Intelligent mining area environment monitoring system based on unmanned aerial vehicle

Abstract

The invention relates to the technical field of mining area detection, in particular to a mining area environment intelligent monitoring system based on an unmanned aerial vehicle. The system comprises an image acquisition module for acquiring a spectral image shot by a hyperspectral camera and an environment image shot by a high-precision camera; the image analysis module receives the spectral image and the environment image, analyzes the spectral image, determines whether vegetation at the boundary of the mining area has abnormal growth or not, and obtains an abnormal analysis result; when it is determined that abnormity exists, the acquisition time of a spectral image is determined, the environment image in a preset time period is analyzed according to the acquisition time, an image analysis result is obtained, whether malicious mining exists or not is determined, and an information feedback module receives an abnormity analysis result, the image analysis result, the environment image and the spectral image; and determining the damage degree according to the abnormal analysis result and the image analysis result, determining feedback information based on the environment image, the spectral image and the damage degree, and sending the feedback information to the management equipment.

Description

Technical Field

[0001] The present application relates to the field of mining area detection technology, and in particular to an intelligent mining area environment monitoring system based on drones. Background Art

[0002] During the mining process, the surface ecological environment is destroyed due to illegal mining and cross-border mining, which can easily lead to a series of environmental problems, such as soil erosion, vegetation degradation, air and water pollution, etc. These environmental problems will not only have a serious impact on the surrounding ecosystem, but also threaten human health and safety. Therefore, how to effectively monitor the mining environment and promptly discover and feedback abnormal situations has become a top priority in current environmental protection and mining management.

[0003] Traditional methods of environmental monitoring in mining areas mainly rely on ground inspections or fixed monitoring equipment. These methods not only have problems such as limited monitoring range and many blind spots, but also have deficiencies such as untimely data collection and slow response speed, making it difficult to meet the needs of efficient and real-time monitoring. In addition, due to the complex terrain of the mining area, some areas may be dangerous, and traditional inspection methods are difficult to cover all areas, making it difficult to ensure the accuracy and integrity of monitoring data. Summary of the invention

[0004] The present application provides a mining environment intelligent monitoring system based on drones to solve the above problems.

[0005] In a first aspect, the present application provides a mining environment intelligent monitoring system based on a drone, comprising a drone and a server; the drone comprises a hyperspectral camera and a high-precision camera; the server comprises an image acquisition module, an image analysis module, and an information feedback module; The image acquisition module is connected to the hyperspectral camera and the high-precision camera respectively, and is used to acquire the spectral image taken by the hyperspectral camera and the environmental image taken by the high-precision camera, and send them to the image analysis module; The image analysis module is connected to the image acquisition module and is used to receive the spectral image and the environmental image, analyze the spectral image, determine whether there is abnormal growth of vegetation at the boundary of the mining area, and obtain an abnormality analysis result; The image analysis module is also used to determine the acquisition time of the spectral image when it is determined that there is an abnormality, and analyze the environmental image of a preset time period according to the acquisition time to obtain an image analysis result, determine whether there is malicious mining, and send the abnormality analysis result, the image analysis result, the environmental image and the spectral image to the information feedback module; The information feedback module is connected to the image analysis module, and is used to receive the abnormal analysis result, the image analysis result, the environmental image and the spectral image; and determine the degree of damage according to the abnormal analysis result and the image analysis result, and based on the environmental image, the spectral image and the degree of damage, determine feedback information and send the feedback information to the management device.

[0006] Through this solution, the drone is equipped with a hyperspectral camera and a high-precision camera to conduct inspection flights along a preset route according to the topography and geological map of the mining area. The drone automatically adjusts the flight altitude and path to cover the mining area boundary and its surrounding ecosystem to obtain comprehensive monitoring data. The image acquisition module is connected to the hyperspectral camera and the high-precision camera respectively. When the drone inspects, it collects spectral images and high-definition environmental images of the mining area environment in real time. The spectral image can accurately capture the plant reflectance spectrum and identify the growth of vegetation, while the environmental image records the overall environmental conditions of the mining area boundary. The image acquisition module transmits the collected spectral image and environmental image to the image analysis module. After receiving the data, this module starts the anomaly detection algorithm to analyze the spectral image to determine whether there is abnormal growth of vegetation at the mining area boundary. If an abnormality is found, an abnormal analysis result is generated. If abnormal vegetation growth is detected, the image analysis module further determines the acquisition time of the spectral image, traces back and analyzes the environmental image of the time period, and determines whether there is malicious mining activity. For example, by analyzing the activity characteristics of people, equipment type and location in the environmental image, it is determined whether there is mining behavior to form a complete image analysis result. The image analysis module transmits the abnormal analysis results, image analysis results, environmental images and spectral images to the information feedback module. The information feedback module evaluates the degree of ecological damage in the mining area based on the analysis results, further confirms the environmental damage that may be caused by malicious mining based on the environmental images and spectral images, and finally generates feedback information and sends it to the mining area management equipment for timely management intervention.

[0007] Optionally, the server further includes an inspection planning module connected to the drone and the image acquisition module. The image acquisition module is used to acquire a geological map of the mining area and send it to the inspection planning module; After receiving the geological map of the mining area, the inspection planning module is used to analyze the geological map of the mining area, and determine the topographic structure and topographic layout of the mining area according to the geological analysis results; determine the flight altitude of the UAV according to the topographic structure; and determine the flight order of the UAV according to the topographic layout; The inspection planning module is further used to plan the inspection route of the UAV according to the flight altitude and the flight sequence, and send the route to the UAV; After receiving the inspection route, the drone performs inspection according to the inspection route.

[0008] Through this solution, drones can cover a larger area, reduce monitoring blind spots, and ensure comprehensive monitoring of the entire mining area; the flight altitude and sequence can be automatically adjusted according to the terrain structure of the mining area to ensure that all areas are covered; the drone transmits the collected data to the server in real time, and managers can immediately obtain the latest information, which improves the response speed; hyperspectral cameras and high-precision cameras provide high-resolution data, which improves the accuracy of monitoring; drones replace personnel to enter dangerous areas for monitoring, reducing personnel safety risks; the inspection planning module automatically plans inspection routes, improves inspection efficiency, and reduces the complexity of manual operations; drone monitoring reduces physical interference with the mining environment and helps protect the environment; drones can complete tasks more efficiently, reducing energy consumption and resource waste.

[0009] Optionally, the server includes a parameter adjustment module connected to the hyperspectral camera and the inspection planning module; The parameter adjustment module is used to obtain the terrain structure, the terrain layout and the flight altitude analyzed by the inspection planning module, determine the flight altitude of the UAV according to the terrain structure; determine the camera exposure and gain according to the terrain layout and the flight altitude, and send the camera exposure and gain to the hyperspectral camera; After receiving the camera exposure and gain, the hyperspectral camera is used to obtain camera information, and determine and adjust the red light band and the near-red light band according to the camera information, the camera exposure and gain.

[0010] Through this solution, the parameter adjustment module can automatically adjust the camera exposure time and gain according to the information provided by the inspection planning module without manual intervention, which improves work efficiency; it can automatically adjust the flight altitude of the drone according to the terrain structure to ensure that high-quality data can be obtained under different terrain conditions. Through precise exposure and gain settings, the hyperspectral camera can obtain the best red and near-red light band data, improving the accuracy and quality of spectral data. The drone can quickly respond to monitoring needs and obtain data in real time, meeting the needs of efficient and real-time monitoring. Drones can replace personnel to enter dangerous areas for monitoring, reducing the chance of personnel being exposed to risks. By adjusting the camera band, the system can more accurately monitor specific environmental parameters, such as vegetation health, soil moisture, etc.

[0011] Optionally, the image analysis module is connected to the inspection planning module to obtain the terrain structure and the terrain layout; When analyzing the spectral image to determine whether the vegetation at the boundary of the mining area has abnormal growth, the image analysis module is used to: Analyzing the spectral image to determine the boundary of the mining area; Determine the developable scope according to the terrain structure and the terrain layout; Determine the interval distance between each vegetation and the boundary of the mining area according to the spectral image; Obtain and analyze the mining area development plan, and determine the development method of the mining area boundary based on the plan analysis results; Determine the growth impact of marginal vegetation based on the development method and the developable scope; According to the interval distance and the growth impact, using NDVI, the vegetation at the boundary of the mining area is evaluated for growth to obtain an evaluation result; Based on the evaluation results, determine whether there is any abnormal growth of vegetation at the boundary of the mining area.

[0012] Through this solution, by analyzing spectral images, the vegetation growth status can be monitored more accurately and the monitoring accuracy can be improved. By using indicators such as the Normalized Difference Vegetation Index (NDVI), the vegetation growth status can be evaluated more carefully. The image analysis module can automatically perform analysis tasks such as image processing, feature extraction, and anomaly detection to reduce manual workload. Combined with the terrain structure and layout, the exploitable scope of the mining area can be determined more accurately. It can intelligently identify abnormal situations, such as abnormal vegetation growth caused by illegal mining activities. If illegal mining or theft is discovered, measures can be taken quickly to intervene.

[0013] Optionally, when the image analysis module uses NDVI to perform growth assessment on vegetation at the boundary of the mining area according to the interval distance and the growth impact, it is used to: Analyze the spectral image according to the red light band and the near-red light band to determine the original red light reflectance and the original near-red light reflectance; analyze the environmental image to determine the dark pixel area; Determine the minimum reflectivity of the red light band and the minimum reflectivity of the near-red light band according to the original red light reflectivity and the original near-red light reflectivity; Determine the dark pixel value of the red light band and the dark pixel value of the near red light band according to the minimum reflectivity of the red light band and the minimum reflectivity of the near red light band; Performing atmospheric correction according to the dark pixel value of the red light band and the dark pixel value of the near-red light band to obtain an actual near-infrared band reflectivity and an actual red light reflectivity; Calculate the NDVI value of each vegetation according to the actual near-infrared band reflectivity and the actual red light reflectivity; Based on the NDVI value of each vegetation and the growth impact, a growth assessment of the vegetation at the boundary of the mining area is performed.

[0014] Through this solution, the image analysis module can automatically process and analyze data, reducing human intervention and improving efficiency; the image analysis module can automatically process and analyze data, reducing human intervention and improving efficiency; through NDVI calculation, the vegetation growth status can be evaluated more accurately and the monitoring accuracy can be improved; through atmospheric correction, the impact of atmospheric conditions on spectral data can be reduced and the accuracy of NDVI calculation can be improved.

[0015] Optionally, when the image analysis module analyzes the environmental image during a preset period to obtain the image analysis result and determines whether there is malicious mining, it is used to: Analyze the environmental image of a preset time period, and determine whether there are human activity features at the boundary of the mining area according to the image analysis results; If it is determined that there are human activity features, then retrieve and analyze continuous environmental images with the human activity features, obtain continuous image analysis results, and determine the human activity behavior; Determining whether there is an accompanying device according to the continuous image analysis result; If so, determining the device type and device function of the accompanying device according to the continuous image analysis result; Analyze the activities of the people and the functions of the equipment to determine whether there is any mining behavior; If it is determined that there is mining behavior, the activity frequency of the person and the working status of the accompanying equipment are determined based on the continuous image analysis results; Determine suspicious mining trends based on the activity frequency of the person and the working status; Determine whether there is malicious mining based on the suspicious mining trends.

[0016] Through this solution, through continuous image analysis, the activity trajectory and behavior pattern of people can be tracked, providing more comprehensive evidence for judging whether there is malicious mining; automated image analysis reduces the burden of manual inspections, improves work efficiency, and reduces the possibility of human error. By analyzing the type and function of accompanying equipment, the nature and scale of mining activities can be more accurately judged; combined with multi-dimensional data such as people's activities, equipment working status and activity frequency, a comprehensive assessment can be conducted to improve the accuracy of judgment; by identifying suspicious mining trends, early warning can be issued to prevent further malicious mining activities.

[0017] Optionally, the intelligent monitoring system for mining area environment further includes a plurality of sensors, which are arranged within the exploitable range and are used to monitor the change of water resources in the mining area; the server further includes a water resource analysis module, which is respectively connected to the image analysis module and the plurality of sensors; When the image analysis module determines that there is no malicious mining based on the suspicious mining trend, the image analysis module sends an analysis signal to the water resource analysis module; After receiving the analysis signal, the water resource analysis module is used to obtain the monitoring data of the plurality of sensors; and determine the monitoring location of each monitoring data according to the acquisition record; Determine the cause of the change in water volume based on the monitoring location; Obtain tailings location; According to the development method, the reason for the change in water volume and the location of the tailings, determine whether the abnormal vegetation growth at the boundary of the mining area is caused by the tailings.

[0018] Through this solution, the changes in water resources in the mining area are monitored in real time through sensors, which can quickly respond to changes in water resource conditions. Once an abnormality is detected, the system can quickly notify management personnel to reduce potential losses; sensors are deployed at key locations within the exploitable range to ensure coverage of water resources in the entire mining area and reduce monitoring blind spots. Even in mining areas with complex terrain, effective monitoring can be carried out through drones or other sensors; the water resource analysis module can conduct in-depth analysis of data to improve the accuracy of monitoring results. Through image analysis and water resource analysis, managers can quickly understand the status of the mining area and make decisions; it integrates multi-source data such as image analysis and sensor data to provide more comprehensive monitoring information; the image analysis module and the water resource analysis module work together to improve the comprehensiveness and efficiency of monitoring.

[0019] Optionally, the server further includes an abnormality tracing module connected to the water resources analysis module; When the water resource analysis module determines that the abnormal vegetation growth at the boundary of the mining area is not caused by tailings, the water resource analysis module sends a tracing signal and the monitoring data to the abnormal tracing module; After receiving the tracing signal, the abnormality tracing module is used to analyze the development mode and determine the terrain changes after the development of the mining area boundary according to the mode analysis result; Analyzing the monitoring data according to the monitoring position to determine the change of water flow; Determine the possibility of soil erosion based on the water flow change and the terrain change; Predicting the growth change of each vegetation at the boundary of the mining area according to the possibility of soil erosion and the spectral image; The growth change is matched with the abnormal analysis result, and based on the matching result, it is determined whether the cause of the abnormal growth is caused by the development method.

[0020] Through this solution, the abnormality tracing module can collect and analyze monitoring data in real time without relying on manual inspections, thereby improving monitoring efficiency. The module integrates data from the water resources analysis module, development method analysis results, terrain change data, water flow change data, spectral images and other multi-source information, providing more comprehensive monitoring and analysis; the module can more accurately determine the cause of abnormal vegetation growth by accurately analyzing monitoring data, thereby improving the accuracy of monitoring; the abnormality tracing module can respond quickly when abnormal vegetation growth is discovered, and issue early warnings in a timely manner, enabling managers to take timely measures; the module reduces the need for personnel to enter dangerous areas through remote monitoring and data analysis, thereby improving the safety of monitoring.

[0021] Optionally, the server further comprises a facial recognition module connected to the image analysis module, and when the image analysis module determines that malicious mining exists based on the suspicious mining trend, the server sends a recognition signal and the continuous environment image to the facial recognition module; After receiving the recognition signal and the continuous environment image, the facial recognition module analyzes the character features in the continuous environment image and determines the character image according to the feature analysis result; Based on the character image, the facial information of the personnel in the preset personnel management database is retrieved, and the character image is compared with the facial information of the personnel to obtain a comparison result, and according to the comparison result, it is determined whether there is malicious mining by internal personnel.

[0022] Through this solution, the facial recognition module can analyze image data in real time. Once the image analysis module detects suspicious mining trends, the facial recognition program is immediately started to quickly respond to potential malicious mining behaviors. Through automated facial recognition technology, the need for traditional ground inspections is reduced, reducing personnel safety risks, especially in complex terrain and dangerous areas. Compared with fixed monitoring equipment and manual inspections, facial recognition modules can cover a wider area, reduce monitoring blind spots, and improve the accuracy and completeness of monitoring data. Facial recognition technology can provide accurate identity recognition, which helps to distinguish between legal and illegal mining behaviors, especially in identifying malicious mining by internal personnel. The facial recognition module can issue early warnings in a timely manner to prevent theft and cross-border mining, which helps protect mineral resources from illegal mining.

[0023] Optionally, the server further includes a reward and punishment determination module connected to the facial recognition module; The facial recognition module compares the character image with the personnel facial information, and after determining that there is malicious mining by an internal personnel, sends a discrimination signal to the reward and punishment discrimination module; The reward and punishment discrimination module is also connected to the image analysis module, and is used to obtain the abnormal analysis result after receiving the discrimination signal; According to the comparison result, the identity information of the internal personnel is determined, and according to the identity information, the abnormal analysis result and the preset reward and punishment standards, the punishment details of the internal personnel are determined.

[0024] Through this solution, rewards and punishments can be determined quickly and accurately through automated processes to improve management efficiency. Real-time monitoring data and facial recognition technology can be combined to ensure rapid response and precise strikes against malicious mining activities. Based on preset reward and punishment standards, the fairness of reward and punishment measures can be ensured to reduce interference from human factors. Through rewards and punishments for internal personnel, it can prevent and deter other employees and reduce the occurrence of malicious mining activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0026] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application; Figure 2 A schematic diagram of the structure of a mining environment intelligent monitoring system based on drones provided in one embodiment of the present application; Figure 3 A schematic diagram of the structure of another drone-based intelligent mining environment monitoring system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0029] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0030] Traditional methods of environmental monitoring in mining areas mainly rely on ground inspections or fixed monitoring equipment. These methods not only have problems such as limited monitoring range and many blind spots, but also have deficiencies such as untimely data collection and slow response speed, making it difficult to meet the needs of efficient and real-time monitoring. In addition, due to the complex terrain of the mining area, some areas may be dangerous, and traditional inspection methods are difficult to cover all areas, making it difficult to ensure the accuracy and integrity of monitoring data.

[0031] Based on this, the present application provides a mining environment intelligent monitoring system based on drones, comprising a drone and a server; the drone comprises a hyperspectral camera and a high-precision camera; the server comprises an image acquisition module, an image analysis module, and an information feedback module; the image acquisition module is respectively connected to the hyperspectral camera and the high-precision camera, and is used to acquire the spectral image taken by the hyperspectral camera and the environmental image taken by the high-precision camera, and send them to the image analysis module; the image analysis module is connected to the image acquisition module, and is used to receive the spectral image and the environmental image, analyze the spectral image, determine whether there is growth abnormality in the vegetation at the boundary of the mining area, and obtain the abnormality analysis result; the image analysis module The block is also used to determine the acquisition time of the spectral image when it is determined that there is an abnormality, and analyze the environmental image of the preset time period according to the acquisition time to obtain the image analysis result, determine whether there is malicious mining, and send the abnormal analysis result, the image analysis result, the environmental image and the spectral image to the information feedback module; the information feedback module is connected to the image analysis module, and is used to receive the abnormal analysis result, the image analysis result, the environmental image and the spectral image; and determine the degree of damage according to the abnormal analysis result and the image analysis result, and determine the feedback information based on the environmental image, the spectral image and the degree of damage and send the feedback information to the management device. The drone is equipped with a hyperspectral camera and a high-precision camera, and conducts inspection flights on a preset route according to the topography and geological map of the mining area. The drone automatically adjusts the flight altitude and path to cover the boundary of the mining area and its surrounding ecosystem to obtain comprehensive monitoring data. The image acquisition module is connected to the hyperspectral camera and the high-precision camera respectively, and collects the spectral image and high-definition environmental image of the mining environment in real time during the drone inspection. Spectral images can accurately capture plant reflectance spectra and identify vegetation growth, while environmental images record the overall environmental conditions at the boundary of the mining area. The image acquisition module transmits the collected spectral images and environmental images to the image analysis module. After receiving the data, this module starts the anomaly detection algorithm to analyze the spectral image to determine whether there is abnormal growth of vegetation at the boundary of the mining area. If an abnormality is found, an abnormal analysis result is generated. If abnormal vegetation growth is detected, the image analysis module further determines the acquisition time of the spectral image, traces back and analyzes the environmental images of the time period, and determines whether there is malicious mining activity. For example, by analyzing the characteristics of human activities, equipment types and locations in the environmental image, it is determined whether there is mining behavior to form a complete image analysis result. The image analysis module transmits the abnormal analysis results, image analysis results, environmental images and spectral images to the information feedback module. The information feedback module evaluates the degree of ecological damage in the mining area based on the analysis results, further confirms the environmental damage that may be caused by malicious mining based on the environmental image and spectral image, and finally generates feedback information and sends it to the mining area management equipment for timely management intervention.

[0032] Figure 1 A schematic diagram of an application scenario provided by the present application is used to prevent theft, illegal mining, and cross-border mining. Specifically, the system provided by the present application is applied to any server, and the server interacts with the drone. The drone is equipped with a hyperspectral camera and a high-precision camera to perform inspection flights on preset routes according to the topography and geological map of the mining area. The drone automatically adjusts the flight altitude and path to cover the mining area boundary and its surrounding ecosystem to obtain comprehensive monitoring data. The image acquisition module is connected to the hyperspectral camera and the high-precision camera respectively. When the drone inspects, the spectral image and high-definition environmental image of the mining area environment are collected in real time. The spectral image can accurately capture the plant reflectance spectrum and identify the growth of vegetation, while the environmental image records the overall environmental conditions of the mining area boundary. The image acquisition module transmits the collected spectral image and environmental image to the image analysis module. After receiving the data, this module starts the anomaly detection algorithm to analyze the spectral image to determine whether there is abnormal growth of vegetation at the mining area boundary, and generates an abnormal analysis result if an abnormality is found. If abnormal vegetation growth is detected, the image analysis module further determines the acquisition time of the spectral image, traces back and analyzes the environmental image of the time period, and determines whether there is malicious mining activity. For example, by analyzing the characteristics of human activities, equipment types and locations in the environmental image, it is determined whether there is any mining behavior and a complete image analysis result is formed. The image analysis module transmits the abnormal analysis results, image analysis results, environmental images and spectral images to the information feedback module. The information feedback module evaluates the degree of ecological damage in the mining area based on the analysis results, further confirms the environmental damage that may be caused by malicious mining based on the environmental image and spectral image, and finally generates feedback information and sends it to the mining area management equipment for timely management intervention.

[0033] For specific implementation methods, please refer to the following embodiments.

[0034] Figure 2 A schematic diagram of a mining environment intelligent monitoring system based on drones is provided in one embodiment of the present application, as shown in FIG. Figure 2As shown, the mining environment intelligent monitoring system 200 based on drones of this embodiment includes: a drone 201 and a server 202; the drone 201 includes a hyperspectral camera 2010 and a high-precision camera 2011; the server 202 includes an image acquisition module 2020, an image analysis module 2021, and an information feedback module 2022; the image acquisition module 2020 is connected to the hyperspectral camera 2010 and the high-precision camera 2011, respectively, for acquiring the spectral image taken by the hyperspectral camera 2010 and the environmental image taken by the high-precision camera 2011, and sending them to the image analysis module 2021; the image analysis module 2021 is connected to the image acquisition module 2020, for receiving the spectral image and the environmental image, analyzing the spectral image, and determining the mining environment. Whether there is any abnormal growth of vegetation at the boundary of the area, and obtaining the abnormal analysis result; the image analysis module 2021 is also used to determine the acquisition time of the spectral image when it is determined that there is an abnormality, and analyze the environmental image of a preset time period according to the acquisition time, to obtain the image analysis result, to determine whether there is malicious mining, and send the abnormal analysis result, image analysis result, environmental image and spectral image to the information feedback module 2022; the information feedback module 2022 is connected to the image analysis module 2021, and is used to receive the abnormal analysis result, image analysis result, environmental image and spectral image; and determine the degree of damage according to the abnormal analysis result and the image analysis result, and determine the feedback information based on the environmental image, the spectral image and the degree of damage and send the feedback information to the management device.

[0035] Spectral images can be images captured by a hyperspectral camera, which contain spectral information of vegetation reflection or emission at different wavelengths. Environmental images can be traditional visual images taken by a high-precision camera, which show the surface landscape of the mining area.

[0036] Growth anomaly can be in vegetation monitoring. Growth anomaly refers to abnormal changes in the growth state of vegetation compared with normal conditions, including reduced vegetation cover, color changes, slower growth rate or changes in growth pattern.

[0037] The environmental image for a preset period may refer to an environmental image collected within a specific time period, which is used to compare with the current image to detect any time-related changes. It can be used to monitor the growth trend of vegetation, changes in environmental conditions, or changes associated with specific events (such as rainfall, drought).

[0038] The degree of damage can be the degree of damage to vegetation, topography or environmental resources in the monitoring area due to mining activities or other factors, such as loss of vegetation cover, soil erosion, terrain changes, etc.

[0039] Specifically, traditional mining area environmental monitoring methods mainly rely on ground inspections or fixed monitoring equipment. These methods have multiple problems in practical applications: on the one hand, the monitoring range is limited by the terrain, and some areas are difficult to cover; on the other hand, the frequency and timeliness of data collection by monitoring equipment are low, resulting in insufficient response speed and difficulty in effectively responding to sudden environmental events. At the same time, the deployment cost of fixed monitoring equipment is high, and the terrain of the mining area is complex. The superposition of multiple factors makes inspections more difficult, especially in areas where geological disasters or other dangerous factors may exist. Traditional inspection methods cannot guarantee the safety of inspection personnel, which increases management difficulty.

[0040] When the system starts, the hyperspectral camera and high-precision camera carried by the drone are first initialized and self-checked to ensure that all parameters are within the standard range; the server starts and loads functional modules such as the image acquisition module, image analysis module, and information feedback module to prepare for subsequent data reception and processing; the drone flies according to the preset inspection route, and uses the hyperspectral camera and high-precision camera to obtain spectral images and environmental images of the mining area boundary respectively. The spectral images collected by the hyperspectral camera can analyze the health of vegetation, while the environmental images taken by the high-precision camera can provide clearer terrain and object contours; the drone transmits the collected spectral images and environmental images to the image acquisition module on the server in real time. The image analysis module receives the spectral image and analyzes the vegetation health at the mining area boundary through parameters such as vegetation index (such as NDVI) to determine whether there is any abnormal growth. If the NDVI value is lower than the preset threshold, it can be preliminarily determined that the vegetation in the area may have been damaged; when the image analysis module detects vegetation anomalies, it will mark the acquisition time of the abnormal spectral image for subsequent comparative analysis of environmental images; the image analysis module analyzes the environmental images of the preset period according to the marking time to determine whether there are signs of human activities or equipment, so as to assist in determining whether there is malicious mining behavior. For example, the presence and use of mining equipment can be determined by identifying people and equipment in the environmental image; the image analysis module sends the detected abnormal analysis results (abnormal vegetation growth, environmental image analysis results, etc.) to the information feedback module; after receiving the data, the information feedback module evaluates the degree of damage based on the analysis results. Specifically, the determination of the degree of damage can be comprehensively evaluated by combining the abnormal features in the spectral image, the environmental image, and the frequency of activities in the time series image; the information feedback module generates feedback information and sends a report including abnormal analysis results, environmental images, and damage degree data to the management device; after receiving the feedback information, the management device can display and record the abnormal situation in real time. Managers can analyze feedback information to determine whether further drone inspections are needed or arrange for ground personnel to conduct detailed inspections. If malicious mining is confirmed, the management equipment can trigger emergency plans, such as alerting relevant departments and recording relevant image data as evidence.

[0041] The system automatically saves the collected spectral images, environmental images and analysis results into the database to form a long-term monitoring record of the mining area environment. These data will provide a historical basis for subsequent analysis and comparison; through the mining and analysis of historical inspection data, the trend of mining area environmental changes can be discovered, providing data support for the formulation of sustainable management plans for mining areas.

[0042] Through this solution, the drone is equipped with a hyperspectral camera and a high-precision camera to conduct inspection flights along a preset route according to the topography and geological map of the mining area. The drone automatically adjusts the flight altitude and path to cover the mining area boundary and its surrounding ecosystem to obtain comprehensive monitoring data. The image acquisition module is connected to the hyperspectral camera and the high-precision camera respectively. When the drone inspects, it collects spectral images and high-definition environmental images of the mining area environment in real time. The spectral image can accurately capture the plant reflectance spectrum and identify the growth of vegetation, while the environmental image records the overall environmental conditions of the mining area boundary. The image acquisition module transmits the collected spectral image and environmental image to the image analysis module. After receiving the data, this module starts the anomaly detection algorithm to analyze the spectral image to determine whether there is abnormal growth of vegetation at the mining area boundary. If an abnormality is found, an abnormal analysis result is generated. If abnormal vegetation growth is detected, the image analysis module further determines the acquisition time of the spectral image, traces back and analyzes the environmental image of the time period, and determines whether there is malicious mining activity. For example, by analyzing the activity characteristics of people, equipment type and location in the environmental image, it is determined whether there is mining behavior to form a complete image analysis result. The image analysis module transmits the abnormal analysis results, image analysis results, environmental images and spectral images to the information feedback module. The information feedback module evaluates the degree of ecological damage in the mining area based on the analysis results, further confirms the environmental damage that may be caused by malicious mining based on the environmental images and spectral images, and finally generates feedback information and sends it to the mining area management equipment for timely management intervention.

[0043] In addition to the above modules and equipment, the UAV-based mining environment intelligent monitoring system 200 also includes other modules and equipment, such as Figure 3 Another structural diagram of an intelligent monitoring system for mining environment based on drones is shown in FIG. In some embodiments, the server 202 also includes an inspection planning module 2023, which is connected to the drone 201 and the image acquisition module 2020. The image acquisition module 2020 is used to obtain the geological map of the mining area and send it to the inspection planning module 2023; after receiving the geological map of the mining area, the inspection planning module 2023 is used to analyze the geological map of the mining area, and determine the terrain structure and terrain layout of the mining area based on the geological analysis results; determine the flight altitude of the drone 201 based on the terrain structure; determine the flight sequence of the drone 201 based on the terrain layout; the inspection planning module 2023 is also used to plan the inspection route of the drone 201 based on the flight altitude and flight sequence, and send it to the drone 201; after receiving the inspection route, the drone 201 performs inspection according to the inspection route.

[0044] A mining area geological map can be a map that shows the geological characteristics of the mining area in detail, including geological profiles, contour lines, faults, folds, and the location of mineral deposits.

[0045] The topographic layout can be the arrangement and distribution of the topographic structure within the mining area, including natural landform features such as mountains, valleys, plains, and slopes.

[0046] The flight sequence can be the order in which the drone flies over various areas of the mine according to certain rules and priorities when performing inspection tasks.

[0047] Specifically, collect geological map data of the mining area, which usually includes information such as topography, geological structure, and distribution of mineral resources; deploy the drone to the designated location of the mining area and ensure that the drone is on standby; start the drone and take off according to the preset program to prepare for inspection; the drone starts and begins to collect geological map information of the mining area; transmits the collected geological map data to the image acquisition module of the server through the wireless network; after the inspection planning module on the server receives the geological map data, it starts geological analysis; analyze the geological map to determine the topographic structure of the mining area, including mountains, valleys, slopes, etc.; plan the topographic layout of the mining area according to the topographic structure , identify key areas and potential risk points; according to the terrain structure, the inspection planning module determines the optimal altitude for the UAV to fly to ensure the accuracy and safety of data collection; according to the terrain layout, the flight sequence of the UAV is planned to ensure that all key areas are covered; the inspection planning module generates a detailed inspection route based on the flight altitude and flight sequence; the generated inspection route is sent to the UAV, and the UAV receives the inspection route instruction and prepares to start the inspection task; the UAV flies along the inspection route, and the hyperspectral camera and high-precision camera collect spectral images and environmental images at the same time; the collected data is transmitted back to the server in real time for analysis.

[0048] Through this solution, drones can cover a larger area, reduce monitoring blind spots, and ensure comprehensive monitoring of the entire mining area; the flight altitude and sequence can be automatically adjusted according to the terrain structure of the mining area to ensure that all areas are covered; the drone transmits the collected data to the server in real time, and managers can immediately obtain the latest information, which improves the response speed; hyperspectral cameras and high-precision cameras provide high-resolution data, which improves the accuracy of monitoring; drones replace personnel to enter dangerous areas for monitoring, reducing personnel safety risks; the inspection planning module automatically plans inspection routes, improves inspection efficiency, and reduces the complexity of manual operations; drone monitoring reduces physical interference with the mining environment and helps protect the environment; drones can complete tasks more efficiently, reducing energy consumption and resource waste.

[0049] In some embodiments, the server 202 includes a parameter adjustment module 2024, which is connected to the hyperspectral camera 2010 and the inspection planning module 2023; the parameter adjustment module 2024 is used to obtain the terrain structure, terrain layout and flight altitude analyzed by the inspection planning module 2023, and determine the flight altitude of the drone according to the terrain structure; determine the camera exposure and gain according to the terrain layout and the flight altitude, and send the camera exposure and gain to the hyperspectral camera 2010; after receiving the camera exposure and gain, the hyperspectral camera 2010 is used to obtain camera information, and determine and adjust the red light band and near-red light band according to the camera information, camera exposure and gain.

[0050] The terrain structure can be the composition and mutual relationship of the surface morphology, which can show the ups and downs, slope, slope direction, river direction, etc. of the terrain.

[0051] Gain can be used to amplify the signal strength received by the camera sensor to obtain the best image quality when lighting conditions change.

[0052] The red light band can be a light band with a wavelength between approximately 620 and 750 nanometers in the electromagnetic spectrum; it plays an important role in plant photosynthesis, so in vegetation monitoring, data from the red light band is very useful for assessing plant health and growth conditions.

[0053] Specifically, the inspection planning module 2023 analyzes the geological map of the mining area to determine the terrain structure, terrain layout and flight altitude; the inspection planning module sends the terrain structure, terrain layout and flight altitude information obtained by the analysis to the server 202; the parameter adjustment module 2024 on the server is ready to receive the inspection planning information; the parameter adjustment module 2024 receives the terrain structure, terrain layout and flight altitude information sent by the inspection planning module 2023; according to the terrain structure and flight altitude, the parameter adjustment module calculates the required camera exposure time and gain setting; the parameter adjustment module determines the optimal exposure time and gain setting of the hyperspectral camera 2010 according to the terrain layout and flight altitude; and sends the calculated camera exposure time and gain setting to the hyperspectral camera 2010. Spectral camera 2010; hyperspectral camera 2010 receives exposure time and gain settings from the parameter adjustment module; the hyperspectral camera obtains its own basic information, such as sensor type, resolution, etc.; the red light band and the near-red light band are determined according to the camera information and the exposure and gain set by the hyperspectral camera; the hyperspectral camera adjusts its own settings according to the determined bands to obtain the best spectral data; the UAV flies according to the route of the inspection planning module; the hyperspectral camera starts to collect spectral data of the red light band and the near-red light band according to the adjusted settings; the collected spectral data is transmitted to the server via the wireless network; the image analysis module on the server processes and analyzes the received spectral data to monitor the mining environment.

[0054] Through this solution, the parameter adjustment module can automatically adjust the camera exposure time and gain according to the information provided by the inspection planning module without manual intervention, which improves work efficiency; it can automatically adjust the flight altitude of the drone according to the terrain structure to ensure that high-quality data can be obtained under different terrain conditions. Through precise exposure and gain settings, the hyperspectral camera can obtain the best red and near-red light band data, improving the accuracy and quality of spectral data. The drone can quickly respond to monitoring needs and obtain data in real time, meeting the needs of efficient and real-time monitoring. Drones can replace personnel to enter dangerous areas for monitoring, reducing the chance of personnel being exposed to risks. By adjusting the camera band, the system can more accurately monitor specific environmental parameters, such as vegetation health, soil moisture, etc.

[0055] In some embodiments, the image analysis module 2021 is connected to the inspection planning module 2023, and is used to obtain the terrain structure and terrain layout; when analyzing the spectral image to determine whether the vegetation at the boundary of the mining area has abnormal growth, the image analysis module 2021 is used to: analyze the spectral image to determine the boundary of the mining area; determine the developable range based on the terrain structure and terrain layout; determine the interval distance between each vegetation and the boundary of the mining area based on the spectral image; obtain and analyze the mining area development plan, and determine the development method of the mining area boundary based on the plan analysis results; determine the growth impact of the marginal vegetation based on the development method and the developable range; based on the interval distance and growth impact, use NDVI to perform a growth assessment on the vegetation at the boundary of the mining area to obtain an assessment result; based on the assessment result, determine whether the vegetation at the boundary of the mining area has abnormal growth.

[0056] The mine boundary may be the boundary between the mine area and the surrounding environment.

[0057] The exploitable scope can be the area within the mining area boundary, which is determined to be exploitable based on geological conditions, environmental impact assessment and other factors.

[0058] A mining area development plan can be a detailed plan for the mining, development and utilization of a mining area, including mining schedules, mining methods, environmental protection measures, etc.

[0059] The development mode may be the mining technology and method specified in the mining area development plan, such as open-pit mining, underground mining, etc.

[0060] Growth impacts can be the effect of mining development activities on the growth of surrounding vegetation.

[0061] NDVI can be an indicator for evaluating vegetation growth conditions, which is obtained by calculating the ratio of the difference and sum of the reflectance of the red light band and the near-red light band.

[0062] Growth assessment can be the process of evaluating the growth status of vegetation at the boundary of the mining area based on NDVI values ​​and growth impact analysis.

[0063] Specifically, the hyperspectral camera carried by the drone collects spectral images of the ground in the mining area; the collected spectral images are transmitted to the ground server via a wireless network; the image analysis module 2021 is started and is ready to analyze the received spectral images; the image analysis module analyzes the spectral images and uses image processing technology to determine the boundaries of the mining area; the module combines terrain structure data to determine the exploitable range of the mining area; according to the terrain layout, the boundaries of the exploitable area are further refined; through image analysis, the vegetation within the boundary of the mining area is identified; according to the spectral image, the interval distance between each vegetation and the boundary of the mining area is calculated; the document of the development plan of the mining area is obtained; the development plan is analyzed to determine the development method of the boundary of the mining area; according to the development method and the exploitable range, the growth impact of the marginal vegetation is evaluated; the growth of the vegetation at the boundary of the mining area is evaluated using the normalized difference vegetation index (NDVI); the growth status of the vegetation is further analyzed in combination with the vegetation interval distance and growth impact; according to the evaluation results, whether there is any abnormal growth of the vegetation at the boundary of the mining area is detected.

[0064] Through this solution, by analyzing spectral images, the vegetation growth status can be monitored more accurately and the monitoring accuracy can be improved. By using indicators such as the Normalized Difference Vegetation Index (NDVI), the vegetation growth status can be evaluated more carefully. The image analysis module can automatically perform analysis tasks such as image processing, feature extraction, and anomaly detection to reduce manual workload. Combined with the terrain structure and layout, the exploitable scope of the mining area can be determined more accurately. It can intelligently identify abnormal situations, such as abnormal vegetation growth caused by illegal mining activities. If illegal mining or theft is discovered, measures can be taken quickly to intervene.

[0065] In some embodiments, when the image analysis module 2021 uses NDVI to perform growth assessment on vegetation at the boundary of a mining area based on the interval distance and growth impact, it is used to: analyze the spectral image based on the red light band and the near red light band to determine the original red light reflectivity and the original near red light reflectivity; analyze the environmental image to determine the dark pixel area; determine the minimum reflectivity of the red light band and the minimum reflectivity of the near red light band based on the original red light reflectivity and the original near red light reflectivity; determine the dark pixel value of the red light band and the dark pixel value of the near red light band based on the minimum reflectivity of the red light band and the minimum reflectivity of the near red light band; perform atmospheric correction based on the dark pixel value of the red light band and the dark pixel value of the near red light band to obtain the actual near infrared band reflectivity and the actual red light reflectivity; calculate the NDVI value of each vegetation based on the actual near infrared band reflectivity and the actual red light reflectivity; and perform growth assessment on vegetation at the boundary of the mining area based on the NDVI value and growth impact of each vegetation.

[0066] The original red light reflectance can be the ratio of the light energy reflected by vegetation in the red light band to the incident light energy in the early stage of mining.

[0067] The original near-infrared reflectance can be the ratio of the light energy reflected by vegetation in the near-infrared band to the incident light energy in the early stage of mining.

[0068] The dark pixel region may be a pixel region in the image with very low reflectivity or close to black.

[0069] The minimum reflectivity may be the reflectivity corresponding to the pixel point with the minimum reflectivity value in the image within a specific band.

[0070] Atmospheric correction is an image processing technology that can improve the accuracy of remote sensing data and make the data closer to the actual surface reflectance. It is used to correct the influence of the atmosphere on remote sensing data, such as aerosols, water vapor, etc.

[0071] Specifically, the drone is equipped with a hyperspectral camera to inspect the boundary of the mining area and collect spectral data of vegetation coverage; the image analysis module analyzes the spectral image and extracts the spectral data of the red light band and the near-red light band; the environmental image is analyzed to identify the dark pixel areas in the image, which usually represent non-vegetated or low-reflectivity surfaces; the original red light reflectance and the original near-red light reflectance are calculated based on the spectral data of the red light band and the near-red light band; the minimum reflectance of the red light band and the near-red light band is determined from the original reflectance, and these values ​​usually correspond to the reflectance of the dark pixel area; based on the minimum reflectance, the dark pixel values ​​of the red light band and the near-red light band are determined, and these values ​​are used for atmospheric correction; the spectral data is atmospherically corrected using the dark pixel values ​​to obtain The actual near-infrared band reflectivity and the actual red light reflectivity are used to reduce the impact of atmospheric conditions on spectral data; the NDVI value of each vegetation is calculated based on the corrected actual near-infrared band reflectivity and the actual red light reflectivity; the NDVI value is the difference between the near-infrared band reflectivity and the red light band reflectivity divided by their sum; the growth of vegetation at the boundary of the mining area is evaluated based on the NDVI value of each vegetation and the growth impact analysis; the higher the NDVI value, the healthier the vegetation is; the abnormally growing vegetation at the boundary of the mining area is identified through the NDVI value and growth impact analysis, which may indicate the existence of illegal mining or cross-border mining; the vegetation growth assessment results are output, including the NDVI value, growth status and location information of abnormal vegetation.

[0072] Through this solution, the image analysis module can automatically process and analyze data, reducing human intervention and improving efficiency; the image analysis module can automatically process and analyze data, reducing human intervention and improving efficiency; through NDVI calculation, the vegetation growth status can be evaluated more accurately and the monitoring accuracy can be improved; through atmospheric correction, the impact of atmospheric conditions on spectral data can be reduced and the accuracy of NDVI calculation can be improved.

[0073] In some embodiments, when the image analysis module 2021 analyzes the environmental images of a preset time period, obtains the image analysis results, and determines whether there is malicious mining, it is used to: analyze the environmental images of the preset time period, and determine whether there are human activity features at the boundary of the mining area based on the image analysis results; if it is determined that there are human activity features, retrieve and analyze continuous environmental images with human activity features to obtain continuous image analysis results, and determine the human activity behavior; determine whether there is accompanying equipment based on the continuous image analysis results; if so, determine the device type and device function of the accompanying equipment based on the continuous image analysis results; analyze the human activity behavior and the device function to determine whether there is mining behavior; if it is determined that there is mining behavior, determine the frequency of human activities and the working status of the accompanying equipment based on the continuous image analysis results; determine suspicious mining trends based on the frequency of human activities and the working status; determine whether there is malicious mining based on the suspicious mining trends.

[0074] The character activity features may be signs of character activities recognized in the environment image, such as human shadows, movement trajectories, etc.

[0075] The continuous environment image may be a sequence of environment images taken continuously within a certain time interval, and is used to track and analyze the activity trajectory and pattern of a person or device.

[0076] The human activity behavior can be the specific behavior of the human observed in the continuous environment image, such as digging, carrying, loading and unloading, etc.

[0077] Accompanying equipment can be mechanical equipment that accompanies human activities observed near the boundary of the mining area, such as excavators, loaders, transport vehicles, etc.

[0078] Mining behavior can be mining activities such as digging, moving ore, etc. carried out by people and accompanying equipment observed at the boundary of the mining area.

[0079] The character activity frequency may be the number of times or frequency at which a character appears at the boundary of a mining area within a certain period of time.

[0080] Suspicious mining activity can be unusual patterns or signs of activity observed at the boundaries of a mining area that may indicate illegal mining activities, such as excavation operations across mining boundaries.

[0081] Specifically, within a preset period of time, the drone is equipped with a high-resolution camera to regularly photograph the boundary of the mining area and collect environmental image data; the image analysis module first analyzes the collected environmental images to identify whether there are any human activity characteristics, such as human shadows, activity trajectories, etc.; if human activity characteristics are detected, the continuous environmental images in the area are retrieved and analyzed to determine the specific behavior of the human activities; the human activity behavior is identified and determined by analyzing the continuous images; while analyzing the human activities, whether there are accompanying mechanical equipment is identified; based on the continuous image analysis results, the type and function of the accompanying equipment are determined, such as excavation function, transportation function, etc.; based on the human activity behavior and the function of the accompanying equipment, it is analyzed whether there is mining behavior; based on the analysis results, whether there is illegal mining behavior is confirmed; if it is determined that there is mining behavior, the frequency of human activities and the working status of the accompanying equipment, such as working hours, work intensity, etc., are further analyzed; based on the frequency of human activities and working status, suspicious mining trends are identified; based on the suspicious mining trends, combined with other monitoring data and background information, whether there is malicious mining behavior is evaluated; if the evaluation results show that there is malicious mining, the alarm mechanism is triggered to notify the management personnel to take corresponding measures.

[0082] Through this solution, through continuous image analysis, the activity trajectory and behavior pattern of people can be tracked, providing more comprehensive evidence for judging whether there is malicious mining; automated image analysis reduces the burden of manual inspections, improves work efficiency, and reduces the possibility of human error. By analyzing the type and function of accompanying equipment, the nature and scale of mining activities can be more accurately judged; combined with multi-dimensional data such as people's activities, equipment working status and activity frequency, a comprehensive assessment can be conducted to improve the accuracy of judgment; by identifying suspicious mining trends, early warning can be issued to prevent further malicious mining activities.

[0083] In some embodiments, the intelligent monitoring system 200 for mining environment also includes a plurality of sensors 203, which are arranged within the exploitable range and are used to monitor changes in water resources in the mining area; the server 202 also includes a water resource analysis module 2025, which is respectively connected to the image analysis module 2021 and the plurality of sensors 203; when the image analysis module 2021 determines that there is no malicious mining based on suspicious mining trends, the image analysis module 2021 sends an analysis signal to the water resource analysis module 2025; after receiving the analysis signal, the water resource analysis module 2025 is used to obtain monitoring data from a plurality of sensors; determine the monitoring location of each monitoring data based on the acquisition records; determine the cause of the water volume change based on the monitoring location; obtain the tailings location; and determine whether the abnormal vegetation growth at the boundary of the mining area is caused by the tailings based on the development method, the cause of the water volume change and the tailings location.

[0084] Water resource changes can be changes in the quantity, quality, distribution, flow state and water ecosystem of water resources in the mining area; including the rise and fall of water levels, increase and decrease of surface flow, changes in reservoir water storage, changes in soil moisture, etc.

[0085] Water quantity change can be the change in the amount of water in a water resource, usually referring to the change in the volume of water within a specific area.

[0086] Specifically, sensors suitable for monitoring changes in water resources are selected, and these sensors are set within the exploitable range to ensure coverage of key water resource monitoring points. The sensors monitor changes in water resources in the mining area in real time and collect relevant data; the sensors transmit data to the server 202 via wired or wireless means; the water resource analysis module 2025 in the server 202 is responsible for receiving and parsing sensor data and performing water resource analysis; the image analysis module 2021 analyzes the mining area environmental image to determine whether there is any suspicious mining trend; if it is determined that there is no malicious mining, the image analysis module 2021 sends an analysis signal to the water resource analysis module 2025; after receiving the signal, the water resource analysis module 2025 obtains the sensor monitoring data, determines the monitoring location of each monitoring data based on the sensor's location information, analyzes the reasons for water volume changes at the monitoring location, such as natural precipitation, artificial pumping, mining activities, etc., and combines the development method, the reasons for water volume changes and the location of tailings to determine whether the abnormal vegetation growth at the boundary of the mining area is caused by tailings.

[0087] Through this solution, the changes in water resources in the mining area are monitored in real time through sensors, which can quickly respond to changes in water resource conditions. Once an abnormality is detected, the system can quickly notify management personnel to reduce potential losses; sensors are deployed at key locations within the exploitable range to ensure coverage of water resources in the entire mining area and reduce monitoring blind spots. Even in mining areas with complex terrain, effective monitoring can be carried out through drones or other sensors; the water resource analysis module 2025 can conduct in-depth analysis of data to improve the accuracy of monitoring results. Through image analysis and water resource analysis, managers can quickly understand the status of the mining area and make decisions; it integrates multi-source data such as image analysis and sensor data to provide more comprehensive monitoring information; the image analysis module and the water resource analysis module work together to improve the comprehensiveness and efficiency of monitoring.

[0088] In some embodiments, the server 202 also includes an abnormality tracing module 2026, which is connected to the water resource analysis module 2025; when the water resource analysis module 2025 determines that the abnormal growth of vegetation at the boundary of the mining area is not caused by tailings, the abnormality tracing module 2026 sends a tracing signal and monitoring data to the abnormality tracing module 2026; after receiving the tracing signal, the abnormality tracing module 2026 is used to analyze the development method, and determine the terrain changes after the development of the mining area boundary according to the method analysis results; analyze the monitoring data according to the monitoring location to determine the water flow changes; determine the possibility of soil erosion according to the water flow changes and the terrain changes; predict the growth changes of each vegetation at the boundary of the mining area according to the possibility of soil erosion and spectral images; match the growth changes with the abnormality analysis results, and determine whether the cause of the growth abnormality is caused by the development method according to the matching results.

[0089] Water flow changes can be the changes in parameters such as flow rate, flow velocity, flow direction, water level, and water quality of water bodies in mining areas (such as rivers, streams, groundwater, etc.) over time.

[0090] Topographic changes can refer to changes in the surface morphology of the mining area as mining progresses, including changes in ground elevation, slope, and landform structure.

[0091] Specifically, in some cases there is underground infiltration, underground seepage, and another situation is that the pH value of the surface soil changes due to soil erosion caused by ponds or surface water, primary wastewater storage pits, tailings storage pits, etc.; the abnormal tracing module 2026 is connected to the water resource analysis module 2025, and receives the tracing signal and monitoring data from the module. The module analyzes the development method of the mining area, including the type of mining, mining intensity, mining cycle, etc., and determines the terrain changes after the development of the mining area boundary according to the development method analysis results. According to the monitoring location, the monitoring data is analyzed to determine the water flow changes. Combined with the water flow changes and terrain changes, the possibility of soil erosion is evaluated, and the spectral image is used to analyze the vegetation health status at the mining area boundary. According to the possibility of soil erosion and spectral images, the growth changes of each vegetation at the mining area boundary are predicted, and the predicted growth changes are matched with the actual monitored vegetation growth anomalies. According to the matching results, it is determined whether the cause of the vegetation growth anomaly is caused by the development method.

[0092] Through this solution, the abnormality tracing module 2026 can collect and analyze monitoring data in real time without relying on manual inspections, thereby improving monitoring efficiency. The module integrates multi-source information such as the data of the water resources analysis module 2025, development method analysis results, terrain change data, water flow change data, and spectral images, providing more comprehensive monitoring and analysis; the module can more accurately determine the cause of abnormal vegetation growth by accurately analyzing the monitoring data, thereby improving the accuracy of monitoring; the abnormality tracing module 2026 can respond quickly when abnormal vegetation growth is discovered, and issue early warnings in a timely manner, so that managers can take timely measures; the module reduces the need for personnel to enter dangerous areas through remote monitoring and data analysis, thereby improving the safety of monitoring.

[0093] In some embodiments, the server 202 also includes a facial recognition module 2027, which is connected to the image analysis module 2021. When the image analysis module 2021 determines that there is malicious mining based on suspicious mining trends, it sends an identification signal and a continuous environmental image to the facial recognition module 2027; after receiving the identification signal and the continuous environmental image, the facial recognition module 2027 analyzes the character features in the continuous environmental image, and determines the character image based on the feature analysis results; based on the character image, retrieves the facial information of the person in the preset personnel management database, and compares the character image with the facial information of the person to obtain a comparison result, and determines whether there is malicious mining by internal personnel based on the comparison result.

[0094] Personal features can be information extracted from facial images during facial recognition and used to identify and distinguish different individuals.

[0095] A character image can be an individual image depicted based on the character's characteristics, or it can be the overall appearance of the character.

[0096] Specifically, when the image analysis module 2021 determines that there is a suspicious mining trend, it will send an identification signal and continuous environmental images to the facial recognition module 2027; the facial recognition module 2027 pre-processes the continuous environmental images, such as adjusting brightness and contrast, denoising, cropping, etc., to facilitate subsequent feature extraction; based on the extracted features, determine the character image; query similar facial information from the preset personnel management database; compare the character image with the facial information in the database, and use a similarity algorithm to determine whether there is a match; based on the facial comparison results, determine whether there are internal personnel involved in malicious mining; if it is determined that there are internal personnel malicious mining, the system will issue an early warning notification.

[0097] Through this solution, the facial recognition module can analyze image data in real time. Once the image analysis module detects suspicious mining trends, the facial recognition program is immediately started to quickly respond to potential malicious mining behaviors. Through automated facial recognition technology, the need for traditional ground inspections is reduced, reducing personnel safety risks, especially in complex terrain and dangerous areas. Compared with fixed monitoring equipment and manual inspections, facial recognition modules can cover a wider area, reduce monitoring blind spots, and improve the accuracy and completeness of monitoring data. Facial recognition technology can provide accurate identity recognition, which helps to distinguish between legal and illegal mining behaviors, especially in identifying malicious mining by internal personnel. The facial recognition module can issue early warnings in a timely manner to prevent theft and cross-border mining, which helps protect mineral resources from illegal mining.

[0098] In some embodiments, the server 202 also includes a reward and punishment determination module 2028, which is connected to the facial recognition module 2027; the facial recognition module 2027 compares the character image with the facial information of the personnel, and after determining that there is malicious mining by an internal personnel, sends a determination signal to the reward and punishment determination module 2028; the reward and punishment determination module 2028 is also connected to the image analysis module 2021, and is used to obtain abnormal analysis results after receiving the determination signal; based on the comparison results, the identity information of the internal personnel is determined, and based on the identity information, the abnormal analysis results and the preset reward and punishment standards, the punishment details of the internal personnel are determined.

[0099] The preset reward and punishment standards can be the rules based on which the reward and punishment determination module 2028 in the mining environment intelligent monitoring system is based, and are stored in a preset database.

[0100] Specifically, the facial recognition module 2027 compares the captured character image with the facial information of the personnel in the personnel management database to determine whether there is malicious mining by an insider; if it is confirmed that there is malicious mining by an insider, the facial recognition module 2027 will send a discrimination signal to the reward and punishment discrimination module 2028; the reward and punishment discrimination module 2028 receives the discrimination signal from the facial recognition module 2027; the module 2028 is also connected to the image analysis module 2021 to obtain abnormal analysis results related to malicious mining by insiders; the reward and punishment discrimination module 2028 combines the comparison results of facial recognition and the abnormal conditions of image analysis to conduct a comprehensive analysis of the mining behavior of the insider; based on the comparison results, the identity information of the insider is determined; based on the identity information of the insider, the abnormal analysis results and the preset reward and punishment standards, the reward and punishment measures for the insider are determined and the specific punishment measures, such as fines, suspensions, dismissals, etc., are determined, and a punishment details are generated.

[0101] Through this solution, rewards and punishments can be determined quickly and accurately through automated processes to improve management efficiency. Real-time monitoring data and facial recognition technology can be combined to ensure rapid response and precise strikes against malicious mining activities. Based on preset reward and punishment standards, the fairness of reward and punishment measures can be ensured to reduce interference from human factors. Through rewards and punishments for internal personnel, it can prevent and deter other employees and reduce the occurrence of malicious mining activities.

Claims

1. An intelligent mining environment monitoring system based on drones, characterized in that: It includes a drone and a server; the drone includes a hyperspectral camera and a high-precision camera; the server includes an image acquisition module, an image analysis module, and an information feedback module; The image acquisition module is connected to the hyperspectral camera and the high-precision camera respectively, and is used to acquire the spectral image taken by the hyperspectral camera and the environmental image taken by the high-precision camera, and send them to the image analysis module; The image analysis module is connected to the image acquisition module and is used to receive the spectral image and the environmental image, analyze the spectral image, determine whether there is abnormal growth of vegetation at the boundary of the mining area, and obtain an abnormality analysis result; The image analysis module is also used to determine the acquisition time of the spectral image when it is determined that there is an abnormality, and analyze the environmental image of a preset time period according to the acquisition time to obtain an image analysis result, determine whether there is malicious mining, and send the abnormality analysis result, the image analysis result, the environmental image and the spectral image to the information feedback module; The information feedback module is connected to the image analysis module, and is used to receive the abnormal analysis result, the image analysis result, the environmental image and the spectral image; and determine the degree of damage according to the abnormal analysis result and the image analysis result, and based on the environmental image, the spectral image and the degree of damage, determine feedback information and send the feedback information to the management device.

2. The system according to claim 1, characterized in that The server also includes an inspection planning module connected to the drone and the image acquisition module. The image acquisition module is used to acquire a geological map of the mining area and send it to the inspection planning module; After receiving the geological map of the mining area, the inspection planning module is used to analyze the geological map of the mining area, and determine the topographic structure and topographic layout of the mining area according to the geological analysis results; determine the flight altitude of the UAV according to the topographic structure; and determine the flight order of the UAV according to the topographic layout; The inspection planning module is further used to plan the inspection route of the UAV according to the flight altitude and the flight sequence, and send the route to the UAV; After receiving the inspection route, the drone performs inspection according to the inspection route.

3. The system according to claim 2, characterized in that The server includes a parameter adjustment module connected to the hyperspectral camera and the inspection planning module; The parameter adjustment module is used to obtain the terrain structure, the terrain layout and the flight altitude analyzed by the inspection planning module, determine the flight altitude of the UAV according to the terrain structure; determine the camera exposure and gain according to the terrain layout and the flight altitude, and send the camera exposure and gain to the hyperspectral camera; After receiving the camera exposure and gain, the hyperspectral camera is used to obtain camera information, and determine and adjust the red light band and the near-red light band according to the camera information, the camera exposure and gain.

4. The system according to claim 3, characterized in that The image analysis module is connected to the inspection planning module and is used to obtain the terrain structure and the terrain layout; When analyzing the spectral image to determine whether the vegetation at the boundary of the mining area has abnormal growth, the image analysis module is used to: Analyzing the spectral image to determine the boundary of the mining area; Determine the developable scope according to the terrain structure and the terrain layout; Determine the interval distance between each vegetation and the boundary of the mining area according to the spectral image; Obtain and analyze the mining area development plan, and determine the development method of the mining area boundary based on the plan analysis results; Determine the growth impact of marginal vegetation based on the development method and the developable scope; According to the interval distance and the growth impact, using NDVI, the vegetation at the boundary of the mining area is evaluated for growth to obtain an evaluation result; Based on the evaluation results, determine whether there is any abnormal growth of vegetation at the boundary of the mining area.

5. The system according to claim 4, characterized in that The image analysis module is used to evaluate the growth of vegetation at the boundary of the mining area using NDVI according to the interval distance and the growth impact: Analyze the spectral image according to the red light band and the near-red light band to determine the original red light reflectance and the original near-red light reflectance; analyze the environmental image to determine the dark pixel area; Determine the minimum reflectivity of the red light band and the minimum reflectivity of the near-red light band according to the original red light reflectivity and the original near-red light reflectivity; Determine the dark pixel value of the red light band and the dark pixel value of the near red light band according to the minimum reflectivity of the red light band and the minimum reflectivity of the near red light band; Performing atmospheric correction according to the dark pixel value of the red light band and the dark pixel value of the near-red light band to obtain an actual near-infrared band reflectivity and an actual red light reflectivity; Calculate the NDVI value of each vegetation according to the actual near-infrared band reflectivity and the actual red light reflectivity; Based on the NDVI value of each vegetation and the growth impact, a growth assessment of the vegetation at the boundary of the mining area is performed.

6. The system according to claim 5, characterized in that The image analysis module analyzes the environmental image of a preset period of time, obtains the image analysis result, and determines whether there is malicious mining, and is used to: Analyze the environmental image of a preset time period, and determine whether there are human activity features at the boundary of the mining area according to the image analysis results; If it is determined that there are human activity features, then retrieve and analyze continuous environmental images with the human activity features, obtain continuous image analysis results, and determine the human activity behavior; Determining whether there is an accompanying device according to the continuous image analysis result; If so, determining the device type and device function of the accompanying device according to the continuous image analysis result; Analyze the activities of the people and the functions of the equipment to determine whether there is any mining behavior; If it is determined that there is mining behavior, the activity frequency of the person and the working status of the accompanying equipment are determined based on the continuous image analysis results; Determine suspicious mining trends based on the activity frequency of the person and the working status; Determine whether there is malicious mining based on the suspicious mining trends.

7. The system according to claim 6, characterized in that The intelligent monitoring system for mining area environment also includes a plurality of sensors, which are arranged within the exploitable range and are used to monitor the changes in water resources in the mining area; the server also includes a water resource analysis module, which is respectively connected to the image analysis module and the plurality of sensors; When the image analysis module determines that there is no malicious mining based on the suspicious mining trend, the image analysis module sends an analysis signal to the water resource analysis module; After receiving the analysis signal, the water resource analysis module is used to obtain the monitoring data of the plurality of sensors; and determine the monitoring location of each monitoring data according to the acquisition record; Determine the cause of the change in water volume based on the monitoring location; Obtain tailings location; According to the development method, the reason for the change in water volume and the location of the tailings, determine whether the abnormal vegetation growth at the boundary of the mining area is caused by the tailings.

8. The system according to claim 7, characterized in that The server also includes an abnormality tracing module connected to the water resources analysis module; When the water resource analysis module determines that the abnormal vegetation growth at the boundary of the mining area is not caused by tailings, the water resource analysis module sends a tracing signal and the monitoring data to the abnormal tracing module; After receiving the tracing signal, the abnormality tracing module is used to analyze the development mode and determine the terrain changes after the development of the mining area boundary according to the mode analysis result; Analyzing the monitoring data according to the monitoring position to determine the change of water flow; Determine the possibility of soil erosion based on the water flow change and the terrain change; Predicting the growth change of each vegetation at the boundary of the mining area according to the possibility of soil erosion and the spectral image; The growth change is matched with the abnormal analysis result, and based on the matching result, it is determined whether the cause of the abnormal growth is caused by the development method.

9. The system according to claim 6, characterized in that The server further comprises a facial recognition module connected to the image analysis module, and when the image analysis module determines that malicious mining exists based on the suspicious mining trend, the server sends a recognition signal and the continuous environment image to the facial recognition module; After receiving the recognition signal and the continuous environment image, the facial recognition module analyzes the character features in the continuous environment image and determines the character image according to the feature analysis result; Based on the character image, the facial information of the personnel in the preset personnel management database is retrieved, and the character image is compared with the facial information of the personnel to obtain a comparison result, and according to the comparison result, it is determined whether there is malicious mining by internal personnel.

10. The system according to claim 9, characterized in that The server also includes a reward and punishment discrimination module connected to the facial recognition module; The facial recognition module compares the character image with the personnel facial information, and after determining that there is malicious mining by an internal personnel, sends a discrimination signal to the reward and punishment discrimination module; The reward and punishment discrimination module is also connected to the image analysis module, and is used to obtain the abnormal analysis result after receiving the discrimination signal; According to the comparison result, the identity information of the internal personnel is determined, and according to the identity information, the abnormal analysis result and the preset reward and punishment standards, the punishment details of the internal personnel are determined.

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