A mine spray dust fall control method and control system based on image recognition

Abstract

The application relates to the technical field of dust control, in particular to a mine spray dust control method and control system based on image recognition, which comprises the following steps: when an actual dust diffusion diagram contains a shadow area, determining a corresponding simulation mine area model based on mine area parameters of a mine area to be controlled, and obtaining a corresponding simulation dust diffusion diagram of the mine area to be controlled based on construction parameters of the mine area to be controlled and the simulation mine area model; determining a target dust diffusion diagram based on the actual dust diffusion diagram and the simulation dust diffusion diagram; performing edge detection and granularity analysis on the target dust diffusion diagram to determine dust information corresponding to the mine area to be controlled from the target dust diffusion diagram; and generating a spray control instruction based on the dust information, wherein the spray control instruction is used for controlling a spray equipment to perform dust reduction operation based on the spray control instruction. The application is convenient for improving the accuracy of dust detection results, thereby improving the dust reduction effect.

Description

Technical Field

[0001] This application relates to the field of dust control technology, and in particular to a method and control system for dust suppression using spray in mining based on image recognition. Background Technology

[0002] Mining operations typically involve large-scale excavation, crushing, and transportation processes, all of which generate significant amounts of dust. In mining environments, dust originates not only from the extraction and processing of raw materials such as coal and ore but also from the wear and vibration generated by machinery during operation. Failure to control this dust can negatively impact the health and well-being of workers, potentially leading to equipment degradation and environmental pollution. Therefore, dust control in mining environments remains a long-term and complex challenge.

[0003] In traditional dust suppression methods, the situation of underground dust is usually assessed by image detection first, and then ventilation or spray equipment is used to suppress dust based on the detection results. Although this can reduce dust concentration to some extent, due to the complexity of the underground environment, the accuracy of the detection results is low when the distribution of underground dust is identified by image detection methods alone. Therefore, using such low-accuracy detection results as the basis for spray dust suppression control may reduce the accuracy of the spray dust suppression strategy, which may affect the dust suppression effect. Summary of the Invention

[0004] To improve the accuracy of dust detection results and thus enhance dust suppression, this application provides a mining spray dust suppression control method and control system based on image recognition.

[0005] In a first aspect, this application provides a method for controlling dust suppression in mining applications based on image recognition, employing the following technical solution:

[0006] A method for controlling dust suppression in mining using spray based on image recognition, comprising:

[0007] Acquire actual mining area images, and determine the actual dust diffusion map corresponding to the mining area to be controlled based on the actual mining area images;

[0008] When the actual dust diffusion map contains a shaded area, a corresponding simulated mining area model is determined based on the mining area parameters of the mining area to be controlled. Based on the construction parameters of the mining area to be controlled and the simulated mining area model, a simulated dust diffusion map corresponding to the mining area to be controlled is obtained. The construction parameters include the amount of construction, the construction area, and environmental parameters, including wind speed and humidity.

[0009] Based on the actual dust diffusion map and the simulated dust diffusion map, the target dust diffusion map is determined;

[0010] Edge detection and particle size analysis are performed on the target dust diffusion map to determine the dust information corresponding to the mining area to be controlled from the target dust diffusion map. The dust information includes the size of the dust area and the dust concentration.

[0011] Based on the dust information, a spray control command is generated. The spray control command is used to control the spray equipment to perform dust suppression operation based on the spray control command. The spray control command includes spray volume and spray attitude.

[0012] By adopting the above technical solution, when the actual mining area image contains shadowed areas, a simulated dust diffusion map can first be obtained by constructing a simulated mining area model and combining it with construction parameters. This can simulate and predict the mining situation underground, thus facilitating an understanding of the dust distribution underground. However, since the simulation results are relatively ideal, a method is adopted that integrates the actual dust diffusion map and the simulated dust diffusion map, and identifies dust information based on the fusion result, rather than directly using the simulated dust diffusion map to identify dust information. This method facilitates a clear understanding of the dust distribution underground while improving the fit between the detection results and the actual situation, thereby improving the accuracy of the detection results. Finally, the spray control command generated based on the highly accurate dust information facilitates the control of the spray volume and spray attitude of the spray equipment, thereby ensuring the accuracy and effectiveness of spray dust suppression, and thus improving the dust suppression effect.

[0013] In one possible implementation, determining the target dust diffusion map based on the actual dust diffusion map and the simulated dust diffusion map includes:

[0014] Identify at least one non-shaded area contained in the actual dust diffusion map, and identify the area edge line corresponding to each non-shaded area;

[0015] By merging the edge lines of at least one non-shaded area, a stitched edge line is obtained;

[0016] Identify the simulated edge line corresponding to the simulated dust diffusion map. If the edge line matching degree between the simulated edge line and the spliced ​​edge line is higher than the preset standard matching degree, then determine the diffusion map of the area to be superimposed based on the shaded area and the simulated dust diffusion map.

[0017] The actual dust diffusion map, which includes the shaded area, is completed based on the diffusion map of the area to be superimposed to obtain the target dust diffusion map.

[0018] By adopting the above technical solution, matching the spliced ​​edge line and the simulated edge line facilitates the verification of the simulation prediction results. When the edge line matching degree is higher than the preset standard matching degree, it indicates that the simulated data and the actual data have a high consistency in the key feature of the edge line, and also indicates that the simulation prediction results are highly adapted to the actual situation. In the actual dust diffusion map, the shaded area may cause the dust information to be missing or distorted. By identifying the part in the simulated dust diffusion map that matches the actual dust diffusion map, and using the simulated data to fill in the shaded area, it is convenient to improve the dust distribution of the entire mining area and make the target dust diffusion map more comprehensive and accurate.

[0019] In one possible implementation, when the number of spraying devices exceeds a preset number of devices, the method further includes:

[0020] Obtain the dust reduction rate of the corresponding spray range for each spraying device, and determine the spray range with a dust reduction rate lower than the preset standard dust reduction rate as the scope of interest;

[0021] Based on the regional construction parameters and historical construction data corresponding to the scope of concern, the predicted dust amount of the scope of concern is determined.

[0022] When the predicted dust amount is higher than the preset dust amount, the location information of the area of ​​interest is identified, and a movement route is generated based on the location information;

[0023] Obtain the spray control command corresponding to the area of ​​interest, and determine the moving spray control command based on the predicted dust amount and the predicted dust amount;

[0024] Based on the movement route and the spray control command, a movement control command is determined. The movement control command is used to control the drone spraying equipment to perform mobile dust suppression treatment on the area of ​​interest according to the generated movement route.

[0025] By adopting the above technical solution, the dust reduction rate of each spraying device within its corresponding spray range is monitored, and a preset standard dust reduction rate is set. This facilitates the accurate identification of areas of concern where dust reduction is not ideal, and helps to concentrate limited mobile spraying resources on the areas most in need of dust reduction, thereby improving dust reduction efficiency. In addition, by using drone spraying equipment in conjunction with the generated mobile routes and spray control commands for mobile dust reduction operations, intelligent mobile dust reduction processing can be achieved while improving the accuracy of dust reduction operations.

[0026] In one possible implementation, the method further includes:

[0027] Based on the regional construction parameters and historical construction data corresponding to each spray range, the predicted amount of regional dust for each spray range in the preset time period is determined.

[0028] Obtain the range parameters for each spray range corresponding to the preset time period, the range parameters including temperature and wind speed;

[0029] Based on the predicted dust volume and range parameters for each region, as well as the simulated mining area model, the adjusted spray volume for each spraying device is determined.

[0030] Based on each adjustment of the spray volume, optimize the spray control command for each spray device corresponding to the preset time period.

[0031] By adopting the above technical solution and combining the regional construction parameters and historical construction data corresponding to each spray range, it is easier to predict the amount of dust in each area within a preset time period. Based on this prediction data, it is easier to adjust the spray volume of the spraying equipment more precisely, thereby controlling dust more effectively. Optimizing the spray control command can ensure that there is enough spray volume to control dust when needed, while reducing the spray volume when unnecessary, thereby avoiding unnecessary interference with the construction progress and improving construction efficiency.

[0032] In one possible implementation, the method further includes:

[0033] Each spray range is identified and labeled from the actual mining area images to obtain a range-labeled image;

[0034] Based on the predicted dust volume of each spray range in the preset time period, determine the dust change information corresponding to each spray range;

[0035] Each dust change information is superimposed onto the corresponding position in the range-marked image to obtain a dust change map, and the dust change map is fed back.

[0036] By adopting the above technical solution, the coverage area of ​​dust suppression operations can be clearly defined by identifying and marking the spray range of the mining area, which facilitates subsequent analysis and management. The dust change information corresponding to each spray range is superimposed with the range marking image to create a dust transformation map, which provides relevant inspection personnel with an intuitive display of the environmental status. In addition, the dust transformation map allows relevant inspection personnel to adjust the spray strategy in a timely manner and optimize the dust suppression effect.

[0037] In one possible implementation, superimposing each dust change information onto the corresponding position in the range-marked image includes:

[0038] From the actual mining area images, identify the dust features corresponding to each spray range;

[0039] Based on the predicted dust amount and corresponding dust characteristics of the area corresponding to each spray range, a dust change cloud map corresponding to the preset time period is determined for each spray range. The dust change cloud map includes dust particles and cloud map color, wherein the dust particles correspond to the dust characteristics and the cloud map color corresponds to the predicted dust amount of the area.

[0040] Each dust change cloud map is overlaid onto the corresponding position in the range-marked image.

[0041] By adopting the above technical solution, the predicted amount and characteristics of regional dust are transformed into a dust cloud map, which makes it easy for relevant inspection personnel to intuitively view the changes in dust within a preset time period. In addition, by corresponding dust particles with dust characteristics and cloud map colors with predicted dust amounts, it is easy to clearly display the changes in dust within different spray ranges, which is convenient for relevant inspection personnel to monitor dynamically. The visualized dust changes help relevant inspection personnel to give early warnings of high dust risk areas, thereby helping to protect the health and safety of relevant workers.

[0042] Secondly, this application provides an electronic device that adopts the following technical solution:

[0043] A control system comprising:

[0044] At least one processor;

[0045] Memory;

[0046] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the above-described image recognition-based mining spray dust suppression control method.

[0047] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0048] A computer-readable storage medium includes: a computer program stored thereon that can be loaded by a processor and execute the above-described image recognition-based mining spray dust suppression control method.

[0049] Fourthly, this application provides a computer program product, which adopts the following technical solution:

[0050] A computer program product includes a computer program that, when executed by a processor, implements the above-described image recognition-based dust suppression control method for mining spraying.

[0051] In summary, this application includes at least one of the following beneficial technical effects:

[0052] When actual mining area images contain shaded areas, a simulated dust diffusion map can first be obtained by constructing a simulated mining area model and combining it with construction parameters. This can simulate and predict the mining conditions underground, thus facilitating an understanding of the dust distribution underground. However, since the simulation results are relatively ideal, a method is adopted that integrates the actual dust diffusion map and the simulated dust diffusion map, and identifies dust information based on the fusion result, rather than directly using the simulated dust diffusion map to identify dust information. This method facilitates a clear understanding of the dust distribution underground while improving the fit between the detection results and the actual situation, thereby improving the accuracy of the detection results. Finally, spray control commands generated based on highly accurate dust information facilitate the control of the spray volume and spray attitude of the spray equipment, thereby ensuring the accuracy and effectiveness of spray dust suppression, and ultimately improving the dust suppression effect.

[0053] By converting regional dust predictions and dust characteristics into dust cloud maps, relevant inspection personnel can intuitively view dust changes within a preset time period. In addition, by mapping dust particles to dust characteristics and cloud map colors to predicted dust amounts, it is easy to clearly display dust changes within different spray ranges, facilitating dynamic monitoring by relevant inspection personnel. The visualized dust changes help relevant inspection personnel to provide early warnings of high-dust-risk areas, thereby ensuring the health and safety of relevant workers. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating a method for controlling dust suppression in mines based on image recognition, as described in an embodiment of this application.

[0055] Figure 2 This is a schematic diagram of a shaded area in an embodiment of this application;

[0056] Figure 3 This is a schematic diagram of a spliced ​​edge line in an embodiment of this application;

[0057] Figure 4 This is a schematic flowchart illustrating a method for determining a dust change pattern in an embodiment of this application.

[0058] Figure 5 This is a schematic diagram of the structure of a control system according to an embodiment of this application. Detailed Implementation

[0059] The following is in conjunction with the appendix Figure 1-5 This application will be described in further detail.

[0060] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0061] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0063] Specifically, this application provides an image recognition-based method for controlling dust suppression in mining spray systems. This method is executed by a control system, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not impose any limitations on this.

[0064] refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for controlling dust suppression in mines based on image recognition, as described in an embodiment of this application. The method includes steps S110-S150, wherein:

[0065] Step S110: Obtain an actual mining area image and determine the actual dust diffusion map corresponding to the mining area to be controlled based on the actual mining area image.

[0066] Specifically, actual mining area images can be acquired by image acquisition devices located in the mining area to be controlled, and then uploaded to the control system. The mining area to be controlled is the area requiring dust suppression spraying. The actual dust diffusion map represents the actual diffusion pattern of dust in the actual mining area image. When determining the actual dust diffusion map from the actual mining area image, image preprocessing can be performed to better highlight the dust diffusion pattern. Preprocessing operations include noise reduction, contrast enhancement, and brightness adjustment. The specific content of the preprocessing operations is not specifically limited in this embodiment and can be defined by relevant technical personnel. Feature detection is then performed on the preprocessed image. Specifically, scale-invariant feature transformation algorithms or accelerated robust feature algorithms can be used to detect dust particles in the preprocessed actual mining area image. The specific feature detection algorithm is not specifically limited in this embodiment, as long as feature detection is possible. Finally, image segmentation technology can be applied to identify and segment dust areas from the actual mining area image to obtain the actual dust diffusion map. The actual dust diffusion map allows for a direct view of the dust diffusion state corresponding to the mining area to be controlled.

[0067] Step S120: When the actual dust diffusion map contains a shaded area, determine the corresponding simulated mining area model based on the mining area parameters of the mining area to be controlled, and obtain the simulated dust diffusion map corresponding to the mining area to be controlled based on the construction parameters of the mining area to be controlled and the simulated mining area model. The construction parameters include the construction amount, construction area and environmental parameters, including wind speed and humidity.

[0068] Specifically, since mining areas are generally located underground, natural light cannot penetrate the surface as the mining depth increases, which may lead to dim lighting within the mining area. Light may be blocked by mining equipment or mine walls, resulting in shadowed areas in the actual dust diffusion image. Shadows are formed because light is blocked by objects, causing some areas to lack direct illumination. Color analysis can be used to determine whether an actual dust diffusion image contains shadowed areas. Since shadowed areas are usually different in color from non-shadowed areas—for example, they may be darker or have a different hue—identifying and comparing the color values ​​of each pixel can determine whether an actual dust diffusion image contains shadowed areas. Other methods for determining whether an actual dust diffusion image contains shadowed areas include edge detection, image segmentation, and optical flow analysis. The specific methods are not limited in this embodiment, as long as they can identify and determine whether an actual dust diffusion image contains shadowed areas.

[0069] The mining area parameters include the location of the mining area, the mining type, and the scale of the mining area. Based on the mining area parameters, the accuracy of determining the simulated mining area model corresponding to the mining area to be controlled can be improved. Different mining area parameters correspond to different simulated mining area models. There is a correspondence between mining area parameters and simulated mining area models. Based on this correspondence, the simulated mining area model corresponding to any mining area parameter can be determined. The specific content of this correspondence is not specifically limited in this embodiment of the application. It can be determined by relevant technical personnel based on historical experimental data and then uploaded to the control system.

[0070] The construction parameters include the amount of work, the construction area, and environmental parameters. Environmental parameters include wind speed and humidity. The construction area is a specific mining area within the controlled mining zone. The amount of work is the quantity of mineral to be mined; when the mineral is coal, the amount of coal to be mined. Since wind speed is the primary driving force for dust movement, higher wind speeds result in greater aerodynamic forces on the dust generated during mining operations, increasing its diffusion range and speed. Air humidity also significantly affects the cohesion and suspension capacity of dust. Increased humidity in the surrounding environment may cause dust particles to absorb water, increasing their weight and reducing their suspension time in the air. Therefore, to better simulate dust diffusion, wind speed and humidity in the surrounding environment need to be considered during the simulation. Environmental parameters may also include temperature. Specific environmental parameters are not limited in this embodiment and can be set by relevant technical personnel according to actual needs.

[0071] When simulating dust diffusion based on the construction parameters of the mining area to be controlled and the simulated mining area model, the simulated mining area model can be set first according to the mining area parameters, and then the construction parameters can be input into the simulated mining area model as simulation conditions to simulate dust behavior. During the simulation process, the simulated mining area model will predict and calculate the movement trajectory of dust under the corresponding construction parameters, and finally the spatial distribution of dust can be displayed through the simulated dust diffusion map.

[0072] Step S130: Determine the target dust diffusion map based on the actual dust diffusion map and the simulated dust diffusion map.

[0073] Specifically, since simulated dust diffusion maps primarily simulate and reproduce the distribution of dust in the air, rather than the lighting effects in physical space, simulated dust diffusion maps generally do not contain shaded areas. Therefore, the simulated dust diffusion map can be used to understand the dust distribution corresponding to the shaded areas in the actual dust diffusion map. Furthermore, to make the target dust diffusion map more comprehensive and accurate, the target dust diffusion map is determined based on the actual dust diffusion map and the simulated dust diffusion map. This can specifically include:

[0074] Identify at least one non-shaded region in the actual dust diffusion map and identify the region edge line corresponding to each non-shaded region; merge the region edge lines of at least one non-shaded region to obtain a stitched edge line; identify the simulated edge line corresponding to the simulated dust diffusion map; if the edge line matching degree between the simulated edge line and the stitched edge line is higher than the preset standard matching degree, then determine the diffusion map of the region to be superimposed based on the shaded region and the simulated dust diffusion map; complete the actual dust diffusion map containing the shaded region based on the diffusion map of the region to be superimposed to obtain the target dust diffusion map.

[0075] Specifically, when an actual dust diffusion map contains shaded areas, the shaded areas may divide the actual dust diffusion map into multiple non-shaded areas. Therefore, when an actual dust diffusion map contains shaded areas, it will also contain at least one non-shaded area. At least one non-shaded area and the shaded area together constitute the entire actual dust diffusion map.

[0076] When determining the edge line of each shaded region, methods such as Canny edge detection, Sobel operator, or Hough transform can be used for identification. Since different lighting conditions and different image shooting angles will cause the shaded areas in actual dust diffusion images to change, at least one non-shaded region in different actual dust diffusion images is not fixed, and the edge line corresponding to each non-shaded region is also different. Figure 2 As shown, Figure 2 It contains three non-shaded regions, and the boundary lines of these three non-shaded regions are indicated by dashed lines. When merging the boundary lines of at least one non-shaded region, the following steps can be used:

[0077] Step 1: Edge recognition. Based on the region edge line of each non-shaded area, locate the boundary of each non-shaded area;

[0078] Step 2: Image segmentation, ensuring that each non-shaded area is completely cut out from the actual dust diffusion image. This process may require thresholding or the use of color spaces to distinguish between shaded and non-shaded areas.

[0079] Step 3: Edge matching. Determine the adjacent regions of adjacent non-shaded regions, and then determine the adjacent edge lines of adjacent regions based on the region edge lines of adjacent regions. This process can be achieved by comparing the coordinates of the region edge points of two shaded regions.

[0080] Step 4: Edge fusion. After determining the adjacent edge lines between adjacent regions, the adjacent edge lines can be merged to eliminate gaps or overlaps and ensure the continuity of the edge lines between adjacent regions. In this process, pixel operations of the image, such as closing operations, can be used to fill holes or connect broken edges. The specific method is not specifically limited in this application embodiment.

[0081] Step 5: Result Integration. All merged edges are integrated into a single, continuous boundary, forming a large area, resulting in the stitched edge line, such as... Figure 3 As shown.

[0082] Before using a simulated dust diffusion map to complete the actual dust diffusion map, the accuracy of the simulated dust diffusion map can be verified by matching edge lines. When the matching degree between the simulated edge line and the stitched edge line is higher than the preset standard matching degree, it indicates that the adaptation between the simulated dust diffusion map and the actual dust diffusion map is high, and image fusion or overlay can be performed. When the matching degree between the simulated edge line and the stitched edge line is not higher than the preset standard matching degree, it indicates that the adaptation between the simulated dust diffusion map and the actual dust diffusion map is low. In order to improve the accuracy of the target dust diffusion map and thus improve the accuracy of the dust control process, a simulated dust diffusion map can be regenerated from the simulated mining area model, and then image fusion or overlay can be performed based on the new simulated dust diffusion map. The preset standard matching degree can be 89% or 95%, and the specific value is not specifically limited in this embodiment of the application, but can be set by relevant technicians.

[0083] When the edge matching degree between the simulated edge line and the stitched edge line is higher than the preset standard matching degree, the characterization can be performed by image fusion or overlay. Specifically, the steps include locating the shadow area from the simulated dust diffusion map, then performing image cutout processing on the image corresponding to the shadow area in the simulated dust diffusion map to obtain the diffusion map of the area to be overlaid, and finally overlaying the diffusion map of the area to be overlaid onto the shadow area of ​​the actual dust diffusion map to obtain the target dust diffusion map. Besides image overlay, the target dust diffusion map can also be obtained through image fusion technology. The specific method for obtaining the target dust diffusion map is not specifically limited in this embodiment. By identifying the parts in the simulated dust diffusion map that match the actual dust diffusion map, and using simulated data to complete the shadow area, it is easier to improve the dust distribution situation of the entire mining area.

[0084] Step S140: Perform edge detection and particle size analysis on the target dust diffusion map to determine the dust information corresponding to the mining area to be controlled from the target dust diffusion map. The dust information includes the size of the dust area and the dust concentration.

[0085] Specifically, edge detection is performed on the target dust diffusion map to facilitate the determination of the dust region size in the target dust diffusion map, and particle size analysis is performed on the target dust diffusion map to facilitate the determination of dust concentration. When determining the dust region size, the target edge line corresponding to the target dust diffusion map can be identified, and the area of ​​the bounding rectangle corresponding to the target edge line can be calculated after determining the target edge line, or the number of pixels within the target edge line can be directly calculated, and then the dust region size is obtained by multiplying the number of pixels by the area of ​​a single pixel. This method is not specifically limited in this embodiment of the application and can be set by relevant technical personnel.

[0086] When performing particle size analysis on a target dust diffusion map, estimation can be based on grayscale values. Since the grayscale value of a dust region is related to the dust concentration, the average grayscale value or grayscale histogram of the dust region can be statistically analyzed. The dust concentration in the target dust diffusion map can then be determined based on known historical experimental data or a preset standard curve. Historical experimental data and preset standard curves can be uploaded to the control system in advance by relevant personnel. Furthermore, if dust particles exhibit obvious textures or spots in the target dust diffusion map, the dust concentration can be determined by statistically analyzing the number, area, or volume of these textures or spots. Texture analysis methods such as local binary mode algorithms, gray-level co-occurrence matrices, or wavelet transforms can be used to extract texture features. Spot detection methods such as Otsu binarization, Canny edge detection, blob detection, or region growing methods can be used to identify spot features. Specific methods are not limited in this embodiment.

[0087] Step S150: Generate spray control instructions based on dust information. The spray control instructions are used to control the spray equipment to perform dust suppression operations based on the spray control instructions. The spray control instructions include spray volume and spray attitude.

[0088] Specifically, the dust information includes the size of the dust area and the dust concentration. A larger dust area and a higher dust concentration correspond to a larger spray volume in the spray control command. A first correspondence exists between dust information and spray volume, which determines the appropriate spray volume for different dust conditions. The specific content of this first correspondence is not specifically limited in this application. The spray attitude includes the nozzle angle of the spraying device. A second correspondence exists between the nozzle angle and the size of the dust area. This second correspondence includes the appropriate dust area size for different nozzle angles. The specific content of this second correspondence is not specifically limited in this application embodiment. The spray attitude may also include the nozzle height, the specific content of which is not specifically limited in this application embodiment and can be set or modified by relevant technical personnel according to actual needs. After generating the spray control command, it can be sent to the spraying device to control the spraying device to perform dust suppression operations according to the spray volume and spray attitude in the spray control command.

[0089] In the embodiments of this application, when the actual mining area image contains shadowed areas, a simulated dust diffusion map can first be obtained by constructing a simulated mining area model and combining it with construction parameters. This can simulate and predict the mining situation underground, thus facilitating an understanding of the dust distribution underground. However, since the simulation results are relatively ideal, a method is adopted to identify dust information based on the fusion result after fusing the actual dust diffusion map and the simulated dust diffusion map, rather than directly using the simulated dust diffusion map to identify dust information. This method facilitates a clear understanding of the dust distribution underground while improving the fit between the detection results and the actual situation, thereby improving the accuracy of the detection results. Finally, the spray control command generated based on the highly accurate dust information facilitates the control of the spray volume and spray attitude of the spray equipment, thereby ensuring the accuracy and effectiveness of spray dust suppression, and thus improving the dust suppression effect.

[0090] When the number of spraying devices exceeds the preset number of devices, the method provided in this application embodiment further includes:

[0091] The process involves acquiring the dust reduction rate for each spraying device's corresponding spray range and identifying spray ranges with dust reduction rates lower than the preset standard dust reduction rate as the scope of interest. Based on the regional construction parameters and historical construction data corresponding to the scope of interest, the predicted dust amount for that scope is determined. When the predicted dust amount is higher than the preset dust amount, the location information of the scope of interest is identified, and a movement route is generated based on the location information. The process also involves acquiring the spray control command corresponding to the scope of interest, determining the movement spray control command based on the predicted dust amount, and determining the movement control command based on the movement route and the spray control command. The movement control command is used to control the UAV spraying device to perform movement dust reduction processing on the scope of interest according to the generated movement route.

[0092] Specifically, the number of preset devices can be 5 or 6. The specific number is not limited in this embodiment and can be set by relevant technical personnel. Each spraying device corresponds to a different spray range. Since the dust area size and dust concentration may differ for different spray ranges, the spray control commands for different spraying devices may also differ. Therefore, the dust reduction rate corresponding to different spray ranges will also be different. To determine the dust reduction rate corresponding to different spray ranges, it can be collected by particle concentration detection devices installed within different spray ranges and uploaded to the control system. Alternatively, it can be determined by detecting the concentration of the spray images corresponding to different spray ranges. The specific determination method and the specific value of the preset standard dust reduction rate are not specifically limited in this embodiment.

[0093] The dustfall rate corresponding to the area of ​​concern is lower than the preset standard dustfall rate. The number of areas of concern can be zero, one, or multiple. A low dustfall rate within the area of ​​concern may lead to potential dust accumulation. In this case, it is necessary to predict the amount of dust in the area of ​​concern over a future period to determine whether the potential dust accumulation will worsen. Predicting the amount of dust in the area of ​​concern over a future period can be based on the regional construction parameters and historical construction data. The regional construction parameters include the construction volume, construction area, and environmental parameters. To determine the predicted dust amount, the simulated mining area model corresponding to the area of ​​concern can be determined from the simulated mining area model. The regional construction parameters are then imported into the simulated mining area model to obtain the predicted dust amount for the future period. The specific prediction method is not specifically limited in this embodiment; the future period can be two hours after the current time or one hour after the current time. The specific duration is not specifically limited in this embodiment.

[0094] When the predicted dust level exceeds the preset dust level, the potential for dust accumulation within the area of ​​concern will worsen. In this case, it is necessary to utilize both drone spraying equipment and spraying equipment corresponding to the area of ​​concern to jointly reduce dust accumulation and improve the dust reduction rate, thereby reducing the probability of dust accumulation within the area. By monitoring the dust reduction rate of each spraying device's corresponding spray range and setting a preset standard dust reduction rate, it is easy to accurately identify areas of concern where dust reduction is ineffective. This helps to concentrate limited mobile spraying resources on the areas most in need of dust reduction, thus improving dust reduction efficiency. Furthermore, by using drone spraying equipment in conjunction with generated movement routes and spray control commands for mobile dust reduction operations, intelligent mobile dust reduction processing can be achieved while improving the accuracy of the dust reduction operation.

[0095] Furthermore, the method provided in this application embodiment also includes:

[0096] Based on the regional construction parameters and historical construction data corresponding to each spray range, the predicted dust volume for each spray range in the preset time period is determined; the range parameters for each spray range in the preset time period are obtained, including temperature and wind speed; based on the predicted dust volume, range parameters, and simulated mining area model, the adjusted spray volume for each spraying device is determined; and the spray control instructions for each spraying device in the preset time period are optimized based on the adjusted spray volume.

[0097] Specifically, the method for determining the predicted dust amount for each spray range can refer to the method for determining the predicted dust amount for the area of ​​interest in the above embodiments, and will not be repeated here. The preset time period can be 1 hour after the current time or 2 hours after the current time; the specific duration is not specifically limited in this application embodiment.

[0098] The range parameters corresponding to each spray range can be estimated and obtained from the real-time recorded parameter log corresponding to each spray range. Each spray range has a corresponding real-time recorded parameter log, which can be collected by sensors set within each spray range and uploaded to the control system. The real-time recorded parameter log contains the temperature and wind speed corresponding to each recording moment in historical periods. The interval between each recording moment can be 20 minutes or 10 minutes, and the specific interval length is not specifically limited in this application embodiment. For any real-time recorded parameter log, the temperature and wind speed can be fitted separately to obtain the temperature fitting trend and the wind speed fitting trend. Then, the range parameters are determined based on the temperature fitting trend, the wind speed fitting trend, and the preset time period. The method of determining the range parameters is not specifically limited in this application embodiment, as long as the range parameters corresponding to each spray range in the preset time period can be obtained.

[0099] After determining the range parameters corresponding to each spray range, based on the predicted dust volume, range parameters, and simulated mining area model for each spray range, the predicted spray volume for each spraying device is determined. Then, the actual spray volume for each spraying device at the current moment is obtained. Finally, based on the predicted and actual spray volumes for each spray range, the adjusted spray volume for each spraying device within each range is determined. For each spraying device, the adjusted spray volume ensures that the actual spray volume reaches the predicted spray volume within a preset time period. This prediction data facilitates more precise adjustment of the spray volume, thereby more effectively controlling dust. Optimizing spray control commands ensures sufficient spray volume to control dust when needed, while reducing spray volume unnecessarily, thus avoiding unnecessary interference with the construction schedule and improving construction efficiency.

[0100] Furthermore, in order to provide relevant testing personnel with an intuitive display of the environmental conditions, the method provided in this application embodiment also includes steps S1-S3, such as... Figure 4 As shown, where:

[0101] Step S1: Identify and label each spray range from the actual mining area image to obtain a range-labeled image.

[0102] Specifically, different spray ranges correspond to different range identifiers. Each spray range can be identified from the actual mining area image based on the range identifier. The range identifier can be a pre-defined range edge line. When marking the spray range in the actual mining area image, a combination of borders and text can be used for marking. The marking method is not specifically limited in this application embodiment, as long as different spray ranges can be distinguished from the range-marked image.

[0103] Step S2: Determine the dust change information corresponding to each spray range based on the predicted dust amount in the area corresponding to each spray range within a preset time period.

[0104] Specifically, the dust change information can be in the form of a data axis or text. In this embodiment, no specific limitation is made. When the dust change information is in the form of a data axis, a data axis coordinate system can be constructed first based on the prediction time period. Then, the predicted dust amount of the area corresponding to each prediction time can be imported into the data axis coordinate system to obtain the dust change information of the spray range corresponding to the preset time period. Based on the above steps, the dust change information corresponding to each spray range can be obtained.

[0105] Step S3: Overlay each dust change information onto the corresponding position in the range-marked image to obtain a dust change map, and then feed back the dust change map.

[0106] Specifically, each spray range corresponds to a dust change information. Before overlaying, the corresponding spray range needs to be determined from the range marking image. Then, the dust change information is overlaid onto the corresponding position using data overlay technology. For easy viewing, the corresponding dust change information can be displayed after a visitor clicks or triggers the corresponding spray range.

[0107] When the dust change information is presented as a change image, each dust change information is superimposed onto the corresponding position in the range-marked image. Specifically, this can include:

[0108] From actual mining area images, identify dust features corresponding to each spray range; determine the dust change cloud map corresponding to each spray range in a preset time period based on the predicted dust amount and corresponding dust features of the area corresponding to each spray range. The dust change cloud map contains dust particles and cloud map color, where dust particles correspond to dust features and cloud map color corresponds to the predicted dust amount of the area; superimpose each dust change cloud map onto the corresponding position in the range-marked image.

[0109] Specifically, the dust feature is a particle feature. Different spray ranges can use the same preset dust feature or different dust features. When using different dust features, it is necessary to identify the particle features of the corresponding spray range from the actual mining area image. The specific method is not specifically limited in this application embodiment. Using different dust features to construct dust change maps corresponding to different spray ranges facilitates the improvement of the fit between the dust change map and the actual dust distribution of the corresponding spray range, thereby facilitating a more intuitive display of the dust increase in the corresponding spray range.

[0110] For any spray range, when determining the dust change cloud map based on dust characteristics and the predicted dust amount in the area corresponding to the preset time period, the frame image and dust prediction amount corresponding to each preset time can be determined first based on the preset time period and the predicted dust amount in the area. Then, the dust characteristics corresponding to the dust prediction amount are superimposed on the frame image to form a dust change frame image. According to the above steps, the dust change frame image corresponding to each preset time can be determined. Then, after integrating multiple dust change frame images in chronological order, the dust change cloud map corresponding to the preset time period is obtained.

[0111] Different dust characteristics correspond to different dust particle sizes. Distinguishing different dust characteristics by particle size facilitates a direct visual observation of dust differences within different spray ranges. A correspondence exists between dust characteristics and dust particles, allowing the identification of the dust particle corresponding to any given dust characteristic. A correspondence also exists between cloud map colors and regional dust prediction levels. Dust levels can be determined based on regional dust prediction levels, with different cloud map colors corresponding to different dust levels. For example, the cloud map color for the first dust level is green, the second for the second, and the third for the third. Higher regional dust prediction levels correspond to higher dust levels, and higher dust levels correspond to darker cloud map colors. A correspondence exists between cloud map colors and dust levels, as well as between dust levels and regional dust prediction levels. The specific details of these correspondences are not limited in this embodiment and can be set by relevant technical personnel.

[0112] After determining the dust change cloud map corresponding to each spray range, the specific implementation method of overlaying the dust change cloud map onto the range annotation image can refer to the method in the above embodiment of overlaying each dust change information onto the corresponding position in the range annotation image, and will not be elaborated here. By associating dust particles with dust characteristics and the cloud map color with the predicted dust amount, it is easy to clearly display the dust changes in different spray ranges, which facilitates dynamic monitoring by relevant inspection personnel. The visualized dust changes help relevant inspection personnel to provide early warning of high dust risk areas, thereby helping to ensure the health and safety of relevant workers.

[0113] This application provides a control system, such as... Figure 5 Show, Figure 5 The control system 500 shown includes a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, for example, via a bus 502. Optionally, the control system 500 may also include a transceiver 504. It should be noted that in practical applications, the transceiver 504 is not limited to one, and the structure of this control system 500 does not constitute a limitation on the embodiments of this application.

[0114] Processor 501 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 501 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0115] Bus 502 may include a pathway for transmitting information between the aforementioned components. Bus 502 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 502 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0116] The memory 503 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0117] The memory 503 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 501. The processor 501 is used to execute the application code stored in the memory 503 to implement the content shown in the foregoing method embodiments. Figure 5 The control system shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0118] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0119] This application provides a computer program product including a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0120] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0121] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for controlling dust suppression in mining using image recognition-based spraying, characterized in that, include: Acquire actual mining area images, and determine the actual dust diffusion map corresponding to the mining area to be controlled based on the actual mining area images; When the actual dust diffusion map contains a shaded area, a corresponding simulated mining area model is determined based on the mining area parameters of the mining area to be controlled. Based on the construction parameters of the mining area to be controlled and the simulated mining area model, a simulated dust diffusion map corresponding to the mining area to be controlled is obtained. The construction parameters include the amount of construction, the construction area, and environmental parameters, including wind speed and humidity. Based on the actual dust diffusion map and the simulated dust diffusion map, the target dust diffusion map is determined; Edge detection and particle size analysis are performed on the target dust diffusion map to determine the dust information corresponding to the mining area to be controlled from the target dust diffusion map. The dust information includes the size of the dust area and the dust concentration. Based on the dust information, a spray control command is generated. The spray control command is used to control the spray equipment to perform dust suppression operation based on the spray control command. The spray control command includes spray volume and spray attitude. The step of determining the target dust diffusion map based on the actual dust diffusion map and the simulated dust diffusion map includes: Identify at least one non-shaded area contained in the actual dust diffusion map, and identify the area edge line corresponding to each non-shaded area; By merging the edge lines of at least one non-shaded area, a stitched edge line is obtained; Identify the simulated edge line corresponding to the simulated dust diffusion map. If the edge line matching degree between the simulated edge line and the spliced ​​edge line is higher than the preset standard matching degree, then determine the diffusion map of the area to be superimposed based on the shaded area and the simulated dust diffusion map. The actual dust diffusion map, which includes the shaded area, is completed based on the diffusion map of the area to be superimposed to obtain the target dust diffusion map; The determination of the diffusion map of the area to be overlaid, based on the shaded area and the simulated dust diffusion map, includes: The process involves locating the shadowed areas from a simulated dust diffusion image; extracting the corresponding shadowed areas from the simulated dust diffusion image to obtain a diffusion image of the area to be overlaid; overlaying the diffusion image of the area to be overlaid onto the shadowed areas of the actual dust diffusion image to obtain the target dust diffusion image; and fusing the edge lines of at least one non-shadowed area to obtain a stitched edge line, including: determining the boundary of each non-shadowed area based on its edge line; distinguishing between shadowed and non-shadowed areas according to color space and segmenting each non-shadowed area from the actual dust diffusion image; determining the adjacent areas of adjacent non-shadowed areas and then determining their adjacent edge lines based on their edge lines; fusing the adjacent edge lines and integrating all the fused edge lines to obtain the stitched edge line. If the edge line matching degree between the simulated edge line and the spliced ​​edge line is not higher than the preset standard matching degree, the simulated dust diffusion map is updated according to the simulated mining area model, and the diffusion map of the area to be superimposed is determined based on the updated simulated dust diffusion map. When the number of spraying devices exceeds the preset number of devices, it also includes: Obtain the dust reduction rate of the corresponding spray range for each spraying device, and determine the spray range with a dust reduction rate lower than the preset standard dust reduction rate as the scope of interest; Based on the regional construction parameters and historical construction data corresponding to the scope of concern, the predicted dust amount of the scope of concern is determined. When the predicted dust amount is higher than the preset dust amount, the location information of the area of ​​interest is identified, and a movement route is generated based on the location information; Obtain the spray control command corresponding to the area of ​​interest, and determine the moving spray control command based on the predicted dust amount and the predicted dust amount; Based on the movement route and the spray control command, a movement control command is determined. The movement control command is used to control the drone spraying equipment to perform mobile dust suppression treatment on the area of ​​interest according to the generated movement route. This also includes: Based on the regional construction parameters and historical construction data corresponding to each spray range, the predicted amount of regional dust for each spray range in the preset time period is determined. Obtain the range parameters for each spray range corresponding to the preset time period, the range parameters including temperature and wind speed; Based on the predicted dust volume and range parameters for each region, as well as the simulated mining area model, the adjusted spray volume for each spraying device is determined. Based on each adjustment of the spray volume, optimize the spray control command for each spray device corresponding to the preset time period.

2. The method for controlling dust suppression in mines based on image recognition according to claim 1, characterized in that, Also includes: Each spray range is identified and labeled from the actual mining area images to obtain a range-labeled image; Based on the predicted dust volume of each spray range in the preset time period, determine the dust change information corresponding to each spray range; Each dust change information is superimposed onto the corresponding position in the range-marked image to obtain a dust change map, and the dust change map is fed back.

3. The method for controlling dust suppression in mines based on image recognition according to claim 2, characterized in that, The step of overlaying each dust change information onto the corresponding position in the range-marked image includes: From the actual mining area images, identify the dust features corresponding to each spray range; Based on the predicted dust amount and corresponding dust characteristics of the area corresponding to each spray range, a dust change cloud map corresponding to the preset time period is determined for each spray range. The dust change cloud map includes dust particles and cloud map color, wherein the dust particles correspond to the dust characteristics and the cloud map color corresponds to the predicted dust amount of the area. Each dust change cloud map is overlaid onto the corresponding position in the range-marked image.

4. A control system, characterized in that, The control system includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform a mining spray dust suppression control method based on image recognition as described in any one of claims 1-3.

5. A computer-readable storage medium, characterized in that, include: The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1-3, which is a method for controlling dust suppression in mining based on image recognition.

6. A computer program product, characterized in that, The method includes a computer program that, when executed by a processor, implements the steps of any one of the image recognition-based dust suppression control methods for mines according to claims 1-3.

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