Mining inspection method and system based on ventilation parameter monitoring

Abstract

The invention provides a mining inspection method and system based on ventilation parameter monitoring, and relates to the technical field of data processing. The method comprises the following steps: constructing a mine tunnel three-dimensional model according to mine tunnel structure information, and generating an optimal inspection path based on the mine tunnel three-dimensional model and distribution characteristics of a monitoring area; the inspection robot is controlled to conduct autonomous inspection based on the optimal inspection path, and mine ventilation parameters collected by the inspection robot are received; according to roadway section information acquired by the inspection robot, mine ventilation parameters corresponding to all wind measurement points in the section are determined, a wind flow distribution feature map is generated, and a ventilation abnormal area is recognized based on the feature map; and constructing a ventilation parameter time sequence model corresponding to the preset wind measurement point based on the mine ventilation parameters, performing ventilation parameter prediction by using the time sequence model, and determining a potential risk area according to a prediction result. According to the technical scheme, the accuracy and comprehensiveness of mine ventilation monitoring can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a mine inspection method and system based on ventilation parameter monitoring. Background Art

[0002] Accurate measurement and inspection of mine ventilation parameters are crucial for mine safety. However, existing technologies rely primarily on fixed wind measuring devices or manual inspections for ventilation parameter monitoring, which have numerous drawbacks in complex mine environments.

[0003] Fixed wind measuring devices are usually placed at key ventilation nodes, but the number of measuring points is limited, making it difficult to fully reflect wind flow changes. Tunnel structure, equipment layout, and production activities affect the wind flow field, which may cause sudden changes in local areas. Fixed devices cannot be flexibly adjusted, resulting in blind spots in key area monitoring, making it difficult to detect ventilation anomalies in a timely manner. Although manual inspections can supplement the shortcomings of fixed wind measuring devices, the inspection cycle is long, is greatly affected by human factors, and measurement accuracy and data integrity are difficult to guarantee. In large-section tunnels, eddy current areas, or high-risk work areas, inspections are difficult to carry out continuously, and missed inspections and errors are prone to occur. In addition, manual recording methods are highly subjective, lack standardized management, and long-term trend analysis is difficult, making it difficult to detect potential ventilation risks in a timely manner.

[0004] In summary, the accuracy and comprehensiveness of mine ventilation monitoring in existing technologies are difficult to guarantee, and cannot meet the needs of mine safety management for high-precision and dynamic monitoring. Summary of the Invention

[0005] The purpose of the embodiments of the present disclosure is to provide a mine inspection method and system based on ventilation parameter monitoring, thereby improving the accuracy and comprehensiveness of mine ventilation monitoring at least to a certain extent.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0007] According to a first aspect of an embodiment of the present disclosure, a mine inspection method based on ventilation parameter monitoring is provided, the method comprising: acquiring measurement data of a target area through a plurality of types of sensors, and generating raw data including spatial position information and measurement attribute information; performing time alignment processing and spatial coordinate conversion processing on the raw data, and adjusting the data generated by each sensor to a unified time base and spatial reference coordinate system; based on the data adjusted by the time-space synchronization module, performing fusion processing on the multi-source data according to a weighted algorithm, and generating three-dimensional terrain data of the target area; dividing the target area into a plurality of task areas and generating corresponding operation instructions based on the distribution characteristics of the three-dimensional terrain data and the operation status information of a plurality of operation platforms; coordinating the measurement behaviors of the operation platforms in different task areas according to the operation instructions, and collecting and updating the operation status information of each operation platform in real time.

[0008] According to a second aspect of an embodiment of the present disclosure, a mine inspection system based on ventilation parameter monitoring is provided, the system comprising: a path generation module for constructing a three-dimensional model of a mine tunnel according to mine tunnel structure information, and generating an optimal inspection path based on the three-dimensional model of the mine tunnel and the distribution characteristics of the monitoring area; a parameter acquisition module for controlling an inspection robot to perform autonomous inspection based on the optimal inspection path, and receive mine ventilation parameters collected by the inspection robot; an abnormality identification module for determining the mine ventilation parameters corresponding to each wind measuring point in the cross-section according to the tunnel cross-section information obtained by the inspection robot, generating a wind flow distribution characteristic map, and identifying ventilation abnormality areas based on the characteristic map; a risk prediction module for constructing a ventilation parameter time series model corresponding to preset wind measuring points based on the mine ventilation parameters, using the time series model to predict ventilation parameters, and determining potential risk areas based on the prediction results.

[0009] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the above-mentioned mine inspection method based on ventilation parameter monitoring is implemented.

[0010] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the mining inspection method based on ventilation parameter monitoring is implemented.

[0011] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects:

[0012] The mine inspection method based on ventilation parameter monitoring in the exemplary embodiments of the present disclosure first constructs a three-dimensional mine tunnel model based on mine tunnel structural information and utilizes the distribution characteristics of the monitored area to generate an optimal inspection path. This allows inspection path planning to adapt to the complexity of the mine environment, reducing measurement blind spots caused by improper path planning during the inspection process and improving inspection coverage and efficiency. Through autonomous inspections based on the optimal inspection path, the inspection robot can operate stably in the mine environment, avoiding the measurement errors and data loss caused by human factors in traditional manual inspections, thereby ensuring the accuracy of ventilation parameter monitoring.

[0013] Secondly, during the inspection process, the inspection robot collects mine ventilation parameters and, combined with tunnel cross-sectional information, determines the ventilation parameters at the wind measurement point. This generates a characteristic wind flow distribution map, making the spatial distribution of wind flow changes more intuitive and clear. Compared to single-point measurement methods, this provides a more comprehensive reflection of the ventilation status within the mine. This characteristic wind flow distribution map can be used to identify areas of ventilation anomalies. This no longer relies on data from a single measurement point, but rather on spatial ventilation characteristics. This improves the accuracy of anomaly detection and helps identify local anomalies in the mine ventilation system.

[0014] Furthermore, a ventilation parameter time series model based on mine ventilation parameters is constructed at pre-set wind measurement points. This allows wind measurement data to not only reflect the current ventilation status but also conduct trend analysis based on historical data, addressing the difficulty of traditional monitoring methods in capturing the patterns of ventilation parameter changes. Using this time series model to predict ventilation parameters, future ventilation status trends can be quantified, enabling predictions before ventilation anomalies occur, improving the foresight of risk identification.

[0015] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0017] Figure 1 A flowchart of a mine inspection method based on ventilation parameter monitoring according to some embodiments of the present disclosure is schematically shown.

[0018] Figure 2 A flowchart of a method for identifying an abnormal ventilation area according to some embodiments of the present disclosure is schematically shown.

[0019] Figure 3 A schematic diagram of a visual interactive interface of a mine inspection system according to some embodiments of the present disclosure is schematically shown.

[0020] Figure 4 A block diagram of a mining inspection system according to some embodiments of the present disclosure is schematically shown.

[0021] Figure 5 A schematic structural diagram of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0022] Figure 6 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0023] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0024] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of this specification, as detailed in the appended claims.

[0025] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. As used in this specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0026] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information without departing from the scope of this specification. Depending on the context, the term "if" as used herein may be interpreted as "when," "when," or "in response to determining."

[0027] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0028] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present disclosure.

[0029] Furthermore, the drawings are schematic illustrations only and are not necessarily drawn to scale. The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically separate entities. In other words, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0030] Existing mine ventilation monitoring relies primarily on fixed wind measuring devices and manual inspections. However, the former has limited measurement points and struggles to cover areas with fluctuating wind flows. The latter, however, suffers from long inspection cycles and significant human influence, leading to insufficient data accuracy and integrity. In complex tunnels and high-risk areas, manual inspections are prone to missed inspections and errors. Furthermore, subjective recording methods and a lack of standardized management make it difficult to detect and issue early warnings of ventilation anomalies.

[0031] In order to solve some or all of the above technical problems, the present disclosure provides a mine inspection method based on ventilation parameter monitoring. Figure 1 The following schematically illustrates a flow chart of a mine inspection method based on ventilation parameter monitoring according to some embodiments of the present disclosure. Figure 1 As shown, the mine inspection method based on ventilation parameter monitoring may include the following steps:

[0032] Step S110, constructing a three-dimensional mine tunnel model according to the mine tunnel structure information, and generating an optimal inspection path based on the three-dimensional mine tunnel model and the distribution characteristics of the monitoring area;

[0033] Step S120, controlling the inspection robot to perform autonomous inspection based on the optimal inspection path, and receiving the mine ventilation parameters collected by the inspection robot;

[0034] Step S130, determining the mine ventilation parameters corresponding to each wind measurement point in the cross-section based on the tunnel cross-section information acquired by the inspection robot, generating a wind flow distribution characteristic map, and identifying ventilation abnormality areas based on the characteristic map;

[0035] Step S140: constructing a ventilation parameter time series model corresponding to a preset wind measurement point based on the mine ventilation parameters, using the time series model to predict the ventilation parameters, and determining the potential risk area according to the prediction result.

[0036] During the actual operation, the mine tunnels are first modeled in detail using 3D modeling technology based on their structural information to ensure that the model accurately reflects the mine's geometry and spatial characteristics. This model then uses an optimization algorithm to generate an optimal inspection route, taking into account the distribution characteristics of the mine's monitoring area, such as the tunnel length, width, height, ventilation equipment layout, and the location of production facilities. This ensures that the inspection route covers all critical areas and avoids blind spots or duplicate inspections caused by improper route selection, thereby improving inspection efficiency and coverage. After the optimal inspection route is generated, the inspection robot is controlled to conduct autonomous inspections along this route. The inspection robot collects real-time ventilation parameters, including target gas concentration, wind speed, temperature, humidity, and air pressure. The inspection robot automatically collects this data using built-in sensors and transmits it to the ground control center via wireless communication, enabling real-time monitoring of the mine ventilation system.

[0037] During the inspection process, the inspection robot acquires tunnel cross-sectional information and, combined with current measurement data, analyzes ventilation parameters at each wind measurement point to generate a corresponding wind flow distribution characteristic map. This characteristic map effectively displays the wind flow conditions within the mine, clearly showing the wind speed, gas concentration distribution, and other key parameters in each area. Based on this wind flow distribution characteristic map, image processing and data analysis techniques are used to identify areas of ventilation anomalies, such as sudden changes in wind speed or excessive gas concentrations, and to promptly alert inspection personnel and mine management to take appropriate measures.

[0038] Finally, based on the collected mine ventilation parameters, a ventilation parameter time series model was constructed for pre-set wind measurement points. This model, combining historical data with real-time monitoring data, applied time series analysis to predict future ventilation conditions. The predictions from the time series model were used to analyze future trends in the mine ventilation system and identify potential risk areas. By combining predictions with real-time monitoring, potential ventilation system failure points can be identified in advance, allowing for timely adjustments and maintenance to ensure safe ventilation in the mine.

[0039] Below, the technical details of the above-mentioned mine inspection method based on ventilation parameter monitoring will be introduced in detail in other embodiments of the present disclosure.

[0040] In step S110, a three-dimensional mine tunnel model is constructed based on the mine tunnel structure information, and an optimal inspection path is generated based on the three-dimensional mine tunnel model and the distribution characteristics of the monitoring area. The mine tunnel structure information can represent the geometric shape, size, relative position, ventilation equipment layout and other physical characteristic data of each tunnel inside the mine, including but not limited to the length, width, height, location of ventilation facilities and the overall layout of the mine. The three-dimensional mine tunnel model can represent a digital three-dimensional representation of the spatial structure of the mine tunnel constructed based on the mine tunnel structure information using computer-aided design or three-dimensional modeling technology. The distribution characteristics of the monitoring area can refer to the spatial distribution characteristics of the area in the mine where ventilation parameters need to be monitored.

[0041] In some embodiments, constructing a three-dimensional mine tunnel model based on mine tunnel structure information specifically includes the following technical steps:

[0042] The first step is to obtain the tunnel structure information corresponding to the target mine and determine the target monitoring area based on this information. Specifically, the appropriate target monitoring area can be selected based on the geometry, size, relative position, ventilation equipment layout, and other physical characteristics of each tunnel within the target mine. The selected target monitoring area will provide the spatial basis for subsequent ventilation parameter monitoring and inspection route planning.

[0043] The second step is to determine the point cloud data set corresponding to the target monitoring area, and construct a tunnel structure surface based on the point cloud data set. The tunnel structure surface is expressed as:

[0044]

[0045] Among them, S(x, y) represents the tunnel structure surface, a mn represents the surface fitting coefficient of the mth row and nth column, φ m (x) represents the mth order basis function, ψ n (y) represents the nth order basis function, M represents the expansion order in the x direction, N represents the expansion order in the y direction, and x and y represent the horizontal coordinate and vertical coordinate in the mine plane coordinate system, respectively.

[0046] For example, a 3D laser scanner, total station, or other high-precision measurement equipment can be used to collect spatial data of the target monitoring area of ​​a mine tunnel to form a point cloud data set. The point cloud data set can be represented as a set of discrete spatial coordinate points, which can be described by the mathematical expression:

[0047]

[0048] Among them, p i =(x i,y i , z i ) represents the spatial coordinate point on the tunnel surface of the target monitoring area, and N is the total number of sampling points.

[0049] The third step is to discretize the tunnel cross-section according to the tunnel structure surface, select multiple measurement cross-sections, and determine the cross-sectional geometric features corresponding to each cross-section based on the discrete point set on the cross-section. Specifically, a series of measurement cross-sections are generated by cutting the tunnel structure surface. The selection of cross-sectional positions can be combined with the geometric changes of the tunnel, the characteristics of the wind flow, and the distribution of key monitoring points. Then, the geometric features of each measurement cross-section are calculated based on the discretization method, including the area A s , perimeter L s And the curvature distribution function κ(x, y), where:

[0050]

[0051] Where Ω represents the cross-sectional area, represents the cross-section boundary, S represents the tunnel structural surface, and x and y represent the horizontal and vertical coordinates, respectively, in the mine plane coordinate system. Extracting cross-section geometric features improves the accuracy of subsequent finite element analysis and provides more accurate geometric input for calculating the distribution of mine ventilation parameters.

[0052] The fourth step is to construct the three-dimensional model of the mine tunnel using the finite element meshing method based on the tunnel surface and the cross-sectional geometric features. Specifically, the three-dimensional space of the mine tunnel is meshed using the finite element method to form a three-dimensional mine tunnel model G, which is defined as:

[0053] G=(V,E)

[0054] Here, V represents the set of mesh vertices, and E represents the connectivity of mesh elements. The construction of a three-dimensional mine tunnel model not only provides tunnel topology information for inspection route planning but can also be used to calculate ventilation parameter distribution and identify abnormal areas.

[0055] In some embodiments, based on the three-dimensional mine tunnel model and the distribution characteristics of the monitoring area, generating an optimal inspection path specifically includes the following technical steps:

[0056] The first step is to determine the inspection points based on the three-dimensional model of the mine tunnel and the distribution of support structures and equipment distribution characteristics of the monitoring area, and establish connections based on the passable relationships between the inspection points to form an inspection path network. Among them, the distribution of support structures can represent the spatial layout of various support systems used to maintain stability inside the mine tunnel, including anchors, steel frames, shotcrete supports, meshes and other supporting structures. The equipment distribution characteristics can represent the spatial position of fixed equipment in the mine tunnel and its impact on the inspection path, including fans, pipelines, cables, conveyor belts and other facilities. The inspection points can represent the locations that the inspection robot needs to reach during the inspection mission. The inspection path network can represent the network structure composed of inspection points and the passable paths between them.

[0057] For example, the three-dimensional model of the mine tunnel can be used to obtain the spatial geometric information of the tunnel, including the tunnel width, slope, intersection location and local curvature changes, to ensure that the inspection path can cover all areas that need to be monitored. Secondly, based on the distribution of the mine tunnel support structure, identify structures that may affect the passage of the inspection robot, such as support piles, anchors, steel frames, etc., and eliminate inaccessible or restricted areas to ensure the feasibility of the inspection path. In addition, considering the equipment distribution characteristics inside the tunnel, such as fans, pipelines, conveyor belts, cables and other fixed facilities, by comparing the equipment layout with the inspection task requirements, the inspection points are reasonably selected so that they can not only cover the key ventilation parameter collection area, but also avoid obstacles and reduce the limiting factors in path planning.

[0058] The second step is to calculate the cost of each inspection path in the inspection path network based on the distance between inspection points, the inspection robot's travel speed in different areas, and the degree of wind disturbance. This calculates the inspection path cost. The inspection path cost represents the cost of the inspection robot traveling along a particular inspection path and is used to evaluate the advantages and disadvantages of different inspection paths.

[0059] In some embodiments, the inspection path cost can be obtained by the following technical steps: First, according to:

[0060] C ij =α d ×d ij +α v ×T ij +α Φ ×Φ ij

[0061] Determine the path cost between different adjacent inspection points in the inspection path, where C ij Indicates that from the inspection point P i to P j The path cost, d ij Indicates inspection point P i to Pj The Euclidean distance between ij Indicates that the inspection robot is on path e ij The travel time at ij Represents path e ij The degree of wind disturbance at α d , α v , α Φ Represents the weighting coefficient.

[0062] Then, based on the inspection path network and the path costs between the different adjacent inspection points, according to:

[0063]

[0064] Determine the inspection path cost corresponding to different inspection paths, where C total represents the inspection path cost, E represents the inspection path network, x ij ∈{0, 1} represents the path selection variable. When the path e ij When selected x ij =1, otherwise x ij =0.

[0065] In the third step, based on the patrol path costs, an optimization algorithm is used to select the optimal patrol path that minimizes the total cost and covers all monitoring areas. Specifically, during the optimization of the patrol path network, to ensure that the patrol path has the lowest total cost and covers all monitoring areas, an optimization method based on integer linear programming can be used to solve the optimal patrol path using the following mathematical modeling and constraints.

[0066] First, define the optimization objective function of the inspection path: min∑ (i，j)∈E C ij ·x ij Then, define the following constraints:

[0067] Inspection path connectivity constraints: Ensure that the inspection path forms a complete closed path and meets the following conditions:

[0068]

[0069] Here, H represents the set of inspection points. This constraint ensures that the starting point and the end point of the path are connected to form a valid inspection cycle. E represents the inspection path network.

[0070] Loop elimination constraints: Prevent unnecessary loops from occurring by eliminating sub-inspection paths:

[0071]

[0072] Among them, u iand u j Represent inspection points P i and P j The order of visits in the path, |V| represents the total number of inspection points. This constraint ensures that the path does not form unnecessary internal loops.

[0073] By solving the above optimization problem, the inspection path with the minimum total cost can be obtained, which can cover all monitoring areas.

[0074] In step S120, the inspection robot is controlled to perform autonomous inspection based on the optimal inspection path, and receive the mine ventilation parameters collected by the inspection robot. Among them, the mine ventilation parameters can represent key physical quantities used to characterize the state of the mine ventilation system, including gas concentration, airflow characteristics, environmental conditions and other aspects. Exemplarily, the inspection robot receives and parses the optimal inspection path, which consists of a sequence of inspection points and connecting paths. The inspection robot traverses the inspection points in turn, travels along the planned path, and adjusts the driving speed and direction in real time to ensure that the path deviation is controlled within the allowable range. During the inspection process, the multi-sensor system carried by the inspection robot collects mine ventilation parameters in real time, specifically including target gas concentration measurement, airflow characteristic measurement and environmental parameter measurement.

[0075] In some embodiments, receiving the mine ventilation parameters collected by the inspection robot specifically includes the following steps:

[0076] First, the wireless communication component is used to obtain the original parameters of mine ventilation collected by the inspection robot, and the original parameters of mine ventilation include one or more of target gas concentration, wind speed, temperature, humidity and air pressure.

[0077] Then, the original parameters of the mine ventilation are subjected to denoising and outlier detection by a filtering algorithm, and the abnormal data are removed to obtain the mine ventilation parameters. For example, the original parameters of the mine ventilation can be denoised by using other suitable methods such as sliding mean filtering, Kalman filtering or wavelet transform denoising. The original parameters of the mine ventilation can be subjected to outlier detection by using other suitable methods such as Z-score method, density clustering detection and outlier detection. Finally, the outliers can be filled and repaired by a linear interpolation method to obtain the mine ventilation parameters.

[0078] In step S130, based on the tunnel cross-section information obtained by the inspection robot, the mine ventilation parameters corresponding to each wind measuring point in the cross-section are determined, a wind flow distribution characteristic map is generated, and ventilation abnormality areas are identified based on the characteristic map. The tunnel cross-section information can represent a set of parameters used to describe the geometric structure and spatial characteristics of the mine tunnel, including information such as cross-sectional shape, area, perimeter, wall roughness, support structure distribution, local obstacles, etc., which can be measured by laser scanning or ultrasonic ranging. The wind measuring point can represent a set of discrete spatial positions selected in the tunnel cross-section for measuring ventilation parameters. The wind flow distribution characteristic map can represent a spatial ventilation parameter distribution map obtained by interpolation calculation based on the mine ventilation parameters at the wind measuring point, which can be in the form of a contour map, a vector flow field map, etc. The ventilation abnormality area refers to an area where the mine ventilation state is abnormal, which is usually manifested as wind flow short circuit, abnormally low wind speed, excessive concentration of high-risk gases, or reverse wind flow.

[0079] In some embodiments, reference Figure 2 As shown, identifying the ventilation abnormality area based on the characteristic map specifically includes the following technical steps:

[0080] Step S210: Based on the location of the wind measurement points, the characteristic map is matched with the three-dimensional model of the mine tunnel to form a spatial distribution data set. Specifically, first, the tunnel cross-section information obtained by the inspection robot is combined with the three-dimensional model of the mine tunnel to perform spatial mapping of the location of the wind measurement points. Assume that the three-dimensional model of the mine tunnel is represented by a finite element mesh. For each wind measurement point P i =(x i ,y i , z i ), and the wind flow distribution characteristic map F(x, y, z) is mapped to the grid cells corresponding to the three-dimensional model of the mine tunnel through the spatial interpolation method:

[0081] F i =F(x i ,y i , z i )

[0082] Among them, F i Indicates wind measurement point P ， The wind flow parameter values ​​at the location include ventilation parameters such as wind speed, wind direction, and target gas concentration.

[0083] During the mapping process, the inverse distance weighted interpolation (IDW) method is used to optimize the matching between wind measurement points and grid vertices:

[0084]

[0085] in, is the weight, d ，represents the Euclidean distance from the wind measurement point to the grid point, and p represents the weight attenuation exponent, which can be set to p=2 to reduce the influence of distant wind measurement points.

[0086] Finally, a spatial distribution dataset is formed:

[0087] D={(P i , F i )|i=1,2,...,N}

[0088] Where D represents the matching set of wind measurement points and wind flow parameters in the three-dimensional space of the mine tunnel, and N represents the number of wind measurement points.

[0089] Step S220: Determine the ventilation parameter change gradient of the wind measurement point based on the spatial distribution data set. Specifically, define the gradient vector:

[0090]

[0091] in, Represent the rate of change of wind flow parameters in the x, y, and z directions respectively.

[0092] The rate of change can be estimated using the central difference method:

[0093]

[0094] Among them, Δx, Δy, and Δz represent the relative position increments of the wind measurement points in the grid cells.

[0095] At the same time, set the modulus of the wind flow change rate:

[0096]

[0097] Among them, G(P i ) represents the wind measurement point P i The gradient of ventilation parameter change at .

[0098] Finally, the wind measurement point change gradient dataset is formed:

[0099] D G ={(P i , G(P i ))|i=1,2,...,N}

[0100] Step S230: Based on the preset ventilation stability threshold, screen out abnormal wind measurement points where the ventilation parameter change gradient is abnormal. For example, in order to identify abnormal wind measurement points, a ventilation stability threshold can be set. When the wind flow change gradient of a wind measurement point exceeds the threshold, it is marked as an abnormal point D. anomaly .

[0101] Step S240, constructs the ventilation anomaly area based on the distribution of the abnormal wind measurement points and the mine tunnel structure information. Specifically, first, based on the spatial distribution characteristics of the abnormal wind measurement points, analyze their distribution pattern in the mine tunnel, identify high-density clustered areas, and eliminate isolated points to eliminate abnormal interference caused by measurement errors. On this basis, combined with the geometric shape of the mine tunnel, the preliminary range of the ventilation anomaly area is divided to ensure that its boundary conforms to the mine ventilation law and matches the actual mine tunnel structure. Next, based on the topological relationship of the mine tunnel, the abnormal area is spatially projected so as to clarify its position and range in the three-dimensional model of the mine tunnel. By analyzing the relative position of the abnormal area in the mine tunnel, it is determined whether it is located in the main air flow channel, the local return air area or the air flow short-circuit point to identify the possible cause of the anomaly. Subsequently, the constructed ventilation anomaly area is graded, and the abnormal area is divided into different levels according to factors such as the degree of anomaly, duration, and wind flow disturbance intensity to provide a more refined ventilation anomaly analysis. Finally, the constructed ventilation abnormality areas will be mapped to the mine ventilation monitoring system, and visual early warning information will be generated so that managers can take ventilation control measures in a timely manner to optimize the mine ventilation environment.

[0102] For example, the construction of ventilation abnormality area can be carried out based on the Euclidean distance clustering method. First, the Euclidean distance clustering is used to identify adjacent abnormal points to form a preliminary abnormal area: R k ={P i ∣P i ∈D anomaly , d(P i , P j ) <d th}, where R k represents the kth ventilation abnormality area, d th is the maximum allowable distance between adjacent abnormal points. Then, combined with the mine tunnel structure information, the abnormal wind measurement points in the preliminary abnormal area are projected onto the three-dimensional mine tunnel model: Proj(R k )={(x, y, z)∣(x, y, z)∈G, (x, y, z) is located in R k The Delaunay triangulation method is used to generate a three-dimensional grid of the abnormal area: T = Delaunay (R k ), where T represents the grid unit of the abnormal area. Finally, the three-dimensional representation of the ventilation abnormal area is obtained. :

[0103]

[0104] Where K represents the number of abnormal areas, and k represents the kth ventilation abnormal area.

[0105] In step S140, a ventilation parameter time series model corresponding to a preset wind measuring point is constructed based on the mine ventilation parameters, the ventilation parameters are predicted using the time series model, and the potential risk area is determined based on the prediction results. Among them, the ventilation parameter time series model can represent a mathematical model used to describe the dynamic change law of mine ventilation parameters in the time dimension. It is constructed based on the ventilation parameter sequence of the wind measuring point at different times, and can reflect the trend and periodic changes of the air flow state in the mine. The model can adopt different time series modeling methods, including but not limited to autoregressive moving average model (ARMA), autoregressive integrated moving average model (ARIMA), long short-term memory neural network (LSTM), variational autoencoder (VAE) time series prediction model, etc.

[0106] In some embodiments, a ventilation parameter time series model corresponding to a preset wind measurement point is constructed based on the mine ventilation parameters, specifically comprising the following technical steps:

[0107] Time series data are constructed based on the mine ventilation parameters at the preset wind measurement points at different times.

[0108] according to:

[0109]

[0110] The time series data is smoothed to obtain the ventilation parameter time series model, wherein: represents the ventilation parameter time series model, W represents the sliding window size, U i (t) represents the ventilation parameter value of wind measurement point i at time t, and k represents the relative time offset within the sliding window.

[0111] Based on the smoothed time series data, a ventilation parameter time series model corresponding to the preset wind measurement point is constructed.

[0112] In some embodiments, based on the smoothed time series data, a ventilation parameter time series model corresponding to the preset wind measurement point is constructed, which specifically includes the following technical steps:

[0113] The smoothed time series data is normalized to obtain normalized time series data corresponding to each of the wind measuring points. Specifically, based on the historical ventilation parameter data of the wind measuring point, the numerical range of each ventilation parameter is determined, including the maximum value, minimum value and statistical distribution characteristics. For different ventilation parameters (such as wind speed, air pressure, temperature, humidity, target gas concentration, etc.), the maximum and minimum values ​​of their time series are calculated respectively to establish a normalized reference range. Then, the Min-Max Normalization method is used to map the original ventilation parameter data to the interval range [0,1] or [-1,1] to reduce the influence of different ventilation parameter dimensions, so that the data of different wind measuring points are comparable.

[0114] Based on the normalized time series data, a ventilation parameter time series model corresponding to the preset wind measurement point is constructed. The ventilation parameter time series model is expressed as:

[0115]

[0116] Among them, V i (t) represents the predicted value vector of all ventilation parameters at wind measurement point i, P and Q represent the autoregressive order and moving average order respectively, α p and β q denote the vectors of autoregressive and moving average coefficients, ε i (t) is the noise term vector, Represents the normalized value of parameter k.

[0117] In some embodiments, ventilation parameter prediction is performed using the time series model, and potential risk areas are determined based on the prediction results, specifically including the following technical steps:

[0118] First, based on the time series model, ventilation parameters at the preset wind measurement points are predicted to determine predicted ventilation parameter values ​​for a target future time period. Specifically, based on the constructed ventilation parameter time series model, ventilation parameter data at the wind measurement points at historical moments are input, and a time series prediction method is used to perform modeling and calculations to obtain predicted ventilation parameter values ​​for the target future time period.

[0119] Then, based on the predicted ventilation parameter values, the deviation between the predicted values ​​and a preset safety threshold is determined. Specifically, after the prediction is completed, the predicted ventilation parameter values ​​are compared with preset safety thresholds in the mine ventilation safety management system to assess whether the future ventilation conditions are within a reasonable range. The safety thresholds can be set according to mine ventilation regulations or adaptively calculated through historical data analysis.

[0120] Finally, the abnormal wind measurement points are determined based on the deviation, and the potential risk area is determined based on the spatial distribution of the abnormal wind measurement points. For example, when the deviation exceeds the set threshold range, the wind measurement point is marked as an abnormal wind measurement point, and the degree of excess is recorded, and the abnormal wind measurement points are spatially clustered to obtain the potential risk area. In addition, the distribution characteristics of the abnormal wind measurement points can be analyzed in combination with the three-dimensional model of the mine tunnel: if the abnormal points are concentrated in a certain area and intersect with the main airflow channel, there may be airflow short circuit or turbulence; if the abnormal points are continuously distributed along the tunnel, it may be an abnormality caused by pressure changes in the ventilation system; if the abnormal points mainly appear in high-gas or low-oxygen areas, there may be a risk of harmful gas accumulation.

[0121] The mine inspection method based on ventilation parameter monitoring in the above embodiment realizes the intelligent, precise and predictive mine ventilation monitoring by integrating multiple technical means such as three-dimensional modeling of mine tunnels, optimal inspection path planning, ventilation parameter measurement and time series analysis, and identification of ventilation abnormality areas.

[0122] First, during the inspection process, the inspection robot acquires tunnel cross-sectional information. Combined with the ventilation parameters corresponding to different wind measurement points, a wind flow distribution characteristic map can be constructed, accurately reflecting the wind flow distribution within the mine. Compared to fixed wind measurement devices, the wind measurement method provided in this disclosure is not restricted by the layout of measurement points. It can establish a complete ventilation status dataset throughout the entire mine tunnel space, avoiding the local monitoring blind spots caused by insufficient measurement point distribution in related technologies.

[0123] Furthermore, by combining time series data from wind measurement points to construct a ventilation parameter time series model, and predicting ventilation parameters based on this time series model, the present disclosure can further achieve active prediction of future ventilation conditions on top of traditional passive monitoring. Through time series data smoothing, normalization, and autoregressive modeling, the prediction results have higher accuracy and stability. The prediction results can not only be used to identify anomalies in advance, but also identify potential risk areas by analyzing the spatial distribution of abnormal wind measurement points, thereby achieving accurate early warning.

[0124] Furthermore, during inspection route planning, the impact of wind disturbances on the inspection robot, path distance, and operating speed are comprehensively considered. The cost of the inspection route is calculated, and an optimization algorithm is used to generate the optimal inspection route. Compared to traditional methods based on the shortest path or fixed inspection routes, the disclosed path optimization solution can dynamically adjust according to real-time changes in the mine tunnel structure and wind environment, thereby ensuring the stability of the inspection process and reducing the impact of wind disturbances on measurement accuracy.

[0125] Furthermore, in terms of identifying ventilation abnormality areas, a density clustering algorithm is used to spatially cluster abnormal wind measurement points, and the regional boundaries are fitted in combination with the three-dimensional model of the mine tunnel, making the delineation of abnormal areas more accurate and avoiding the limitations of traditional methods that rely on a single wind measurement point for local judgment.

[0126] It should be noted that although the steps of the method disclosed herein are depicted in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in that particular order, or that all steps must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one, and / or one step may be decomposed into multiple steps.

[0127] Secondly, in the exemplary embodiment of the present disclosure, a mine inspection system based on ventilation parameter monitoring is also provided. The visual interactive interface of the mine inspection system is as follows: Figure 3 As shown in the figure, it includes multiple functional modules, including main fan online monitoring, main fan air volume details, resistance distribution monitoring, total required air volume, total return air volume, local fan online monitoring, working face air volume, and early warning alarms, enabling real-time monitoring and dynamic analysis of the mine ventilation system. The main fan online monitoring section displays the real-time operating status of the main fan, including air volume measurement data, fan operating status indication, and key parameter trend changes. The main fan air volume details section provides a historical curve of air volume changes over time, supporting analysis of fan air volume trend changes and assisting in determining ventilation system stability. The resistance distribution monitoring section displays the resistance contribution of different mining areas, including the specific resistance values ​​of the pit parking lot, mid-block mining area, mid-block water tank, and pit parking lot, as well as their percentage of the total resistance. This helps analyze the ventilation resistance distribution of each area and optimize air volume allocation. The total required air volume and total return air volume sections display the mine's overall ventilation demand and actual return air volume, ensuring that the ventilation system meets production needs and preventing insufficient air volume or localized accumulation of harmful gases. The local fan online monitoring part monitors the operating status of the local fans in the mine in real time and displays its parameters such as air volume, wind speed, and wind pressure to ensure the stability of local ventilation. The working face air volume part provides real-time air volume measurement data and its corresponding timestamp for each measuring point, covering multiple wind measurement points such as the return air lane and the return air chute in the second mining area, so that inspection personnel can obtain the ventilation status of the working face at any time and conduct trend analysis in combination with historical data. The early warning alarm part is used to display the abnormal air volume, abnormal wind speed and parameter alarm information detected by the current system, ensuring that inspection personnel can detect potential risks in a timely manner and take corresponding measures to prevent the occurrence of mine ventilation accidents. This visual interactive interface realizes the real-time collection, storage, display and analysis of mine ventilation monitoring data through multi-module integration, providing accurate and intuitive monitoring methods for mine ventilation management, and improving the intelligence level and safety of the inspection system.

[0128] refer to Figure 4 As shown, the mine inspection system based on ventilation parameter monitoring can be composed of a path generation module 401, a parameter acquisition module 402, an anomaly identification module 403, and a risk prediction module 404. Among them, the path generation module 401 can be used to construct a three-dimensional mine tunnel model based on the mine tunnel structure information, and generate an optimal inspection path based on the three-dimensional mine tunnel model and the distribution characteristics of the monitoring area; the parameter acquisition module 402 can be used to control the inspection robot to perform autonomous inspection based on the optimal inspection path and receive the mine ventilation parameters collected by the inspection robot; the anomaly identification module 403 can be used to determine the mine ventilation parameters corresponding to each wind measurement point in the cross-section based on the tunnel cross-section information obtained by the inspection robot, generate a wind flow distribution characteristic map, and identify ventilation abnormality areas based on the characteristic map; the risk prediction module 404 can be used to construct a ventilation parameter time series model corresponding to the preset wind measurement points based on the mine ventilation parameters, use the time series model to predict the ventilation parameters, and determine the potential risk area based on the prediction results.

[0129] It should be noted that while the detailed description above mentions several modules or units of the mine inspection system based on ventilation parameter monitoring, this division is not mandatory. In fact, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in a single module or unit. Conversely, the features and functions of a single module or unit described above can be further divided and embodied by multiple modules or units.

[0130] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned mine inspection method based on ventilation parameter monitoring is also provided.

[0131] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods, or program products. Therefore, various aspects of the present disclosure may be implemented in the following forms: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which may be collectively referred to herein as a "circuit," "module," or "system."

[0132] Refer to the following Figure 5 The electronic device 500 according to the above embodiment of the present disclosure is described. Figure 5 The electronic device 500 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0133] like Figure 5As shown, electronic device 500 is implemented as a general-purpose computing device. Components of electronic device 500 may include, but are not limited to, the aforementioned at least one processing unit 510, the aforementioned at least one storage unit 520, a bus 530 connecting various system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0134] The storage unit stores program code, which can be executed by the processing unit 510, so that the processing unit 510 performs the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of the present disclosure. The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 521 and / or a cache memory unit 522, and may further include a read-only memory unit (ROM) 523.

[0135] The storage unit 520 may also include a program / utility 524 having a set (at least one) of program modules 525, such program modules 525 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0136] Bus 530 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0137] The electronic device 500 can also communicate with one or more external devices 570 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 500, and / or any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 550. Furthermore, the electronic device 500 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 560. As shown, the network adapter 560 communicates with other modules of the electronic device 500 via a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 500, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0138] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0139] In exemplary embodiments of the present disclosure, a computer-readable storage medium is also provided, on which is stored a program product capable of implementing the aforementioned methods of this specification. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product is executed on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Methods" section of this specification.

[0140] refer to Figure 6 As shown, a program product 600 for implementing the above-mentioned mine inspection method based on ventilation parameter monitoring according to an embodiment of the present disclosure is described. The program product 600 can be a portable compact disk read-only memory (CD-ROM) and includes program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0141] The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0142] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0143] The program code contained on the readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, electromagnetic waves, etc., or any suitable combination of the foregoing.

[0144] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0145] Furthermore, the figures above are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the figures above do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0146] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0147] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0148] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A mine inspection method based on ventilation parameter monitoring, characterized in that: include: Constructing a three-dimensional mine tunnel model based on the mine tunnel structure information, and generating an optimal inspection path based on the three-dimensional mine tunnel model and the distribution characteristics of the monitoring area; Controlling the inspection robot to perform autonomous inspection based on the optimal inspection path, and receiving the mine ventilation parameters collected by the inspection robot; Determine the mine ventilation parameters corresponding to each wind measurement point in the cross-section based on the tunnel cross-section information acquired by the inspection robot, generate a wind flow distribution characteristic map, and identify ventilation abnormality areas based on the characteristic map; A ventilation parameter time series model corresponding to a preset wind measurement point is constructed based on the mine ventilation parameters, the ventilation parameters are predicted using the time series model, and potential risk areas are determined according to the prediction results.

2. The mine inspection method based on ventilation parameter monitoring according to claim 1 is characterized in that: The method of constructing a three-dimensional mine tunnel model based on the mine tunnel structure information includes: Acquire the mine tunnel structure information corresponding to the target mine, and determine the target monitoring area according to the mine tunnel structure information; Determine the point cloud data set corresponding to the target monitoring area, and construct a tunnel structure surface based on the point cloud data set. The tunnel structure surface is expressed as: Among them, S(x,y) represents the tunnel structure surface, a nn represents the surface fitting coefficient of the mth row and nth column, φ m (x) represents the mth order basis function, ψ n (y) represents the nth order basis function, M represents the expansion order in the x direction, N represents the expansion order in the y direction, x and y represent the horizontal coordinate and vertical coordinate in the mine tunnel plane coordinate system respectively; Discretizing the tunnel cross-section according to the tunnel structure surface, selecting a plurality of measurement cross-sections, and determining cross-sectional geometric features corresponding to each cross-section based on a set of discrete points on the cross-sections; According to the tunnel surface and the cross-sectional geometric features, a finite element meshing method is used to construct the three-dimensional model of the mine tunnel.

3. The mine inspection method based on ventilation parameter monitoring according to claim 1 is characterized in that: The generating of the optimal inspection path based on the three-dimensional model of the mine tunnel and the distribution characteristics of the monitoring area includes: Based on the three-dimensional model of the mine tunnel and the distribution characteristics of the support structure and equipment in the monitoring area, inspection points are determined, and connections are established between the inspection points according to the passable relationships between them to form an inspection path network; In the inspection path network, a cost is calculated for each inspection path based on the distance between inspection points, the speed of the inspection robot in different areas, and the degree of wind disturbance, so as to obtain an inspection path cost; Based on the inspection path cost, an optimization algorithm is used to select the optimal inspection path with the minimum total cost and capable of covering all monitoring areas.

4. The mine inspection method based on ventilation parameter monitoring according to claim 3 is characterized in that: The cost calculation for each inspection path to obtain the inspection path cost includes: according to: C ij =a d ×d ij +a v ×T ij +a Φ ×F ij Determine the path cost between different adjacent inspection points in the inspection path, where C ij Indicates that from the inspection point P i to P j The path cost, d ij Indicates inspection point P i to P j The Euclidean distance between ij Indicates that the inspection robot is on path e ij The travel time at ij Represents path e ij The degree of wind disturbance at α d ,α v ,α Φ represents the weighting coefficient; Based on the inspection path network and the path costs between the different adjacent inspection points, according to: Determine the inspection path cost corresponding to different inspection paths, where C total represents the inspection path cost, E represents the inspection path network, x ij ∈{0,1} represents the path selection variable. When the path e ij When selected x ij =1, otherwise x ij =0.

5. The mine inspection method based on ventilation parameter monitoring according to claim 1 is characterized in that: The receiving of the mine ventilation parameters collected by the inspection robot includes: Using a wireless communication component to obtain the original mine ventilation parameters collected by the inspection robot, the original mine ventilation parameters including one or more of target gas concentration, wind speed, temperature, humidity, and air pressure; The original parameters of the mine ventilation are subjected to denoising and outlier detection through a filtering algorithm, and the abnormal data are removed to obtain the mine ventilation parameters.

6. The mine inspection method based on ventilation parameter monitoring according to claim 1 is characterized in that: The identifying the abnormal ventilation area based on the characteristic graph includes: Based on the locations of the wind measurement points, the characteristic map is matched with a three-dimensional model of the mine tunnel to form a spatial distribution data set; determining a ventilation parameter change gradient at the wind measurement point according to the spatially distributed data set; Based on a preset ventilation stability threshold, abnormal wind measurement points where the ventilation parameter change gradient is abnormal are screened out; The ventilation abnormality area is constructed according to the distribution of the abnormal wind measurement points and the mine tunnel structure information.

7. The mine inspection method based on ventilation parameter monitoring according to claim 1 is characterized in that: The method of constructing a ventilation parameter time series model corresponding to a preset wind measurement point based on the mine ventilation parameters includes: constructing time series data based on the mine ventilation parameters at the preset wind measurement points at different times; according to: The time series data is smoothed to obtain the ventilation parameter time series model, wherein: represents the ventilation parameter time series model, W represents the sliding window size, U i (t) represents the ventilation parameter value of wind measurement point i at time t, and k represents the relative time offset within the sliding window; Based on the smoothed time series data, a ventilation parameter time series model corresponding to the preset wind measurement point is constructed.

8. The mine inspection method based on ventilation parameter monitoring according to claim 7 is characterized in that: The step of constructing a ventilation parameter time series model corresponding to the preset wind measurement point based on the smoothed time series data includes: Normalizing the smoothed time series data to obtain normalized time series data corresponding to each wind measurement point; Based on the normalized time series data, a ventilation parameter time series model corresponding to the preset wind measurement point is constructed. The ventilation parameter time series model is expressed as: Among them, V i (t) represents the predicted value vector of all ventilation parameters at wind measurement point i, P and Q represent the autoregressive order and moving average order respectively, α p and β q denote the vectors of autoregressive and moving average coefficients, ε i (t) is the noise term vector, Represents the normalized value of parameter k.

9. The mine inspection method based on ventilation parameter monitoring according to claim 1, characterized in that: The method of using the time series model to predict ventilation parameters and determining potential risk areas based on the prediction results includes: Based on the time series model, the ventilation parameters of the preset wind measurement points are predicted to determine the predicted values ​​of the ventilation parameters in the target future time period; Based on the predicted value of the ventilation parameter, determining a deviation between the predicted value and a preset safety threshold; Abnormal wind measurement points are determined according to the deviations, and the potential risk areas are determined according to the spatial distribution of the abnormal wind measurement points.

10. A mine inspection system based on ventilation parameter monitoring, characterized in that: The system comprises: A path generation module is used to construct a three-dimensional model of the mine tunnel according to the mine tunnel structure information, and generate an optimal inspection path based on the three-dimensional model of the mine tunnel and the distribution characteristics of the monitoring area; a parameter acquisition module, configured to control the inspection robot to perform autonomous inspection based on the optimal inspection path, and to receive the mine ventilation parameters collected by the inspection robot; an abnormality identification module, configured to determine the mine ventilation parameters corresponding to each wind measurement point within the cross-section based on the tunnel cross-section information acquired by the inspection robot, generate a wind flow distribution characteristic map, and identify ventilation abnormality areas based on the characteristic map; The risk prediction module is used to construct a ventilation parameter time series model corresponding to a preset wind measurement point based on the mine ventilation parameters, use the time series model to predict the ventilation parameters, and determine the potential risk area according to the prediction results.

Images