculate a concentration management deviation degree based on the actual gas concentration data and the predicted gas concentration evolution trend data; the concentration management deviation degree is used to quantify the difference between the actual gas concentration change and the predicted trend; the concentration management deviation degree is calculated by the predicted concentration value and the measured concentration value at the same time node; The calculation module is further configured to calculate an equipment execution deviation degree based on the running feedback data and the target control parameter; the equipment execution deviation degree is used to represent the deviation between the actual running state of the hot air unit and the target control parameter; the equipment execution deviation degree is calculated by the set value of the target control parameter and the actual output value of the equipment; A correction module is configured to dynamically correct the preset response time threshold of the conduction time delay in the gas concentration evolution trend data, the adjustable parameter range of the hot air unit, and the division rule of the concentration gradient region of the mine according to the concentration management deviation degree and the equipment execution deviation degree.

6. A method for intelligent control of gas management based on a hot air unit, characterized in that, The method comprises: Collecting real-time gas concentration data of multiple detection points of a mine and running state data of corresponding hot air units of the mine; Based on the real-time gas concentration data, analyzing the spatial distribution characteristics and diffusion trend of the gas concentration of the mine to generate current gas concentration dynamic trend data of the mine; According to the gas concentration dynamic trend data, predicting gas concentration evolution trend data of the mine within a preset first period; Based on the gas concentration evolution trend data and the running state data, generating a target control parameter of the hot air unit to dynamically regulate the running state of the hot air unit; And, based on the real-time gas concentration data, analyzing the spatial distribution characteristics and diffusion trend of the gas concentration of the mine to generate current gas concentration dynamic trend data of the mine, comprises: According to the real-time gas concentration data of each detection point and the spatial position relationship between each detection point and the corresponding target space perceived by each detection point, dividing the concentration gradient region of the mine; the concentration gradient region is used to represent a block in which the spatial position relationship is adjacent and the real-time gas concentration data are within the same preset concentration threshold range; Based on the concentration change data of each of the concentration gradient regions within a preset second period, a gas migration path and a migration rate between adjacent gradient regions are calculated; the gas migration path is used to describe the diffusion trajectory and direction of gas between the concentration gradient regions, and the migration rate is used to quantify the diffusion rate; According to the perceived spatial layout data of each of the concentration gradient regions and the gas migration path between the adjacent gradient regions, a gas spatial distribution feature is generated; the gas spatial distribution feature is used to represent the spatial correlation structure and concentration gradient distribution law between the concentration gradient regions; Based on the coupling relationship between the gas migration path and the migration rate, a gas diffusion trend is generated; the gas diffusion trend is used to predict the change of gas concentration in the conduction path and intensity of the mine within a preset third period; Based on the gas spatial distribution feature and the gas diffusion trend, current gas concentration dynamic trend data of the mine is generated.

7. A gas management intelligent regulation system based on a hot air unit, characterized in that, The system comprises: a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory to execute the intelligent control method for gas management based on the hot air unit group according to claim 6.

8. A computer storage medium, characterized in that The computer storage medium stores computer instructions, which when invoked, are used to execute the intelligent control method for gas management based on the hot air unit group according to claim 6.

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