Coal mine monitoring data visualization method and device

Abstract

The present invention proposes a method and device for visualizing coal mine monitoring data, wherein the method includes: for a three-dimensional model of a roadway of any three-dimensional format type, the three-dimensional model of the roadway is merged with the roadway centerline corresponding to the three-dimensional model of the roadway to obtain a three-dimensional model of a target roadway; the target roadway to be visualized in the three-dimensional model of the target roadway is determined, and according to the three-dimensional model of the target roadway, the roadway identification and roadway path information of the target roadway and the distance attribute of each discrete triangular vertex in the target roadway are determined; according to the roadway identification and roadway path information of the target roadway and the distance attribute of each discrete triangular vertex in the target roadway, the acquired monitoring data is visualized in the target roadway according to the corresponding color. Therefore, by introducing the centerline of the roadway, the visualization of coal mine monitoring data can be realized on the three-dimensional model of the roadway of any three-dimensional format type.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for visualizing coal mine monitoring data. Background Art

[0002] Coal mine monitoring data includes ventilation data, personnel positioning data, sensor data, etc. Among them, the traditional approach to ventilation data visualization is to produce a three-dimensional model of the tunnel through the geometric information and material information of the tunnel, and then use texture flow technology on the model to achieve the wind flow effect. However, this method is limited to the fact that the three-dimensional model of the tunnel must be implemented through coding, so that the vertex and texture offset coordinates of the tunnel model can be controlled. However, in actual applications, the three-dimensional model of the tunnel may come from different modeling software, such as 3dsmax, bentley, etc., so the geometric information of the tunnel may not be obtained, resulting in the inability to achieve the wind flow effect.

[0003] The visualization of personnel positioning data and sensor data monitoring is achieved by continuously coloring the lanes to show the intensity information of specific attributes of each part of the lanes. This process is also called hot spot analysis or heat map. The traditional approach in three dimensions is generally a plane map that is mapped vertically downward to a specific object through a ground-based mode. However, this method cannot be applied to lanes because lanes are three-dimensional structures. There may be multiple lanes above and below the same location (same longitude and latitude). If a vertical downward mapping method is used, it does not meet actual needs. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the first purpose of the present invention is to propose a method for visualizing coal mine monitoring data, so as to realize the visualization of coal mine monitoring data on a three-dimensional model of a tunnel of any three-dimensional format type by introducing the tunnel centerline.

[0006] The second object of the present invention is to provide a coal mine monitoring data visualization device.

[0007] A third objective of the present invention is to provide an electronic device.

[0008] A fourth object of the present invention is to provide a computer-readable storage medium.

[0009] A fifth object of the present invention is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, a first embodiment of the present invention proposes a method for visualizing coal mine monitoring data, comprising:

[0011] For a lane three-dimensional model of any three-dimensional format type, the lane three-dimensional model is merged with the lane centerline corresponding to the lane three-dimensional model to obtain a target lane three-dimensional model, wherein the target lane three-dimensional model stores a lane identification and lane path information of at least one lane, and a distance attribute of each discrete triangular face vertex in each lane, the lane path information includes a centerline starting point and a centerline end point of the corresponding lane, and the distance attribute is a distance between a corresponding discrete triangular face vertex and a centerline starting point of the corresponding lane along the lane centerline;

[0012] Determine a target lane to be visualized in the target lane three-dimensional model, and determine a lane identification and lane path information of the target lane, and a distance attribute of each discrete triangular vertex in the target lane according to the target lane three-dimensional model;

[0013] According to the lane identification and lane path information of the target lane and the distance attribute of each discrete triangle vertex in the target lane, the acquired monitoring data is visualized in the target lane according to the corresponding color.

[0014] To achieve the above-mentioned purpose, a second embodiment of the present invention proposes a coal mine monitoring data visualization device, comprising:

[0015] A processing module, for a lane three-dimensional model of any three-dimensional format type, to fuse the lane three-dimensional model with the lane centerline corresponding to the lane three-dimensional model, so as to obtain a target lane three-dimensional model, wherein the target lane three-dimensional model stores a lane identification and lane path information of at least one lane, and a distance attribute of each discrete triangular face vertex in each lane, the lane path information includes a centerline starting point and a centerline end point of the corresponding lane, and the distance attribute is a distance between a corresponding discrete triangular face vertex and a centerline starting point of the corresponding lane along the lane centerline;

[0016] A determination module, used to determine a target lane to be visualized in the target lane three-dimensional model, and determine a lane identification and lane path information of the target lane, and a distance attribute of each discrete triangular vertex in the target lane according to the target lane three-dimensional model;

[0017] The visualization module is used to visualize the acquired monitoring data in the target lane according to the lane identification and lane path information of the target lane and the distance attribute of each discrete triangle vertex in the target lane in the corresponding color.

[0018] To achieve the above-mentioned purpose, an embodiment of the third aspect of the present invention proposes an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the coal mine monitoring data visualization method of the first aspect mentioned above.

[0019] In order to achieve the above-mentioned purpose, the fourth aspect of the present invention proposes a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the coal mine monitoring data visualization method of the first aspect.

[0020] In order to achieve the above-mentioned purpose, a fifth aspect of the present invention proposes a computer program product, including a computer program, which implements the coal mine monitoring data visualization method of the first aspect when executed by a processor.

[0021] The technical solution provided by the embodiment of the present invention has the following beneficial effects:

[0022] 1. In view of the technical problem in the related art that the wind flow effect cannot be achieved using texture flow technology when the three-dimensional model of the lane comes from different modeling software, the present invention introduces the lane center line to simulate the wind flow effect on the three-dimensional model of the lane produced by any software, and can simulate the wind flow effect in a certain middle section of any lane, and the flow direction can be controlled at the same time.

[0023] 2. In view of the technical problem that traditional vertical projection technology in related technologies cannot meet the needs of hot spot analysis in coal industry tunnels, the present invention introduces the tunnel centerline to accurately display the hot spot information of monitoring data in the tunnel in three-dimensional space, and supports three-dimensional tunnel models generated by any modeling software, and supports dynamic personnel hot spot analysis and static sensor hot spot analysis.

[0024] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0026] Figure 1 A schematic diagram of a flow chart of a method for visualizing coal mine monitoring data provided by an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of lane path information provided by an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of the distance from a designated position to a starting point of a lane provided by an embodiment of the present invention;

[0029] Figure 4 An overall technical roadmap for ventilation data visualization provided by an embodiment of the present invention;

[0030] Figure 5 A flow chart of the overall technology of personnel positioning and sensor data visualization provided by an embodiment of the present invention;

[0031] Figure 6 A schematic diagram of an original lane model provided by an embodiment of the present invention;

[0032] Figure 7 A schematic diagram of the ventilation airflow simulation effect corresponding to the original tunnel model provided in an embodiment of the present invention;

[0033] Figure 8 A schematic diagram of a thermal map simulation effect corresponding to the original tunnel model provided in an embodiment of the present invention;

[0034] Fig. 9 A schematic diagram of the wind flow effect in a certain lane section provided by an embodiment of the present invention;

[0035] Fig.10 A schematic diagram of the wind flow effects between multiple lanes provided by an embodiment of the present invention;

[0036] Fig.11 A schematic diagram of the overall ventilation effect of a mine tunnel provided by an embodiment of the present invention;

[0037] Fig.12 A schematic diagram of the structure of a coal mine monitoring data visualization device provided by an embodiment of the present invention;

[0038] Fig.13 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0040] The following describes a method and apparatus for visualizing coal mine monitoring data according to an embodiment of the present invention with reference to the accompanying drawings.

[0041] Figure 1A schematic flow chart of a method for visualizing coal mine monitoring data provided by an embodiment of the present invention.

[0042] like Figure 1 As shown, the coal mine monitoring data visualization method includes the following steps:

[0043] Step 101 , for a lane 3D model of any 3D format type, the lane 3D model is merged with a lane centerline corresponding to the lane 3D model to obtain a target lane 3D model.

[0044] Among them, the three-dimensional model of the target lane stores the lane identification and lane path information of at least one lane, as well as the distance attribute of each discrete triangle vertex in each lane. The lane path information includes the centerline starting point and centerline end point of the corresponding lane, and the distance attribute is the distance between the corresponding discrete triangle vertex and the centerline starting point of the corresponding lane along the centerline of the lane.

[0045] The three-dimensional model of the laneway may be realized by coding, or may be produced by any modeling software, which is not limited in the present invention.

[0046] In some embodiments, before implementing the rendering effect in 3D, it is necessary to obtain basic lane path information through the lane centerline data. The lane path itself has a starting point and an end point, and the same point can be the starting point of a lane and the end point of another lane. Figure 2 As shown, the starting point of the center line of lane A is Figure 2 The starting point A in the tunnel is the center line end point of the tunnel A. Figure 2 The end point A in the tunnel and the starting point of the center line of the tunnel B are Figure 2 The starting point B in the tunnel is the center line end point of the tunnel B. Figure 2 The end point B in the tunnel and the starting point of the center line of the tunnel C are Figure 2 The starting point B in the tunnel and the end point of the center line of the tunnel C are Figure 2 The end point C in the figure, where the end point of the center line of lane A is at the same position as the starting point of the center line of lane B and the starting point of the center line of lane C, that is, the end point of lane A is the starting point of lane B and also the starting point of lane C.

[0047] In some embodiments, after obtaining the basic lane path information, the distance from the designated location of the lane to the starting point can also be obtained. Note that this distance is not the straight-line distance, but the distance along the center line of the lane. Therefore, when processing the lane 3D model data, the distance attribute from the starting point to each lane triangle vertex can be added. Figure 3 As shown, the distance between point S1 in lane A and the starting point of the center line of lane A (starting point A) is Figure 3 The distance D1 shown is the distance between point S2 in lane B and the starting point of the center line of lane B (starting point B). Figure 3 The distance D2 shown is the distance between point S3 in lane C and the starting point of the center line of lane C (starting point C). Figure 3 The distance D3 is shown. In this way, during the GPU (Graphics Processing Unit) rendering process, when the position of a vertex of a certain lane triangle surface is obtained, its distance information from the starting point can also be obtained. Then, according to the strength information of the lane based on the starting point distance, the distance attribute of the vertex, and the set initial color, the color information of the vertex can be calculated. Then, the color of the vertex is updated according to time, and a heat map effect can be generated.

[0048] In some embodiments, to specify lane shading, the GPU needs to be told which lanes need to be shaded. Optionally, each lane has an ID (Identification) attribute, which is identified by a digital type. Then when processing the lane model geometry data, a lane ID attribute can be added to each vertex of the lane discrete triangle surface. At this time, the information of which lane the lane belongs to is available. Before the lane is rendered, the GPU is told the ID of the lanes that need to specify wind flow effects and heat map effects. The GPU then decides which vertices are ultimately required to be rendered. At the same time, when the heat map rendering starts, all lanes are defaulted to a uniform intensity, for example, all are 0, and use a uniform color.

[0049] It should be noted that the target laneway 3D model is identical to the laneway 3D model in terms of geometric appearance, the only difference being that the target laneway 3D model stores all data required for rendering.

[0050] Step 102, determining the target lane to be visualized in the target lane three-dimensional model, and determining the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangle vertex in the target lane according to the target lane three-dimensional model.

[0051] The target lane to be visualized in the target lane 3D model may be any lane among the lanes stored in the target lane 3D model and / or a set section of any lane among the lanes stored in the target lane 3D model.

[0052] In some embodiments, since the target lane three-dimensional model stores the lane identification and lane path information of each lane, as well as the distance attribute of each discrete triangular vertex in each lane, and the target lane to be visualized in the target lane three-dimensional model can be any lane of each lane stored in the target lane three-dimensional model and / or a set interval of any lane of each lane stored in the target lane three-dimensional model, the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangular vertex in the target lane can be determined based on the target lane three-dimensional model.

[0053] Step 103 , based on the lane identification and lane path information of the target lane and the distance attribute of each discrete triangle vertex in the target lane, the acquired monitoring data is visualized in the target lane according to the corresponding color.

[0054] In some embodiments, after determining the relevant information of the target lane to be visualized (lane identification and lane path information of the target lane, distance attributes of each discrete triangle vertex in the target lane), the acquired monitoring data can be visualized in the target lane according to corresponding colors based on this information.

[0055] In the present invention, any three-dimensional roadway model produced by modeling software can produce a thermal map effect as long as there is a corresponding center line. This is because the data used for rendering can be calculated in advance by using the center line of the roadway and stored in the three-dimensional model of the target roadway. Therefore, no matter whether the three-dimensional roadway model is realized by coding or generated by modeling software, the coal mine monitoring data can be visualized based on the data used for rendering stored in the three-dimensional model of the target roadway.

[0056] The coal mine monitoring data visualization method provided by the embodiment of the present invention obtains a target laneway three-dimensional model by fusing the laneway three-dimensional model with the laneway centerline corresponding to the laneway three-dimensional model for any three-dimensional format type, wherein the target laneway three-dimensional model stores a laneway identification and laneway path information of at least one lane, and a distance attribute of each discrete triangular face vertex in each lane, the laneway path information includes a centerline starting point and a centerline end point of the corresponding lane, and the distance attribute is a distance between the corresponding discrete triangular face vertex and the centerline starting point of the corresponding lane along the lane centerline, thereby determining the target lane to be visualized in the target lane three-dimensional model, and based on the target lane three-dimensional model, determining the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangular face vertex in the target lane, and then based on the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangular face vertex in the target lane, the acquired monitoring data is visualized in the target lane according to the corresponding color. Therefore, by introducing the tunnel centerline, the coal mine monitoring data can be visualized on the tunnel 3D model of any 3D format type.

[0057] As a possible implementation, when executing step 101, for a lane 3D model of any 3D format type, the lane 3D model is merged with the lane center line corresponding to the lane 3D model to obtain a target lane 3D model, the following steps may be specifically performed:

[0058] Step 1011: for a lane three-dimensional model of any three-dimensional format type, parse the lane centerline corresponding to the lane three-dimensional model to obtain lane path information of at least one lane.

[0059] In some embodiments, for a lane three-dimensional model of any three-dimensional format type, when there is no intersection on the center line of the lane three-dimensional model, the lane center line corresponding to the lane three-dimensional model is parsed to obtain lane path information of a lane, and the starting point of the center line of the lane is the starting point of the lane center line corresponding to the lane three-dimensional model, and the end point of the center line of the lane is the end point of the lane center line corresponding to the lane three-dimensional model. Alternatively, for a lane three-dimensional model of any three-dimensional format type, when there is an intersection on the center line of the lane three-dimensional model, the lane center line corresponding to the lane three-dimensional model is parsed, and any intersection on the center line of the lane three-dimensional model is used as the starting point or end point of the center line of the corresponding lane, thereby obtaining lane path information of multiple lanes, and the starting point of the center line of any lane is the starting point of the lane center line corresponding to any intersection or the lane three-dimensional model, and the end point of the center line of any lane is the end point of the lane center line corresponding to any intersection or the lane three-dimensional model.

[0060] Step 1012: Determine the distance attribute of each discrete triangle vertex in each lane according to the lane path information of each lane.

[0061] In some embodiments, for any lane, the distance along the pipeline between each discrete triangular surface vertex in the lane and the starting point of the center line of the lane can be calculated based on the lane path information of the lane and the position of each discrete triangular surface vertex in the lane, thereby obtaining the distance attribute of each discrete triangular surface vertex in the lane.

[0062] Step 1013: The lane identification and lane path information of each lane and the distance attribute of each discrete triangle vertex in each lane are integrated into the lane 3D model to obtain the target lane 3D model.

[0063] In some embodiments, in the process of determining the lane path information of the lane, the lane identification of each lane can be configured so that the corresponding lane is uniquely indicated by the lane identification of each lane. Thus, after obtaining the lane identification and lane path information of each lane and the distance attribute of each discrete triangular vertex in each lane, the lane identification and lane path information of each lane and the distance attribute of each discrete triangular vertex in each lane can be correspondingly stored in the lane three-dimensional model, thereby obtaining the target lane three-dimensional model.

[0064] As a possible implementation method, when executing step 102, determining the target lane to be visualized in the target lane three-dimensional model, and determining the lane identification and lane path information of the target lane and the distance attribute of each discrete triangle vertex in the target lane according to the target lane three-dimensional model, the following steps can be specifically performed:

[0065] Step 1021 : Determine any lane and / or a set section of any lane selected by the user from the target lane 3D model as the target lane to be visualized.

[0066] The set interval of any lane is determined by the two lane coordinates selected by the user. The lane coordinates are the lane interval coordinates.

[0067] It should be noted that the user can select multiple lanes and / or multiple lane sections from the target lane three-dimensional model, and the present invention does not limit this.

[0068] Step 1022, when the target lane is any lane, the lane identification and lane path information of the lane stored in the three-dimensional model of the target lane, and the distance attribute of each discrete triangular vertex in the lane are determined as the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangular vertex in the target lane.

[0069] In some embodiments, if the target lane is any lane, the lane identification and lane path information of the lane and the distance attribute of each discrete triangular vertex in the lane stored in the three-dimensional model of the target lane can be directly determined as the lane identification and lane path information of the target lane and the distance attribute of each discrete triangular vertex in the target lane.

[0070] Step 1023, when the target lane is a set interval of any lane, the lane identifier of the lane stored in the three-dimensional model of the target lane is determined as the lane identifier of the target lane, and the lane path information of the target lane is determined according to the interval coordinates selected by the user, and the distance attribute of each discrete triangle vertex in the target lane is determined according to the lane path information of the target lane.

[0071] In some embodiments, if the target lane is a set interval of any lane, the lane identification of the lane stored in the three-dimensional model of the target lane can also be directly determined as the lane identification of the target lane, but the lane path information of the target lane is different from the lane path information of the lane stored in the three-dimensional model of the target lane, and needs to be determined based on the interval coordinates selected by the user, and if the centerline starting point of the determined target lane is different from the centerline starting point of the lane stored in the three-dimensional model of the target lane, then the distance attribute of each discrete triangular face vertex in the target lane is also different from the distance attribute of each discrete triangular face vertex in the lane stored in the three-dimensional model of the target lane, and needs to be determined based on the lane path information of the target lane.

[0072] Optionally, the projection coordinates of the interval point on the centerline of the target lane can be calculated based on the interval coordinates selected by the user, so that the calculated projection coordinates are correspondingly determined as the centerline starting point and centerline end point of the target lane, thereby determining the lane path information of the target lane.

[0073] Optionally, the distance along the pipeline between each discrete triangular surface vertex in the target lane and the starting point of the center line of the target lane can be calculated based on the lane path information of the target lane and the position of each discrete triangular surface vertex in the target lane, thereby determining the distance attribute of each discrete triangular surface vertex in the target lane.

[0074] As an example, during the ventilation data visualization process, when interacting with the 3D scene, the mouse will click on a certain section on the tunnel model, that is, the custom start and end point, and two position coordinates will be generated. Because it is clicked on the 3D model of the tunnel, the position is a spatial coordinate. The two coordinates are vertically projected onto the corresponding tunnel centerline, and the two coordinates on the centerline are calculated. When clicking on the tunnel model, you can know the tunnel ID, and you can get the start and end coordinates of the tunnel. Combined with the two selected coordinates, you can calculate the distance from the starting point.

[0075] As a possible implementation, when executing step 103, based on the lane identification and lane path information of the target lane and the distance attribute of each discrete triangle vertex in the target lane, the acquired monitoring data is visualized in the target lane according to the corresponding color, which can be specifically performed according to the following steps:

[0076] Step 1031 , storing at least one of the lane identification and lane path information of the target lane, the distance attribute of each discrete triangle vertex in the target lane, and the target information determined based on the monitoring data into the target texture in a specified format.

[0077] Optionally, the monitoring data includes ventilation data, personnel positioning data and sensor data, and the target information includes at least one of personnel strength information, personnel strength distribution and sensor strength information. Thus, when the monitoring data is ventilation data, the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangular vertex in the target lane can be stored in the target texture in a specified format. Alternatively, when the monitoring data is personnel positioning data, the lane identification and lane path information of the target lane, the distance attribute of each discrete triangular vertex in the target lane, the personnel strength information and personnel strength distribution of the target lane are stored in the target texture in a specified format, wherein the personnel strength information and personnel strength distribution of the target lane are determined based on the personnel positioning data. Alternatively, when the monitoring data is sensor data, the lane identification and lane path information of the target lane, the distance attribute of each discrete triangular vertex in the target lane, and the sensor strength information of the target lane are stored in the target texture in a specified format, wherein the sensor strength information of the target lane is determined based on the sensor data.

[0078] As a possible implementation manner, the target lane includes multiple areas, the personnel strength information of the target lane includes the personnel strength information of each area, the personnel strength distribution of the target lane includes the personnel strength distribution of each area, and the determination process of the personnel strength information and the personnel strength distribution of the target lane includes:

[0079] For any area, determine the personnel intensity information of the area according to the number of personnel indicated by the personnel location data of the area;

[0080] For any area, the following formula is used to determine the personnel intensity distribution of the area:

[0081]

[0082] Wherein, d represents the distance between any pixel in the region and the position of the person indicated by the personnel positioning data of the region, σ represents the standard deviation of the Gaussian function, and scaleFactor(d) represents the scaling factor related to d.

[0083] As a possible implementation manner, the target lane includes multiple areas, the sensor strength information of the target lane includes sensor strength information of each area, and the process of determining the sensor strength information of the target lane includes:

[0084] For any area, when there is only one sensor in the area, the maximum value method is used to determine the sensor strength information of the area;

[0085] For any area, when there are multiple sensors in the area, numerical linear interpolation calculation is performed between the sensors to determine the sensor strength information of the area.

[0086] In some embodiments, the intensity information calculation may need to be adjusted differently depending on the information to be described. When displaying the density of personnel, a personnel positioning system is deployed underground in a coal mine, and the system can obtain the location information of underground personnel in real time and accurately. This information will be transmitted to the rendering system through a data interface. Thus, the personnel intensity information and personnel intensity distribution are realized as follows:

[0087] If there is only one person in a certain area, the intensity value of the area is considered to be 1. If there are N people in a certain area, then its intensity is N. According to the position of the people, in order to better reflect the distribution of personnel density, so that the intensity information approaches 0 more quickly when it is far away from the center of the personnel, the above formula (1) can be used to determine the personnel intensity distribution of each area in the target lane. In formula (1), a distance-related scaling factor is introduced to transform the Gaussian function. The scaling factor is a function of the distance d -k (k is a constant greater than 0), which makes the function approach 0 more quickly when it is far from the center. The intensity of a certain position is accumulated in this way.

[0088] However, this method cannot be used for sensor data. For example, the sensor for carbon dioxide concentration displays the concentration value. If two sensors are in the same position, they cannot be accumulated, which will result in an incorrect result. If there is only one sensor in a lane, the intensity value is calculated by taking the maximum value; if there are multiple sensors in a lane, numerical linear interpolation is performed between the sensors. At the same time, the color mapping method is required. Traditional color mapping methods often use fixed color ranges and thresholds. This solution uses an adaptive threshold adjustment method to dynamically calculate the threshold range of the concentration distribution based on the real-time data and historical data of the sensor concentration. This method can more accurately reflect the actual situation of concentration changes, especially when the concentration fluctuates greatly, and can provide a more intuitive and accurate display of the concentration distribution.

[0089] Step 1032, using the GPU fragment shader to parse the target texture, and determine whether the fragment identifier of the target processing fragment matches the lane identifier stored in the target texture.

[0090] During the rendering process, the specified lane ID, segment information, and intensity data volume need to be passed to the GPU. Because GPU data transmission has some restrictions, GPU code writing can only pass parameters through Uniform variables. However, the storage capacity of Uniform variables is limited, and can only store 4*32 bits of information at most. The data of the id and segment information that need to be specified is not fixed, and there may be several ids and segment information that need to be transmitted at the same time. This leads to the uncertainty of the amount of data transmitted. In the present invention, a technical method is adopted to store this information in a texture in a specified format. When the GPU is rendering, the parameters are not read through the Uniform variables, but all the parameter data are read through a large texture.

[0091] Step 1033: When the fragment identifier of the target processing fragment does not match the lane identifier stored in the target texture, the target processing fragment is rendered as a set color.

[0092] If the fragment identifier of the target processing fragment does not match the lane identifier stored in the target texture, it means that the target processing fragment is not within the visualization range of the monitoring data. In this case, the target processing fragment only needs to be rendered as the set color.

[0093] Step 1034, when the fragment identifier of the target processing fragment matches the lane identifier stored in the target texture, the color distribution of the target processing fragment is determined and rendered based on the monitoring data, the target information, the distance attribute of the lane point corresponding to the target processing fragment and at least one of the preset information.

[0094] If the fragment identifier of the target processing fragment matches the lane identifier stored in the target texture, it means that the target processing fragment is within the visualization range of the monitoring data. At this time, it is necessary to determine the color distribution of the target processing fragment and render it based on the monitoring data, target information, distance attributes of the lane points corresponding to the target processing fragment, and preset information.

[0095] Optionally, the monitoring data includes ventilation data, personnel positioning data and sensor data, the target information includes at least one of personnel strength information, personnel strength distribution and sensor strength information, and the preset information includes a preset initial color and a preset color mapping rule. Thus, when the monitoring data is ventilation data, the color distribution of the target processing element can be determined and rendered according to the direction and speed of the lane wind flow indicated by the ventilation data, the distance attribute of the lane point corresponding to the target processing element and the preset initial color. Alternatively, when the monitoring data is personnel positioning data, the color distribution of the target processing element can be determined and rendered according to the personnel strength information, personnel strength distribution, the distance attribute of the lane point corresponding to the target processing element and the preset color mapping rule. Alternatively, when the monitoring data is sensor data, the color distribution of the target processing element can be determined and rendered according to the sensor strength information, the distance attribute of the lane point corresponding to the target processing element and the preset color mapping rule.

[0096] Among them, the process of determining the preset color mapping rules may include: normalizing the monitoring data to scale the monitoring data to a target range; determining color breakpoints based on the data distribution of the normalized monitoring data, wherein the color breakpoints are used to divide the color mapping into at least one interval, each interval corresponding to a color range, and, in the process of determining the color breakpoints, dynamically adjusting the color mapping parameters involved in the color breakpoints, and dynamically updating the distribution threshold range of the monitoring data based on the real-time data and historical data of the monitoring data.

[0097] In some embodiments, the color mapping principle of the monitoring data is as follows:

[0098] (1) First, normalize the data and scale it to the range of [0,1]. Assume that C is the original value of the monitoring, C_min is the minimum value in the data set, and C_max is the maximum value in the data set. The normalized value C_norm can be calculated by the following formula: C_norm = (C-C_min) / (C_max-C_min) This formula scales the original value to the range of [0,1] so that all values ​​can be compared and mapped within this range.

[0099] (2) Determine the color breakpoints based on the data distribution. Color breakpoints are points that divide the color map into different intervals, and each interval corresponds to a color range.

[0100] The gradient color mapping table from blue (small number of people or low sensor concentration) to red (large number of people or high sensor concentration) is as follows:

[0101] When C_norm is in [0,1 / 3], it transitions from blue to green

[0102] When C_norm(1 / 3,2 / 3], keep green.

[0103] When C_norm is in (2 / 3,1], it transitions from green to red.

[0104] The mapping formula is as follows:

[0105] # Transition from blue to green

[0106] R=0

[0107] G=255*(C_norm / (1 / 3))**beta

[0108] B=255*(1-(C_norm / (1 / 3))**beta)

[0109] #KeepItGreen

[0110] R=0

[0111] G = 255

[0112] B=0

[0113] # Transition from green to red

[0114] C_norm_adj=(C_norm-2 / 3) / (1-2 / 3)

[0115] R = 255 * (C_norm_adj**beta)

[0116] G = 255 * (1-C_norm_adj**beta)

[0117] B=0

[0118] # Ensure that RGB values ​​are in the range [0,255]

[0119] R = int(max(0,min(R,255)))

[0120] G = int(max(0,min(G,255)))

[0121] B = int(max(0,min(B,255)))

[0122] In the above formula, alpha = 5.0, beta = 2.0, and C_norm is the normalized concentration value. Alpha and beta are parameters that control the shape and rate of color transition. When the value of beta is large, the color transition will be faster, and when the value of beta is small, the color transition will be slower or smoother.

[0123] (4) Intensity and color value mapping. According to the adjusted color mapping parameters, the intensity is mapped to the color space. For each data point, the corresponding color is found in the color mapping table according to its intensity, and the color is used to draw the heat map.

[0124] (5) Dynamically update the threshold. Based on real-time data and historical data, dynamically update a threshold range and recalculate the threshold range to display the thermal distribution.

[0125] Optionally, the overall technical roadmap for ventilation data visualization is as follows Figure 4 As shown in the figure, the overall technical flow chart of personnel positioning and sensor data visualization is as follows Figure 5 shown.

[0126] In summary, any three-dimensional tunnel model produced by any modeling software, which supports a variety of geometric shapes, such as tunnel bends, tunnel intersections, etc., can use this method to generate heat map effects. And the heat map effect supports the degree of personnel gathering at dynamic locations and the degree of change of sensor monitoring values ​​at static locations.

[0127] In the present invention, as long as there is a corresponding center line, the heat map effect can be generated. Because the center line of the lane is used, the data used for rendering are calculated in advance and stored in the new lane 3D model. The new lane model is the same as the original lane model in terms of geometric appearance. The only difference is that the new model stores all the data required for rendering. For example, assuming that the original lane model is as follows Figure 6 As shown in the figure, the corresponding ventilation flow simulation effect can be shown as Figure 7 As shown, the corresponding thermal map simulation effect can be Figure 8 shown.

[0128] In addition, in the present invention, the wind flow effect of any section of the lane can be customized. By clicking a certain lane section with the mouse, the rendering related information of the selected section can be obtained, so as to simulate the wind flow effect. For example, Fig. 9 The wind flow effect in a certain lane is shown. Fig.10 The wind flow effects between multiple lanes are shown.

[0129] Moreover, in the present invention, the wind flow effect can set parameters such as direction, speed, and color. According to the flow speed of the tunnel wind flow, the distance attribute of the vertex, and the set initial color, the color information of the vertex can be calculated, and the color of the vertex can be updated according to time, thereby generating a flow effect. Therefore, according to the real-time data of coal mine ventilation, the wind direction, wind speed, and air intake and return can be set to generate a real-time simulation effect. For example, Fig.11 It shows the overall ventilation effect of a mine tunnel.

[0130] In order to implement the above embodiment, the present invention also proposes a coal mine monitoring data visualization device.

[0131] Fig.12 A schematic structural diagram of a coal mine monitoring data visualization device provided by an embodiment of the present invention.

[0132] like Fig.12 As shown, the coal mine monitoring data visualization device includes: a processing module 11, a determination module 12 and a visualization module 13.

[0133] The processing module 11 is used for fusing the lane 3D model with the lane centerline corresponding to the lane 3D model for any lane 3D format type to obtain a target lane 3D model, wherein the target lane 3D model stores lane identification and lane path information of at least one lane, and distance attributes of each discrete triangular face vertex in each lane, the lane path information includes a centerline start point and a centerline end point of the corresponding lane, and the distance attribute is a distance between the corresponding discrete triangular face vertex and the centerline start point of the corresponding lane along the lane centerline;

[0134] A determination module 12 is used to determine the target lane to be visualized in the target lane three-dimensional model, and determine the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangle vertex in the target lane according to the target lane three-dimensional model;

[0135] The visualization module 13 is used to visualize the acquired monitoring data in the target lane according to the lane identification and lane path information of the target lane and the distance attribute of each discrete triangle vertex in the target lane.

[0136] Furthermore, in a possible implementation of the embodiment of the present invention, the visualization module 13 includes:

[0137] A storage unit, used to store at least one of a lane identification and lane path information of a target lane, a distance attribute of each discrete triangle vertex in the target lane, and target information determined based on monitoring data into a target texture in a specified format;

[0138] A processing unit, used for parsing a target texture using a GPU fragment shader, and determining whether a fragment identifier of a target processing fragment matches a lane identifier stored in the target texture;

[0139] A first rendering unit, configured to render the target processing fragment into a set color when the fragment identifier of the target processing fragment does not match the lane identifier stored in the target texture;

[0140] The second rendering unit is used to determine the color distribution of the target processing fragment and render it according to the monitoring data, target information, the distance attribute of the lane point corresponding to the target processing fragment and at least one of the preset information when the fragment identifier of the target processing fragment matches the lane identifier stored in the target texture.

[0141] Further, in a possible implementation of the embodiment of the present invention, the monitoring data includes ventilation data, personnel positioning data and sensor data, the target information includes at least one of personnel strength information, personnel strength distribution and sensor strength information, and the preset information includes a preset initial color and a preset color mapping rule; the second rendering unit is further used to:

[0142] In the case where the monitoring data is ventilation data, the color distribution of the target processing fragment is determined and rendered according to the direction and speed of the lane wind flow indicated by the ventilation data, the distance attribute of the lane point corresponding to the target processing fragment, and the preset initial color;

[0143] In the case where the monitoring data is personnel positioning data, the color distribution of the target processing fragment is determined and rendered according to the personnel intensity information, personnel intensity distribution, the distance attribute of the lane point corresponding to the target processing fragment and the preset color mapping rule;

[0144] When the monitoring data is sensor data, the color distribution of the target processing fragment is determined and rendered according to the sensor strength information, the distance attribute of the lane point corresponding to the target processing fragment and the preset color mapping rule.

[0145] Furthermore, in a possible implementation of the embodiment of the present invention, the process of determining the preset color mapping rule includes:

[0146] Normalize the monitoring data to scale it to the target range;

[0147] Color breakpoints are determined based on the data distribution of the normalized monitoring data, wherein the color breakpoints are used to divide the color mapping into at least one interval, each interval corresponding to a color range, and in the process of determining the color breakpoints, the color mapping parameters involved in the color breakpoints are dynamically adjusted, and the distribution threshold range of the monitoring data is dynamically updated based on the real-time data and historical data of the monitoring data.

[0148] Further, in a possible implementation of the embodiment of the present invention, the monitoring data includes ventilation data, personnel positioning data and sensor data, and the target information includes at least one of personnel intensity information, personnel intensity distribution and sensor intensity information; the storage unit is further used to:

[0149] When the monitoring data is ventilation data, the lane identification and lane path information of the target lane and the distance attribute of each discrete triangle vertex in the target lane are stored in the target texture through a specified format;

[0150] In the case where the monitoring data is personnel positioning data, the lane identification and lane path information of the target lane, the distance attribute of each discrete triangle vertex in the target lane, the personnel strength information and personnel strength distribution of the target lane are stored in the target texture through a specified format, wherein the personnel strength information and personnel strength distribution of the target lane are determined based on the personnel positioning data;

[0151] When the monitoring data is sensor data, the lane identification and lane path information of the target lane, the distance attribute of each discrete triangle vertex in the target lane, and the sensor strength information of the target lane are stored in the target texture in a specified format, wherein the sensor strength information of the target lane is determined based on the sensor data.

[0152] Further, in a possible implementation of the embodiment of the present invention, the target lane includes multiple areas, the personnel strength information of the target lane includes the personnel strength information of each area, the personnel strength distribution of the target lane includes the personnel strength distribution of each area, and the determination process of the personnel strength information and the personnel strength distribution of the target lane includes:

[0153] For any area, according to the number of personnel indicated by the personnel positioning data of the area, the personnel intensity information and personnel intensity distribution of the target lanes of the area are determined;

[0154] For any area, the following formula is used to determine the personnel intensity distribution of the area:

[0155]

[0156] Wherein, d represents the distance between any pixel in the region and the position of the person indicated by the personnel positioning data of the region, σ represents the standard deviation of the Gaussian function, and scaleFactor(d) represents the scaling factor related to d.

[0157] Furthermore, in a possible implementation of the embodiment of the present invention, the target lane includes multiple areas, the sensor strength information of the target lane includes sensor strength information of each area, and the process of determining the sensor strength information of the target lane includes:

[0158] For any area, when there is only one sensor in the area, the maximum value method is used to determine the sensor strength information of the area;

[0159] For any area, when there are multiple sensors in the area, numerical linear interpolation calculation is performed between the sensors to determine the sensor strength information of the area.

[0160] Furthermore, in a possible implementation of the embodiment of the present invention, the determination module 12 is further configured to:

[0161] Determine any lane and / or a set section of any lane selected by a user from the three-dimensional model of the target lane as the target lane to be visualized;

[0162] In the case where the target lane is any lane, the lane identification and lane path information of the lane stored in the three-dimensional model of the target lane, and the distance attribute of each discrete triangular vertex in the lane are determined as the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangular vertex in the target lane;

[0163] When the target lane is a set interval of any lane, the lane identifier of the lane stored in the three-dimensional model of the target lane is determined as the lane identifier of the target lane, and the lane path information of the target lane is determined according to the interval coordinates selected by the user. In addition, the distance attribute of each discrete triangle vertex in the target lane is determined according to the lane path information of the target lane.

[0164] Furthermore, in a possible implementation of the embodiment of the present invention, the processing module 11 is further configured to:

[0165] For a lane three-dimensional model of any three-dimensional format type, analyzing a lane centerline corresponding to the lane three-dimensional model to obtain lane path information of at least one lane;

[0166] According to the lane path information of each lane, the distance attribute of each discrete triangle vertex in each lane is determined;

[0167] The lane identification and lane path information of each lane and the distance attribute of each discrete triangle vertex in each lane are integrated into the lane 3D model to obtain the target lane 3D model.

[0168] It should be noted that the aforementioned explanation of the embodiment of the coal mine monitoring data visualization method is also applicable to the coal mine monitoring data visualization device of this embodiment, and will not be repeated here.

[0169] The coal mine monitoring data visualization device provided by the embodiment of the present invention obtains a target laneway three-dimensional model by fusing the laneway three-dimensional model with the laneway centerline corresponding to the laneway three-dimensional model for any three-dimensional format type, wherein the target laneway three-dimensional model stores a laneway identification and laneway path information of at least one lane, as well as a distance attribute of each discrete triangular face vertex in each lane, the laneway path information includes a centerline starting point and a centerline end point of the corresponding lane, and the distance attribute is a distance between the corresponding discrete triangular face vertex and the centerline starting point of the corresponding lane along the lane centerline, thereby determining the target lane to be visualized in the target lane three-dimensional model, and based on the target lane three-dimensional model, determining the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangular face vertex in the target lane, and then based on the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangular face vertex in the target lane, the acquired monitoring data is visualized in the target lane according to the corresponding color. Therefore, by introducing the tunnel centerline, the coal mine monitoring data can be visualized on the tunnel 3D model of any 3D format type.

[0170] In order to implement the above embodiments, the present invention also proposes an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the coal mine monitoring data visualization method proposed in any of the above embodiments of the present invention.

[0171] Fig.13 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0172] It should be noted that Fig.13 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0173] like Fig.13 As shown, the electronic device includes:

[0174] A memory 21 , a processor 22 , and a computer program stored in the memory 21 and executable on the processor 22 .

[0175] When the processor 22 executes the program, the coal mine monitoring data visualization method provided in any of the above embodiments is implemented.

[0176] Furthermore, the electronic device further comprises:

[0177] The communication interface 23 is used for communication between the memory 21 and the processor 22 .

[0178] The memory 21 is used to store computer programs that can be run on the processor 22 .

[0179] The memory 21 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0180] The processor 22 is used to implement the coal mine monitoring data visualization method described in any of the above embodiments when executing the program.

[0181] If the memory 21, the processor 22 and the communication interface 23 are implemented independently, the communication interface 23, the memory 21 and the processor 22 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.13 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0182] Optionally, in a specific implementation, if the memory 21, the processor 22 and the communication interface 23 are integrated on a chip, the memory 21, the processor 22 and the communication interface 23 can communicate with each other through an internal interface.

[0183] The processor 22 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0184] In order to implement the above embodiments, the present invention further proposes a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the coal mine monitoring data visualization method proposed in any of the above embodiments of the present invention.

[0185] In order to implement the above embodiments, the present invention further proposes a computer program product, including a computer program, which, when executed by a processor, implements the coal mine monitoring data visualization method proposed in any of the above embodiments of the present invention.

[0186] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0187] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0188] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0189] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a system based on a computer, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0190] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0191] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0192] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0193] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for visualizing coal mine monitoring data, characterized in that: include: For a lane three-dimensional model of any three-dimensional format type, the lane three-dimensional model is merged with the lane centerline corresponding to the lane three-dimensional model to obtain a target lane three-dimensional model, wherein the target lane three-dimensional model stores a lane identification and lane path information of at least one lane, and a distance attribute of each discrete triangular face vertex in each lane, the lane path information includes a centerline starting point and a centerline end point of the corresponding lane, and the distance attribute is a distance between a corresponding discrete triangular face vertex and a centerline starting point of the corresponding lane along the lane centerline; Determine a target lane to be visualized in the target lane three-dimensional model, and determine a lane identification and lane path information of the target lane, and a distance attribute of each discrete triangular vertex in the target lane according to the target lane three-dimensional model; According to the lane identification and lane path information of the target lane and the distance attribute of each discrete triangle vertex in the target lane, the acquired monitoring data is visualized in the target lane according to the corresponding color.

2. The method according to claim 1, characterized in that The method of visualizing the acquired monitoring data in the target lane in accordance with corresponding colors according to the lane identification and lane path information of the target lane and the distance attribute of each discrete triangle vertex in the target lane includes: storing at least one of the lane identification and lane path information of the target lane, the distance attribute of each discrete triangle vertex in the target lane, and the target information determined based on the monitoring data into a target texture in a specified format; Using a GPU fragment shader to parse the target texture, and determining whether a fragment identifier of a target processing fragment matches a lane identifier stored in the target texture; When the fragment identifier of the target processing fragment does not match the lane identifier stored in the target texture, rendering the target processing fragment into a set color; When the fragment identifier of the target processing fragment matches the lane identifier stored in the target texture, the color distribution of the target processing fragment is determined and rendered based on the monitoring data, the target information, the distance attribute of the lane point corresponding to the target processing fragment and at least one of the preset information.

3. The method according to claim 2, characterized in that The monitoring data includes ventilation data, personnel positioning data and sensor data, the target information includes at least one of personnel strength information, personnel strength distribution and sensor strength information, and the preset information includes a preset initial color and a preset color mapping rule; the color distribution of the target processing fragment is determined and rendered according to the monitoring data, the target information, the distance attribute of the lane point corresponding to the target processing fragment and at least one of the preset information, including: In the case where the monitoring data is ventilation data, the color distribution of the target processing fragment is determined and rendered according to the direction and speed of the lane wind flow indicated by the ventilation data, the distance attribute of the lane point corresponding to the target processing fragment, and the preset initial color; In the case where the monitoring data is personnel positioning data, the color distribution of the target processing fragment is determined and rendered according to the personnel intensity information, the personnel intensity distribution, the distance attribute of the lane point corresponding to the target processing fragment and the preset color mapping rule; In the case where the monitoring data is sensor data, the color distribution of the target processing fragment is determined and rendered according to the sensor strength information, the distance attribute of the lane point corresponding to the target processing fragment and the preset color mapping rule.

4. The method according to claim 3, characterized in that The process of determining the preset color mapping rule includes: Normalizing the monitoring data to scale the monitoring data to a target range; Color breakpoints are determined based on the data distribution of the normalized monitoring data, wherein the color breakpoints are used to divide the color mapping into at least one interval, each of the intervals corresponds to a color range, and in the process of determining the color breakpoints, color mapping parameters involved in the color breakpoints are dynamically adjusted, and the distribution threshold range of the monitoring data is dynamically updated based on the real-time data and historical data of the monitoring data.

5. The method according to claim 2, characterized in that: The monitoring data includes ventilation data, personnel positioning data and sensor data, and the target information includes at least one of personnel strength information, personnel strength distribution and sensor strength information; the storing of at least one of the lane identification and lane path information of the target lane, the distance attribute of each discrete triangular vertex in the target lane and the target information determined based on the monitoring data to the target texture in a specified format includes: In the case where the monitoring data is ventilation data, the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangle vertex in the target lane are stored in the target texture in a specified format; In the case where the monitoring data is personnel positioning data, the lane identification and lane path information of the target lane, the distance attribute of each discrete triangle vertex in the target lane, the personnel intensity information and the personnel intensity distribution of the target lane are stored in the target texture through a specified format, wherein the personnel intensity information and the personnel intensity distribution of the target lane are determined based on the personnel positioning data; In the case where the monitoring data is sensor data, the lane identification and lane path information of the target lane, the distance attribute of each discrete triangle vertex in the target lane, and the sensor strength information of the target lane are stored in the target texture in a specified format, wherein the sensor strength information of the target lane is determined based on the sensor data.

6. The method according to claim 5, characterized in that The target lane includes a plurality of areas, the personnel strength information of the target lane includes the personnel strength information of each of the areas, the personnel strength distribution of the target lane includes the personnel strength distribution of each of the areas, and the determination process of the personnel strength information and the personnel strength distribution of the target lane includes: For any of the areas, determining the personnel intensity information of the area according to the number of personnel indicated by the personnel positioning data of the area; For any of the above areas, the following formula is used to determine the personnel intensity distribution in the area: Wherein, d represents the distance between any pixel in the area and the position of the person indicated by the personnel positioning data of the area, σ represents the standard deviation of the Gaussian function, and scaleFactor(d) represents the scaling factor related to d.

7. The method according to claim 5, characterized in that The target lane includes a plurality of regions, the sensor strength information of the target lane includes sensor strength information of each of the regions, and the process of determining the sensor strength information of the target lane includes: For any of the areas, when there is only one sensor in the area, a maximum value method is used to determine the sensor strength information of the area; For any of the regions, when there are multiple sensors in the region, numerical linear interpolation calculation is performed between the sensors to determine the sensor strength information of the region.

8. The method according to any one of claims 1 to 7, characterized in that The step of determining a target lane to be visualized in the three-dimensional model of the target lane, and determining a lane identification and lane path information of the target lane, and a distance attribute of each discrete triangular vertex in the target lane according to the three-dimensional model of the target lane, includes: Determine any lane and / or a set section of any lane selected by a user from the three-dimensional model of the target lane as the target lane to be visualized; In the case where the target lane is any of the lanes, the lane identification and lane path information of the lane, and the distance attribute of each discrete triangular vertex in the lane stored in the three-dimensional model of the target lane are determined as the lane identification and lane path information of the target lane, and the distance attribute of each discrete triangular vertex in the target lane; In the case where the target lane is a set interval of any of the lanes, the lane identifier of the lane stored in the three-dimensional model of the target lane is determined as the lane identifier of the target lane, and the lane path information of the target lane is determined according to the interval coordinates selected by the user, and the distance attribute of each discrete triangular vertex in the target lane is determined according to the lane path information of the target lane.

9. The method according to any one of claims 1 to 7, characterized in that: The method of fusing the three-dimensional lane model with the lane center line corresponding to the three-dimensional lane model of any three-dimensional format type to obtain a target three-dimensional lane model includes: For a lane three-dimensional model of any three-dimensional format type, parsing a lane centerline corresponding to the lane three-dimensional model to obtain lane path information of at least one lane; Determining the distance attribute of each discrete triangular vertex in each lane according to the lane path information of each lane; The lane identification and lane path information of each lane and the distance attribute of each discrete triangle vertex in each lane are integrated into the lane three-dimensional model to obtain the target lane three-dimensional model.

10. A coal mine monitoring data visualization device, characterized in that: include: A processing module, for a lane three-dimensional model of any three-dimensional format type, to fuse the lane three-dimensional model with the lane centerline corresponding to the lane three-dimensional model, so as to obtain a target lane three-dimensional model, wherein the target lane three-dimensional model stores a lane identification and lane path information of at least one lane, and a distance attribute of each discrete triangular face vertex in each lane, the lane path information includes a centerline starting point and a centerline end point of the corresponding lane, and the distance attribute is a distance between a corresponding discrete triangular face vertex and a centerline starting point of the corresponding lane along the lane centerline; A determination module, used to determine a target lane to be visualized in the target lane three-dimensional model, and determine a lane identification and lane path information of the target lane, and a distance attribute of each discrete triangular vertex in the target lane according to the target lane three-dimensional model; The visualization module is used to visualize the acquired monitoring data in the target lane according to the lane identification and lane path information of the target lane and the distance attribute of each discrete triangle vertex in the target lane in the corresponding color.

Images