Coal mine coal mining wastewater treatment system and method

Abstract

The invention belongs to the technical field of wastewater treatment, and discloses a coal mining wastewater treatment system and method.The method comprises the steps that a route planning module plans an optimal flight route for an unmanned aerial vehicle; the accumulated water and gushing monitoring mechanism cruises the interior of the coal mine to collect high-definition images; the movable drainage mechanism moves to the water accumulation or water gushing position in the coal mine to conduct drainage operation; the sewage treatment mechanism is used for deeply treating sewage discharged from a coal mine; the underground water level monitoring mechanism monitors coal mine underground water level change conditions in real time; and the abnormity monitoring mechanism analyzes the collected images and timely identifies abnormal conditions. Through unmanned aerial vehicle cruise monitoring, the number of times that personnel enter a dangerous coal mine area is reduced; the ponding and gushing monitoring mechanism can accurately identify the positions and severity of ponding and gushing by using a high-definition camera and an image analysis technology; and the movable drainage mechanism can automatically move to a water accumulation or water gushing point for drainage operation, and sewage can be treated in time.

Description

Technical Field

[0001] This application relates to the field of wastewater treatment technology, and more specifically, to a coal mine wastewater treatment system and method. Background Technology

[0002] Coal mining wastewater mainly originates from various stages of coal mining and processing, specifically including: mine inflow, coal washing wastewater, surface flushing water, and domestic sewage. During coal mining, groundwater flows into the mine, forming mine inflow. This wastewater may contain pollutants such as suspended solids, salts, and heavy metals, exhibiting significant characteristics of the coal industry. Direct discharge of coal mining wastewater without proper treatment can cause multiple environmental hazards: wastewater intruding into farmland or being used for irrigation can cause soil calcification, disrupt soil structure, lead to crop withering and death, resulting in reduced yields or even crop failure. Harmful substances in the wastewater may enter the human body through the food chain, causing diseases or infectious diseases; wastewater containing heavy metals may also cause poisoning or long-term health problems. Wastewater discharge disrupts the ecological balance of water bodies, affecting their self-purification capacity, leading to water quality deterioration, and consequently impacting the health of the entire ecosystem.

[0003] The prior art publication CN111285509A provides a heavy medium velocity sedimentation treatment device for underground coal mines. By setting up a pretreatment zone and filter plates, the mine water undergoes preliminary filtration through the filter plates in the pretreatment zone before flocculation, improving the subsequent flocculation effect. Simultaneously, by setting up slots, insert plates, vertical grooves, vertical plates, fixing plates, connecting plates, through screw holes, connecting screw holes, and connecting screws, the filter plates are easily installed and disassembled using the interlocking of slots, insert plates, vertical grooves, and vertical plates. The filter plates are fixed by threaded connections of the connecting screws to the through screw holes and connecting screw holes. By setting up a collection base plate, uprights, horizontal plates, and perforations, when cleaning the pretreatment zone is required, ropes are used to pass through the perforations and external equipment to lift the horizontal plates out, thereby discharging solid impurities from the pretreatment zone. By setting up a collection box, lifting rods, and lifting lugs, ropes are used to pass through the lifting lugs and external equipment to lift the collection box out, thereby cleaning other impurities after flocculation.

[0004] While the existing technical solutions described above can achieve the relevant beneficial effects through their structure, they still have the following drawbacks: 1. They cannot quickly and timely detect water accumulation or inrush in coal mines, and cannot drain water in a timely manner. 2. The drainage equipment can only quickly and automatically move to the location where drainage is needed; it cannot automatically drain water.

[0005] In view of this, we propose a coal mine wastewater treatment system and method. Summary of the Invention

[0006] 1. Technical problems to be solved

[0007] The purpose of this application is to provide a coal mine wastewater treatment system and method, which solves the technical problems mentioned in the background art above. It realizes the following technical effects: reducing the number of times personnel enter dangerous coal mine areas and reducing accident risks through drone patrol monitoring; real-time monitoring of gas concentration and groundwater level to promptly detect and warn of potential safety hazards; accurate identification of the location and severity of water accumulation and inrush through a water accumulation and inrush monitoring mechanism using high-definition cameras and image analysis technology; and automatic movement to water accumulation or inrush points for drainage operations through a mobile drainage mechanism, enabling timely treatment of wastewater.

[0008] 2. Technical Solution

[0009] This application provides a coal mine wastewater treatment system, including:

[0010] Mobile drainage system: includes an automatically controlled mobile trolley, a high-definition camera, and a drainage pump; it is used to drain water from locations where water accumulates or flows in within the coal mine.

[0011] Water accumulation and inrush monitoring systems include drones, GPS locators, gas concentration detectors, high-definition cameras, and LED lights. These systems patrol and collect high-definition images of the coal mine interior to promptly detect water accumulation or inrush problems. High-definition cameras capture clear images of the mine's interior to help identify the specific location and severity of water accumulation or inrush. Simultaneously, gas concentration detectors monitor gas concentrations at different locations within the mine.

[0012] Route planning module: Based on the three-dimensional structure map of the coal mine, topography, obstacle distribution and actual monitoring needs, the module plans the optimal flight route for the UAV.

[0013] Wastewater treatment facilities: These facilities perform advanced treatment on wastewater discharged from coal mines, removing pollutants such as suspended solids, heavy metals, and harmful chemicals to meet discharge or reuse standards.

[0014] Groundwater level monitoring agencies: These agencies monitor changes in groundwater levels in coal mines in real time using underground water level sensors. When water levels become abnormal, they can issue timely warnings to prevent flooding accidents.

[0015] Anomaly monitoring agencies analyze the collected images to promptly identify anomalies;

[0016] Alarm module: Includes an alarm that promptly issues an alert when an abnormal situation is detected;

[0017] Central control unit: Connected to mobile drainage system, water accumulation and inrush monitoring system, route planning module, sewage treatment system and groundwater level monitoring system via wireless network.

[0018] As an optional embodiment of the present invention, the route planning module plans the optimal flight route for the UAV inside the coal mine, including the following steps:

[0019] 1. Data Collection and Preprocessing: A 3D structural map of the coal mine, including detailed structures such as underground roadways, stops, and pillars, is obtained using a Geographic Information System (GIS). High-resolution remote sensing imagery or UAV-captured mining area imagery is used to conduct a detailed analysis of the topography. Key information such as slope, height, and geomorphic features is identified, and obstacles within the coal mine, such as large machinery, mine cars, and roadway intersections, are marked. Key monitoring areas are determined, such as historically waterlogged areas, low-lying areas, and roadway intersections.

[0020] 2. Model Establishment: Based on the UAV model and performance parameters, establish a flight performance model for the UAV. Performance parameters include key indicators such as maximum flight speed, maximum rate of climb, and minimum turning radius, to ensure the UAV's flight capabilities are considered during route planning. Based on the actual environment and monitoring needs within the coal mine, set flight constraints for the UAV, including altitude limits (to avoid collisions with the tunnel roof), speed limits (to ensure flight stability and safety), and turning limits (to avoid excessively large or small turning angles).

[0021] 3. Route Planning: The RRT (Random Search Tree) algorithm, suitable for path planning in complex 3D environments, is selected to find the optimal or near-optimal path from the starting point to the destination. Information such as the 3D structure map of the coal mine, terrain features, and obstacle distribution is input into the algorithm. Based on the set constraints and the UAV's performance model, path search and optimization are performed. The optimal or near-optimal flight route is output and displayed on the 3D map for operator review and confirmation.

[0022] · Path planning using the RRT algorithm includes the following steps:

[0023] Randomly select a point p within the drone's search space. sample This point is a candidate point that the algorithm is trying to connect to in the explored path tree.

[0024] Find the distance p in the tree sample The nearest point p nearest Find the distance p from the currently constructed path tree. sample The nearest point p nearest This point is the node in the current tree that is closest to the new sampling point.

[0025] Try from p nearest to p sample Connection: Try to connect from p nearest The straight line extends to p sampleThis creates a new path segment. This step requires checking whether this path meets the drone's flight constraints.

[0026] Add p sample To the tree: If from p nearest to p sample If the straight path of p satisfies the following condition, then p sample Add to the path tree: distance d(p) between two points nearest ,p sample The path must be less than or equal to a preset threshold, which ensures the continuity and smoothness of the path. The path must meet flight constraints, including but not limited to altitude limits, speed limits, and turning radius limits. This is a sufficiently small positive number to control the smoothness of the path. It ensures that the path is not too convoluted, thus preventing the drone from making overly aggressive maneuvers during flight.

[0027] Flight constraints: Flight constraints that need to be considered during path planning, including limitations on altitude, speed, and turning angle.

[0028] 4. Route Evaluation and Optimization: Conduct a safety assessment of the planned flight route to check for any risk of collision with obstacles. If potential risks are found, replan the route or adjust the UAV's flight parameters to ensure safety. Iterate and optimize the route multiple times based on the assessment results until safety and efficiency requirements are met.

[0029] 5. Real-time monitoring: Real-time monitoring during drone flight ensures the drone follows its planned route and allows for timely handling of emergencies. Data such as images and videos captured during flight are collected, processed, and analyzed to meet monitoring requirements.

[0030] u(t) = K p e(t)+K d {[d e [(t)] / (dt)}+K i ∫[e(t)dt];

[0031] In the formula, u(t) represents the control input at time t, which is the control input applied to the UAV at time t. This can be control commands such as throttle, steering, pitch, roll, or yaw. e(t) represents the error at time t, i.e., the difference between the expected value and the actual value. Error is the difference between the UAV's current position or flight path and the predetermined path. It can be a position error (the difference between the UAV's current position and the position of a corresponding point on the predetermined path), or a speed error or heading error. K p K represents the proportional gain. The control input is adjusted based on the current value of the error; a larger proportional gain results in a stronger response to the current error. dK is the differential gain. Adjusting the control input based on the rate of change of the error (i.e., the derivative of the error) helps reduce system overshoot and improve system response speed. i The integral gain is used to adjust the control input based on the accumulation of error over time, which helps to eliminate steady-state error and improve system stability.

[0032] Through the above technical solutions, the route planning module can plan the optimal flight route for the UAV within the coal mine, improving monitoring efficiency, reducing collision risks, and ensuring safe operation of the UAV. The real-time monitoring system continuously measures the UAV's current position, speed, and heading, among other status information.

[0033] As an optional solution of the present invention, the water accumulation and inrush monitoring agency monitors the water accumulation and inrush situation, including the following steps:

[0034] 1. Drone Patrol: The drone is smoothly launched and patrols along a planned route. During the patrol, a high-definition camera captures real-time high-definition images of the coal mine's interior. A gas concentration detector monitors the gas concentration inside the mine to ensure safety.

[0035] 2. Image Analysis and Recognition: High-definition images captured by the drone are transmitted in real time to a ground station or designated storage device. The transmitted images undergo preprocessing, including noise reduction, contrast enhancement, and color deviation correction, to improve image quality and facilitate subsequent analysis. Image processing software is then used to analyze the preprocessed images. By comparing images from different times and areas, the specific location and severity of water accumulation or flooding are identified. Image recognition algorithms or machine learning models are used to assist the recognition process, improving accuracy and efficiency. This includes the following steps:

[0036] Image Transmission and Storage: Activate the image transmission system on the drone to transmit images captured by the high-definition camera to a ground station or designated storage device in real time. During transmission, implement data integrity verification mechanisms, such as CRC or MD5 checksums, to ensure that the image data is not tampered with or damaged during transmission. Store the received image data in encrypted storage devices, or use encryption technology to protect the security of data transmission and prevent data leakage.

[0037] Image preprocessing includes denoising, image cropping and scaling, contrast enhancement, and color deviation correction.

[0038] Image analysis: Using image processing software to extract key features from images, such as edges, textures, and color distribution. The currently captured image is compared with previous images (historical images of the same area or images from different time points). By comparing changes in these features, signs of water accumulation or surging can be identified.

[0039] Water accumulation and gushing water identification: Water accumulation or gushing water areas in an image are separated from the background using image segmentation techniques. The shape, size, and other features of the water accumulation or gushing water areas are analyzed to assess their severity. Images at different time points are compared to detect trends in the water accumulation or gushing water areas, such as area expansion and rising water levels.

[0040] Assisted identification: Image recognition algorithms (such as template matching, feature matching, etc.) are applied to automatically identify areas of water accumulation or water inrush. A convolutional neural network (CNN) is selected to train a machine learning model to identify water accumulation or water inrush features in images. The model is trained using a large amount of labeled image data, enabling it to automatically identify and classify water accumulation or water inrush situations.

[0041] Using the squared difference as the matching metric: SSD(T,I)=∑ x,y I(x,y)-T(x,y)] 2 Where T is the template image, I is the target image, and SSD represents the squared difference, which is used to measure the difference between the template image and the target image.

[0042] Then, Hamming distance is used for feature point matching:

[0043]

[0044] Where D A Let D be the feature descriptor vector of image A. B Let d(D) be the feature descriptor vector of image B. A D B ) represents the Hamming distance between feature descriptors.

[0045] A convolutional neural network (CNN) model is trained using a large amount of labeled coal mine image data, enabling the model to automatically identify features of water accumulation or inrush. The CNN forward propagation is represented by the following steps: Z [l] =W [l] A [l-1] +b [l] ;

[0046] A [l] =f(Z) [l] The activation function is f(x) = max(0,x);

[0047] The loss function is: L = -∑ C c＝1 [y o,c log(p o,c )]. Among them, Z [l] This is the input for the l-th layer (inactive). W [l] Let b be the weight matrix of the l-th layer. [l] Let A be the bias vector for the l-th layer.[l] y is the activation output of layer l. f is the activation function, such as ReLU. C is the total number of classes. o,c p is the one-hot encoding of the real label, where c represents the category index. o,c Let L be the probability of class c predicted by the model. L is the loss function. The system can effectively extract features from images, perform matching, and use a deep learning model to accurately identify areas of standing water or rushing water. This can significantly improve the accuracy and robustness of the identification.

[0048] Model parameters were adjusted using optimization algorithms such as gradient descent. Data augmentation techniques were applied to expand the training dataset, improving the model's ability to identify water accumulation and inrush conditions under different lighting and angle conditions. Image segmentation techniques were used to separate water accumulation or inrush areas from the background, and then the shape, size, and changing trends of these areas were analyzed.

[0049] Results Output: The analysis results will be output in the form of images, charts, or reports, showing the specific location, area, severity, and other information of the water accumulation or inrush. When the detected water accumulation or inrush reaches a preset threshold, an alarm mechanism will be triggered.

[0050] As an alternative solution invented by En, the anomaly monitoring agency conducts in-depth analysis of images collected by water accumulation and inrush monitoring agencies (especially high-definition cameras mounted on drones) to promptly identify any possible anomalies, including the following steps:

[0051] Image reception: The anomaly monitoring agency first receives high-definition images transmitted in real time or periodically from devices such as drones.

[0052] Image preprocessing: Preprocessing the received image, including noise reduction, contrast enhancement, and color deviation correction.

[0053] Feature extraction: Utilizing advanced image processing and computer vision techniques, key features such as shape, texture, and color are extracted from preprocessed images. These features help distinguish between normal and abnormal regions. This includes edge detection, texture analysis, color segmentation, and shape recognition.

[0054] Edge detection: Using edge detection algorithms such as Canny and Sobel, edge information in images is identified, such as water boundaries and cracks.

[0055] Texture analysis: By using methods such as gray-level co-occurrence matrix (GLCM) and local binary mode (LBP), the texture features of image regions are analyzed to distinguish between normal geological structures and anomalous changes.

[0056] Shape recognition: Using techniques such as Hough transform and shape template matching, specific shapes in images are identified, such as the circular or elliptical features of a water inlet.

[0057] Color segmentation: Threshold segmentation based on color space (such as RGB, HSV) to distinguish different objects or regions, such as the blue or dark features of a water accumulation area.

[0058] Anomaly Detection: A machine learning model based on Support Vector Machine (SVM) is constructed and trained using historical data to improve the accuracy of anomaly detection. Convolutional Neural Network (CNN) deep learning technology is used to automatically learn image features and classify anomalies, resulting in higher accuracy and adaptability to complex and changing scenarios. Combining multiple algorithms and feature information, a comprehensive judgment is made to identify anomalies such as expanding water accumulation areas, the appearance of new water inlets, and precursors to ground subsidence with high confidence.

[0059] Alarms and Response: Once an anomaly is detected, the anomaly monitoring agency will immediately send an alarm to the central control unit and may trigger a series of emergency response measures.

[0060] The aforementioned technical solutions enable timely understanding and monitoring of the actual conditions within coal mines, identification of potential safety hazards, and the implementation of corresponding measures to address them. This effectively prevents accidents such as flooding, ensuring the safety of miners and maintaining the normal production order of the coal mine. Furthermore, timely monitoring and handling of abnormal situations also contributes to protecting the natural environment and ecological balance surrounding the coal mine.

[0061] As an optional embodiment of the present invention, the mobile drainage mechanism includes an automatically controlled mobile trolley, a high-definition camera, and a drainage pump; the high-definition camera is fixedly mounted on the automatically controlled mobile trolley; the drainage pump is detachably and fixedly mounted on the automatically controlled mobile trolley. The mobile drainage mechanism drains accumulated and gushing water from locations within the coal mine; including the following steps:

[0062] 1. The intelligent navigation system on the automatically controlled mobile vehicle plans the optimal path from the current location to the target location.

[0063] 2. The automatically controlled mobile vehicle starts autonomously according to the instructions of the intelligent navigation system and travels along the planned path to the target location.

[0064] 3. Upon reaching the target location, the drainage pump will automatically start to pump out the accumulated or gushing water.

[0065] 4. During the pumping process, high-definition cameras continuously monitor the pumping effect to ensure that water accumulation or inrush is effectively controlled. The high-definition cameras capture and transmit real-time images of the coal mine, helping operators remotely monitor the specific situation of water accumulation or inrush.

[0066] 5. The drainage pump automatically stops working when the accumulated or gushing water is drained to a safe level. The automatically controlled mobile trolley evacuates to a safe area along the original route or a newly planned path, according to the instructions of the intelligent navigation system.

[0067] Through the above technical solutions, the mobile drainage system can efficiently and accurately complete the drainage tasks of accumulated water and gushing water in coal mines, effectively reducing the impact of water hazards on production and ensuring the safety and stability of the working environment inside the coal mine.

[0068] As an optional embodiment of the present invention, the wastewater treatment facility includes: a pre-sedimentation and equalization tank, a coagulation sedimentation tank, a maturation tank, a biofilm filter tank, a clear water tank, a water pump, a sludge tank, a plate and frame filter press, a stirrer, and a hydrocyclone.

[0069] The output end of the drainage pump is connected to the pre-settling and equalization tank; a coagulation sedimentation tank is fixedly installed on the right side of the pre-settling and equalization tank; a water pump is installed in the coagulation sedimentation tank and the pre-settling and equalization tank, the input end of the water pump is connected to the pre-settling and equalization tank, and the output end of the water pump is connected to the coagulation sedimentation tank.

[0070] A maturation tank is fixedly installed on the right side of the coagulation sedimentation tank, and a water pump is installed between the maturation tank and the coagulation sedimentation tank; both the coagulation sedimentation tank and the maturation tank are equipped with agitators.

[0071] A biofilm filter is fixedly installed on the right side of the maturation tank; an aeration device is installed inside the biofilm filter; a water pump is installed between the biofilm filter and the maturation tank.

[0072] A clear water tank is fixedly installed on the right side of the biofilm filtration tank; a clear water pump is installed between the clear water tank and the biofilm filtration tank.

[0073] A hydrocyclone is installed at the lower end of the biofilm filter tank. The input end of the hydrocyclone is connected to the lower end of the biofilm filter tank, and the output end of the hydrocyclone is connected to the pre-sedimentation and conditioning tank.

[0074] An additive storage tank A is fixedly installed above the coagulation sedimentation tank; a discharge pipe with a flow meter is installed at the lower end of additive storage tank A; additive storage tank A contains polyaluminum chloride (PAC); an additive storage tank B is fixedly installed above the coagulation sedimentation tank; a discharge pipe with a flow meter is installed at the lower end of additive storage tank B; additive storage tank B contains polyacrylamide (PAM); by adding polyaluminum chloride (PAC) and polyacrylamide (PAM) to the coagulation sedimentation tank, suspended solids and colloids in the wastewater form flocs and settle.

[0075] A micro-sand storage tank is fixedly installed above the maturation tank; a discharge pipe is installed at the lower end of the micro-sand storage tank, and a flow meter is installed on the discharge pipe; the micro-sand storage tank is filled with micro-sand; the micro-sand particle size is about 100-150um.

[0076] The biofilm filtration tank is equipped with a biofilm filter and an aeration device. A blower is fixedly installed on the outside of the biofilm filtration tank; the output end of the blower is connected to the aeration device. Microorganisms on the biofilm filter degrade organic matter in the wastewater, while the aeration device provides oxygen to promote microbial growth. A portion of the treated water is returned to the pre-sedimentation and equalization tank via a hydrocyclone, simultaneously separating and removing heavier coarse particles such as silt and sand to improve treatment efficiency.

[0077] The input end of the water pump is connected to the biofilm filter; the output end of the water pump is connected to the water tank; the water tank stores the water treated by the biofilm filter for subsequent use or discharge.

[0078] A fine screen is fixedly installed inside the pre-settling and equalization tank; a sludge storage tank is fixedly installed below the pre-settling and equalization tank; the pre-settling and equalization tank is used for the initial sedimentation of large suspended solids in wastewater and to regulate water quality and quantity. The built-in fine screen further intercepts larger impurities.

[0079] A collection box is fixedly installed at the bottom of the coagulation sedimentation tank;

[0080] Both the collection box and the sludge storage box are connected to the sludge tank via pipes.

[0081] A plate and frame filter press is connected to the sludge tank, which presses the sludge into sludge cakes.

[0082] Online water quality monitoring equipment (including pH meters, turbidity meters, dissolved oxygen meters, etc.) is installed in the pre-sedimentation equalization tank, coagulation sedimentation tank, maturation tank, biofilm filtration tank and clear water tank to monitor water quality changes in real time and provide accurate data for the automatic control system.

[0083] An oil suction device is installed on the maturation tank to remove oil from the water surface; an automatic oil suction machine is selected to ensure that the floating oil on the water surface can be removed quickly and effectively.

[0084] The above technical solution effectively removes pollutants such as suspended solids, colloids, and organic matter from wastewater, achieving wastewater purification. Simultaneously, the system also considers sludge treatment and disposal, ensuring the integrity and environmental friendliness of the entire treatment process.

[0085] As an optional solution of this invention, the groundwater level monitoring agency employs advanced sensor technology, such as pressure type, float type, or ultrasonic type, to achieve accurate measurement of groundwater level. Wireless communication technology is used to transmit the data collected by the water level sensors to the monitoring center in real time. The monitoring center uses data processing software to parse, store, and analyze the received data, generating intuitive charts and reports. When the monitored groundwater level data exceeds a preset threshold, the system automatically triggers a warning or alarm mechanism, promptly notifying relevant personnel to take appropriate measures through audible and visual alarms, SMS notifications, or telephone calls.

[0086] This invention provides a method for treating coal mining wastewater, comprising the following steps:

[0087] S1, the route planning module, plans the optimal flight route for the UAV based on the three-dimensional structure map of the coal mine, topography, obstacle distribution, and actual monitoring needs.

[0088] S2. The water accumulation and inrush monitoring agency conducts patrols of the coal mine along a planned route, collecting high-definition images to promptly detect water accumulation or inrush issues. High-definition cameras capture detailed images of the mine's interior, helping to identify the specific location and severity of water accumulation or inrush. Simultaneously, gas concentration detectors monitor gas concentrations at different locations within the mine.

[0089] S3. The mobile drainage mechanism uses an automatically controlled mobile trolley to drive a drainage pump to the location of water accumulation or gushing water in the coal mine to carry out drainage operations and pump the accumulated water or gushing water to the sewage treatment facility.

[0090] S4. Wastewater treatment facilities conduct advanced treatment on wastewater discharged from coal mines to remove pollutants such as suspended solids, heavy metals, and harmful chemicals, so that it meets discharge standards or reuse standards.

[0091] S5. Simultaneously, groundwater level monitoring agencies use underground water level sensors to monitor changes in groundwater levels in coal mines in real time, providing crucial data support for safe production and scientific management of groundwater resources. When water levels are abnormal, timely alarms can be issued to prevent flooding accidents.

[0092] S6. The anomaly monitoring agency analyzes the collected images and promptly identifies abnormal situations;

[0093] S7. When an abnormal situation is detected, the alarm module will issue an alarm in a timely manner.

[0094] 3. Beneficial effects

[0095] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0096] 1. This invention utilizes drone-based patrol monitoring to reduce the number of times personnel need to enter hazardous coal mine areas, thereby lowering the risk of accidents. Real-time monitoring of methane concentration and groundwater levels allows for the timely detection and early warning of potential safety hazards, such as methane accumulation and flooding, effectively preventing major safety accidents.

[0097] 2. The water accumulation and inrush monitoring system utilizes high-definition cameras and image analysis technology to accurately identify the location and severity of water accumulation and inrush, providing accurate information for rapid response. When an abnormality is detected, the alarm module immediately issues an alert, enabling a rapid response and minimizing losses.

[0098] 3. The mobile drainage mechanism can automatically move to the point of water accumulation or gushing water to carry out drainage operations, which improves drainage efficiency and shortens the processing time.

[0099] 4. Wastewater treatment facilities perform advanced treatment of wastewater to ensure that the discharged water quality meets or reaches the reuse standard, which protects the environment and saves water resources.

[0100] 5. Groundwater level monitoring agencies provide real-time and accurate water level data, offering crucial information for safe coal mine production and the scientific management of groundwater resources. Comprehensive monitoring data and analysis results help managers make informed and rational decisions, optimizing coal mining and wastewater treatment processes. Attached Figure Description

[0101] Figure 1 This is a flowchart of a preferred embodiment of a coal mine wastewater treatment method disclosed in this application;

[0102] Figure 2 This is a schematic diagram of the overall structure of a coal mine wastewater treatment system disclosed in a preferred embodiment of this application;

[0103] Figure 3 This is a schematic diagram of the wastewater treatment mechanism of a coal mine wastewater treatment system disclosed in a preferred embodiment of this application.

[0104] Figure label:

[0105] 1. Pre-sedimentation and equalization tank; 2. Drainage pump; 3. Automatically controlled moving trolley; 4. Coagulation sedimentation tank; 5. Maturation tank; 6. Biofilm filter tank; 7. Clear water tank; 8. Water pump; 9. Sludge tank; 10. Plate and frame filter press; 11. Fine screen; 12. Sludge storage tank; 13. Agitator; 41. Collection tank; 61. Biofilm filter; 62. Hydrocyclone; 63. Blower; 64. Aeration device; 71. Clear water pump. Detailed Implementation

[0106] The present application will be further described in detail below with reference to the accompanying drawings.

[0107] Reference Figure 1 and Figure 2 This application provides a coal mine wastewater treatment system, including:

[0108] Mobile drainage system: Includes an automatically controlled mobile trolley, high-definition camera, and drainage pump; it drains accumulated and gushing water from locations within the coal mine; capable of autonomous or remote-controlled movement to specific locations of accumulated or gushing water within the mine. Through an intelligent navigation system, the trolley can accurately locate and respond quickly, effectively draining water and reducing the impact of flooding on production. The water pump is used to remove accumulated or gushing water, ensuring a safe and stable working environment within the coal mine.

[0109] Water accumulation and inrush monitoring system: This system includes drones, GPS locators, gas concentration detectors, high-definition cameras, and LED lights. It patrols the interior of coal mines to collect high-definition images, promptly detecting water accumulation or inrush issues. Drones can flexibly navigate the complex environment inside coal mines to perform high-definition image acquisition tasks. High-definition cameras capture high-definition images of the mine interior, helping to identify the specific location and severity of water accumulation or inrush. Simultaneously, gas concentration detectors monitor gas concentrations at different locations within the coal mine.

[0110] Route planning module: Based on the 3D structural map of the coal mine, topography, obstacle distribution, and actual monitoring needs, the module plans the optimal flight route for the UAV. This not only improves monitoring efficiency but also reduces the risk of UAV collisions, ensuring safe operation.

[0111] Wastewater treatment facilities: These facilities provide advanced treatment for wastewater discharged from coal mines, removing suspended solids, heavy metals, harmful chemicals, and other pollutants to meet discharge or reuse standards. This helps protect the surrounding water environment and promotes the recycling of water resources.

[0112] Groundwater level monitoring agencies: These agencies monitor changes in groundwater levels in coal mines in real time using underground water level sensors, providing crucial data support for safe production and scientific management of groundwater resources. When water levels are abnormal, they can issue timely alerts to prevent flooding accidents.

[0113] Anomaly monitoring agencies analyze the collected images to promptly identify anomalies;

[0114] Alarm module: Includes an alarm that promptly issues an alert when an abnormal situation is detected;

[0115] Central control unit: Connected to mobile drainage system, water accumulation and inrush monitoring system, route planning module, sewage treatment system and groundwater level monitoring system via wireless network.

[0116] Furthermore, the route planning module plans the optimal flight route for the UAV within the coal mine. This requires comprehensive consideration of the mine's 3D structural map, terrain, obstacle distribution, and actual monitoring needs. Specifically, it includes the following steps:

[0117] 1. Data Collection and Preprocessing: Obtain 3D structural maps of the coal mine using a Geographic Information System (GIS), including detailed structures such as underground roadways, mine blocks, and pillars. Ensure the accuracy and timeliness of the 3D structural maps to provide reliable foundational data for UAV planning. Utilize high-resolution remote sensing imagery or UAV-captured mining area images for detailed topographic analysis. Identify key information such as slope, altitude, and topographic features to consider UAV flight limitations and safety when planning routes. Mark obstacles within the coal mine, such as large machinery, mine carts, and roadway intersections. Ensure the accuracy and completeness of obstacle information for effective route avoidance. Identify key monitoring areas, such as historically waterlogged areas, low-lying areas, and roadway intersections.

[0118] 2. Model building: Select a suitable drone model and build a flight performance model for the drone based on the model and performance parameters. The performance parameters include key indicators such as maximum flight speed, maximum rate of climb, and minimum turning radius, so as to take the drone's flight capabilities into account when planning routes.

[0119] Based on the actual environment and monitoring needs within the coal mine, flight constraints for the drones are set, including altitude limits (to avoid collisions with the top of the tunnel), speed limits (to ensure flight stability and safety), and turning limits (to avoid excessively large or small turning angles).

[0120] 3. Route Planning: The RRT (Random Search Tree) algorithm, suitable for path planning in complex 3D environments, is selected to find the optimal or near-optimal path from the starting point to the destination. Information such as the 3D structure map of the coal mine, terrain features, and obstacle distribution is input into the algorithm. Based on the set constraints and the UAV's performance model, path search and optimization are performed. The optimal or near-optimal flight route is output and displayed on the 3D map for operator review and confirmation.

[0121] · Path planning using the RRT algorithm includes the following steps:

[0122] Randomly select a point p within the drone's search space. sample This point is a candidate point that the algorithm is trying to connect to in the explored path tree.

[0123] Find the distance p in the tree sample The nearest point p nearest Find the distance p from the currently constructed path tree. sample The nearest point p nearest This point is the node in the current tree that is closest to the new sampling point.

[0124] Try from p nearest to p sampleConnection: Try to connect from p nearest The straight line extends to p sample This creates a new path segment. This step requires checking whether this path meets the drone's flight constraints.

[0125] Add p sample To the tree: If from p nearest to p sample If the straight path of p satisfies the following condition, then p sample Add to the path tree: distance d(p) between two points nearest ,p sample The path must be less than or equal to a preset threshold, which ensures the continuity and smoothness of the path. The path must meet flight constraints, including but not limited to altitude limits, speed limits, and turning radius limits. This is a sufficiently small positive number to control the smoothness of the path. It ensures that the path is not too convoluted, thus preventing the drone from making overly aggressive maneuvers during flight.

[0126] Flight constraints: Flight constraints that need to be considered during path planning, including limitations on altitude, speed, and turning angle.

[0127] In this technical solution, the RRT algorithm can find a path from the starting point to the destination for a UAV in complex three-dimensional environments, such as inside a coal mine. This path not only meets the flight performance requirements of the UAV but also takes into account the complexity and safety of the environment.

[0128] 4. Route Evaluation and Optimization: Conduct a safety assessment of the planned flight route to check for any risk of collision with obstacles. If potential risks are identified, replan the route or adjust the drone's flight parameters to ensure safety. Evaluate the efficiency of the flight route, including metrics such as flight time and monitoring coverage area. If low efficiency is found, attempt to optimize the route or increase parameters such as the drone's flight speed to improve efficiency. Iterate and optimize the route multiple times based on the evaluation results until both safety and efficiency requirements are met.

[0129] 5. Real-time Monitoring: Before takeoff, conduct final checks and preparations for the drone, including confirming the flight route and checking the drone's status. During flight, conduct real-time monitoring to ensure the drone follows the planned route and handle any unforeseen circumstances promptly. Collect images, videos, and other data captured by the drone during flight, and perform subsequent processing and analysis to meet monitoring needs.

[0130] u(t) = K p e(t)+K d {[d e [(t)] / (dt)}+K i ∫[e(t)dt];

[0131] In the formula, u(t) represents the control input at time t, which is the control input applied to the UAV at time t. This can be control commands such as throttle, steering, pitch, roll, or yaw. e(t) represents the error at time t, i.e., the difference between the expected value and the actual value. Error is the difference between the UAV's current position or flight path and the predetermined path. It can be a position error (the difference between the UAV's current position and the position of a corresponding point on the predetermined path), or a speed error or heading error. K p K represents the proportional gain. The control input is adjusted based on the current value of the error; a larger proportional gain results in a stronger response to the current error. d K is the differential gain. Adjusting the control input based on the rate of change of the error (i.e., the derivative of the error) helps reduce system overshoot and improve system response speed. i The integral gain is used to adjust the control input based on the accumulation of error over time, which helps to eliminate steady-state error and improve system stability.

[0132] In this technical solution, the route planning module can plan the optimal flight route for the UAV within the coal mine, improving monitoring efficiency, reducing collision risks, and ensuring safe operation of the UAV. The real-time monitoring system continuously measures the UAV's current position, speed, and heading. This actual state information is compared with the target state information at corresponding time points along the predetermined path, calculating the error e(t). Based on the error and its rate of change, the required control input u(t) is calculated using a PID controller. This control input is applied to the UAV's control system, adjusting the UAV's flight state to return it to the predetermined path. The system dynamically responds to the UAV's flight state and path deviations, adjusting the control input in real time to ensure flight safety and path tracking accuracy.

[0133] Furthermore, water accumulation and inrush monitoring agencies monitor water accumulation and inrush conditions, including the following steps:

[0134] 1. Drone Cruise: Control the drone for a smooth takeoff and cruise along a planned route. During the cruise, use a high-definition camera to capture real-time high-definition images of the coal mine interior. Adjust the camera angle and focus as needed to obtain clearer images and a more comprehensive field of view. Monitor the methane concentration inside the coal mine using a methane detector to ensure safety. When there is insufficient light inside the coal mine, promptly turn on the LED lights on the drone to provide illumination.

[0135] 2. Image Analysis and Recognition: High-definition images captured by the drone are transmitted in real time to a ground station or designated storage device. The transmitted images undergo preprocessing, including noise reduction, contrast enhancement, and color deviation correction, to improve image quality and facilitate subsequent analysis. Image processing software is then used to analyze the preprocessed images. By comparing images from different times and areas, the specific location and severity of water accumulation or flooding are identified. Image recognition algorithms or machine learning models are used to assist the recognition process, improving accuracy and efficiency. This includes the following steps:

[0136] Image Transmission and Storage: Activate the image transmission system on the drone to transmit images captured by the high-definition camera to a ground station or designated storage device in real time. During transmission, implement data integrity verification mechanisms, such as CRC or MD5 checksums, to ensure that the image data is not tampered with or damaged during transmission. Store the received image data in encrypted storage devices, or use encryption technology to protect the security of data transmission and prevent data leakage.

[0137] Image preprocessing includes denoising, image cropping and scaling, contrast enhancement, and color deviation correction.

[0138] Denoising: Applying denoising algorithms (such as Gaussian filtering, median filtering, etc.) to remove noise from an image and improve image quality.

[0139] Enhance contrast: Enhance image contrast through methods such as histogram equalization and Laplacian sharpening to make details in the image clearer.

[0140] Correcting color deviation: Using color correction algorithms (such as white balance adjustment) to correct color deviations in the image, ensuring that the image colors accurately reflect the actual scene.

[0141] Image cropping and scaling: Crop the useless parts of the image as needed and scale the image appropriately for subsequent analysis.

[0142] Image analysis: Image processing software is used to extract key features from the image, such as edges, textures, and color distribution. These features will be used in subsequent recognition processes. The currently captured image is compared with previous images (historical images of the same area or images from different time points). By comparing changes in features within the images, signs of water accumulation or surging can be identified.

[0143] Water accumulation and gushing water identification: Water accumulation or gushing water areas in an image are separated from the background using image segmentation techniques. The shape, size, and other features of the water accumulation or gushing water areas are analyzed to assess their severity. Images at different time points are compared to detect trends in the water accumulation or gushing water areas, such as area expansion and rising water levels.

[0144] Assisted identification: Image recognition algorithms (such as template matching, feature matching, etc.) are applied to automatically identify areas of water accumulation or water inrush. A convolutional neural network (CNN) is selected to train a machine learning model to identify water accumulation or water inrush features in images. The model is trained using a large amount of labeled image data, enabling it to automatically identify and classify water accumulation or water inrush situations.

[0145] Using the squared difference as the matching metric: SSD(T,I)=∑ x,y I(x,y)-T(x,y)] 2 Where T is the template image, I is the target image, and SSD represents the squared difference, which is used to measure the difference between the template image and the target image.

[0146] Then, Hamming distance is used for feature point matching:

[0147]

[0148] Where D A Let D be the feature descriptor vector of image A. B Let d(D) be the feature descriptor vector of image B. A D B ) represents the Hamming distance between feature descriptors.

[0149] A convolutional neural network (CNN) model is trained using a large amount of labeled coal mine image data, enabling the model to automatically identify features of water accumulation or inrush. The CNN forward propagation is represented by the following steps: Z [l] =W [l] A [l-1] +b [l] ;

[0150] A [l] =f(Z) [l] The activation function is f(x) = max(0,x);

[0151] The loss function is: L = -∑ C c＝1 [y o,c log(p o,c )]. Among them, Z [l] This is the input for the l-th layer (inactive). W [l] Let b be the weight matrix of the l-th layer. [l] Let A be the bias vector for the l-th layer. [l] y is the activation output of layer l. f is the activation function, such as ReLU. C is the total number of classes. o,c p is the one-hot encoding of the real label, where c represents the category index. o,cLet L be the probability of class c predicted by the model. L is the loss function. The system can effectively extract features from images, perform matching, and use a deep learning model to accurately identify areas of standing water or rushing water. This can significantly improve the accuracy and robustness of the identification.

[0152] Model parameters were adjusted using optimization algorithms such as gradient descent. Data augmentation techniques were applied to expand the training dataset, improving the model's ability to identify water accumulation and inrush conditions under different lighting and angle conditions. Image segmentation techniques were used to separate water accumulation or inrush areas from the background, and then the shape, size, and changing trends of these areas were analyzed.

[0153] Output Results: The analysis results will be output in the form of images, charts, or reports, displaying information such as the specific location, area, and severity of water accumulation or inrush. An alarm mechanism will be triggered when water accumulation or inrush reaches a preset threshold. Alarm information should include the specific location, time, and severity of the water accumulation or inrush, as well as suggested response measures. Notification to Relevant Personnel: The alarm information will be promptly communicated to relevant departments and personnel, including the specific location, area, and depth of the water accumulation, so that they can take appropriate response measures.

[0154] Furthermore, the anomaly monitoring agency conducts in-depth analysis of images collected by water accumulation and inrush monitoring agencies (especially high-definition cameras mounted on drones) to promptly identify any possible anomalies, including the following steps:

[0155] Image reception: The anomaly monitoring agency first receives high-definition images transmitted in real time or periodically from devices such as drones.

[0156] Image preprocessing: The received image undergoes preprocessing, including denoising, contrast enhancement, and color deviation correction. Denoising employs digital image processing techniques such as median filtering and Gaussian filtering to effectively remove noise and improve image quality. Contrast enhancement uses methods like histogram equalization and gamma correction to adjust image contrast, making details more vivid and facilitating subsequent analysis. Color correction addresses color deviations caused by factors such as changes in ambient lighting and differences in camera white balance, performing automatic or manual color correction to ensure the image colors accurately reflect the scene.

[0157] Feature extraction: Utilizing advanced image processing and computer vision techniques, key features such as shape, texture, and color are extracted from preprocessed images. These features help distinguish between normal and abnormal regions. This includes edge detection, texture analysis, color segmentation, and shape recognition.

[0158] Edge detection: Using edge detection algorithms such as Canny and Sobel, edge information in images is identified, such as water boundaries and cracks.

[0159] Texture analysis: By using methods such as gray-level co-occurrence matrix (GLCM) and local binary mode (LBP), the texture features of image regions are analyzed to distinguish between normal geological structures and anomalous changes.

[0160] Shape recognition: Using techniques such as Hough transform and shape template matching, specific shapes in images are identified, such as the circular or elliptical features of a water inlet.

[0161] Color segmentation: Threshold segmentation based on color space (such as RGB, HSV) to distinguish different objects or regions, such as the blue or dark features of a water accumulation area.

[0162] Anomaly Detection: A machine learning model based on Support Vector Machine (SVM) is constructed and trained using historical data to improve the accuracy of anomaly detection. Convolutional Neural Network (CNN) deep learning technology is used to automatically learn image features and classify anomalies, resulting in higher accuracy and adaptability to complex and changing scenarios. Combining multiple algorithms and feature information, a comprehensive judgment is made to identify anomalies such as expanding water accumulation areas, the appearance of new water inlets, and precursors to ground subsidence with high confidence.

[0163] Alarm and Response: Once an anomaly is detected, the anomaly monitoring agency will immediately send an alarm to the central control unit and may trigger a series of emergency response measures, such as activating mobile drainage units to handle the situation and notifying relevant personnel to conduct safety inspections.

[0164] This technical solution helps coal mine managers to understand and grasp the actual situation inside the mine in a timely manner, identify potential safety hazards, and take corresponding measures to deal with them, thereby effectively preventing accidents such as flooding and ensuring the safety of miners and the normal production order of the coal mine. At the same time, the timely monitoring and handling of abnormal situations also helps to protect the natural environment and ecological balance around the coal mine.

[0165] Furthermore, the mobile drainage mechanism includes an automatically controlled mobile trolley 3, a high-definition camera, and a drainage pump 2. The drainage pump 2 is detachably and fixedly mounted on the automatically controlled mobile trolley 3. The drainage pump 2 is a BOS400-25-55KW / 4, Q=400m³ / h, H=25m, N=55kW, made of alloy cast iron, equipped with a maintenance bracket, and its start and stop are controlled by a level gauge. A stirrer 13 is installed in the pre-settling and regulating tank 1. The automatically controlled mobile trolley 3 serves as the carrier for the drainage pump 2, allowing for flexible movement to adapt to different working environments. The mobile drainage mechanism drains accumulated and gushing water from locations within the coal mine, including the following steps:

[0166] 1. The automatically controlled mobile vehicle 3 is equipped with an intelligent navigation system that plans the optimal path from the current location to the target location. The intelligent navigation system uses sensors such as GPS, LiDAR, or gyroscopes to achieve precise positioning and path planning for the vehicle. It can perceive environmental changes in real time and automatically adjust the driving route to ensure the vehicle reaches the target location quickly and accurately.

[0167] 2. The automatically controlled mobile vehicle 3 starts autonomously according to the instructions of the intelligent navigation system and travels along the planned path to the target location.

[0168] 3. Upon reaching the target location, drainage pump 2 will start automatically (or according to the operator's instructions) and begin pumping out accumulated or gushing water.

[0169] 4. During the pumping process, high-definition cameras continuously monitor the pumping effect to ensure effective control of water accumulation or inrush. The high-definition cameras capture and transmit real-time images of the coal mine, helping operators remotely monitor the specific situation of water accumulation or inrush. The high-definition cameras feature night vision capabilities and a dustproof and waterproof design, ensuring clear imaging even in harsh environments.

[0170] 5. When the accumulated or gushing water is drained to a safe level, the drainage pump 2 will automatically stop working (or stop according to the operator's instructions). The automatically controlled mobile trolley 4 will evacuate to a safe area along the original route or a newly planned path according to the instructions of the intelligent navigation system.

[0171] In this technical solution, the mobile drainage mechanism can efficiently and accurately complete the drainage of accumulated water and gushing water in the coal mine, effectively reducing the impact of water hazards on production and ensuring the safety and stability of the working environment inside the coal mine.

[0172] Reference Figure 3 The wastewater treatment system includes: a pre-sedimentation equalization tank 1, a coagulation sedimentation tank 4, a maturation tank 5, a biofilm filter tank 6, a clear water tank 7, a water pump 8, a sludge tank 9, a plate and frame filter press 10, a mixer 13, and a hydrocyclone 62.

[0173] The output end of the drainage pump 2 is connected to the pre-settling and equalization tank 1; a coagulation sedimentation tank 4 is fixedly installed on the right side of the pre-settling and equalization tank 1; a water pump 8 is installed in the coagulation sedimentation tank 4 and the pre-settling and equalization tank 1, the input end of the water pump 8 is connected to the pre-settling and equalization tank 1, and the output end of the water pump 8 is connected to the coagulation sedimentation tank 4.

[0174] A maturation tank 5 is fixedly installed on the right side of the coagulation sedimentation tank 4, and a water pump 8 is installed between the maturation tank 5 and the coagulation sedimentation tank 4; both the coagulation sedimentation tank 4 and the maturation tank 5 are equipped with a stirrer 13.

[0175] A biofilm filter tank 6 is fixedly installed on the right side of the maturation tank 5; an aeration device is installed inside the biofilm filter tank 6; a water pump 8 is installed between the biofilm filter tank 6 and the maturation tank 5.

[0176] A clear water tank 7 is fixedly installed on the right side of the biofilm filtration tank 6; a clear water pump 71 is installed between the clear water tank 7 and the biofilm filtration tank 6.

[0177] A hydrocyclone 62 is installed at the lower end of the biofilm filter tank 6. The input end of the hydrocyclone 62 is connected to the lower end of the biofilm filter tank 6, and the output end of the hydrocyclone 62 is connected to the pre-sedimentation and conditioning tank 1.

[0178] An additive storage tank A is fixedly installed above the coagulation sedimentation tank 4; a discharge pipe is installed at the lower end of the additive storage tank A, and a flow meter is installed on the discharge pipe; the additive storage tank A contains polyaluminum chloride (PAC).

[0179] An additive storage tank B is fixedly installed above the coagulation sedimentation tank 4; a discharge pipe is installed at the lower end of the additive storage tank B, and a flow meter is installed on the discharge pipe; the additive storage tank B contains polyacrylamide (PAM);

[0180] By adding polyaluminum chloride (PAC) and polyacrylamide (PAM) to the coagulation sedimentation tank 4, suspended solids and colloids in the wastewater are formed into flocs and precipitated.

[0181] A micro-sand storage tank is fixedly installed above the maturation tank 5; a discharge pipe with a flow meter is installed at the lower end of the micro-sand storage tank; the micro-sand storage tank contains micro-sand with a particle size of approximately 100-150 μm; micro-sand is added (from the micro-sand storage tank) to enhance the sedimentation effect. An internal agitator 13 promotes mixing of the micro-sand with the wastewater. A flushing pipeline should be adequately considered in the micro-sand conveying system. A screw pump is used for adding the micro-sand, and a dust absorption device is installed on the sand hopper for adding new micro-sand. A dedicated sand separator should be used.

[0182] The biofilm filter tank 6 is equipped with a biofilm filter 61 and an aeration device 64. A blower 63 is fixedly installed on the outside of the biofilm filter tank 6; the output end of the blower 63 is connected to the aeration device 64. Microorganisms on the biofilm filter 61 degrade organic matter in the wastewater, while the aeration device 64 provides oxygen to promote microbial growth. Part of the treated water is returned to the pre-sedimentation and equalization tank 1 via a hydrocyclone 62, simultaneously separating and removing heavier coarse particles such as silt and sand to improve treatment efficiency. A hydrocyclone is a classifying device that uses centrifugal force to accelerate the settling of mineral particles; it can also be used to separate and remove heavier coarse particles such as silt and sand from wastewater. Its working principle is based on centrifugal sedimentation and density difference. When water enters the equipment tangentially from the desander inlet under a certain pressure, it generates a strong rotational motion. Because sand and water (or water and solid particles in sand-containing flocs) have different densities, they are subjected to different forces under the action of centrifugal force, centripetal buoyancy, and fluid drag. This causes the water (or clear water) with lower density to rise and be discharged from the overflow outlet, while the sand (or solid particles in sand-containing flocs) with higher density sinks and be discharged from the bottom sand discharge outlet, thus achieving the purpose of separation.

[0183] The input end of the clear water pump 71 is connected to the biofilm filter 61; the output end of the clear water pump 71 is connected to the clear water tank 7; the clear water tank 7 stores the clear water treated by the biofilm filter 61 for subsequent use or discharge. The clear water is pumped from the biofilm filter 61 to the clear water tank 7 by the clear water pump 71.

[0184] A fine screen 11 is fixedly installed inside the pre-sedimentation and equalization tank 1; a sludge storage tank 12 is fixedly installed below the pre-sedimentation and equalization tank 1; the pre-sedimentation and equalization tank 1 is used for the initial sedimentation of large suspended solids in wastewater and to regulate water quality and quantity. The built-in fine screen 11 further intercepts larger impurities; a stirrer 13 is equipped to help evenly mix the water. The sludge storage tank 12 collects and stores the sludge settled from the pre-sedimentation and equalization tank.

[0185] A collection box 41 is fixedly installed below the coagulation sedimentation tank 4;

[0186] Both the collection box 41 and the sludge storage box 12 are connected to the sludge tank 9 via pipes.

[0187] A plate and frame filter press 10 is connected to the sludge tank 9, which presses the sludge into sludge cakes. The sludge tank 9 collects and processes sludge from the pre-sedimentation and equalization tank 1, the coagulation sedimentation tank 4, and the maturation tank 5. The plate and frame filter press 10 presses the sludge into sludge cakes for easy subsequent treatment and disposal.

[0188] Online water quality monitoring equipment (including pH meters, turbidity meters, dissolved oxygen meters, etc.) is installed in the pre-sedimentation equalization tank 1, coagulation sedimentation tank 4, maturation tank 5, biofilm filtration tank 6, and clear water tank 7 to monitor water quality changes in real time and provide accurate data for the automatic control system. Based on the water quality monitoring data, the automatic control system algorithm is optimized to achieve more precise start-up, shutdown, and adjustment of equipment such as pumps, agitators, and valves, thereby improving system response speed and processing efficiency.

[0189] An oil suction device is installed on maturation tank 5 to remove oil from the water surface; an automatic oil suction machine is selected to ensure rapid and effective removal of floating oil. The suction position and speed are automatically adjusted. During the suction process, the automatic oil suction machine automatically filters out large particles to prevent clogging of the suction port and maintain suction efficiency. A water level monitoring device is installed in the maturation tank to monitor water level changes in real time, providing accurate operating position information for the oil suction device. The oil suction device is rationally arranged according to the shape, size, and oil distribution of the maturation tank to ensure complete coverage of the oil-contaminated area.

[0190] This technical solution effectively removes pollutants such as suspended solids, colloids, and organic matter from wastewater through a series of physical, chemical, and biological processes, achieving wastewater purification. Simultaneously, the system also considers sludge treatment and disposal, ensuring the integrity and environmental friendliness of the entire treatment process.

[0191] Furthermore, groundwater level monitoring agencies employ advanced sensor technologies, such as pressure-type, float-type, or ultrasonic sensors, to achieve accurate measurement of groundwater levels. These sensors are installed in boreholes or monitoring wells, transmitting water level data to the monitoring center in real time. Wireless communication technology is used to transmit the data collected by the water level sensors to the monitoring center in real time. The monitoring center uses data processing software to analyze, store, and process the received data, generating intuitive charts and reports. When the monitored groundwater level exceeds a preset threshold, the system automatically triggers a warning or alarm mechanism, promptly notifying relevant personnel to take appropriate measures through audible and visual alarms, SMS notifications, or telephone calls. Sudden changes in groundwater levels can trigger flooding accidents, such as water inrush and sudden water surges, seriously threatening the lives of miners and the stability of mine production. Groundwater level monitoring agencies can promptly detect water level anomalies, providing a window of opportunity for coal mining enterprises to take preventative measures, effectively avoiding or reducing the occurrence of flooding accidents.

[0192] Reference Figure 1 This invention provides a method for treating coal mine wastewater, comprising the following steps:

[0193] S1, the route planning module, plans the optimal flight route for the UAV based on the three-dimensional structure map of the coal mine, topography, obstacle distribution, and actual monitoring needs.

[0194] S2. The water accumulation and inrush monitoring agency conducts patrols of the coal mine along a planned route, collecting high-definition images to promptly detect water accumulation or inrush issues. High-definition cameras capture detailed images of the mine's interior, helping to identify the specific location and severity of water accumulation or inrush. Simultaneously, gas concentration detectors monitor gas concentrations at different locations within the mine.

[0195] S3. The mobile drainage mechanism uses an automatically controlled mobile trolley to drive a drainage pump to the location of water accumulation or gushing water in the coal mine to carry out drainage operations and pump the accumulated water or gushing water to the sewage treatment facility.

[0196] S4. Wastewater treatment facilities conduct advanced treatment on wastewater discharged from coal mines to remove pollutants such as suspended solids, heavy metals, and harmful chemicals, so that it meets discharge standards or reuse standards.

[0197] S5. Simultaneously, groundwater level monitoring agencies use underground water level sensors to monitor changes in groundwater levels in coal mines in real time, providing crucial data support for safe production and scientific management of groundwater resources. When water levels are abnormal, timely alarms can be issued to prevent flooding accidents.

[0198] S6. The anomaly monitoring agency analyzes the collected images and promptly identifies abnormal situations;

[0199] S7. When an abnormal situation is detected, the alarm module will issue an alarm in a timely manner.

[0200] The working principle of this coal mine wastewater treatment system is as follows: The route planning module plans the optimal flight route for the drone based on the three-dimensional structure map, topography, obstacle distribution, and actual monitoring needs of the coal mine. The water accumulation and inrush monitoring mechanism cruises the coal mine along the planned route, collecting high-definition images to promptly detect water accumulation or inrush problems. High-definition cameras capture high-definition images of the mine's interior, helping to identify the specific location and severity of water accumulation or inrush. Simultaneously, a gas concentration detector monitors the gas concentration at different locations within the mine. A mobile drainage mechanism, through an automatically controlled trolley, moves a drainage pump to the location of water accumulation or inrush within the mine to pump the water to a wastewater treatment facility. The wastewater treatment facility performs deep treatment on the wastewater discharged from the coal mine, removing suspended solids, heavy metals, harmful chemicals, and other pollutants to meet discharge or reuse standards. The groundwater level monitoring mechanism uses underground water level sensors to monitor changes in the coal mine's groundwater level in real time, providing crucial data support for safe coal mine production and the scientific management of groundwater resources. When water levels are abnormal, an alarm can be issued promptly to prevent flooding. The anomaly monitoring agency analyzes the collected images to identify abnormalities in a timely manner; when an anomaly is detected, the alarm module issues an alert immediately.

[0201] This invention utilizes drone-based patrol monitoring to reduce the number of times personnel enter hazardous coal mine areas, thereby lowering the risk of accidents. Real-time monitoring of methane concentration and groundwater levels allows for timely detection and early warning of potential safety hazards, such as methane accumulation and flooding, effectively preventing major safety accidents. The water accumulation and inrush monitoring system employs high-definition cameras and image analysis technology to accurately identify the location and severity of water accumulation and inrush, providing accurate information for rapid response. When an anomaly is detected, the alarm module immediately issues an alert, enabling rapid response and minimizing losses. The mobile drainage system can automatically move to water accumulation or inrush points to perform drainage operations, improving drainage efficiency and shortening processing time. Wastewater is deeply treated by a wastewater treatment system to ensure that the discharged water meets or reaches reuse standards, protecting the environment and conserving water resources.

[0202] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for treating coal mine wastewater, characterized in that, Includes the following steps: S1. The route planning module plans the optimal flight route for the UAV based on the three-dimensional structure map of the coal mine, the terrain, the distribution of obstacles, and the actual monitoring needs. S2. The water accumulation and inrush monitoring agency conducts patrols of the coal mine according to the planned route to collect high-definition images and promptly detect water accumulation or inrush problems in the coal mine; at the same time, it monitors the gas concentration at different locations in the coal mine through a gas concentration detector. S3. The mobile drainage mechanism uses an automatically controlled mobile trolley to drive a drainage pump to the location of water accumulation or gushing water in the coal mine to carry out drainage operations and pump the accumulated water or gushing water to the sewage treatment facility. S4. Wastewater treatment facilities conduct advanced treatment on wastewater discharged from coal mines to remove suspended solids, heavy metals and harmful chemicals, so that it meets discharge standards or reuse standards. S5. At the same time, the groundwater level monitoring agency monitors the changes in the groundwater level in the coal mine in real time through water level sensors installed underground. S6. The anomaly monitoring agency analyzes the collected images and promptly identifies abnormal situations; S7. When an abnormal situation is detected, the alarm module will issue an alarm in a timely manner.

2. The method for treating coal mining wastewater according to claim 1, characterized in that: Step S1 includes the following steps: S11. Data Collection and Preprocessing: Obtain a three-dimensional structural map of the coal mine through the Geographic Information System (GIS). Utilize high-resolution remote sensing images or images of the mining area taken by drones to conduct a detailed analysis of the topography and geomorphology, identify slope, height, and geomorphic features, and mark obstacles inside the coal mine; determine key monitoring areas. S12. Model Building: Based on the UAV model and performance parameters, build a flight performance model for the UAV. Based on the actual environment and monitoring needs inside the coal mine, set flight constraints for the UAV, including altitude limits, speed limits, and turning limits. S13. Route Planning: The RRT (Random Tree Search) algorithm is selected to find the optimal or suboptimal path from the starting point to the destination in a complex 3D environment. The 3D structure map of the coal mine, the terrain and obstacle distribution information are input into the algorithm. Based on the set constraints and the performance model of the UAV, the path search and optimization are performed. The optimal or suboptimal flight route is output. S14. Route Evaluation and Optimization: Conduct a safety assessment of the planned flight route to check for the risk of collision with obstacles; perform multiple iterative optimizations of the route based on the assessment results until the safety and efficiency requirements are met. S15. Real-time monitoring: Real-time monitoring during drone flight to ensure drones fly along planned routes and to handle emergencies promptly.

3. The method for treating coal mining wastewater according to claim 1, characterized in that: Step S2 includes the following steps: S21. Drone patrol: Drones patrol along a planned route; high-definition cameras capture high-definition images of the coal mine interior in real time; and methane concentration detectors monitor the methane concentration inside the coal mine. S22. Image Analysis and Recognition: Preprocess the transmitted images, including noise reduction, contrast enhancement, and color deviation correction. Analyze the preprocessed images using image processing software. By comparing images from different times and regions, identify the specific location and severity of water accumulation or gushing water. S23. Output Results: Output the analysis results in the form of images, charts, or reports, showing the specific location, area, and severity of water accumulation or gushing water.

4. The method for treating coal mining wastewater according to claim 3, characterized in that: Step S22 includes the following steps: S221, Image Transmission and Storage: Activate the image transmission system on the UAV to transmit images captured by the high-definition camera to the ground station or designated storage device in real time; S222, Image preprocessing: including noise reduction, image cropping and scaling, contrast enhancement and color deviation correction; S223. Image Analysis: Use image processing software to extract key features from images, including edges, textures, and color distribution; compare the currently captured image with previous images, and identify signs of water accumulation or gushing by comparing changes in features in the images; S224. Water accumulation and water inrush identification: Using image segmentation technology, the water accumulation or water inrush area in the image is separated from the background. The shape and size features of the water accumulation or water inrush area are analyzed to assess its severity. By comparing images at different time points, the changing trend of the water accumulation or water inrush area is detected. S225. Assisted Recognition: Select a Convolutional Neural Network (CNN) to train a machine learning model to identify water accumulation or gushing features in images. Train the model with a large amount of labeled image data so that it can automatically identify and classify water accumulation or gushing situations.

5. The method for treating coal mining wastewater according to claim 4, characterized in that: In step S225, the squared difference is used as the matching metric; Then, Hamming distance is used for feature point matching; A convolutional neural network (CNN) model was trained using a large amount of labeled coal mine image data to enable the model to automatically identify water accumulation or water inrush features. The model parameters were adjusted using a gradient descent optimization algorithm. Data augmentation techniques were applied to expand the training dataset to improve the model's ability to identify water accumulation and water inrush under different lighting and angle conditions. Image segmentation techniques were used to separate water accumulation or water inrush areas from the background, and then the shape, size, and changing trends of these areas were analyzed.

6. The method for treating coal mine wastewater according to claim 2, characterized in that: Step S6 includes the following steps: S61, Image Reception: Receives high-definition images transmitted in real time from a high-definition camera; S62. Image preprocessing: Preprocess the received image, including noise reduction, contrast enhancement, and color deviation correction; S63. Feature Extraction: Extract key features from the preprocessed image. Key features include shape, texture, and color. Feature extraction includes edge detection, texture analysis, color segmentation, and shape recognition. S64. Anomaly Identification: Construct a machine learning model based on Support Vector Machine (SVM), train the model using historical data, and use Convolutional Neural Network (CNN) deep learning technology to automatically learn image features and classify anomalies. Combine multiple algorithms and feature information to make a comprehensive judgment and identify anomalies such as the expansion of water accumulation areas, the appearance of new water inlets, and signs of ground collapse. S65. Alarm and Response: Once an abnormal situation is identified, the anomaly monitoring agency will immediately send an alarm to the central control unit.

7. The method for treating coal mining wastewater according to claim 1, characterized in that: Step S3 includes the following steps: S31. The intelligent navigation system on the automatically controlled mobile vehicle plans the optimal path from the current position to the target position; S32. The automatically controlled mobile vehicle starts autonomously according to the instructions of the intelligent navigation system and travels along the planned path to the target location. S33. Upon reaching the target location, the drainage pump will automatically start to pump out the accumulated or gushing water. S34. During the pumping process, a high-definition camera continuously monitors the pumping effect to ensure that the accumulated water or gushing water is effectively controlled. S35. When the accumulated or gushing water is drained to a safe level, the drainage pump automatically stops working; the automatically controlled mobile trolley evacuates to a safe area along the original route or a newly planned route according to the instructions of the intelligent navigation system.

8. The method for treating coal mining wastewater according to claim 1, characterized in that: Wastewater treatment facilities include: pre-sedimentation equalization tank, coagulation sedimentation tank, maturation tank, biofilm filter tank, clear water tank, water pump, sludge tank, plate and frame filter press, agitator and hydrocyclone; The output end of the drainage pump is connected to the pre-settling and equalization tank; a coagulation sedimentation tank is fixedly installed on the right side of the pre-settling and equalization tank; a water pump is installed in both the coagulation sedimentation tank and the pre-settling and equalization tank. A maturation tank is fixedly installed on the right side of the coagulation sedimentation tank, and a water pump is installed between the maturation tank and the coagulation sedimentation tank; both the coagulation sedimentation tank and the maturation tank are equipped with agitators; a biofilm filter tank is fixedly installed on the right side of the maturation tank; an aeration device is installed in the biofilm filter tank; and a water pump is installed between the biofilm filter tank and the maturation tank. A clear water tank is fixedly installed on the right side of the biofilm filter tank; a clear water pump is installed between the clear water tank and the biofilm filter tank; a hydrocyclone is installed at the lower end of the biofilm filter tank, the input end of the hydrocyclone is connected to the lower end of the biofilm filter tank, and the output end of the hydrocyclone is connected to the pre-sedimentation and conditioning tank. An additive storage tank A is fixedly installed above the coagulation sedimentation tank; additive storage tank A contains polyaluminum chloride (PAC); additive storage tank B is fixedly installed above the coagulation sedimentation tank; additive storage tank B contains polyacrylamide (PAM). A micro-sand storage tank is fixedly installed above the maturation tank; the micro-sand storage tank contains micro-sand; the micro-sand particle size is approximately 100-150 μm; the input end of the clear water pump is connected to the biofilm filter; the output end of the clear water pump is connected to the clear water tank; a fine screen is fixedly installed in the pre-sedimentation and equalization tank; a sludge storage tank is fixedly installed below the pre-sedimentation and equalization tank; a collection tank is fixedly installed below the coagulation sedimentation tank; both the collection tank and the sludge storage tank are connected to the sludge tank through pipes; a plate and frame filter press is connected to the sludge tank.

9. The method for treating coal mine wastewater according to claim 8, characterized in that: The biofilm filtration tank is equipped with a biofilm filter and an aeration device. A blower is fixedly installed on the outside of the biofilm filtration tank. The output end of the blower is connected to the aeration device. An oil suction device is installed on the maturation tank. Online water quality monitoring equipment is installed in the pre-sedimentation and conditioning tank, coagulation sedimentation tank, maturation tank, biofilm filtration tank and clear water tank to monitor water quality changes in real time.

10. A coal mine wastewater treatment system, comprising: The system comprises a mobile drainage system, a water accumulation and inrush monitoring system, a route planning module, a sewage treatment system, an anomaly monitoring system, an alarm module, and a central control unit; its features are: Mobile drainage system: includes an automatically controlled mobile trolley, a high-definition camera, and a drainage pump; it is used to drain water from locations where water accumulates or flows in within the coal mine. Water accumulation and inrush monitoring agencies: These include drones, GPS locators, gas concentration detectors, high-definition cameras, and LED lights. They patrol the interior of coal mines to collect high-definition images and promptly detect water accumulation or inrush problems. They also monitor the gas concentration at different locations within the coal mine using gas concentration detectors. Route planning module: Based on the 3D structural map of the coal mine, topography, obstacle distribution, and actual monitoring needs, the module plans the optimal flight route for the UAV. Wastewater treatment facilities: provide advanced treatment for wastewater discharged from coal mines to remove suspended solids, heavy metals and harmful chemicals, so that it meets discharge standards or reuse standards; Groundwater level monitoring agencies: monitor changes in groundwater levels in coal mines in real time using water level sensors installed underground; Anomaly monitoring agencies analyze the collected images to promptly identify anomalies; Alarm module: Includes an alarm that promptly issues an alert when an abnormal situation is detected; Central control unit: Connected to mobile drainage system, water accumulation and inrush monitoring system, route planning module, sewage treatment system and groundwater level monitoring system via wireless network.

Images