Dynamic ventilation management method and system based on coal mine air quality multi-parameter detection

Abstract

The invention provides a dynamic ventilation management method and system based on coal mine air quality multi-parameter detection, and relates to the technical field of intelligent monitoring, and the management method comprises the steps: obtaining point cloud data collected by an unmanned aerial vehicle in a coal mine channel, and constructing a coal mine channel construction model; performing sensor deployment analysis based on the coal mine channel structure model to obtain deployment data of a sensor cluster so as to deploy the sensor cluster; according to the air quality sensing parameters and the real-time excavation data of the coal mine channel, optimizing and adjusting the coal mine channel structure model to obtain a channel structure optimization model; and performing air parameter simulation evaluation through the channel construction optimization model to obtain a risk node section for ventilation optimization, and generating a corresponding ventilation management configuration scheme. According to the dynamic ventilation management method and system provided by the invention, corresponding dynamic ventilation management based on the air quality parameters changing in real time is realized, and the ventilation efficiency and the ventilation effect of the coal mine channel are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent monitoring, in particular to a dynamic ventilation management method and system based on coal mine air quality multi-parameter detection. BACKGROUND

[0002] In the traditional coal mine ventilation management process, various sensors are installed and deployed in the coal mine according to relevant experience by manual means, and the configuration management of ventilation equipment and ventilation parameters is realized according to experience to achieve the purpose of coal mine air quality safety.

[0003] However, the existing sensor and ventilation equipment deployment method based on manual experience can realize air quality parameter monitoring and ventilation management to a certain extent, but due to insufficient manual experience and complex coal mine channels, it is difficult to ensure the rationality of sensor equipment arrangement and coverage of risk areas, and the ventilation deployment of ventilation equipment also cannot guarantee complete coverage of different risk level areas, which leads to insufficient rationalization and greatly reduces the safety index of coal mines. SUMMARY

[0004] The dynamic ventilation management method and system based on coal mine air quality multi-parameter detection provided by the present application solve the technical problem that in the prior art, due to insufficient manual experience and complex coal mine channels, it is difficult to ensure the rationality of sensor equipment arrangement and coverage of risk areas, and the ventilation deployment of ventilation equipment also cannot guarantee complete coverage of different risk level areas, which leads to insufficient rationalization and greatly reduces the safety index of coal mines.

[0005] To achieve the above-mentioned purpose and other related purposes, the present application provides a dynamic ventilation management method based on coal mine air quality multi-parameter detection, comprising: acquiring point cloud data collected by a UAV in a coal mine channel, the point cloud data comprising point cloud coordinates and image data corresponding to each point cloud coordinate; based on the point cloud data, performing coal mine geological layer analysis to construct a coal mine channel structure model; based on the coal mine channel structure model, performing sensor deployment analysis to obtain deployment data of a sensor cluster for deployment of the sensor cluster, the deployment data comprising deployment positions and deployment types; acquiring air quality sensor parameters of the deployed sensor cluster; according to the air quality sensor parameters and real-time excavation data of the coal mine channel, optimizing and adjusting the coal mine channel structure model to obtain a channel structure optimization model; performing air parameter simulation evaluation through the channel structure optimization model to obtain a risk node section; based on the risk node section, performing ventilation optimization to generate a corresponding ventilation management configuration scheme.

[0006] In an embodiment of the present application, based on point cloud data, coal mine geological layer analysis is performed, and a coal mine passage structure model is constructed, including: based on point cloud coordinates, simulating and building a spatial shape structure of a coal mine passage; performing geological layer analysis on image data corresponding to each point cloud coordinate in the spatial shape structure to obtain corresponding geological layer data, the geological layer data including geological layer types, geological layer textures, and geological layer looseness; based on the geological layer data, generating a corresponding geological layer model; and adding the geological layer model to the spatial shape structure to construct the coal mine passage structure model.

[0007] In an embodiment of the present application, the image data corresponding to each point cloud coordinate in the spatial shape structure is subjected to geological layer analysis to obtain corresponding geological layer data, including: based on each point cloud coordinate in the spatial shape structure, stitching the image data to obtain a geological layer segment; comparing the geological layer segment with each reference geological texture image in a geological layer storage library for similarity to obtain a similarity value; when the similarity value reaches a similarity threshold value, taking the reference geological layer type corresponding to the reference geological texture image as the geological layer type of the geological layer segment; according to the geological layer type of the geological layer segment and the corresponding geological texture, obtaining the geological layer looseness corresponding to each geological layer segment; and taking the geological layer looseness, the geological layer type, and the geological texture as the geological layer data.

[0008] In an embodiment of the present application, based on the coal mine passage structure model, sensor deployment analysis is performed to obtain deployment data of a sensor cluster for deployment of the sensor cluster, including: according to the geological layer types and the geological layer looseness corresponding to each node in the coal mine passage structure model, performing passage divisibility evaluation on the coal mine passage structure model to obtain an indivisible model passage segment; dividing other model passage segments in the coal mine passage structure model except the indivisible model passage segment into multiple model passage slices; sequentially and incrementally stitching the model passage slices of the corresponding order at both ends of the indivisible model passage segment to obtain a stitched passage model; comparing the stitched passage structure of each stitched passage model with reference passage structures in a reference model library for similarity to obtain a reference passage structure that reaches a preset similarity with the stitched passage structure; calling reference deployment data of a sensor cluster corresponding to the reference passage structure as stitching deployment data of a sensor cluster of the stitched passage model; and based on the stitching deployment data, obtaining deployment data of a sensor cluster of the coal mine passage structure model for deployment of the sensor cluster.

[0009] In an embodiment of the present application, according to the geological layer type and the geological layer loose degree, the passageway divisibility of the coal mine passageway structure model is evaluated, and the non-divisible model passageway section is obtained, including: obtaining a first non-divisibility index according to the geological layer type of each node of the coal mine passageway structure model along the linear direction of the passageway; obtaining a second non-divisibility index according to the geological layer loose degree and an index conversion factor; and obtaining a non-divisibility comprehensive index according to the first non-divisibility index and the second non-divisibility index, wherein the calculation formula of the non-divisibility comprehensive index is , wherein, represents the first non-divisibility index, represents the geological layer loose degree, represents the index conversion factor; when the non-divisibility comprehensive index is less than an index threshold value, the corresponding node is taken as a non-divisible node; and all adjacent non-divisible nodes are connected to obtain the non-divisible model passageway section.

[0010] In an embodiment of the present application, according to the air quality sensing parameter and the real-time excavation data of the coal mine passageway, the coal mine passageway structure model is optimized and adjusted to obtain a passageway structure optimization model, including: performing emission simulation on the coal mine passageway structure model through the initial emission parameters corresponding to each passageway point to obtain the air quality simulation parameters corresponding to each deployment point; comparing the air quality simulation parameters with the air quality sensing parameter to optimize and adjust the coal mine passageway structure model to obtain a passageway structure intermediate model; and based on the real-time excavation data of the coal mine passageway, the passageway structure intermediate model is optimized and adjusted to obtain the passageway structure optimization model.

[0011] In an embodiment of the present application, the air quality simulation parameters are compared with the air quality sensing parameter to optimize and adjust the coal mine passageway structure model to obtain a passageway structure intermediate model, including: obtaining the first emission blocking strength corresponding to each deployment point and the second emission blocking strength corresponding to each non-deployment point between each deployment point according to the initial emission parameters of the coal mine passageway structure model and the corresponding air quality simulation parameters; performing difference calculation on the air quality simulation parameters and the air quality sensing parameter to obtain a parameter difference; monitoring the parameter difference; when the parameter difference is greater than a parameter threshold value, the initial emission parameters, the first emission blocking strength and the second emission blocking strength of the coal mine passageway structure model are optimized and adjusted in a direction in which the parameter difference is reduced, and the air quality simulation parameters obtained by using the adjusted coal mine passageway structure model are used to continue the difference calculation with the air quality sensing parameter until the parameter difference is less than the parameter threshold value; and when the parameter difference is less than the parameter threshold value, the coal mine passageway structure model corresponding to the parameter difference less than the parameter threshold value is taken as the passageway structure intermediate model.

[0012] In an embodiment of the present application, the real-time excavation data includes an excavation point, an excavation depth, and an excavation geological layer image; based on the real-time excavation data of the coal mine passage, an intermediate model of the passage structure is optimized and adjusted to obtain an optimized model of the passage structure, including: according to the excavation point of the coal mine passage, a target discharge resistance strength and a target geological layer image of a corresponding passage point in the intermediate model of the passage structure are extracted; according to the target discharge resistance strength, the target geological layer image, and the excavation depth, an image prediction is performed through the intermediate model of the passage structure to obtain a predicted geological layer image corresponding to the excavation depth and a predicted discharge resistance strength; according to the excavation depth, the excavation geological layer image, the predicted discharge resistance strength, and the predicted geological layer image, the predicted discharge resistance strength corresponding to the depth in the intermediate model of the passage structure is corrected to obtain a discharge resistance strength correction value, and a calculation formula of the discharge resistance strength correction value is: wherein, the discharge resistance strength correction value is represented by, the predicted discharge resistance strength is represented by, the excavation depth is represented by, a first discharge resistance strength correction coefficient corresponding to the excavation depth is represented by, an image difference value between the excavation geological layer image and the predicted geological layer image is represented by, a second discharge resistance strength correction coefficient corresponding to the image difference value is represented by; and the intermediate model of the passage structure is optimized and adjusted through the predicted discharge resistance strength to obtain the optimized model of the passage structure.

[0013] In an embodiment of the present application, air parameter simulation evaluation is performed through the optimized model of the passage structure to obtain a risk node section, including: after each excavation of the coal mine passage, discharge simulation is performed through the optimized model of the passage structure to obtain air quality optimization simulation parameters corresponding to each passage point; a plurality of threshold sections are monitored for the air quality optimization simulation parameters to obtain a passage point set corresponding to each threshold section, wherein each threshold section corresponds to a risk level; in the passage point set corresponding to each threshold section, all adjacent passage points form a risk node section corresponding to the same risk level.

[0014] To achieve the above object and other related objects, the present application also provides a dynamic ventilation management system based on multi-parameter detection of air quality of a coal mine, comprising: a first obtaining unit configured to obtain point cloud data collected by a UAV in a coal mine passage, the point cloud data comprising point cloud coordinates and image data corresponding to each point cloud coordinate; a constructing unit configured to analyze a coal mine geological layer based on the point cloud data, and construct a coal mine passage structure model; a deployment analysis unit configured to analyze sensor deployment based on the coal mine passage structure model, and obtain deployment data of a sensor cluster for deployment of the sensor cluster, the deployment data comprising deployment points and deployment types; a second obtaining unit configured to obtain air quality sensing parameters of the deployed sensor cluster; an adjusting unit configured to optimize and adjust the coal mine passage structure model according to the air quality sensing parameters and real-time excavation data of the coal mine passage, to obtain a passage structure optimization model; an evaluation unit configured to simulate and evaluate air parameters through the passage structure optimization model, to obtain risk node sections; and a generating unit configured to optimize ventilation based on the risk node sections, and generate a corresponding ventilation management configuration scheme.

[0015] The present application has the following beneficial effects: The dynamic ventilation management method and system based on multi-parameter detection of air quality of a coal mine proposed by the present application can preliminarily and accurately construct a coal mine passage structure model by collecting point cloud data of a coal mine passage by a UAV and obtaining coal mine geological layer distribution from the point cloud data, and can also realize accurate deployment of a sensor cluster based on the coal mine passage structure model after the coal mine passage structure model is constructed. After the sensor cluster is deployed by using the deployment data, the air quality sensing parameters collected by the sensor cluster can be further used to realize optimization and adjustment of the coal mine passage structure model, and the real-time excavation data of the coal mine passage can also be used to realize optimization and adjustment of the coal mine passage structure model, to obtain a passage structure optimization model for air parameter simulation, thereby ensuring the accuracy and reliability of the passage structure optimization model in air parameter simulation. After the risk node sections of each passage point are simulated, the ventilation equipment and quantity of the coal mine ventilation are further optimized and managed, which can realize intelligent dynamic ventilation management based on real-time variable air parameters, and ensure the ventilation efficiency, ventilation effect and ventilation reliability of the coal mine passage. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application. It is readily apparent to one of ordinary skill in the art that the accompanying drawings are merely some embodiments of the present application, and other drawings can be obtained from the accompanying drawings without any creative effort.

[0017] In the drawings: Figure 1 A flowchart of a dynamic ventilation management method based on multi-parameter detection of air quality in a coal mine according to an embodiment of the present application is shown.

[0018] Figure 2 A schematic diagram of a channel splicing process according to an embodiment of the present application is shown.

[0019] Figure 3 A schematic diagram of a gas emission simulation process using a coal mine channel construction model according to an embodiment of the present application is shown.

[0020] Figure 4 A block diagram of a dynamic ventilation management system based on multi-parameter detection of air quality in a coal mine according to an embodiment of the present application is shown.

[0021] Figure 5 A structural diagram of an electronic device according to an embodiment of the present application is shown.

[0022] Reference signs are as follows: Electronic device 1; dynamic ventilation management system based on multi-parameter detection of air quality in a coal mine 11; memory 12; processor 13; first acquisition unit 111; construction unit 112; deployment analysis unit 113; second acquisition unit 114; adjustment unit 115; evaluation unit 116; generation unit 117. DETAILED DESCRIPTION

[0023] The present application can be implemented or applied in other different specific embodiments, and each detail in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. The following embodiments and features in the embodiments can be combined with each other in the case of no conflict.

[0024] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present application, and the drawings only show the components related to the present application, not the number, shape and size of the components when actually implemented. The type, number and ratio of each component when actually implemented can be arbitrarily changed, and the layout type of the components can also be more complex.

[0025] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application, however, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details, and in other embodiments, the known structures and devices are shown in the form of block diagrams rather than in the form of details to avoid making the embodiments of the present application difficult to understand.

[0026] The application provides a dynamic ventilation management method based on multi-parameter detection of coal mine air quality. The point cloud data of the coal mine passage is collected by using a UAV, so that the distribution of the coal mine geological layer can be determined by using the point cloud data, and a coal mine passage structure model is constructed based on the distribution of the coal mine geological layer. After the construction of the coal mine passage structure model is completed, the accurate deployment of a sensor cluster can be completed based on the coal mine passage structure model. Moreover, after the deployment is completed, the air quality sensing parameters collected by the sensor cluster can be further used to realize the optimization and adjustment of the coal mine passage structure model based on the air quality sensing parameters, and the optimization and adjustment of the coal mine passage structure model is realized by using the real-time excavation data of the coal mine passage, so as to obtain a passage structure optimization model for air parameter simulation, thereby determining the risk node section of each passage point, and realizing the optimization and management configuration of the ventilation equipment and quantity of the coal mine ventilation based on the real-time change of the air parameters, so as to ensure the ventilation efficiency and effect of the coal mine passage.

[0027] Figure 1 A flowchart of the dynamic ventilation management method based on multi-parameter detection of coal mine air quality in an example embodiment of the application is shown, which is applied to a dynamic ventilation management system based on multi-parameter detection of coal mine air quality, and includes steps S10-S70. The technical solutions of the application will be described in detail below. Figure 1

[0028] Firstly, step S10 is performed to obtain the point cloud data collected by the UAV in the coal mine passage, and the point cloud data includes point cloud coordinates and image data corresponding to each point cloud coordinate.

[0029] When the point cloud data is obtained, the UAV can be controlled to fly and monitor in the coal mine passage by using the UAV control system, so as to collect the point cloud coordinates and image data corresponding to each position in the coal mine passage as the point cloud data. Then, the point cloud data collected by the UAV in the coal mine passage can be obtained by the dynamic ventilation management system of the application actively collecting the point cloud data of the UAV control system, or by the UAV control system actively uploading the point cloud data to the dynamic ventilation management system.

[0030] Then, step S20 is performed to analyze the coal mine geological layer based on the point cloud data, and construct a coal mine passage structure model.

[0031] After the point cloud data is obtained by the dynamic ventilation management system of the application, the image data and the corresponding point cloud coordinates in the point cloud data are used to realize the analysis of the coal mine geological layer, so as to determine the coal mine geological layer information, i.e., the geological layer data, and then the coal mine passage structure model can be constructed based on the geological layer data, so as to ensure the simulation and restoration effect of the coal mine passage structure.​

[0032] In step S20, based on point cloud data, coal mine geological layer analysis is performed, and a coal mine passage structure model is constructed, including: Based on point cloud coordinates, the spatial shape and structure of a coal mine tunnel are simulated and constructed. Geological layer analysis is performed on the image data corresponding to the point cloud coordinates in the spatial shape structure to obtain the corresponding geological layer data, which includes the geological layer type, geological layer texture and geological layer porosity. Based on geological layer data, corresponding geological layer models are generated; Geological layer models are incorporated into spatial shape structures to construct coal mine passageway structural models.

[0033] Point cloud coordinates can be used to pre-simulate the spatial shape and structure of coal mine tunnels. Then, based on the image data corresponding to the point cloud coordinates, geological layers can be analyzed to determine the geological layer data corresponding to the point cloud coordinates. This geological layer data can include the type of geological layer, its texture, and its porosity. Geological layer types can include rock strata and coal seams. Rock strata include the roof and floor strata supporting the coal seams, as well as non-coal strata existing in or near the coal seams; specifically, rock strata can be sandstone, limestone, mudstone, shale, limestone, etc. Geological layer textures can be the texture shape composed of coal seams and rock strata, or the texture shape composed of stacked coal seams, or the texture shape composed of stacked rock strata. In the following steps, when comparing the similarity of geological layer fragments with the baseline geological texture maps in the geological layer repository, the comparison mainly focuses on the texture shapes composed of stacked coal seams and the texture shapes composed of stacked rock strata. The looseness of geological layers is mainly used to indicate the looseness between coal seams and rock strata, between stacked coal seams, and between stacked rock strata. By utilizing the looseness of geological layers, the gas permeability of the rock or coal seam corresponding to each point cloud location can be further assessed.

[0034] This includes performing geological layer analysis on the image data corresponding to the point cloud coordinates in the spatial shape structure to obtain the corresponding geological layer data, which may further include: Based on the cloud coordinates of each point in the spatial shape structure, the image data is stitched together to obtain geological layer fragments; The similarity values ​​are obtained by comparing the geological layer fragments with the baseline geological texture maps in the geological layer repository; When the similarity value reaches the similarity threshold, the base geological layer type corresponding to the base geological texture map is taken as the geological layer type of the geological layer fragment. Based on the geological layer type and corresponding geological texture of the geological layer fragments, the degree of looseness of the geological layer corresponding to each geological layer fragment is obtained; The geological layer data includes a geological layer type, a geological layer looseness degree, and a geological texture.

[0035] In the process of utilizing the image data corresponding to each point cloud coordinate, the splicing of the corresponding image data can be performed based on each point cloud coordinate in the spatial shape structure, so that each adjacent image data is spliced together to obtain a geological layer segment in a preset image size format. A large number of reference geological texture maps are pre-stored in the geological layer storage library, and therefore, the similarity comparison between the corresponding geological layer segment and each reference geological texture map in the geological layer storage library can be performed to find a reference geological texture map corresponding to a similarity value reaching a similarity threshold, and the reference geological layer type corresponding to the reference geological texture map is taken as the geological layer type of the current geological layer segment. After the geological layer type is determined, the geological layer looseness degree of the corresponding geological layer segment can be selected in combination with the geological texture corresponding to the address texture map. Then, the geological layer looseness degree, the geological layer type, and the geological texture are further taken as the geological layer data.

[0036] In the process of obtaining the geological layer looseness degree of each geological layer segment according to the geological layer type of the geological layer segment and the corresponding geological texture, each reference geological texture map stored in the geological layer storage library corresponds to multiple reference geological textures, and each reference geological texture corresponds to a reference geological layer looseness degree. After the geological layer type of the geological layer segment is determined, the reference geological texture closest to the current geological layer segment can be found to be taken as the geological texture of the geological layer segment, and the reference geological layer looseness degree corresponding to the reference geological texture is taken as the geological layer looseness degree of the geological layer segment. Therefore, the subsequent automatic deployment analysis of the sensor cluster can be facilitated according to the geological layer looseness degree at the corresponding position of the channel to determine the deployment data corresponding to the sensor cluster.

[0037] In the process of generating the corresponding geological layer model based on the geological layer data, the process can further include: According to the geological layer type, the corresponding geological layer reference model is called; According to the geological layer looseness degree, the adjustment parameter of the geological layer reference model is generated; According to the adjustment parameter, the geological layer reference model is optimized to generate the corresponding geological layer model.

[0038] That is, the geological layer reference model corresponding to each geological layer type can be pre-stored. Then, the corresponding geological layer reference model pre-stored is called based on the geological layer type corresponding to the geological layer segment. In order to ensure that the geological layer reference model can be better applied to the corresponding geological layer segment, the corresponding adjustment parameter can be further generated by using the geological layer looseness degree to adjust the model looseness of the geological layer reference model, so as to optimize the geological layer reference model and obtain the geological layer model after optimization.

[0039] Then, step S30 is performed, sensor deployment analysis is performed based on the coal mine passage structure model to obtain deployment data of the sensor cluster for deployment of the sensor cluster, and the deployment data includes deployment points and deployment types.

[0040] After the coal mine passage structure model is constructed by the dynamic ventilation management system of the application, sensor deployment analysis can be performed by using the coal mine passage structure model to determine the corresponding deployment data of the sensor cluster. The sensor cluster can include an explosion-proof sensor cluster that requires high protection, a simple version sensor group that does not require high protection, and other sensor combinations. The specific deployment mode of the sensor cluster can be manually programmed in advance based on each type of coal mine passage structure, so that the corresponding reference passage structure of the manually programmed reference passage structure can be found in the model segment processing of the coal mine passage structure model, and the corresponding reference deployment data of the reference passage structure can be used as the deployment data of the corresponding model segment, thereby obtaining the deployment data of the sensor cluster of the entire coal mine passage structure model. The deployment of the sensor cluster can be realized according to the obtained deployment data of the sensor cluster by the field staff, so that the sensor cluster can be used to collect air quality sensing parameters.

[0041] The explosion-proof sensor cluster can include a methane sensor, a laser dust sensor, a temperature and humidity composite sensor, a CO / CO2 dual gas detector, etc., which can be manually combined according to the specific conditions of the coal mine passage. The methane sensor can be a GJC4 infrared methane sensor with a range of 0-5% vol, an accuracy of ±0.05%, and an explosion-proof level of Ex ib I. The laser dust sensor can be a GD42 mine laser dust sensor, which is a PM2.5 / PM10 dual-channel sensor with a resolution of 1 μg / m 3 ; the temperature and humidity composite sensor can be a KGF2 temperature and humidity composite sensor with a temperature sensitivity of ±0.3℃ and a humidity sensitivity of ±2% RH; the CO / CO2 dual gas detector can be a GTH1000 type CO / CO2 dual gas detector, which mainly uses electrochemical + NDIR technology. When the explosion-proof sensor cluster is executed, the sensor data can be sampled at 1 second / second without interruption, and the sensor data can be transmitted to the substation through the MHY32 mine flame-retardant cable. Of course, other sampling strategies and other data transmission methods can also be set according to the specific needs of the coal mine passage.

[0042] The simplified sensor group can be obtained by simply combining methane, CO, dust, etc. sensors, and the collection mode of the collected sensor data can be a 3-second / second sampling strategy, which is returned through an industrial WiFi Mesh network. Of course, other sampling strategies and other data return modes can also be set according to the specific needs of the coal mine passageway.

[0043] Specifically, based on the coal mine passageway structure model, sensor deployment analysis is performed to obtain deployment data of the sensor cluster for deployment of the sensor cluster, including: According to the geological layer type and the loose degree of the geological layer, the passageway divisibility of the coal mine passageway structure model is evaluated to obtain an indivisible model passageway segment; The other model passageway segments in the coal mine passageway structure model except the indivisible model passageway segment are segmented into multiple model passageway slices; The model passageway slices of the corresponding sequence are sequentially and incrementally spliced at both ends of the indivisible model passageway segment to obtain a spliced passageway model; The spliced passageway structure of each spliced passageway model is compared with the reference passageway structure in the reference model library for similarity to obtain a reference passageway structure that reaches a preset similarity with the spliced passageway structure; The reference deployment data of the sensor cluster corresponding to the reference passageway structure is retrieved as the spliced deployment data of the sensor cluster of the spliced passageway model; Based on the spliced deployment data, deployment data of the sensor cluster of the coal mine passageway structure model is obtained for deployment of the sensor cluster.

[0044] In the sensor deployment analysis of the coal mine passageway structure model, the passageway divisibility of each node (also referred to as a passageway point) of the coal mine passageway structure model can be evaluated based on the geological layer type and the loose degree of the geological layer of each node of the coal mine passageway structure model, so as to determine the indivisible model passageway segment that needs to be monitored with high risk. In order to realize the division of each model passageway segment, after determining the indivisible model passageway segment, the other model passageway segments in the coal mine passageway structure model except the indivisible model passageway segment also need to be further segmented into model passageway small segments, that is, model passageway slices. Then, the model passageway slices are sequentially added at both ends of the corresponding indivisible model passageway segment, and the model passageway slices are added at both ends of the corresponding indivisible model passageway segment in an incremental number, and then compared with the reference passageway structure in the reference model library for similarity to determine the reference passageway structure that reaches a preset similarity with the spliced passageway structure.

[0045] For example, a first model channel segment is added before the non-separable model channel segment to obtain a first spliced channel model, and then the first spliced channel structure corresponding to the first spliced channel model is compared with the reference channel structure in the reference model library for similarity, when the similarity is low, a second model channel segment is added after the first model channel segment or after the non-separable model channel segment to obtain a second spliced channel model, and then the second spliced channel structure corresponding to the second spliced channel model is compared with the reference channel structure in the reference model library for similarity, until the similarity between the spliced channel structure after splicing and the reference channel structure in the reference model library reaches the preset similarity, and then the deployment data of the sensor cluster corresponding to the reference channel structure capable of reaching the preset similarity is directly used as the splicing deployment data of the sensor cluster of the spliced channel model. Through the search of the entire coal mine channel structure model, the splicing deployment data of the plurality of non-separable model channel segments is obtained, and then the deployment of the entire sensor cluster is composed based on all the splicing deployment data.

[0046] Of course, between the spliced channel models corresponding to the two adjacent non-separable model channel segments, since they do not have non-separability, they can not be the key section for air quality parameter monitoring, or they can also be installed and deployed with simple sensors at a preset interval based on a conventional deployment scheme.

[0047] Please refer to Figure 2 , Figure 2 In the embodiment, when the spliced channel structure is spliced, the spliced channel structure obtained each time is compared with all the reference channel structures in the reference model library to find the spliced channel structure capable of reaching the preset similarity with a certain reference channel structure after splicing, and the reference deployment data of the sensor cluster corresponding to the corresponding reference channel structure is obtained as the splicing deployment data of the sensor cluster of the spliced channel structure. For the spliced channel structure, it includes a non-separable model channel segment and a plurality of channel slices at both ends, and in the illustration, the spliced channel structure obtained by splicing the channel slice am-1, the channel slice am, the non-separable model channel segment, the channel slice b1 and the channel slice b2 can reach the preset similarity with a reference channel structure in the reference model library. Through the search of the reference channel structure in this way, the deployment efficiency, the deployment order and the deployment accuracy of the sensor can be improved.

[0048] In addition, according to the geological layer type and the loose degree of the geological layer corresponding to each node in the coal mine channel structure model, the channel separability of the coal mine channel structure model is evaluated to obtain the non-separable model channel segment, which can further include: According to the geological layer type of each node of the coal mine passage structure model along the linear direction of the passage, a first indivisibility index is obtained; According to the geological layer looseness degree and the index conversion factor, a second indivisibility index is obtained; According to the first indivisibility index and the second indivisibility index, a comprehensive indivisibility index is calculated, wherein the calculation formula of the comprehensive indivisibility index is , wherein, represents the first indivisibility index, represents the geological layer looseness degree, represents the index conversion factor; When the comprehensive indivisibility index is less than the index threshold value, the corresponding node is taken as an indivisible node; All adjacent indivisible nodes are grouped to form a connection, and a non-separable model passage section is obtained.

[0049] In the process of evaluating the passage divisibility of the coal mine passage structure model, the first indivisibility index of each node of the coal mine passage structure model can be determined based on the geological layer type of the node, and specifically, each geological layer type corresponds to a first indivisibility index stored in the dynamic ventilation management system of the present application. Therefore, the corresponding first indivisibility index can be obtained by looking up the table according to the geological layer type. Then, based on the geological layer looseness degree of the corresponding node, the second indivisibility index which can be used to evaluate the degree of indivisibility of the node is obtained by using the index conversion factor corresponding to the geological layer looseness degree. Then, the first indivisibility index and the second indivisibility index are fused to obtain a comprehensive indivisibility index, and the formula can be expressed as After the comprehensive indivisibility index is calculated, the index threshold value of the comprehensive indivisibility index can be monitored, and when the comprehensive indivisibility index is less than the index threshold value, the corresponding node can be taken as an indivisible node. Then, all the adjacent indivisible nodes are grouped to form a connection, thereby obtaining a non-separable model passage section. Similarly, when the comprehensive indivisibility index is greater than the index threshold value, the corresponding node can be taken as a divisible node. Then, all the adjacent divisible nodes are grouped to form a connection, thereby obtaining a separable model passage section. Then, the separable model passage section is cut into a plurality of model passage slices, which are combined with the non-separable model passage section, thereby obtaining a spliced passage structure with a similarity between the reference passage structure and the preset similarity.

[0050] Next, step S40 is executed to obtain the air quality sensing parameters of the deployed sensor cluster.

[0051] In the dynamic ventilation management system, the air quality sensor parameters of the deployed sensor cluster can be actively reported to the dynamic ventilation management system, or the dynamic ventilation management system can collect data from the sensor cluster.

[0052] Then, step S50 is performed, and the air quality sensor parameters and the real-time excavation data of the coal mine passage are used to optimize and adjust the coal mine passage construction model to obtain a passage construction optimization model.

[0053] After the dynamic ventilation management system obtains the air quality sensor parameters, the coal mine passage construction model can be optimized and adjusted based on the air quality sensor parameters, and then the coal mine passage construction model can be optimized and adjusted again based on the real-time excavation data of the coal mine passage, thereby obtaining a real-time optimized and updated passage construction optimization model, thereby ensuring the accuracy of the passage construction optimization model reflecting the coal mine passage environment.

[0054] In step S50, the air quality sensor parameters and the real-time excavation data of the coal mine passage are used to optimize and adjust the coal mine passage construction model to obtain a passage construction optimization model, including: The initial emission parameters corresponding to each passage point are used to simulate the emission of the coal mine passage construction model, and the air quality simulation parameters corresponding to each deployment point are obtained; The air quality simulation parameters and the air quality sensor parameters are compared, the emission of the coal mine passage construction model is optimized, and a passage construction intermediate model is obtained; Based on the real-time excavation data of the coal mine passage, the passage construction intermediate model is optimized and adjusted to obtain a passage construction optimization model.

[0055] In the optimization and adjustment of the coal mine passage construction model, the initial emission parameters corresponding to each passage point can be used to simulate the emission of the coal mine passage construction model, thereby obtaining the air quality simulation parameters corresponding to each deployment point. The comparison of the air quality simulation parameters and the air quality sensor parameters can be used to optimize the emission of the coal mine passage construction model based on the comparison results, and the optimized passage construction intermediate model can be obtained. Finally, the real-time excavation data of the coal mine passage can be used to further optimize and adjust the passage construction intermediate model to obtain a real-time dynamic updated passage construction optimization model, thereby accurately determining the corresponding risk node segment based on the real-time dynamic updated passage construction optimization model, and ensuring the rationality and reliability of the ventilation configuration.

[0056] Specifically, the comparison of the air quality simulation parameters and the air quality sensor parameters, the emission optimization of the coal mine passage construction model, and the obtaining of the passage construction intermediate model can further include: According to the initial emission parameter of the coal mine passage structure model and the corresponding air quality simulation parameter, the first emission blocking strength corresponding to each deployment point and the second emission blocking strength corresponding to each non-deployment point between the deployment points are obtained. The air quality simulation parameter and the air quality sensing parameter are differentially calculated to obtain a parameter difference; The parameter difference is monitored; When the parameter difference is greater than a parameter threshold, the initial emission parameter of the coal mine passage structure model, the first emission blocking strength and the second emission blocking strength are adjusted in a direction in which the parameter difference is reduced, and the air quality simulation parameter obtained by using the adjusted coal mine passage structure model is used to continue the step of differentially calculating the air quality sensing parameter until the parameter difference is less than the parameter threshold; When the parameter difference is less than the parameter threshold, the coal mine passage structure model corresponding to the parameter difference being less than the parameter threshold is taken as an intermediate model of the passage structure.

[0057] In the process of comparing the air quality simulation parameter and the air quality sensing parameter, the first emission blocking strength corresponding to each deployment point and the second emission blocking strength corresponding to each non-deployment point between the deployment points can be calculated using the initial emission parameter of the coal mine passage structure model and the corresponding air quality simulation parameter. The calculation formula of the emission blocking strength can be expressed as wherein, represents the initial emission parameter, represents the air quality simulation parameter, represents an emission blocking adjustment coefficient.

[0058] Please refer to Figure 3 , Figure 3 In an embodiment given, the geological layer data composed of the geological layer type, the geological layer texture and the geological layer looseness degree on the outside of the coal mine passage can preliminarily construct the geological layer model of the coal mine passage structure model, that is, the blocking layer. When the initial emission parameter is discharged through the blocking layer to the coal mine passage, the first emission blocking strength is applied at the deployment point, and the second emission blocking strength is applied at each non-deployment point between the deployment points, so that the air quality simulation parameter is finally formed when reaching the coal mine passage. Therefore, the formation of the first emission blocking strength and the second emission blocking strength is mainly determined by the geological layer type, the geological layer texture and the geological layer looseness degree of the coal mine passage. Moreover, in the case where the geological layer type and the geological layer texture are determined, the geological layer looseness degree is not accurate enough based on the geological layer texture, so it is further necessary to compare the parameter difference between the air quality simulation parameter and the air quality sensing parameter with the parameter threshold to further optimize the first emission blocking strength and the second emission blocking strength and the initial emission parameter.

[0059] Specifically, when the parameter difference between the air quality simulation parameter and the air quality sensing parameter is greater than the parameter threshold, the initial emission parameter, the first emission barrier strength and the second emission barrier strength of the coal mine passage construction model are adjusted and optimized so that the initial emission parameter, the first emission barrier strength and the second emission barrier strength of the coal mine passage construction model can gradually change towards the direction of reducing the parameter difference. And after adjusting the initial emission parameter, the first emission barrier strength and the second emission barrier strength, the coal mine passage construction model is updated synchronously, the air quality simulation parameter is simulated again, and the comparison with the air quality sensing parameter is continued until the calculated parameter difference is less than the parameter threshold. At this time, the optimized initial emission parameter, the first emission barrier strength and the second emission barrier strength are obtained to adjust the coal mine passage construction model to obtain the passage construction intermediate model, so that the optimized initial emission parameter, the first emission barrier strength and the second emission barrier strength can accurately adapt to the passage construction intermediate model. It is worth noting that when the coal mine passage construction model is adjusted by using the optimized initial emission parameter, the first emission barrier strength and the second emission barrier strength, the geological layer loosening degree of the barrier layer can be adjusted accordingly by the first emission barrier strength and the second emission barrier strength. Of course, the corresponding point of the initial emission parameter in the coal mine passage construction model can also be adjusted accordingly.

[0060] Wherein, when the excavation work of a certain point of the coal mine passage is carried out, the real-time excavation data including the excavation point, the excavation depth and the excavation geological layer image can be uploaded to the dynamic ventilation management system of the present application.

[0061] Specifically, based on the real-time excavation data of the coal mine passage, the passage construction intermediate model is adjusted and optimized to obtain the passage construction optimization model, including: According to the excavation point of the coal mine passage, the target emission barrier strength and the target geological layer image of the corresponding passage point in the passage construction intermediate model are extracted; According to the target emission barrier strength, the target geological layer image and the excavation depth, the image prediction is carried out through the passage construction intermediate model to obtain the predicted geological layer image and the predicted emission barrier strength corresponding to the excavation depth; According to the excavation depth, the excavation geological layer image, the predicted emission barrier strength and the predicted geological layer image, the predicted emission barrier strength of the corresponding depth in the passage construction intermediate model is corrected to obtain the emission barrier strength correction value, and the calculation formula of the emission barrier strength correction value is: , wherein, represents the emission barrier strength correction value, represents the predicted emission barrier strength, represents the excavation depth, represents a first emission barrier strength correction coefficient corresponding to the excavation depth, represents an image difference value between the excavation geological layer image and the predicted geological layer image, represents a second emission barrier strength correction coefficient corresponding to the image difference value; The channel structure intermediate model is optimized and adjusted by the predicted emission barrier strength to obtain a channel structure optimization model.

[0062] When the channel structure intermediate model is optimized and adjusted by the real-time excavation data, the staff can upload the excavation point, the excavation depth, and the excavation geological layer image of the corresponding excavation point to the dynamic ventilation management system. The dynamic ventilation management system will find the target emission barrier strength and the target geological layer image of the corresponding channel point in the channel structure intermediate model based on the excavation point, and will also use the channel structure intermediate model after adjusting the geological layer looseness of the barrier layer to realize model cutting of the corresponding depth of the barrier layer according to the corresponding excavation depth, so as to obtain the predicted geological layer image and the predicted emission barrier strength corresponding to the predicted excavation depth when the channel structure intermediate model is simulated again.

[0063] After obtaining the predicted geological layer image and the predicted emission barrier strength, the predicted emission barrier strength can be further corrected to ensure the accuracy of the emission barrier strength. Specifically, the predicted emission barrier strength under the corresponding excavation depth in the channel structure intermediate model is corrected by using the excavation depth, the excavation geological layer image, and the predicted emission barrier strength and the predicted geological layer image obtained by prediction, so as to obtain an emission barrier strength correction value, and the calculation formula can be represented as Moreover, when the excavation geological layer image is used, the excavation geological layer image can be compared and analyzed with the predicted geological layer image to obtain an image difference value therebetween, and then a first emission barrier strength adjustment value related to the geological layer image is obtained based on a second emission barrier strength correction coefficient corresponding to the image difference value, and a second emission barrier strength adjustment value corresponding to the excavation depth is obtained based on the excavation depth and the corresponding first emission barrier strength correction coefficient, so that the predicted emission barrier strength is corrected based on the first emission barrier strength adjustment value and the second emission barrier strength adjustment value, thereby ensuring the accuracy and conservatism of the emission barrier strength correction value.

[0064] Then, step S60 is performed, air parameter simulation evaluation is performed by the channel structure optimization model, and a risk node section is obtained.

[0065] After the channel structure optimization model is derived by the dynamic ventilation management system of the application, the air quality optimization simulation parameters in the coal mine channel are derived by further using the corresponding initial emission parameters for emission modeling based on the channel structure optimization model, so as to realize air parameter simulation evaluation, obtain risk node sections, and thus the same ventilation configuration scheme can be generated based on different levels of risk node sections.

[0066] In step S60, the air parameter simulation evaluation is performed by the channel structure optimization model to obtain the risk node sections, including: After each coal mine channel excavation, the emission simulation is performed by the optimized channel structure optimization model to obtain the air quality optimization simulation parameters corresponding to each channel point; The air quality optimization simulation parameters are monitored by multiple threshold sections to obtain a channel point set corresponding to each threshold section, wherein each threshold section corresponds to a risk level; In the channel point set corresponding to each threshold section, all adjacent channel points form a risk node section corresponding to the same risk level.

[0067] After each coal mine channel excavation, the channel structure optimization model is updated according to the corresponding real-time excavation data. After optimization, the emission simulation can be performed by the optimized channel structure optimization model to derive the air quality optimization simulation parameters corresponding to each channel point in the channel structure optimization model. Then, all air quality optimization simulation parameters are monitored by multiple threshold sections designed by artificial to collect each air quality optimization simulation parameter into the corresponding threshold section, so as to form a corresponding channel section based on the channel points having an adjacent relationship in each threshold section as the risk node section corresponding to the corresponding threshold section, and thus the corresponding ventilation management configuration scheme can be more accurately selected based on the risk level corresponding to the risk node section, to ensure the reliability of dynamic ventilation.

[0068] Then, step S70 is performed to perform ventilation optimization based on the risk node sections to generate the corresponding ventilation management configuration scheme.

[0069] After all risk levels and risk node sections corresponding to the risk levels in the coal mine channel are derived by the dynamic ventilation management system of the application, the ventilation optimization can be further performed to derive the ventilation management configuration scheme to realize dynamic ventilation management of the coal mine channel.

[0070] Specifically, the ventilation optimization based on the risk node sections to generate the corresponding ventilation management configuration scheme can further include: According to the length of the risk node section and the risk level, the corresponding ventilation management configuration equipment and the configuration quantity of the ventilation management configuration equipment are obtained; The ventilation management configuration device and the configuration quantity of the ventilation management configuration device are taken as the ventilation management configuration scheme.

[0071] That is, all ventilation management configuration devices corresponding to the requirements can be found through the risk level, and then the configuration quantity of the ventilation management configuration device actually required can be determined based on the length of the risk node segment, and the ventilation management configuration device and the configuration quantity of the ventilation management configuration device analyzed are taken as the ventilation management configuration scheme and sent to the management personnel for issuing and configuring the ventilation management configuration scheme, so as to ensure the safety and reliability of the coal mine air quality after the configuration management based on the ventilation management configuration scheme.

[0072] Specifically, in the dynamic ventilation management process, the ventilation adjustment can be realized by configuring the counter-rotating fan matched with the ABB frequency converter to the corresponding risk node segment, or the ventilation adjustment can be realized by configuring and installing the servo motor matched with the opening control mechanism and the starting regulation damper to the corresponding risk node segment, and the like, and the configuration and installation can be performed according to the risk level and the like of the relevant application scene.

[0073] Please refer to Figure 4 The application further provides a dynamic ventilation management system 11 based on coal mine air quality multi-parameter detection, comprising: a first acquisition unit 111, configured to acquire point cloud data collected by a UAV in a coal mine passage, the point cloud data comprising point cloud coordinates and image data corresponding to each point cloud coordinate; a construction unit 112, configured to analyze a coal mine geological layer based on the point cloud data, and construct a coal mine passage structure model; a deployment analysis unit 113, configured to perform sensor deployment analysis based on the coal mine passage structure model, to obtain deployment data of a sensor cluster for deployment of the sensor cluster, the deployment data comprising deployment points and deployment types; a second acquisition unit 114, configured to acquire air quality sensing parameters of the deployed sensor cluster; an adjustment unit 115, configured to optimize and adjust the coal mine passage structure model according to the air quality sensing parameters and real-time excavation data of the coal mine passage, to obtain a passage structure optimization model; an evaluation unit 116, configured to perform air parameter simulation evaluation through the passage structure optimization model, to obtain a risk node segment; and a generation unit 117, configured to perform ventilation optimization based on the risk node segment, to generate a corresponding ventilation management configuration scheme.

[0074] It should be noted that the dynamic ventilation management system based on coal mine air quality multi-parameter detection 11 provided by the above embodiment and the dynamic ventilation management method based on coal mine air quality multi-parameter detection provided by the above embodiment belong to the same concept, wherein the specific operation of each module and unit has been described in detail in the method embodiment, and will not be repeated here. The dynamic ventilation management system based on coal mine air quality multi-parameter detection 11 provided by the above embodiment can complete the above-mentioned functions by different functional modules according to the actual application, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above-mentioned functions, and this is not limited here.

[0075] Please refer to Figure 5 , the electronic device 1 can include a memory 12, a processor 13 and a bus, and can also include a computer program stored in the memory 12 and executable on the processor 13, such as a dynamic ventilation management program based on coal mine air quality multi-parameter detection.

[0076] Among them, the memory 12 at least includes one type of readable storage medium, which includes flash memory, mobile hard disk, multimedia card, card type memory (such as SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 12 can be an internal storage unit of the electronic device 1 in some embodiments, such as the mobile hard disk of the electronic device 1. The memory 12 can also be an external storage device of the electronic device 1 in other embodiments, such as the plug-in mobile hard disk, smart storage card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card, etc. equipped on the electronic device 1. Further, the memory 12 can include both the internal storage unit and the external storage device of the electronic device 1. The memory 12 can be used not only to store application software and various data installed on the electronic device 1, such as the code of the dynamic ventilation management based on coal mine air quality multi-parameter detection, but also to temporarily store data that has been output or will be output.

[0077] The processor 13 can be composed of integrated circuits in some embodiments, for example, can be composed of a single packaged integrated circuit, or can be composed of multiple packaged integrated circuits with the same function or different functions, including one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, etc. The processor 13 is the control core of the electronic device 1, which connects various components of the entire electronic device 1 through various interfaces and lines, executes programs or modules stored in the memory 12 (for example, a dynamic ventilation management program based on multi-parameter detection of coal mine air quality, etc.), and calls data stored in the memory 12 to perform various functions and process data of the electronic device 1.

[0078] The processor 13 executes the operating system of the electronic device 1 and various installed application programs. The processor 13 executes the application programs to implement the steps in the above-mentioned dynamic ventilation management method based on multi-parameter detection of coal mine air quality.

[0079] For example, the computer program can be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules can be a series of computer program instruction segments that can complete a specific function, which are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program can be divided into various units in the dynamic ventilation management system based on multi-parameter detection of coal mine air quality.

[0080] The integrated units implemented in the form of software function modules described above can be stored in a computer readable storage medium, which can be non-volatile or volatile. The software function modules described above are stored in a storage medium, including a number of instructions for making a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor execute part of the dynamic ventilation management method based on multi-parameter detection of coal mine air quality described in various embodiments of the present application.

[0081] In summary, the coal mine air quality multi-parameter detection based dynamic ventilation management method and system disclosed by the application can collect point cloud data of the coal mine passageway by using the unmanned aerial vehicle, and can obtain the coal mine geological layer distribution by using the point cloud data, so that the coal mine passageway structure model can be preliminarily and accurately constructed, and the precise deployment of the sensor cluster based on the coal mine passageway structure model can be realized after the construction of the coal mine passageway structure model is completed. After the deployment data is used to complete the deployment of the sensor cluster, the air quality sensing parameters collected by the sensor cluster can be further used to realize the optimization and adjustment of the coal mine passageway structure model, and the real-time excavation data of the coal mine passageway is used to realize the optimization and adjustment of the coal mine passageway structure model, so as to obtain the passageway structure optimization model for air parameter simulation, so as to ensure the accuracy and reliability of the passageway structure optimization model for air parameter simulation. After the risk node segments of each passageway point are simulated, the ventilation equipment and quantity of the coal mine ventilation are further optimized and managed, so that the intelligent dynamic ventilation management based on the real-time change of the air parameters can be realized, and the ventilation efficiency, ventilation effect and ventilation reliability of the coal mine passageway are ensured. Therefore, the application effectively overcomes the shortcomings of the prior art and has high industrial utilization value.

[0082] The above embodiments only exemplarily illustrate the principles and effects of the application, and are not used to limit the application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the application. Therefore, all equivalent modifications or changes completed by those skilled in the art without departing from the spirit and technical thought of the application should be covered by the claims of the application.

Claims

1. A dynamic ventilation management method based on multi-parameter detection of air quality in coal mines, characterized in that, include: The point cloud data collected by the UAV in the coal mine tunnel includes point cloud coordinates and image data corresponding to each point cloud coordinate. Based on the point cloud data, a coal mine geological layer analysis is performed to construct a coal mine passage structure model. Sensor deployment analysis is performed based on the coal mine tunnel construction model to obtain sensor cluster deployment data for sensor cluster deployment. The deployment data includes deployment locations and deployment types. Obtain the air quality sensing parameters of the deployed sensor cluster; Based on the air quality sensing parameters and the real-time excavation data of the coal mine tunnel, the coal mine tunnel construction model is optimized and adjusted to obtain an optimized tunnel construction model. By constructing an optimization model for the channel, air parameters are simulated and evaluated to obtain risk node segments; Based on the aforementioned risk node segments, ventilation optimization is performed to generate corresponding ventilation management configuration schemes.

2. The dynamic ventilation management method based on multi-parameter detection of coal mine air quality according to claim 1, characterized in that, Based on the point cloud data, a coal mine geological layer analysis is performed, and a coal mine passage structure model is constructed, including: Based on the point cloud coordinates, the spatial shape and structure of the coal mine tunnel are simulated and constructed. Geological layer analysis is performed on the image data corresponding to each point cloud coordinate in the spatial shape structure to obtain the corresponding geological layer data, which includes the geological layer type, geological layer texture and geological layer porosity. Based on the geological layer data, a corresponding geological layer model is generated; The geological layer model is added to the spatial shape structure to construct the coal mine passage construction model.

3. The dynamic ventilation management method based on multi-parameter detection of coal mine air quality according to claim 2, characterized in that, Geological layer analysis is performed on the image data corresponding to the point cloud coordinates in the spatial shape structure to obtain the corresponding geological layer data, including: Based on the point cloud coordinates in the spatial shape structure, the image data is stitched together to obtain a geological layer fragment. The similarity values ​​are obtained by comparing the geological layer fragments with the reference geological texture maps in the geological layer repository. When the similarity value reaches the similarity threshold, the corresponding reference geological layer type of the reference geological texture map is taken as the geological layer type of the geological layer fragment. Based on the geological layer type and corresponding geological texture of the geological layer segment, the porosity of the geological layer corresponding to each geological layer segment is obtained; The degree of looseness of the geological layer, the type of the geological layer, and the geological texture are used as the geological layer data.

4. The dynamic ventilation management method based on multi-parameter detection of coal mine air quality according to claim 1, characterized in that, Based on the aforementioned coal mine tunnel construction model, sensor deployment analysis is performed to obtain sensor cluster deployment data for sensor cluster deployment, including: Based on the geological layer type and looseness of each node in the coal mine passage construction model, the divisibility of the passage is evaluated to obtain indivisible passage segments. The coal mine tunnel construction model, excluding the indivisible model tunnel segment, is divided into multiple model tunnel slices. By sequentially splicing model channel slices in a corresponding order at both ends of the indivisible model channel segment, a spliced ​​channel model is obtained; The similarity of the spliced ​​channel construction of each spliced ​​channel model with the benchmark channel construction in the benchmark model library is compared to obtain the benchmark channel construction that reaches the preset similarity with the spliced ​​channel construction. The baseline deployment data of the sensor cluster corresponding to the baseline channel is retrieved as the splicing deployment data of the sensor cluster in the splicing channel model; Based on the spliced ​​deployment data, the deployment data of the sensor cluster of the coal mine channel construction model is obtained, so as to deploy the sensor cluster.

5. The dynamic ventilation management method based on multi-parameter detection of coal mine air quality according to claim 4, characterized in that, Based on the type and porosity of the geological strata, the divisibility of the coal mine passage structure model is assessed, resulting in indivisible passage segments, including: Based on the geological strata types at each node along the linear direction of the coal mine passage construction model, the first indivisibility index is obtained. Based on the degree of looseness of the geological layer and the index conversion factor, the second indivisibility index is obtained; Based on the first indivisibility index and the second indivisibility index, an indivisibility composite index is calculated, wherein the calculation formula for the indivisibility composite index is as follows: ,in, Indicates the first indivisibility index. Indicates the degree of looseness of the geological layer. Indicates the indicator conversion factor; When the indivisibility comprehensive index is less than the index threshold, the corresponding node is regarded as an indivisible node. Connect all adjacent inseparable nodes to obtain the inseparable model channel segment.

6. The dynamic ventilation management method based on multi-parameter detection of air quality in coal mines according to claim 1, characterized in that, Based on the air quality sensing parameters and real-time excavation data of the coal mine tunnel, the coal mine tunnel construction model is optimized and adjusted to obtain an optimized tunnel construction model, including: The emission simulation of the coal mine channel construction model is performed by using the initial emission parameters corresponding to each channel location to obtain the air quality simulation parameters corresponding to each deployment location. The simulated air quality parameters are compared with the air quality sensing parameters, and the emission optimization is performed on the coal mine tunnel construction model to obtain an intermediate tunnel construction model. Based on the real-time excavation data of the coal mine tunnel, the intermediate model of the tunnel structure is optimized and adjusted to obtain the optimized tunnel structure model.

7. The dynamic ventilation management method based on multi-parameter detection of air quality in coal mines according to claim 6, characterized in that, The simulated air quality parameters are compared with the sensed air quality parameters to optimize the emissions of the coal mine tunnel construction model, resulting in an intermediate tunnel construction model, including: Based on the initial emission parameters and the corresponding air quality simulation parameters of the coal mine tunnel construction model, the first emission blocking force corresponding to each of the deployment points and the second emission blocking force corresponding to each non-deployed point between the deployment points are obtained. The difference between the simulated air quality parameters and the sensed air quality parameters is calculated to obtain the parameter difference. Monitor the difference in the parameters; When the parameter difference is greater than the parameter threshold, the initial emission parameters, the first emission blocking force and the second emission blocking force of the coal mine tunnel construction model are optimized and adjusted in the direction that reduces the parameter difference. The air quality simulation parameters obtained from the adjusted coal mine tunnel construction model are then used to continue the step of calculating the difference with the air quality sensing parameters until the parameter difference is less than the parameter threshold. When the parameter difference is less than the parameter threshold, the adjusted coal mine tunnel construction model corresponding to the parameter difference being less than the parameter threshold is used as the intermediate tunnel construction model.

8. The dynamic ventilation management method based on multi-parameter detection of air quality in coal mines according to claim 6, characterized in that, The real-time excavation data includes excavation locations, excavation depths, and images of the geological layers being excavated; Based on the real-time excavation data of the coal mine tunnel, the intermediate model of the tunnel structure is optimized and adjusted to obtain an optimized tunnel structure model, including: Based on the excavation points of the coal mine tunnel, extract the target emission blocking force and target geological layer image of the corresponding tunnel points in the intermediate model of the tunnel structure; Based on the target emission blocking force, the target geological layer image, and the excavation depth, image prediction is performed using the intermediate model constructed through the channel to obtain the predicted geological layer image and the predicted emission blocking force corresponding to the excavation depth; Based on the excavation depth, the image of the excavated geological layer, the predicted emission blocking force, and the image of the predicted geological layer, the predicted emission blocking force at the corresponding depth in the intermediate model of the channel structure is corrected to obtain a corrected emission blocking force value. The formula for calculating the corrected emission blocking force value is as follows: ,in, This indicates the correction value for emission blocking force. Indicates the predicted emission blocking strength, Indicates the excavation depth. This represents the correction factor for the first emission barrier force corresponding to the excavation depth. This represents the image difference value between the excavated geological layer image and the predicted geological layer image. This represents the second emission barrier strength correction coefficient corresponding to the image difference value; The intermediate model of the channel construction is optimized and adjusted by the predicted emission blocking force to obtain the optimized model of the channel construction.

9. The dynamic ventilation management method based on multi-parameter detection of air quality in coal mines according to claim 1, characterized in that, By constructing an optimization model for the aforementioned channel and simulating and evaluating air parameters, risk node segments are obtained, including: After each coal mine tunnel is excavated, emission simulation is performed using the optimized tunnel structure optimization model to obtain the air quality optimization simulation parameters corresponding to each tunnel location. Multiple threshold segments are monitored for the air quality optimization simulation parameters to obtain a set of channel points corresponding to each threshold segment, wherein each threshold segment corresponds to a risk level; In the set of channel points corresponding to each threshold segment, all adjacent channel points form the risk node segment corresponding to the same risk level.

10. A dynamic ventilation management system based on multi-parameter detection of air quality in coal mines, characterized in that, include: The first acquisition unit is used to acquire point cloud data collected by the UAV in the coal mine tunnel. The point cloud data includes point cloud coordinates and image data corresponding to each point cloud coordinate. The construction unit is used to perform coal mine geological layer analysis and construct a coal mine passage structure model based on the point cloud data. The deployment analysis unit is used to perform sensor deployment analysis based on the coal mine tunnel construction model to obtain sensor cluster deployment data for sensor cluster deployment. The deployment data includes deployment locations and deployment types. The second acquisition unit is used to acquire the air quality sensing parameters of the deployed sensor cluster; The adjustment unit is used to optimize and adjust the coal mine tunnel construction model based on the air quality sensing parameters and the real-time excavation data of the coal mine tunnel, so as to obtain an optimized tunnel construction model. The evaluation unit is used to simulate and evaluate air parameters through the channel construction optimization model to obtain risk node segments; as well as The generation unit is used to optimize ventilation based on the risk node segment and generate a corresponding ventilation management configuration scheme.

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