Rare earth mining area monitoring method and related equipment based on intelligent remote sensing

Abstract

The present application discloses a rare earth mining area monitoring method based on intelligent remote sensing and related equipment, which relates to the field of remote sensing monitoring technology. The rare earth mining area monitoring method based on intelligent remote sensing includes: when receiving an aerial image, preprocessing the aerial image to obtain initial image information; extracting rare earth mining area feature information of the initial image information based on a preset feature extraction model; identifying the rare earth mining area feature information based on a preset artificial intelligence model, obtaining rare earth mining area monitoring identification results, and executing corresponding alarm schemes. The present application uses a feature extraction model to extract rare earth mining area feature information of the initial image information, identifies the rare earth mining area feature information through an artificial intelligence model, obtains monitoring results, and realizes the automation of rare earth mining area monitoring, thereby improving the monitoring efficiency of rare earth mining areas.

Description

Technical Field

[0001] The present application relates to the field of remote sensing monitoring technology, and in particular to a rare earth mining area monitoring method and related equipment based on intelligent remote sensing. Background Art

[0002] Rare earth minerals are a type of mineral resource with important strategic significance, which are widely used in many fields such as machinery manufacturing, petrochemical industry, agriculture, forestry, animal husbandry, aerospace and military technology. With the continuous growth of global demand for rare earth resources, the scale of mining in rare earth mining areas is constantly expanding, and it is of great significance to improve the efficiency and accuracy of rare earth mining area monitoring.

[0003] The traditional method of monitoring rare earth mining areas is to manually control drones for dynamic inspections. However, due to the limited shooting range of drones and the incomplete and lost data records of manual inspections, the monitoring efficiency of rare earth mining areas is low.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a rare earth mining area monitoring method and related equipment based on intelligent remote sensing, aiming to solve the technical problem of low monitoring efficiency of rare earth mining areas.

[0006] To achieve the above objectives, this application proposes a rare earth mining area monitoring method based on intelligent remote sensing, the method comprising:

[0007] When receiving the aerial image, preprocessing the aerial image to obtain initial image information;

[0008] Based on a preset feature extraction model, extracting rare earth mining area feature information of the initial image information;

[0009] Based on a preset artificial intelligence model, the characteristic information of the rare earth mining area is identified, the rare earth mining area monitoring and identification results are obtained, and the corresponding alarm plan is executed.

[0010] In one embodiment, the aerial images include high spatial resolution aerial images and high spectral aerial resolution aerial images.

[0011] In one embodiment, the step of extracting the rare earth mining area feature information of the initial image information based on a preset feature extraction model includes:

[0012] Obtain image information samples and rare earth mining area feature labels;

[0013] Sending the image information sample to a preset model to be trained;

[0014] Based on the model to be trained, feature extraction is performed on the spectral information and spatial information of the image information sample to obtain a rare earth mining area feature extraction result;

[0015] Analyze the difference between the rare earth mining area feature extraction result and the rare earth mining area feature label to obtain an extraction loss value;

[0016] If the extraction loss value does not meet the preset loss standard, return to the step of performing feature extraction on the spectral information and spatial information of the image information sample based on the model to be trained to obtain the feature extraction result of the rare earth mining area, and stop training until the extraction loss value meets the loss standard to obtain a feature extraction model that meets the loss standard.

[0017] In one embodiment, the step of obtaining the rare earth mining area monitoring and identification result and executing the corresponding alarm scheme includes:

[0018] If the spectral information of the rare earth mining area characteristic information is identified to be similar to the preset spectral information of the near-mine surrounding rock alteration, an alarm message of the discovery of a new mineral is sent to the preset mineral management equipment.

[0019] In one embodiment, the step of obtaining the rare earth mining area monitoring and identification result and executing the corresponding alarm scheme includes:

[0020] If it is identified that the difference between the vegetation coverage space information of the rare earth mining area characteristic information and the preset historical vegetation coverage information exceeds a preset threshold, an alarm message of abnormal vegetation coverage is sent to a preset environmental management device.

[0021] In one embodiment, the step of obtaining the rare earth mining area monitoring and identification result and executing the corresponding alarm scheme includes:

[0022] If it is identified that the rare earth mineral spatial information of the rare earth mining area characteristic information is different from the reported rare earth mineral mining area, an alarm message of rare earth mineral mining abnormality is sent to the mineral management device.

[0023] In addition, to achieve the above purpose, the present application also proposes a rare earth mining area monitoring device based on intelligent remote sensing, and the rare earth mining area monitoring device based on intelligent remote sensing includes:

[0024] A processing module, configured to pre-process the aerial image to obtain initial image information when the aerial image is received;

[0025] An extraction module, used for extracting rare earth mining area feature information of the initial image information based on a preset feature extraction model;

[0026] The alarm module is used to identify the characteristic information of the rare earth mining area based on a preset artificial intelligence model, obtain the monitoring and identification results of the rare earth mining area, and execute the corresponding alarm plan.

[0027] In addition, to achieve the above-mentioned objectives, the present application also proposes a rare earth mining area monitoring device based on intelligent remote sensing, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the rare earth mining area monitoring method based on intelligent remote sensing as described above.

[0028] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the rare earth mining area monitoring method based on intelligent remote sensing as described above are implemented.

[0029] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the rare earth mining area monitoring method based on intelligent remote sensing as described above.

[0030] One or more technical solutions proposed in this application have at least the following technical effects:

[0031] Compared with the related art, in which the drone is manually controlled for dynamic inspection, but the drone's shooting range is limited, and the data records of manual inspections may be incomplete or lost, resulting in low monitoring efficiency of rare earth mining areas, this application pre-processes the aerial images when receiving them to obtain initial image information; extracts the rare earth mining area feature information of the initial image information based on a preset feature extraction model; identifies the rare earth mining area feature information based on a preset artificial intelligence model, obtains rare earth mining area monitoring and identification results, and executes the corresponding alarm scheme. It is understandable that this application uses a feature extraction model to extract the rare earth mining area feature information of the initial image information, identifies the rare earth mining area feature information through an artificial intelligence model, obtains monitoring results, and realizes the automation of rare earth mining area monitoring, thereby improving the monitoring efficiency of rare earth mining areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0034] Figure 1 A flow chart of the first embodiment of the rare earth mining area monitoring method based on intelligent remote sensing provided in this application;

[0035] Figure 2 This is a schematic diagram of the module structure of a rare earth mining area monitoring device based on intelligent remote sensing according to an embodiment of the present application;

[0036] Figure 3 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the rare earth mining area monitoring method based on intelligent remote sensing in the embodiment of the present application.

[0037] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0038] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0039] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0040] The main solutions of the embodiments of this application are:

[0041] When receiving the aerial image, preprocessing the aerial image to obtain initial image information;

[0042] Based on a preset feature extraction model, extracting rare earth mining area feature information of the initial image information;

[0043] Based on a preset artificial intelligence model, the characteristic information of the rare earth mining area is identified, the rare earth mining area monitoring and identification results are obtained, and the corresponding alarm plan is executed.

[0044] In this embodiment, the present application uses a rare earth mining area monitoring device based on intelligent remote sensing as the execution body. For the convenience of expression, it is specifically described below as "device".

[0045] The existing technology is to manually control drones for dynamic inspections, but the drone's shooting range is limited, and the data records of manual inspections may be incomplete or lost, resulting in low monitoring efficiency of rare earth mining areas.

[0046] The present application provides a solution, by pre-processing the aerial image when receiving it to obtain the initial image information; extracting the rare earth mining area characteristic information of the initial image information based on a preset feature extraction model; identifying the rare earth mining area characteristic information based on a preset artificial intelligence model, obtaining the rare earth mining area monitoring identification result, and executing the corresponding alarm scheme. It can be understood that the present application uses a feature extraction model to extract the rare earth mining area characteristic information of the initial image information, identifies the rare earth mining area characteristic information through the artificial intelligence model, obtains the monitoring result, realizes the automation of rare earth mining area monitoring, and thereby improves the monitoring efficiency of rare earth mining areas.

[0047] Based on this, the embodiment of the present application provides a rare earth mining area monitoring method based on intelligent remote sensing, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the rare earth mining area monitoring method based on intelligent remote sensing of the present application.

[0048] In this embodiment, the rare earth mining area monitoring method based on intelligent remote sensing includes steps S10 to S30:

[0049] Step S10, when an aerial image is received, preprocessing the aerial image to obtain initial image information;

[0050] It should be noted that the aerial images refer to surface images captured using remote sensing equipment carried by drones or aircraft. These images are usually high-resolution and can provide detailed information on surface features, which is crucial for rare earth mining area monitoring. The preprocessing refers to a series of processing steps performed on aerial images before further analysis, aiming to improve image quality, eliminate noise and distortion, and prepare image data for subsequent feature extraction and analysis. The initial image information is the preprocessed aerial image data, which contains the surface feature information that has been preliminarily processed, providing a basis for subsequent feature extraction and analysis.

[0051] Exemplarily, a drone equipped with a high-resolution camera is used to take aerial photos of a designated rare earth mining area. The drone flies according to a preset flight path and altitude to ensure coverage of the entire mining area, and automatically takes high-resolution images during the flight. The aerial images are sent to the ground control station in real time via wireless transmission technology. The image data includes RGB (red, green, and blue) images and multispectral or hyperspectral images, which can capture detailed surface features and spectral information of the rare earth mining area. After receiving the aerial images, the device automatically preprocesses the images. The preprocessing steps include:

[0052] a. Radiometric correction: Adjusts the radiometric properties of an image to remove image distortion caused by differences in sensor response or changes in lighting conditions.

[0053] b. Atmospheric correction: Eliminate the effects of the atmosphere on the image, such as atmospheric scattering and absorption, to improve the spectral accuracy of the image.

[0054] c. Geometric correction: Correct the image to a common coordinate system to ensure that the image's geographic information is accurate.

[0055] d. Cloud and shadow removal: Automatically identify and remove clouds and shadows in images to reduce the impact of these factors on subsequent analysis.

[0056] e. Image enhancement: By adjusting parameters such as contrast, brightness and color balance, the features of rare earth mining areas in the image are enhanced to make them easier to identify.

[0057] After preprocessing, the noise in the image is effectively reduced, the features are enhanced, and the initial image information is obtained. This information includes the clear boundaries of the rare earth mining area, surface structure, vegetation coverage, and spectral characteristics. The initial image information is stored in the database to provide basic data for the next step of feature extraction and analysis.

[0058] Through the above embodiments, the present application can effectively monitor rare earth mining areas, and improve the efficiency and accuracy of rare earth mining area monitoring through intelligent remote sensing technology. The preprocessing step ensures the quality of image data and provides a reliable basis for subsequent rare earth mining area feature extraction and monitoring identification.

[0059] Step S20, extracting rare earth mining area feature information of the initial image information based on a preset feature extraction model;

[0060] It should be noted that the feature extraction model is an algorithm or calculation model used to automatically identify and extract features related to rare earth mining areas from initial image information, such as color, texture, shape, etc. These features are essential for distinguishing rare earth mining areas from other surface features. The rare earth mining area feature information refers to information directly related to rare earth mining areas extracted from aerial images. This information may include specific spectral features, spatial distribution features, etc., which are the key to identifying rare earth mining areas.

[0061] Exemplarily, it is assumed that high-resolution images of rare earth mining areas have been acquired through drone aerial photography, and preprocessing steps have been completed, including radiation correction, atmospheric correction, geometric correction, cloud and shadow removal, and image enhancement, to obtain clear initial image information. A feature extraction model is set up, which is based on a machine learning algorithm, especially a deep learning network, such as a convolutional neural network (CNN), to automatically extract features related to rare earth mining areas from images. The model uses a large number of labeled rare earth mining area images during the training phase, which contain typical features of rare earth mining areas, such as specific color patterns, texture structures, and shape features. The preprocessed initial image information is input into the feature extraction model. The model automatically identifies and extracts features related to rare earth mining areas, including but not limited to:

[0062] a. Spectral features: Identify the spectral reflectance that is unique to rare earth mining areas. These features are particularly evident in hyperspectral images.

[0063] b.Texture features: Analyze texture patterns in the image, such as the roughness and structure of mineral exposure areas.

[0064] c. Shape features: Identify the geometric shape and boundaries of the mining area. These features help distinguish the mining area from other surface structures. The model outputs a feature vector that contains all the extracted feature information, providing key data for the next step of rare earth mining area identification. The extracted feature information is stored in a database for further analysis and monitoring. By comparing the feature information extracted at different time points, changes in rare earth mining areas, such as mining area expansion, mining activities, etc., can be monitored. The extracted feature information can be used to train and optimize artificial intelligence models to improve the accuracy and efficiency of rare earth mining area monitoring. This feature information can also be used to generate monitoring reports for rare earth mining areas to provide a scientific basis for decision makers.

[0065] Through the above embodiments, the present application uses a preset feature extraction model to effectively extract feature information of rare earth mining areas from initial image information, providing an efficient and automated method for monitoring and management of rare earth mining areas. This method not only improves the accuracy of monitoring, but also reduces the need for manual intervention and reduces costs.

[0066] Step S30, based on a preset artificial intelligence model, the characteristic information of the rare earth mining area is identified, the rare earth mining area monitoring and identification result is obtained, and a corresponding alarm plan is executed.

[0067] It should be noted that the artificial intelligence model refers to a model built using machine learning, deep learning and other technologies, which is used to analyze and identify the extracted rare earth mining area feature information to determine the location and status of the rare earth mining area. The rare earth mining area monitoring and identification results are the results obtained based on the analysis of the artificial intelligence model. It shows the location, scope, possible mining activities and other information of the rare earth mining area, which is of great significance for monitoring and managing rare earth resources. The alarm scheme refers to a notification or warning device that is automatically triggered when abnormal activities or illegal mining activities are detected in rare earth mining areas. This scheme may include sending alarms to regulators, recording event logs, taking on-site intervention measures, etc.

[0068] Exemplarily, aerial images of rare earth mining areas are obtained by high-resolution cameras and hyperspectral sensors carried by drones. These images contain high spectral resolution and high spatial resolution data of rare earth mining areas, providing rich information for subsequent feature identification. Use historical rare earth mining area image data and known mining activity data to train a deep learning model, such as a convolutional neural network (CNN). The model structure may include multiple convolutional layers, pooling layers, and fully connected layers, and use classification cross entropy as a loss function and Adam optimizer for training. The preprocessed initial image information is input into the trained artificial intelligence model. The model automatically identifies the characteristics of rare earth mining areas in the image, such as changes in vegetation cover, exposed soil areas, signs of mining activities, etc. The artificial intelligence model outputs monitoring and identification results, which include key information such as the location of the rare earth mining area, the possibility of mining activities, and the degree of vegetation damage. Based on the monitoring and identification results, the device automatically determines whether there is illegal mining activity or environmental damage. If an abnormality is detected, the device will execute the preset alarm plan:

[0069] a. Automatic alarm: The device sends automatic alarm information to the mine management department, including the coordinates of the abnormal location, timestamp and possible risk level.

[0070] b. Data recording: The device records detailed information of alarm events, including alarm time, location and related image data, to facilitate subsequent review and analysis.

[0071] c. On-site intervention: When necessary, the device can link on-site security personnel or drones to conduct on-site intervention to prevent illegal mining activities. The device collects feedback data, including on-site verification results after the alarm, for further training and optimization of artificial intelligence models to improve the accuracy and efficiency of monitoring.

[0072] Through the above embodiments, the present application provides an automated and intelligent rare earth mining area monitoring solution, which can effectively identify abnormal activities in rare earth mining areas and promptly implement alarm plans to protect rare earth resources and environmental safety.

[0073] The present application provides a rare earth mining area monitoring method based on intelligent remote sensing, which adopts a feature extraction model to extract the rare earth mining area feature information of the initial image information, identifies the rare earth mining area feature information through an artificial intelligence model, obtains monitoring results, and realizes the automation of rare earth mining area monitoring, thereby improving the monitoring efficiency of rare earth mining areas.

[0074] In a feasible implementation manner, before step S20, the following may be included:

[0075] Obtain image information samples and rare earth mining area feature labels;

[0076] Sending the image information sample to a preset model to be trained;

[0077] Based on the model to be trained, feature extraction is performed on the spectral information and spatial information of the image information sample to obtain a rare earth mining area feature extraction result;

[0078] Analyze the difference between the rare earth mining area feature extraction result and the rare earth mining area feature label to obtain an extraction loss value;

[0079] If the extraction loss value does not meet the preset loss standard, return to the step of performing feature extraction on the spectral information and spatial information of the image information sample based on the model to be trained to obtain the feature extraction result of the rare earth mining area, and stop training until the extraction loss value meets the loss standard to obtain a feature extraction model that meets the loss standard.

[0080] It is understood that the image information samples refer to aerial image data sets obtained from rare earth mining areas. These samples contain information such as surface features, vegetation coverage, soil changes, etc. of rare earth mining areas, which are used to train and verify remote sensing monitoring models. The rare earth mining area feature labels refer to the annotation data corresponding to the image information samples. These labels describe in detail the specific features of the rare earth mining areas in the images, such as the location of the mining area, ore type, mining activities, etc., which are used to supervise model training and ensure that the model can accurately identify the characteristics of rare earth mining areas. The model to be trained refers to an artificial intelligence model that has not been trained or needs further training. The model will be used to extract feature information of rare earth mining areas from image information samples. This model may be a convolutional neural network based on deep learning or other machine learning algorithms.

[0081] The spectral information refers to the spectral data in the image information samples, which reflect the reflection or emission characteristics of light of different wavelengths and are crucial for identifying the mineral composition and surface changes of rare earth mining areas. The spatial information refers to the geospatial data in the image information samples, including the geographical location and terrain features of the image, which help the model understand the spatial distribution and structure of rare earth mining areas. The rare earth mining area feature extraction results refer to the feature information extracted by the model from the image information samples. These results include spectral features and spatial features, which are key data for identifying and monitoring rare earth mining areas.

[0082] The extraction loss value refers to the difference measure between the feature information extracted by the model and the feature label of the actual rare earth mining area, which is usually calculated by a specific loss function. The extraction loss value is used to evaluate the performance of the model and guide the training and optimization of the model. The loss standard refers to a preset performance standard used to determine whether the extraction loss value is within an acceptable range. When the extraction loss value is lower than this standard, it indicates that the performance of the model has reached the expected goal and training can be stopped. The feature extraction model refers to a model that has been trained and meets the loss standard. The model can accurately extract the feature information of the rare earth mining area from the image information sample and is used for actual rare earth mining area monitoring tasks.

[0083] Exemplarily, a high-resolution camera and a hyperspectral sensor carried by a drone are used to take aerial photos of a specific rare earth mining area to obtain image information samples containing surface features of the rare earth mining area. At the same time, geological experts provide rare earth mining area feature labels corresponding to these images, including information such as the location of the mining area, ore type, and mining activities. The collected image information samples are input into a preset deep learning model, which is a convolutional neural network (CNN) specially designed for processing remote sensing image data. The spectral information and spatial information of the image information samples are extracted using the model to be trained. The model automatically identifies the spectral features (such as the spectral response of a specific mineral) and spatial features (such as the morphology and distribution of the mining area) in the image through multi-layer convolution and pooling operations. The rare earth mining area feature results extracted by the model are compared with the feature labels provided by the experts, and the difference between the two is calculated to obtain the extraction loss value. This loss value is calculated by a loss function (such as cross entropy loss or mean square error). If the extraction loss value does not meet the preset loss standard, it means that the recognition accuracy of the model is not high enough and further training is required. The model will return to the feature extraction step and continue to train with more image information samples until the loss value meets the preset standard. Through continuous iterative training, the model parameters are adjusted until the extraction loss value is lower than the preset loss standard, indicating that the model can accurately extract the feature information of the rare earth mining area from the image information sample. Once the model's extraction loss value meets the preset standard, the training process stops and a fully trained feature extraction model is obtained. The model can accurately identify and monitor the characteristics of rare earth mining areas, providing a scientific basis for the monitoring and management of rare earth mining areas.

[0084] In a feasible implementation, step S30 may include:

[0085] If the spectral information of the rare earth mining area characteristic information is identified to be similar to the preset spectral information of the near-mine surrounding rock alteration, an alarm message of the discovery of a new mineral is sent to the preset mineral management equipment.

[0086] Exemplarily, a rare earth mining area is scanned using a hyperspectral remote sensing device to obtain detailed spectral information of the mining area. This information includes the spectral reflectance of different minerals, which can reflect the composition and distribution of minerals in the mining area. Based on geological research and historical data, the spectral information of the near-mine surrounding rock alteration is preset. This information is used as a benchmark for comparison with the actual spectral information of the mining area. The acquired spectral information of the rare earth mining area is analyzed for similarity with the preset spectral information of the near-mine surrounding rock alteration. The similarity between the two is evaluated by principal component analysis (PCA) or other spectral matching techniques. If the analysis results show that the spectral information of the rare earth mining area is highly similar to the preset spectral information of the near-mine surrounding rock alteration, this may indicate the presence of new minerals or mineralized alteration in the mining area. Once the spectral information of the rare earth mining area characteristic information is identified to be similar to the preset spectral information of the near-mine surrounding rock alteration, the device will automatically send an alarm message of the discovery of new minerals to the preset mineral management device. The alarm information includes the results of the similarity analysis, the possible location of new minerals, and the recommended follow-up exploration actions. After receiving the alarm information, the mineral management equipment can take corresponding response measures, such as dispatching a geological exploration team to conduct on-site verification, adjusting the exploration plan, or taking protective measures to prevent illegal exploitation of resources.

[0087] In a feasible implementation, step S30 may include:

[0088] If it is identified that the difference between the vegetation coverage space information of the rare earth mining area characteristic information and the preset historical vegetation coverage information exceeds a preset threshold, an alarm message of abnormal vegetation coverage is sent to a preset environmental management device.

[0089] For example, a high-resolution multispectral camera carried by a drone is used to regularly take aerial photos of rare earth mining areas to obtain high-resolution image information samples containing vegetation cover. These images can capture the vegetation cover on the surface of the mining area, including vegetation type, density and health. The obtained image information samples are input into a preset feature extraction model, which can automatically identify and extract the spatial information of vegetation cover in the image. This may include key parameters such as the area, distribution and change of vegetation cover. The extracted spatial information of vegetation cover is compared with the preset historical vegetation cover information. Historical vegetation cover information is data previously obtained and stored through similar aerial photography activities, which is used to monitor changes in vegetation cover. The difference value between the current spatial information of vegetation cover and the historical vegetation cover information is calculated. This difference value may include the percentage change in vegetation cover area, the transformation of vegetation type, etc. A preset threshold is set to determine whether the change in vegetation cover is significant. This threshold is based on professional knowledge of environmental science and rare earth mining area management and reflects the safe range of vegetation cover change. If the calculated difference value exceeds the preset threshold, it indicates that the vegetation cover has changed significantly, which may indicate illegal mining activities or environmental damage. In this case, the device will automatically send an alarm message of abnormal vegetation cover to the preset environmental management device. The alarm message includes the specific location, time, degree of change and possible cause of the abnormal vegetation cover. This information is sent to the environmental management device through the wireless communication network, such as the computer system of the monitoring center or the mobile device of the on-site manager. After receiving the alarm message, the environmental management device can take follow-up actions, such as sending on-site personnel to investigate, initiating legal proceedings or taking emergency environmental protection measures.

[0090] In a feasible implementation, step S30 may include:

[0091] If it is identified that the rare earth mineral spatial information of the rare earth mining area characteristic information is different from the reported rare earth mineral mining area, an alarm message of rare earth mineral mining abnormality is sent to the mineral management device.

[0092] Exemplarily, a high-resolution camera and a hyperspectral sensor carried by a drone are used to take regular aerial photos of a rare earth mining area to obtain high-resolution image information samples containing the distribution of rare earth minerals. These images can capture the spatial information of rare earth minerals on the surface of the mining area, including the distribution range and mining area of ​​the minerals. The acquired image information samples are input into a preset feature extraction model, which can automatically identify and extract the spatial information of rare earth minerals in the image. This may include key parameters such as the distribution area, distribution shape and distribution change of rare earth minerals. The extracted rare earth mineral spatial information is compared with the reported rare earth mineral mining area. The reported area is the legal mining area data provided by the rare earth mining area management department, which is used to monitor and verify whether the actual mining activities are within the legal scope. The difference value between the current rare earth mineral spatial information and the reported mining area is calculated. If the difference value exceeds the preset threshold, it indicates that the actual mining activities may exceed the legal scope, which may indicate illegal mining activities or changes in the mining area. Once it is identified that the rare earth mineral spatial information is different from the reported rare earth mineral mining area, and the difference value exceeds the preset threshold, the device will automatically send an alarm message of rare earth mineral mining anomaly to the preset mineral management device. The alarm information includes the specific location, time, difference value and possible cause of the rare earth mineral mining anomaly. This information is sent to the mineral management equipment through the wireless communication network, such as the computer device of the monitoring center or the mobile device of the on-site manager. After receiving the alarm information, the mineral management equipment can take follow-up actions, such as sending on-site personnel to investigate, initiating legal proceedings or taking emergency environmental protection measures.

[0093] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the rare earth mining area monitoring method based on intelligent remote sensing of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0094] This application also provides a rare earth mining area monitoring device based on intelligent remote sensing, please refer to Figure 2 The rare earth mining area monitoring device based on intelligent remote sensing includes:

[0095] The processing module 10 is used to pre-process the aerial image to obtain initial image information when the aerial image is received;

[0096] An extraction module 20 is used to extract rare earth mining area feature information from the initial image information based on a preset feature extraction model;

[0097] The alarm module 30 is used to identify the characteristic information of the rare earth mining area based on a preset artificial intelligence model, obtain the rare earth mining area monitoring and identification results, and execute the corresponding alarm plan.

[0098] And / or, the rare earth mining area monitoring device based on intelligent remote sensing includes:

[0099] The first acquisition module is used to acquire image information samples and rare earth mining area feature labels;

[0100] A first sending module, used for sending the image information sample to a preset model to be trained;

[0101] A first extraction module is used to extract features from the spectral information and spatial information of the image information sample based on the model to be trained, so as to obtain a rare earth mining area feature extraction result;

[0102] The first analysis module is used to analyze the difference between the rare earth mining area feature extraction result and the rare earth mining area feature label to obtain an extraction loss value;

[0103] The first loop module is used to return to the step of performing feature extraction on the spectral information and spatial information of the image information sample based on the model to be trained to obtain the rare earth mining area feature extraction result if the extraction loss value does not meet the preset loss standard, and stop training after the extraction loss value meets the loss standard to obtain a feature extraction model that meets the loss standard.

[0104] And / or, the alarm module 30 includes:

[0105] The first alarm module is used to send an alarm message of discovering a new mineral to a preset mineral management device if it is identified that the spectral information of the rare earth mining area characteristic information is similar to the preset spectral information of the near-mine surrounding rock alteration.

[0106] And / or, the alarm module 30 includes:

[0107] The second alarm module is used to send an alarm message of abnormal vegetation coverage to a preset environmental management device if the difference between the vegetation coverage spatial information of the rare earth mining area characteristic information and the preset historical vegetation coverage information exceeds a preset threshold.

[0108] And / or, the alarm module 30 includes:

[0109] The third alarm module is used to send an alarm message of rare earth mineral mining abnormality to the mineral management device if it is identified that the rare earth mineral spatial information of the rare earth mining area characteristic information is different from the reported rare earth mineral mining area.

[0110] The rare earth mining area monitoring device based on intelligent remote sensing provided by the present application adopts the rare earth mining area monitoring method based on intelligent remote sensing in the above embodiment, which can solve the technical problem of low monitoring efficiency of rare earth mining areas. Compared with the prior art, the beneficial effects of the rare earth mining area monitoring device based on intelligent remote sensing provided by the present application are the same as the beneficial effects of the rare earth mining area monitoring method based on intelligent remote sensing provided by the above embodiment, and the other technical features of the rare earth mining area monitoring device based on intelligent remote sensing are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0111] The present application provides a rare earth mining area monitoring device based on intelligent remote sensing, and the rare earth mining area monitoring device based on intelligent remote sensing includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the rare earth mining area monitoring method based on intelligent remote sensing in the above-mentioned embodiment one.

[0112] Reference below Figure 3 , which shows a schematic diagram of the structure of a rare earth mining area monitoring device based on smart remote sensing suitable for implementing the embodiment of the present application. The rare earth mining area monitoring device based on smart remote sensing in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, tablet computers, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The rare earth mining area monitoring equipment based on intelligent remote sensing shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0113] like Figure 3As shown, the rare earth mining area monitoring equipment based on intelligent remote sensing may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the rare earth mining area monitoring equipment based on intelligent remote sensing are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the rare earth mine area monitoring device based on smart remote sensing to communicate wirelessly or wired with other devices to exchange data. Although the rare earth mine area monitoring device based on smart remote sensing with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0114] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0115] The rare earth mining area monitoring equipment based on intelligent remote sensing provided by the present application adopts the rare earth mining area monitoring method based on intelligent remote sensing in the above embodiment, which can solve the technical problem of low monitoring efficiency of rare earth mining areas. Compared with the prior art, the beneficial effects of the rare earth mining area monitoring equipment based on intelligent remote sensing provided by the present application are the same as the beneficial effects of the rare earth mining area monitoring method based on intelligent remote sensing provided by the above embodiment, and the other technical features of the rare earth mining area monitoring equipment based on intelligent remote sensing are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0116] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0117] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0118] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the rare earth mining area monitoring method based on intelligent remote sensing in the above-mentioned embodiment.

[0119] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0120] The above-mentioned computer-readable storage medium may be included in the rare earth mining area monitoring equipment based on intelligent remote sensing; or it may exist independently without being assembled into the rare earth mining area monitoring equipment based on intelligent remote sensing.

[0121] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the rare earth mining area monitoring equipment based on intelligent remote sensing, the rare earth mining area monitoring equipment based on intelligent remote sensing: when receiving the aerial image, pre-process the aerial image to obtain the initial image information; based on the preset feature extraction model, extract the rare earth mining area feature information of the initial image information; based on the preset artificial intelligence model, identify the rare earth mining area feature information, obtain the rare earth mining area monitoring identification result, and execute the corresponding alarm plan.

[0122] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0124] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0125] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned rare earth mining area monitoring method based on intelligent remote sensing, and can solve the technical problem of low monitoring efficiency of rare earth mining areas. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the rare earth mining area monitoring method based on intelligent remote sensing provided in the above-mentioned embodiment, and will not be repeated here.

[0126] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the rare earth mining area monitoring method based on intelligent remote sensing as described above.

[0127] The computer program product provided by this application can solve the technical problem of low monitoring efficiency of rare earth mining areas. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the rare earth mining area monitoring method based on intelligent remote sensing provided by the above embodiment, which will not be repeated here.

[0128] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A rare earth mining area monitoring method based on intelligent remote sensing, characterized in that: The method includes: When receiving the aerial image, preprocessing the aerial image to obtain initial image information; Based on a preset feature extraction model, extracting rare earth mining area feature information of the initial image information; Based on a preset artificial intelligence model, the characteristic information of the rare earth mining area is identified, the rare earth mining area monitoring and identification results are obtained, and the corresponding alarm plan is executed.

2. The method according to claim 1, characterized in that The aerial images include high spatial resolution aerial images and high spectral resolution aerial images.

3. The method according to claim 1, characterized in that The step of extracting the rare earth mining area feature information of the initial image information based on a preset feature extraction model includes: Obtain image information samples and rare earth mining area feature labels; Sending the image information sample to a preset model to be trained; Based on the model to be trained, feature extraction is performed on the spectral information and spatial information of the image information sample to obtain a rare earth mining area feature extraction result; Analyze the difference between the rare earth mining area feature extraction result and the rare earth mining area feature label to obtain an extraction loss value; If the extraction loss value does not meet the preset loss standard, return to the step of performing feature extraction on the spectral information and spatial information of the image information sample based on the model to be trained to obtain the feature extraction result of the rare earth mining area, and stop training until the extraction loss value meets the loss standard to obtain a feature extraction model that meets the loss standard.

4. The method according to claim 1, characterized in that The steps of obtaining the rare earth mining area monitoring and identification results and executing the corresponding alarm scheme include: If the spectral information of the rare earth mining area characteristic information is identified to be similar to the preset spectral information of the near-mine surrounding rock alteration, an alarm message of the discovery of a new mineral is sent to the preset mineral management equipment.

5. The method according to claim 1, characterized in that The steps of obtaining the rare earth mining area monitoring and identification results and executing the corresponding alarm scheme include: If it is identified that the difference between the vegetation coverage space information of the rare earth mining area characteristic information and the preset historical vegetation coverage information exceeds a preset threshold, an alarm message of abnormal vegetation coverage is sent to a preset environmental management device.

6. The method according to claim 1, characterized in that The steps of obtaining the rare earth mining area monitoring and identification results and executing the corresponding alarm scheme include: If it is identified that the rare earth mineral spatial information of the rare earth mining area characteristic information is different from the reported rare earth mineral mining area, an alarm message of rare earth mineral mining abnormality is sent to the mineral management device.

7. A rare earth mining area monitoring device based on intelligent remote sensing, characterized in that: The device comprises: A processing module, configured to pre-process the aerial image to obtain initial image information when the aerial image is received; An extraction module, used for extracting rare earth mining area feature information of the initial image information based on a preset feature extraction model; The alarm module is used to identify the characteristic information of the rare earth mining area based on a preset artificial intelligence model, obtain the monitoring and identification results of the rare earth mining area, and execute the corresponding alarm plan.

8. A rare earth mining area monitoring device based on intelligent remote sensing, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the rare earth mining area monitoring method based on intelligent remote sensing as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the rare earth mining area monitoring method based on intelligent remote sensing as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the rare earth mining area monitoring method based on intelligent remote sensing as described in any one of claims 1 to 6 are implemented.

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