Fully mechanized coal mining face cutting planning method and device

Abstract

The invention relates to the technical field of coal mining cutting planning, and provides a fully mechanized coal mining face cutting planning method and device.The method comprises the steps that through an acquisition device arranged on a fully mechanized coal mining face, face image data obtained after pushing and sliding of a hydraulic support are completed are obtained, the working face image data comprises environment data of a fully-mechanized working face and operation data of fully-mechanized mining equipment, and the fully-mechanized mining equipment at least comprises a coal mining machine and a hydraulic support; the working face image data are analyzed, cutting basic data are obtained, and the cutting basic data comprise at least one of geological anomaly data, roof and floor boundary data and coal wall state evaluation data; and performing coal mining cutting planning on the fully mechanized coal mining face according to the cutting basic data, and determining cutting parameters and a cutting path. The cutting path can be adaptively, dynamically and accurately planned according to the change of the occurrence condition of the fully mechanized coal mining face coal seam, and the cutting precision and efficiency are improved.

Description

Technical Field

[0001] This application relates to the field of coal mining cutting planning technology, specifically to a cutting planning method and apparatus for fully mechanized mining faces. Background Technology

[0002] Existing fully mechanized mining faces are rapidly developing towards less manpower and intelligent operation, and the precision and adaptive capability of the coal mining machine's autonomous cutting have become the key to achieving efficient and safe mining.

[0003] However, in related technologies, coal cutting planning often relies on preset models, programs, or human experience, which has poor adaptability. The collected working face data is easily affected by environmental interference, resulting in poor accuracy. It cannot provide an effective basis for parameter adjustment, affecting cutting accuracy and efficiency. At the same time, there is a time lag between parameter adjustment and on-site working conditions. When the coal seam interface and roof conditions change abruptly, it can easily lead to risks such as cutting tooth wear and roof collapse, affecting mining safety. Summary of the Invention

[0004] The purpose of this application is to provide a cutting planning method and apparatus for fully mechanized mining faces, so as to at least solve the technical problems of low cutting accuracy and efficiency caused by the reliance on manual labor and poor adaptability of existing cutting planning methods, which affect mining efficiency and mining safety.

[0005] To solve the above-mentioned technical problems, the embodiments of this application adopt the following technical solutions:

[0006] In a first aspect, embodiments of this application provide a method for cutting and planning a fully mechanized mining face, including:

[0007] The acquisition device installed at the fully mechanized mining face acquires image data of the mining face after the hydraulic support has been pushed through. The image data of the mining face includes environmental data of the fully mechanized mining face and operating data of the fully mechanized mining equipment, which includes at least a coal mining machine and the hydraulic support.

[0008] The working face image data is analyzed to obtain basic cutting data, wherein the basic cutting data includes at least one of geological anomaly data, roof and floor boundary data, and coal wall and roof condition assessment data;

[0009] Based on the cutting baseline data, coal cutting planning is carried out for the fully mechanized mining face to determine cutting parameters and cutting paths.

[0010] In some embodiments, the working surface image data is analyzed to obtain basic truncating data, including:

[0011] The roof and floor lines are identified from the working face image data using an edge detection algorithm, and the coal seam thickness and roof and floor dip angles are determined based on the positions of the roof and floor lines.

[0012] The geological anomaly regions in the working face image data are identified by using a target detection model or anomaly detection algorithm. The geological anomaly regions include at least one of faults, interbedded rock, iron sulfide nodules, and coal wall spalling.

[0013] Extract the coal wall edge contour from the working face image data, and perform integrity detection on the coal wall;

[0014] The displacement changes of the roof rock strata in the working face image data are analyzed to assess the stability of the roof.

[0015] In some embodiments, after analyzing the working surface image data, the method further includes:

[0016] If the working face image data meets the preset conditions, the working face image data is sent to the terminal for manual analysis. The preset conditions include at least one of the following: the identification confidence level of the working face image data is lower than a preset threshold, a major anomaly is identified from the working face image data, and the geological conditions of the fully mechanized mining face are complex.

[0017] In some embodiments, coal cutting planning is performed on the fully mechanized mining face based on the cutting baseline data, and cutting parameters are determined, including adjusting the cutting parameters of the coal mining machine and the shifting parameters of the hydraulic support.

[0018] Adjusting the cutting parameters of the coal mining machine includes at least one of the following:

[0019] Adjust the mining height parameters of the coal mining machine based on the identified coal seam thickness and roof and floor lines;

[0020] Adjust the shearing machine's undercut and cutting tool lifting amounts based on the identified floor undulations.

[0021] Based on the identified geological anomaly data, adjust the traction speed and drum speed of the coal mining machine;

[0022] Adjusting the moving parameters of the hydraulic support, including at least one of the following:

[0023] Adjust the support strength of the hydraulic supports according to the integrity and / or degree of cracks in the roof;

[0024] Adjust the moving speed and timing of the hydraulic support according to the stability of the roof;

[0025] Based on the pressure and / or geological anomaly data of the roof, adjust the group relocation strategy of the hydraulic supports, which includes the relocation sequence and the timing of the relocation.

[0026] In some embodiments, the method further includes:

[0027] The working face image data is analyzed to determine the operating parameters of the fully mechanized mining equipment, wherein the operating parameters include at least one of equipment parameters and operating status parameters;

[0028] Based on the operating parameters of the fully mechanized mining equipment and the basic cutting data, a coal cutting plan is made for the fully mechanized mining face.

[0029] In some embodiments, coal cutting planning is performed on the fully mechanized mining face based on the cutting baseline data, including:

[0030] With the goal of stabilizing the current of the cutting motor and ensuring the quality of the coal, the drum speed and traction speed of the coal mining machine are adjusted.

[0031] In some embodiments, coal cutting planning is performed on the fully mechanized mining face based on the cutting baseline data to determine the cutting path, including:

[0032] The objective function is to plan the drum cutting path of the coal mining machine with the highest cutting efficiency and shortest path.

[0033] In some embodiments, the method further includes:

[0034] During the cutting process, image data of the coal mining machine is acquired, and the operation of the drum of the coal mining machine is monitored based on the image data.

[0035] If there is a deviation in the operation of the drum of the coal mining machine, the deviation shall be adjusted.

[0036] In some embodiments, there are multiple data acquisition devices, which are arranged along the top beam of the hydraulic support or the sidewall of the working face roadway in the fully mechanized mining face. The method further includes:

[0037] The deployment density of the acquisition device is determined based on the length of the fully mechanized mining face.

[0038] Secondly, embodiments of this application provide a fully mechanized mining face cutting and planning device, comprising:

[0039] The acquisition module is configured to acquire working face image data after the hydraulic support pushes the conveyor through an acquisition device installed on the fully mechanized mining face. The working face image data includes environmental data of the fully mechanized mining face and operating data of the fully mechanized mining equipment, which includes at least a coal mining machine and the hydraulic support.

[0040] The analysis module is configured to analyze the working face image data to obtain basic cutting data, wherein the basic cutting data includes at least one of geological anomaly data, roof and floor boundary data, and coal wall and roof condition assessment data;

[0041] The planning module is configured to perform coal cutting planning for the fully mechanized mining face based on the cutting baseline data, and to determine the cutting parameters and cutting path.

[0042] Thirdly, embodiments of this application provide an electronic device, including at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-described fully mechanized mining face cutting planning method when executing the computer program in the memory.

[0043] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described fully mechanized mining face cutting planning method.

[0044] This application provides a method and apparatus for cutting and planning a fully mechanized mining face. By using a data acquisition device installed at the fully mechanized mining face, image data of the face after the hydraulic support has pushed the conveyor is acquired. The face image data includes environmental data of the fully mechanized mining face and operational data of the fully mechanized mining equipment, which includes at least a coal mining machine and the hydraulic support. The face image data is analyzed to obtain basic cutting data, which includes at least one of geological anomaly data, roof and floor boundary data, and coal face condition assessment data. Based on the basic cutting data, coal cutting and planning is performed on the fully mechanized mining face to determine cutting parameters and cutting paths. This allows for adaptive, dynamic, and precise cutting path planning based on coal seam changes and geological anomalies, improving cutting accuracy and efficiency. It effectively solves the problems of inaccurate cutting planning and poor adaptability caused by reliance on static models and insufficient real-time performance in related technologies. Furthermore, the data acquisition device can acquire high-quality face image data in complex environments in real time, and it does not require manual intervention, thus improving the real-time performance, accuracy, and reliability of the cutting planning. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of the fully mechanized mining face cutting planning method according to an embodiment of this application;

[0047] Figure 2 This is another flowchart of the fully mechanized mining face cutting planning method according to an embodiment of this application;

[0048] Figure 3 This is a schematic diagram of the structure of the longwall mining face cutting and planning device according to an embodiment of this application. Detailed Implementation

[0049] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0050] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0051] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0052] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0053] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0054] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0055] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0056] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0057] In related technologies, coal mining machine cutting planning methods are highly model-dependent, relying on pre-set 3D geological models or static mining models. They lack the ability to adapt to dynamic geological conditions during mining, leading to deviations between the planned cutting path and actual working conditions. For example, they heavily rely on the accuracy of 3D geological models and historical data. If the model construction has deviations or geological conditions change abruptly (such as a previously unseen fault), the cutting plan will have significant errors and will be unable to adapt to new geological conditions. Furthermore, mining planning models are mainly constructed based on previous geological data and are static, making it difficult to reflect dynamic changes in the coal seam during mining, such as localized sharpening or thinning of the coal seam, resulting in significant deviations between actual mining and planning.

[0058] In addition, there is a time lag between the adjustment of cutting planning parameters and the actual working conditions (insufficient real-time performance). The cutting curve is generated based on historical data statistical analysis, which has a delay in responding to real-time changes. When the coal seam thickness and dip angle change instantaneously, the cutting path cannot be adjusted in time, affecting mining efficiency and equipment safety.

[0059] Furthermore, the cutting plan lacks comprehensive perception capabilities, failing to incorporate intuitive information such as real-time video images and relying solely on data-driven planning. This makes it impossible to monitor and provide feedback on equipment operating status (such as cutting drum wear and hydraulic system leakage) in real time, hindering the long-term stable operation of the equipment. The cutting plan also suffers from a lack of equipment status feedback, focusing only on equipment dimensions and constraints to construct the cutting model. It fails to monitor the impact of equipment operating status (such as coal mining machine cutting load and hydraulic support pressure) on the cutting process in real time, making it impossible to dynamically adjust cutting parameters based on the actual operating conditions of the equipment.

[0060] Finally, the coal mining machine cutting planning method also has the defect of poor environmental adaptability. It has not optimized the cutting system for complex underground environments (such as electromagnetic interference and humidity). In actual operation, the accuracy of sensor data and communication stability are easily affected, reducing the accuracy and reliability of coal mining control.

[0061] As can be seen from the above, the coal cutting planning of mining machines has problems such as reliance on manual labor, low sensor accuracy, poor adaptability, and lack of global optimization planning, which limit the accuracy of coal cutting, mining safety and mining efficiency.

[0062] In view of this, this application discloses a method and apparatus for cutting and planning a fully mechanized mining face.

[0063] Example 1

[0064] Figure 1 A flowchart of the fully mechanized mining face cutting planning method according to an embodiment of this application is shown, such as... Figure 1 As shown in the embodiment of this application, a method for cutting and planning a fully mechanized mining face includes:

[0065] S101: Acquire working face image data after the hydraulic support pushes the conveyor through a data acquisition device installed at the fully mechanized mining face. The working face image data includes environmental data of the fully mechanized mining face and operating data of the fully mechanized mining equipment. The fully mechanized mining equipment includes at least a coal mining machine and the hydraulic support.

[0066] Among them, the fully mechanized mining face cutting planning method is applied to the fully mechanized mining face cutting planning system (hereinafter referred to as the system), which includes at least a controller.

[0067] After the hydraulic support push-through is completed, meaning the coal mining machine has passed through the fully mechanized mining face and the hydraulic support has completed its push-through and support positioning, the field of vision is optimal, with no equipment obstructing the view, allowing for the acquisition of images that most accurately reflect the newly exposed coal seam geological conditions. Specifically, after the hydraulic support push-through is complete, the controller can send a data acquisition command to the acquisition device, which then collects image data of the working face.

[0068] The working face image data includes environmental data of the fully mechanized mining face and operational data of the fully mechanized mining equipment. Environmental data refers to environmental information surrounding the fully mechanized mining face, such as coal and rock, roof and floor. The fully mechanized mining equipment includes at least a coal mining machine and hydraulic supports. Operational data of the fully mechanized mining equipment refers to its operational status information, such as the position of the coal mining machine drum relative to the coal face, and the attitude of the hydraulic supports. Multiple data acquisition devices are used, and these devices are arranged along the top beam of the hydraulic supports or the sidewall of the working face roadway. The method further includes:

[0069] S201: Determine the deployment density of the acquisition device based on the length of the fully mechanized mining face.

[0070] Specifically, multiple data acquisition devices are deployed along the top beam of the hydraulic support or on the sidewall of the working face roadway, and the shooting angle of the acquisition devices covers the entire working face area. The acquisition devices are preferably high-definition explosion-proof cameras.

[0071] The deployment density of the acquisition devices is determined based on the length of the working face. When the working face length is less than a certain threshold, one device is deployed every 10-15 meters; when the working face length is greater than or equal to a certain threshold, one device is deployed every 8-12 meters to ensure complete image coverage. Simultaneously, the acquisition devices must have image preprocessing functions such as noise reduction, defogging, contrast enhancement, and illumination equalization enabled to overcome interference from underground dust and water mist, ensuring the acquisition of high-quality initial image data. In this step, firstly, the acquisition devices acquire real-time video streams of the fully mechanized mining face. The video stream is a continuous sequence of images, recording the dynamic changes of the coal wall, roof, floor, and mining machine in front of the hydraulic supports within a specific time period. Then, after the hydraulic supports complete the push-conveyor operation, video images of the working face are captured to obtain initial working face image data. Finally, the acquired working face image data is stored in the database.

[0072] S102: Analyze the working face image data to obtain basic cutting data, wherein the basic cutting data includes at least one of geological anomaly data, roof and floor boundary data, and coal wall condition assessment data.

[0073] In this step, the controller analyzes the working face image data to extract core information, providing basic data for planned cutting. Because the basic cutting data is updated in real time, the system no longer relies on preset geological models or historical data, thus allowing the planned cutting path and parameters to change dynamically, adapting to the changing geological conditions of the working face.

[0074] Specifically, in step S102, the working surface image data is analyzed to obtain basic cutting data, including:

[0075] S1021: Using an edge detection algorithm, identify the roof line and floor line from the working face image data, and determine the coal seam thickness and roof and floor dip angles based on the positions of the roof line and floor line;

[0076] S1022: Identify geologically abnormal areas in the working face image data using a target detection model or anomaly detection algorithm, wherein the geologically abnormal areas include at least one of faults, interbedded rock, iron sulfide nodules, and coal wall spalling;

[0077] S1023: Extract the coal wall edge contour from the working face image data and perform integrity detection on the coal wall;

[0078] S1024: Analyze the displacement changes of the roof rock strata in the working face image data to assess the stability of the roof.

[0079] In step S1021, the roof and floor boundary data includes the coal seam thickness and the roof and floor dip angles. In this step, an edge detection algorithm is used to accurately extract the positions of the roof and floor lines from the image, thereby calculating the real-time coal seam thickness and roof and floor dip angles. The coal seam thickness is obtained by calculating the vertical distance between the roof and floor lines, and the roof and floor dip angles are obtained by analyzing the orientation of the boundary lines.

[0080] In step S1022, the geological anomaly data includes attribute information such as the anomaly type, precise location on the working face, and size of the anomaly area. In this step, a target detection model or anomaly detection algorithm is used to automatically identify and locate anomaly areas in the image, such as faults, interbedded rock, iron sulfide nodules, and coal wall spalling. A fault refers to a structural feature where rock strata or coal seams fracture under crustal movement, resulting in significant relative displacement along the fracture surface. On a coal mining face, a fault manifests as a sudden break or displacement of the coal seam. Interbedded rock refers to thin layers of other rocks mixed within the coal seam, commonly including mudstone, sandstone, and carbonaceous mudstone. On the coal mining face, a layer of rock with a different color and texture than the coal can be seen on the coal wall. The thickness varies from a few centimeters to tens of centimeters. Iron sulfide nodules appear as particularly hard, shiny, isolated or irregularly distributed hard lumps on the coal wall. Coal wall collapse refers to the phenomenon where, after the coal mining machine cuts through the coal wall, the coal wall in front of it loses stability under the pressure of the mine, resulting in local or large-scale collapse or landslide.

[0081] The coal wall condition assessment data in step S102 may include coal wall integrity detection data and roof stability assessment data.

[0082] In step S1023, the coal wall integrity detection involves extracting the coal wall edge contour from the video image using an algorithm, comparing it with the "normal coal wall contour template," and rating the integrity and stability of the roof (e.g., complete, relatively complete, broken). If a fracture (crack) or local depression (precursor to spalling) is found in the contour, the abnormal area is immediately marked and an early warning is output, providing a basis for the support strength.

[0083] In step S1024, the stability assessment of the roof is carried out by analyzing the displacement changes of the roof rock strata through video images and combining the data from the support pressure sensor. If the roof sinking speed is greater than 10m / min or fracturing and spalling occur, it is determined to be an unstable state, triggering the cutting parameter adjustment command.

[0084] S103: Based on the cutting baseline data, perform coal cutting planning for the fully mechanized mining face, and determine the cutting parameters and cutting path.

[0085] In this step, based on the basic cutting data obtained in step S102, a set of preset optimization rules are used to plan the coal cutting of the fully mechanized mining face, thereby determining the cutting parameters and cutting path.

[0086] Specifically, in step S103, coal cutting planning is performed on the fully mechanized mining face based on the cutting baseline data, and cutting parameters are determined, including adjusting the cutting parameters of the coal mining machine and the shifting parameters of the hydraulic support.

[0087] Adjusting the cutting parameters of the coal mining machine includes at least one of the following:

[0088] S1031: Adjust the mining height parameters of the coal mining machine based on the identified coal seam thickness and roof and floor lines;

[0089] S1032: Adjust the bottom-laying amount and the cutting edge lifting amount of the coal mining machine according to the identified bottom plate undulation.

[0090] S1033: Adjust the traction speed and drum speed of the coal mining machine based on the identified geological anomaly data;

[0091] Adjusting the moving parameters of the hydraulic support, including at least one of the following:

[0092] S1034: Adjust the support strength of the hydraulic support according to the integrity and / or degree of cracks in the top plate;

[0093] S1035: Adjust the moving speed and timing of the hydraulic support according to the stability of the top plate;

[0094] S1036: Adjust the group relocation strategy of hydraulic supports based on the pressure and / or geological anomaly data of the roof, wherein the group relocation strategy includes the relocation sequence and the relocation timing.

[0095] In step S1031, the precise coal seam thickness is calculated based on the real-time identified roof and floor lines. Then, using a preset formula: Target mining height = Identified current coal seam thickness - (Reserved roof coal thickness / floor coal thickness), the mining height parameters of the coal mining machine are automatically calculated. Based on the target mining height value, the system automatically modifies the control program of the coal mining machine's height adjustment cylinder, causing the drum to run along a new trajectory parallel to the roof and floor, avoiding roof or floor cutting.

[0096] In step S1032, when unevenness is detected on the bottom plate, the system dynamically adjusts the compensation amount of the roller in the vertical direction to ensure the flatness of the bottom plate after mining. Specifically, if the bottom plate is concave, the undercut amount is increased so that the roller cuts into the bottom plate to a certain depth, ensuring that the scraper conveyor can be smoothly pushed to the new position; if the bottom plate is convex, the lifting amount is increased to prevent the roller from cutting into the hard bottom plate.

[0097] In step S1033, the operating speed strategy is adjusted based on the identified geological anomaly type and attributes. Specifically, when encountering faults or hard interbedded rock, the system immediately reduces the traction speed to decrease cutting resistance and impact load on the cutting teeth, protecting the mechanical structure. Simultaneously, the drum speed may be adjusted appropriately to optimize cutting efficiency. When traversing geological structural zones, in conjunction with step S1031, a "top-lifting" or "bottom-laying" path planning is adopted, and the system passes smoothly at a lower speed. When coal seam conditions are favorable, the traction speed can be automatically increased to maximize mining efficiency, provided that the equipment load does not exceed limits.

[0098] In step S1034, when image recognition detects that the roof is broken and cracks are developed, the system will automatically increase the initial support force and working resistance setting value of the hydraulic support in that area, thereby increasing the support strength to suppress further deterioration of the roof and prevent roof collapse accidents.

[0099] In step S1035, in areas where the roof is unstable, the system will shorten the interval between coal cutting by the coal mining machine and the movement of the hydraulic support. By accelerating the movement speed, the system can provide immediate support for the newly exposed roof, minimize the roof exposure time and subsidence, and ensure safety.

[0100] In step S1036, in areas with high roof pressure, the "follow-up operation" process can be temporarily adjusted, adopting a more conservative frame-moving sequence to avoid large-scale simultaneous frame lowering. For example, large-area group frame moving can be changed to small-scale segmented or single-frame sequential frame moving; simultaneous frame lowering in the pressure peak area can be avoided to reduce the overall disturbance to roof stability.

[0101] Optionally, in step S103, coal cutting planning is performed on the fully mechanized mining face based on the cutting baseline data to determine the cutting path, including:

[0102] S301: Plan the drum cutting path of the coal mining machine with the objective function of maximizing cutting efficiency and minimizing path length.

[0103] In step S301, when determining the cutting path, an algorithm is used to plan the drum cutting path with "highest cutting efficiency and shortest path" as the objective function. Among them, the highest cutting efficiency mainly includes: (1) maximizing coal recovery rate: extracting as much coal as possible under the premise of safety and reducing the residue of top and bottom coal; (2) minimizing gangue mixing rate: the planned path should prioritize avoiding gangue layers or geologically abnormal areas. When it is not possible to completely avoid them (such as thin-layer gangue), the cutting depth should be controlled to reduce the amount of gangue mined; (3) maximizing output per unit time: seeking a more efficient cutting path within the allowable range of equipment capacity. The shortest path refers to minimizing the idle or repeated stroke of the coal mining machine drum under the premise of completing the established mining task.

[0104] Specifically, when localized rock inclusions or faults are identified, the algorithm treats these areas as "obstacles" or "high-cost zones" during path planning. The optimization objective drives the path to automatically bypass these areas or pass through them with minimal cutting depth, directly achieving the goal of "minimizing rock loss." When the coal seam thickness changes, the algorithm plans a smooth path that conforms to the coal-rock interface based on real-time roof and floor lines. This path ensures the highest coal recovery rate while avoiding cutting through the roof and floor rocks, and it is also the shortest and most effective cutting path under the current geological conditions.

[0105] The fully mechanized mining face cutting planning method provided in this application embodiment acquires face image data after the hydraulic support pushes the conveyor through a data acquisition device installed on the fully mechanized mining face. The face image data includes environmental data of the fully mechanized mining face and operational data of the fully mechanized mining equipment, which includes at least a coal mining machine and the hydraulic support. The face image data is analyzed to obtain basic cutting data, which includes at least one of geological anomaly data, roof and floor boundary data, and coal wall and roof condition assessment data. Based on the basic cutting data, coal cutting planning is performed on the fully mechanized mining face to determine cutting parameters and cutting paths. This method can adaptively and dynamically plan cutting paths accurately based on coal seam changes and geological anomalies, improving cutting accuracy and efficiency. It effectively solves the problems of inaccurate cutting planning and poor adaptability caused by reliance on static models and insufficient real-time performance in related technologies. Furthermore, the data acquisition device can acquire high-quality face image data in complex environments in real time, and it does not require manual intervention, thus improving the real-time performance, accuracy, and reliability of cutting planning.

[0106] In some embodiments, after analyzing the working surface image data in step S102, the method further includes:

[0107] S401: If the working face image data meets the preset conditions, the working face image data is sent to the terminal for manual analysis. The preset conditions include at least one of the following: the recognition confidence of the working face image data is lower than a preset threshold, a major anomaly is identified from the working face image data, and the geological conditions of the fully mechanized mining face are complex.

[0108] In this embodiment, the system automatically analyzes the images and provides preliminary judgments and adjustment suggestions. When the working face image data meets preset conditions, key images are pushed to the ground control room or mobile terminal for manual interpretation by technicians. Ground technicians interpret the received information, potentially confirming the system's suggestions or making modifications. The final confirmed instructions are then manually input into the system as the final basis for execution. The preset conditions include at least one of the following: the confidence level of the working face image data is below a preset threshold; a major anomaly is identified from the working face image data; and the geological conditions of the fully mechanized mining face are complex.

[0109] Among these, "confidence level below the preset threshold" refers to the fact that image recognition algorithms (such as object detection models) include a confidence score in their output, indicating the degree of certainty in their judgment. The system sets a minimum acceptable confidence threshold. "Major anomalies" refers to geological structures or working conditions that, if not handled properly, could immediately lead to serious safety accidents or major equipment damage. "Complex geological conditions in the fully mechanized mining face" means that the working face is located in an extremely complex geological section, such as a densely faulted zone or a region with severe folding.

[0110] In some embodiments, step S103, the method further includes:

[0111] S501: Analyze the working face image data to determine the operating parameters of the fully mechanized mining equipment;

[0112] S502: Based on the operating parameters of the fully mechanized mining equipment and the basic cutting data, a coal cutting plan is made for the fully mechanized mining face.

[0113] like Figure 2 As shown in this embodiment, analyzing the working face image data can not only identify basic cutting data such as the safety status of coal and rock, roof and coal wall, but also identify the operating parameters of the fully mechanized mining equipment (such as the position and attitude of the coal mining machine drum). At this time, the basic cutting data and the operating parameters of the fully mechanized mining equipment can be combined to determine whether they meet the input and constraint conditions of the cutting planning model. Based on meeting the conditions, the constructed cutting planning model can be used to carry out coal cutting planning.

[0114] In this embodiment, by combining basic cutting data with the operating parameters of the fully mechanized mining equipment, the cutting planning of the fully mechanized mining face can not only accurately adapt to changes in coal seam thickness and coal-rock interface based on the basic cutting data, but also dynamically adjust the cutting parameters according to the actual working conditions of the equipment based on video image recognition technology, considering the impact of equipment operating status (such as the cutting load of the coal mining machine and the pressure of the hydraulic support). This allows for real-time monitoring and feedback of equipment operating status (such as wear of the cutting drum and leakage in the hydraulic system), ensuring long-term stable operation of the equipment. For example, it can monitor unsafe conditions of the working face equipment and environment in real time. For instance, by extrapolating the spatial motion trajectory of the drum and its relative position to the roof beam, it can achieve anti-collision early warning for fully mechanized mining cutting, avoiding dangerous abnormal situations such as the coal mining machine drum cutting the hydraulic support roof beam, and improving the level of intelligent safety production management and control at the working face.

[0115] In some embodiments, step S103, which involves planning the coal cutting of the fully mechanized mining face based on the cutting baseline data, includes:

[0116] S601: To ensure stable cutting motor current and qualified coal quality, the drum speed and traction speed of the coal mining machine are adjusted.

[0117] In this embodiment, the drum speed and traction speed are optimized to achieve the goals of "stable cutting motor current and qualified coal quality". Stable cutting motor current refers to smooth equipment operation, minimal load fluctuations, and low energy consumption. Qualified coal quality mainly refers to controlling the proportion of gangue mixed in the raw coal; reducing gangue mixing is essential to improving product value and profit.

[0118] Specifically, when the current sensor detects a current greater than 110% of the rated value, it indicates that the cutting resistance is high, and the traction speed needs to be automatically reduced to allow the drum more time to crush hard coal or gangue, thereby avoiding motor overload and allowing the current to drop back to the stable range; when the video identifies a gangue rate greater than 5%, the drum height needs to be reduced to reduce the volume of gangue to be cut.

[0119] In some embodiments, step S103, the method further includes:

[0120] S701: During the cutting process, acquire coal mining machine image data, and monitor whether the coal mining machine drum runs according to the cutting path based on the coal mining machine image data;

[0121] S702: If there is a deviation in the operation of the drum of the coal mining machine, the deviation shall be adjusted.

[0122] In this embodiment, during the cutting process, a camera deployed on the working face is used to continuously acquire image data of the coal mining machine. Through image recognition algorithm, the image is analyzed in real time to calculate the current actual spatial position and height of the drum. The actual position of the drum is compared in real time with the expected cutting path planned in step S103, thereby monitoring in real time whether the drum is running according to the planned path.

[0123] If the coal mining machine's drum deviates from its planned path, the deviation information is immediately sent to the system's control center. The system then performs path fine-tuning or parameter correction based on the magnitude and direction of the deviation. Path fine-tuning generates a new instruction to smoothly transition back to the original planned path from the current deviation position. Parameter correction directly adjusts the control signal of the height adjustment cylinder. For example, if the drum is found to be 10 centimeters higher than planned, an instruction is immediately issued to lower it by 10 centimeters to return to the correct path. This ensures that the final execution result remains consistent with the initial planning target, guaranteeing the accuracy of the cutting.

[0124] The reasons for the deviation may include: the coal mining machine may slip on the rugged floor; there may be precision errors in the actuators such as the height adjustment cylinder; and the extremely localized soft coal or hard rock that was not identified during the planning may cause the drum to shift momentarily, etc.

[0125] In summary, the fully mechanized mining face cutting planning method provided in this application uses video image recognition technology to identify coal, rock, roof, and floor, and performs cutting planning based on the identification results (including determining cutting parameters and planning the cutting path of the coal mining machine). It can accurately adapt to changes in coal seam thickness and coal-rock interface, effectively solving the bottlenecks of existing drilling and geophysical exploration technologies that restrict the adaptive control of the coal mining machine, and realizing adaptive cutting of coal seam occurrence conditions in fully mechanized mining faces. At the same time, through intelligent cutting path planning, the traditional "onboard memory cutting" is transformed into "flexible scheduling by the host computer and precise execution by the coal mining machine", which greatly improves the effectiveness of the cutting path planning and reduces invalid and repeated cutting by the coal mining machine. In addition, precise cutting path planning can prevent the coal mining machine drum from excessively cutting hard materials such as rock, reduce the wear and damage of the cutting teeth, extend the service life of the equipment, and reduce equipment maintenance and replacement costs. In this embodiment of the application, reasonable cutting planning can also reduce energy consumption, improve energy utilization, and further reduce mining costs. The above-mentioned cutting planning method relies on coal mining process drive engine, planning and cutting control, intelligent video recognition, digital twin and other technologies to realize flexible coal mining process compilation, adjustment and online switching, meet a variety of coal mining operation scenarios, and realize a new coal mining mode of "ground planning coal mining and unmanned operation of working face".

[0126] Example 2

[0127] Figure 3 A schematic diagram of the structure of the fully mechanized mining face cutting and planning device according to an embodiment of this application is shown, as follows: Figure 3 As shown in the figure, this application provides a fully mechanized mining face cutting and planning device, including:

[0128] The acquisition module 10 is configured to acquire working face image data after the hydraulic support pushes the conveyor through an acquisition device installed on the fully mechanized mining face. The working face image data includes environmental data of the fully mechanized mining face and operating data of the fully mechanized mining equipment. The fully mechanized mining equipment includes at least a coal mining machine and the hydraulic support.

[0129] Analysis module 20 is configured to analyze the working face image data to obtain basic cutting data, wherein the basic cutting data includes at least one of geological anomaly data, roof and floor boundary data, and coal wall and roof condition assessment data;

[0130] The planning module 30 is configured to perform coal cutting planning for the fully mechanized mining face based on the cutting basic data, and to determine the cutting parameters and cutting path.

[0131] In some embodiments, the analysis module 20 is further configured as follows:

[0132] The roof and floor lines are identified from the working face image data using an edge detection algorithm, and the coal seam thickness and roof and floor dip angles are determined based on the positions of the roof and floor lines.

[0133] The geological anomaly regions in the working face image data are identified by using a target detection model or anomaly detection algorithm. The geological anomaly regions include at least one of faults, interbedded rock, iron sulfide nodules, and coal wall spalling.

[0134] Extract the coal wall edge contour from the working face image data, and perform integrity detection on the coal wall;

[0135] The displacement changes of the roof rock strata in the working face image data are analyzed to assess the stability of the roof.

[0136] In some embodiments, the analysis module 20 is further configured as follows:

[0137] If the working face image data meets the preset conditions, the working face image data is sent to the terminal for manual analysis. The preset conditions include at least one of the following: the identification confidence level of the working face image data is lower than a preset threshold, a major anomaly is identified from the working face image data, and the geological conditions of the fully mechanized mining face are complex.

[0138] In some embodiments, the planning module 30 is further configured as follows:

[0139] Adjust the cutting parameters of the coal mining machine and the shifting parameters of the hydraulic support.

[0140] Adjusting the cutting parameters of the coal mining machine includes at least one of the following:

[0141] Adjust the mining height parameters of the coal mining machine based on the identified coal seam thickness and roof and floor lines;

[0142] Adjust the shearing machine's undercut and cutting tool lifting amounts based on the identified floor undulations.

[0143] Based on the identified geological anomaly data, adjust the traction speed and drum speed of the coal mining machine;

[0144] Adjusting the moving parameters of the hydraulic support, including at least one of the following:

[0145] Adjust the support strength of the hydraulic supports according to the integrity and / or degree of cracks in the roof;

[0146] Adjust the moving speed and timing of the hydraulic support according to the stability of the roof;

[0147] Based on the pressure and / or geological anomaly data of the roof, adjust the group relocation strategy of the hydraulic supports, which includes the relocation sequence and the timing of the relocation.

[0148] In some embodiments, the planning module 30 is further configured as follows:

[0149] The working face image data is analyzed to determine the operating parameters of the fully mechanized mining equipment, wherein the operating parameters include at least one of equipment parameters and operating status parameters;

[0150] Based on the operating parameters of the fully mechanized mining equipment and the basic cutting data, a coal cutting plan is made for the fully mechanized mining face.

[0151] In some embodiments, the planning module 30 is further configured as follows:

[0152] With the goal of stabilizing the current of the cutting motor and ensuring the quality of the coal, the drum speed and traction speed of the coal mining machine are adjusted.

[0153] In some embodiments, the planning module 30 is further configured as follows:

[0154] The objective function is to plan the drum cutting path of the coal mining machine with the highest cutting efficiency and shortest path.

[0155] In some embodiments, the planning module 30 is further configured as follows:

[0156] During the cutting process, image data of the coal mining machine is acquired, and the operation of the drum of the coal mining machine is monitored based on the image data.

[0157] If there is a deviation in the operation of the drum of the coal mining machine, the deviation shall be adjusted.

[0158] In some embodiments, the acquisition module 10 is further configured as follows:

[0159] The data acquisition devices are multiple, and these multiple data acquisition devices are arranged along the top beam of the hydraulic support or the sidewall of the working face roadway in the fully mechanized mining face. The fully mechanized mining face cutting planning device also includes a data acquisition device arrangement module, configured as follows:

[0160] The deployment density of the acquisition device is determined based on the length of the fully mechanized mining face.

[0161] The fully mechanized mining face cutting and planning device provided in this application corresponds to the fully mechanized mining face cutting and planning method in the above embodiments. Any option in the embodiments of the fully mechanized mining face cutting and planning method is also applicable to the embodiments of the fully mechanized mining face cutting and planning device, and will not be repeated here.

[0162] Example 3

[0163] This application also provides an electronic device, which includes at least a memory and a processor. The memory stores a computer program, and the processor implements the above-described fully mechanized mining face cutting planning method when executing the computer program in the memory.

[0164] In some embodiments, the processor executing a computer program may be a processing device that includes one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that runs other instruction sets, or a processor that runs a combination of instruction sets. The processor may also be one or more special-purpose processing devices, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), system-on-a-chip (SoCs), etc.

[0165] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, PHP, and Python, as well as conventional procedural programming languages ​​such as "C" or similar languages. The program code can be executed entirely on the passenger's computer, partially on the passenger's computer, as a standalone software package, partially on the passenger's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the passenger's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0166] The memory may be a read-only memory (ROM), random access memory (RAM), phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), electrically erasable programmable read-only memory (EEPROM), other types of random access memory (RAM), flash drives or other forms of flash memory, cache, registers, static memory, optical disc read-only memory (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape cassette or other magnetic storage devices, or any other possible non-transitory medium used to store information or instructions that can be accessed by computer equipment.

[0167] The electronic devices in this application embodiment may include, but are not limited to, fixed terminal devices such as servers, desktop computers, and digital TVs, as well as mobile terminal devices such as in-vehicle devices, handheld devices (e.g., mobile phones, tablets, etc.), and wearable devices (e.g., smartwatches, smart bracelets, etc.).

[0168] Example 4

[0169] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described fully mechanized mining face cutting planning method.

[0170] The computer-readable storage medium of this application embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. The computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device; for example, it can be the aforementioned memory.

[0171] The computer programs of embodiments of this application can be organized into one or more computer-executable components or modules. Various aspects of this application can be implemented with any number and combination of such components or modules. For example, aspects of this application are not limited to the specific computer-executable instructions or specific components or modules shown in the drawings and described herein. Other embodiments may include different computer-executable instructions or components having more or fewer functions than those shown and described herein.

[0172] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0173] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Multitasking and parallel processing may be advantageous in certain environments. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

Claims

1. A method for planning the cutting of a fully mechanized mining face, characterized in that, include: The acquisition device installed at the fully mechanized mining face acquires image data of the mining face after the hydraulic support has been pushed through. The image data of the mining face includes environmental data of the fully mechanized mining face and operating data of the fully mechanized mining equipment, which includes at least a coal mining machine and the hydraulic support. The working face image data is analyzed to obtain basic cutting data, wherein the basic cutting data includes at least one of geological anomaly data, roof and floor boundary data, and coal wall condition assessment data; Based on the cutting baseline data, coal cutting planning is carried out for the fully mechanized mining face to determine cutting parameters and cutting paths.

2. The method according to claim 1, characterized in that, The image data of the working surface is analyzed to obtain basic cutting data, including: The roof and floor lines are identified from the working face image data using an edge detection algorithm, and the coal seam thickness and roof and floor dip angles are determined based on the positions of the roof and floor lines. The geological anomaly regions in the working face image data are identified by using a target detection model or anomaly detection algorithm. The geological anomaly regions include at least one of faults, interbedded rock, iron sulfide nodules, and coal wall spalling. Extract the coal wall edge contour from the working face image data, and perform integrity detection on the coal wall; The displacement changes of the roof rock strata in the working face image data are analyzed to assess the stability of the roof.

3. The method according to claim 1, characterized in that, After analyzing the working surface image data, the method further includes: If the working face image data meets the preset conditions, the working face image data is sent to the terminal for manual analysis. The preset conditions include at least one of the following: the identification confidence level of the working face image data is lower than a preset threshold, a major anomaly is identified from the working face image data, and the geological conditions of the fully mechanized mining face are complex.

4. The method according to claim 1, characterized in that, Based on the aforementioned basic cutting data, a coal cutting plan is developed for the fully mechanized mining face, determining the cutting parameters, including adjusting the cutting parameters of the coal mining machine and the shifting parameters of the hydraulic support. Adjusting the cutting parameters of the coal mining machine includes at least one of the following: Adjust the mining height parameters of the coal mining machine based on the identified coal seam thickness and roof and floor lines; Adjust the shearing machine's undercut and cutting tool lifting amounts based on the identified floor undulations. Based on the identified geological anomaly data, adjust the traction speed and drum speed of the coal mining machine; Adjusting the moving parameters of the hydraulic support, including at least one of the following: Adjust the support strength of the hydraulic supports according to the integrity and / or degree of cracks in the roof; Adjust the moving speed and timing of the hydraulic support according to the stability of the roof; Based on the pressure and / or geological anomaly data of the roof, adjust the group relocation strategy of the hydraulic supports, which includes the relocation sequence and the timing of the relocation.

5. The method according to claim 4, characterized in that, The method further includes: The working face image data is analyzed to determine the operating parameters of the fully mechanized mining equipment, wherein the operating parameters include at least one of equipment parameters and operating status parameters; Based on the operating parameters of the fully mechanized mining equipment and the basic cutting data, a coal cutting plan is made for the fully mechanized mining face.

6. The method according to claim 5, characterized in that, Based on the aforementioned basic cutting data, a coal cutting plan is developed for the fully mechanized mining face, including: With the goal of stabilizing the current of the cutting motor and ensuring the quality of the coal, the drum speed and traction speed of the coal mining machine are adjusted.

7. The method according to claim 1, characterized in that, Based on the aforementioned basic cutting data, a coal cutting plan is developed for the fully mechanized mining face to determine the cutting path, including: The objective function is to plan the drum cutting path of the coal mining machine with the highest cutting efficiency and shortest path.

8. The method according to claim 1, characterized in that, The method further includes: During the cutting process, image data of the coal mining machine is acquired, and the operation of the drum of the coal mining machine is monitored based on the image data. If there is a deviation in the operation of the drum of the coal mining machine, the deviation shall be adjusted.

9. The method according to claim 1, characterized in that, The data acquisition device is multiple, and the multiple data acquisition devices are arranged along the top beam of the hydraulic support or the sidewall of the working face roadway in the fully mechanized mining face. The method further includes: The deployment density of the acquisition device is determined based on the length of the fully mechanized mining face.

10. A cutting and planning device for a fully mechanized mining face, characterized in that, include: The acquisition module is configured to acquire working face image data after the hydraulic support pushes the conveyor through an acquisition device installed on the fully mechanized mining face. The working face image data includes environmental data of the fully mechanized mining face and operating data of the fully mechanized mining equipment, which includes at least a coal mining machine and the hydraulic support. The analysis module is configured to analyze the working face image data to obtain basic cutting data, wherein the basic cutting data includes at least one of geological anomaly data, roof and floor boundary data, and coal wall and roof condition assessment data; The planning module is configured to perform coal cutting planning for the fully mechanized mining face based on the cutting baseline data, and to determine the cutting parameters and cutting path.

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