An ecological quality evaluation device for a mine restoration area based on a remote sensing ecological index

Abstract

The application discloses a kind of based on remote sensing ecological index's mine repair area ecological quality evaluation device, belong to production repair technical field, comprising: remote sensing data acquisition module, data preprocessing module, ecological index calculation module, weight assignment module, comprehensive evaluation module, dynamic monitoring module, result output module and main control module, each module is connected through data bus, realize the bidirectional transmission of data and collaborative work;For the defects of existing mine repair area ecological quality evaluation technology, combined with the technical advantages of remote sensing ecological index, the integrity and accuracy of data are improved;The ecological characteristics of mine repair area and the importance difference of each index are considered, the calculation accuracy and pertinence of RSEI index are improved, so that the evaluation result is more in line with the actual ecological condition of mine repair area;A multidimensional comprehensive evaluation system is built, and comprehensive, accurate and dynamic evaluation of ecological quality is realized.

Description

Technical Field

[0001] This invention belongs to the field of ecological restoration technology, specifically relating to an ecological quality evaluation device for mine restoration areas based on remote sensing ecological indices. Background Technology

[0002] Mining operations can lead to ecological problems such as damage to surface vegetation and soil erosion. To mitigate the ecological damage caused by mining, mine ecological environment restoration is typically implemented through human intervention, such as planting vegetation and controlling soil erosion. Assessing the quality of mine ecological environment restoration is fundamental to ensuring its effectiveness. Traditional assessment methods often rely on manual sampling and single-indicator analysis, lacking a quantitative assessment of the overall ecosystem's integrity and dynamics. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an ecological quality assessment device for mine restoration areas based on remote sensing ecological indices. The device includes: a remote sensing data acquisition module, a data preprocessing module, an ecological index calculation module, a weight assignment module, a comprehensive evaluation module, a dynamic monitoring module, a result output module, and a main control module. These modules are connected via a data bus to enable bidirectional data transmission and collaborative operation. The main control module is electrically connected to each module and controls the working sequence and parameter settings of each module to ensure stable operation of the device.

[0004] Furthermore, the remote sensing data acquisition module is used to acquire multi-source remote sensing data of the mine restoration area, including optical remote sensing data, thermal infrared remote sensing data, and radar remote sensing data, providing basic data support for ecological index calculation and ecological quality assessment. The remote sensing data acquisition module includes a satellite remote sensing receiving unit, an UAV remote sensing unit, and a data storage unit. The satellite remote sensing receiving unit is used to receive remote sensing data transmitted by Landsat and Sentinel-2 satellites, covering the visible light band, near-infrared band, mid-infrared band, and thermal infrared band, with a data resolution of not less than 10m, and is able to acquire basic ecological information such as vegetation cover, soil moisture, and surface temperature of the mine restoration area.

[0005] Furthermore, the UAV remote sensing unit is used to conduct high-precision remote sensing photography of key areas in the mine restoration area, acquire high-resolution optical image data, make up for the lack of resolution of satellite remote sensing data, and achieve accurate monitoring of key areas; The data storage unit uses a combination of solid-state drives and cloud storage to store acquired multi-source remote sensing data, preprocessed data, calculation results and evaluation reports. The storage capacity is no less than 1TB, supporting long-term data preservation and fast retrieval, and also has a data backup function to prevent data loss. The data preprocessing module is used to preprocess the multi-source remote sensing data acquired by the remote sensing data acquisition module, eliminate data noise, correct data bias, ensure data accuracy and consistency, and provide a high-quality data foundation for subsequent ecological index calculations.

[0006] Furthermore, the data preprocessing module includes a radiometric correction unit, a geometric correction unit, an image fusion unit, a cloud removal and noise reduction unit, and a data normalization unit. The specific functions of each unit are as follows: The radiometric correction unit is used to perform radiometric correction on satellite and UAV remote sensing data, eliminating the effects of atmospheric absorption, scattering, and sensor errors on the remote sensing data. It converts the grayscale values ​​of the remote sensing image into the true surface reflectance. The radiometric correction formula is as follows:

[0007] in, This represents the corrected surface radiance. The original grayscale values ​​of the remote sensing image. , These are the minimum and maximum gray values ​​for this band, respectively. , These are the minimum and maximum radiance for that band, respectively, which can be obtained from the satellite sensor parameter manual.

[0008] Furthermore, the geometric correction unit is used to perform geometric correction on remote sensing images, eliminating geometric distortions caused by satellite attitude, orbital offset, and terrain undulation. It adopts a quadratic polynomial correction method, combined with ground control points in the mine restoration area, to correct the remote sensing images to a unified geographic coordinate system. The correction error does not exceed 1 pixel, ensuring spatial consistency of remote sensing data from different periods and sources.

[0009] Furthermore, the image fusion unit is used to fuse satellite remote sensing data with UAV remote sensing data. It employs a wavelet transform fusion algorithm to combine the wide coverage advantage of satellite remote sensing data with the high resolution advantage of UAV remote sensing data, generating a fused image that combines both wide coverage and high resolution. The fusion formula is as follows:

[0010] in, The pixel values ​​of the merged image. Let be the weighting coefficient for the i-th type of remote sensing image. Let be the pixel value of the i-th type of remote sensing image at position (x,y), and n be the number of remote sensing image types. The weighting coefficient is calculated using the entropy weighting method to ensure the clarity and information integrity of the fused image.

[0011] The beneficial effects of this invention include: This invention addresses the shortcomings of existing ecological quality assessment technologies for mine restoration areas. Combining the technological advantages of remote sensing ecological indices, it proposes a multi-source remote sensing data fusion solution. This solution integrates satellite remote sensing and UAV remote sensing data, leveraging the advantages of satellite remote sensing's wide coverage and the high resolution of UAV remote sensing. It solves the technical problems of insufficient accuracy in large-scale monitoring and limited range of high-precision monitoring in traditional remote sensing assessments. This achieves a combination of full coverage of the mine restoration area and precise monitoring of key areas, improving the completeness and accuracy of the data.

[0012] The calculation algorithm for the RSEI index was optimized by introducing the Dung Beetle Optimization (DBO) algorithm to improve the traditional Principal Component Analysis (PCA). This solved the problems of unreasonable weight allocation and poor adaptability of principal components in the traditional PCA method. At the same time, the combined weighting method of the Analytic Hierarchy Process (AHP) and the entropy weight method was used to consider the ecological characteristics of the mine restoration area and the differences in the importance of each indicator, which improved the calculation accuracy and pertinence of the RSEI index. This made the assessment results more consistent with the actual ecological conditions of the mine restoration area. The results have been verified to control the assessment bias RMSE within 0.05, which is more than 30% more accurate than the traditional method.

[0013] A multi-dimensional comprehensive evaluation system was constructed, which not only classifies ecological quality levels based on the RSEI index, but also combines artificial sampling data for deviation analysis to verify the accuracy of the evaluation. At the same time, it analyzes the dynamic trend of ecological quality changes and evaluates the effectiveness of mine restoration projects. This solves the problem of traditional evaluation methods "lacking in integrity, dynamism and accuracy verification", and realizes a comprehensive, accurate and dynamic evaluation of ecological quality. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the structure of an ecological quality assessment device for a mine restoration area based on a remote sensing ecological index, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the process for an ecological quality assessment device for mine restoration areas based on remote sensing ecological indices, according to the present invention. Figure 3 This is a schematic diagram of the greenness index data of the present invention; Figure 4 This is a schematic diagram of humidity index data for the present invention; Figure 5This is a schematic diagram of the heat index data of the present invention. Detailed Implementation

[0016] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0017] like Figure 1-5 As shown, this invention discloses an ecological quality assessment device for mine restoration areas based on remote sensing ecological indices, comprising: a remote sensing data acquisition module, a data preprocessing module, an ecological index calculation module, a weight assignment module, a comprehensive evaluation module, a dynamic monitoring module, a result output module, and a main control module. The modules are connected via a data bus to achieve bidirectional data transmission and collaborative operation. The main control module is electrically connected to each module and is used to control the working sequence and parameter settings of each module to ensure stable operation of the device.

[0018] The remote sensing data acquisition module is used to acquire multi-source remote sensing data of the mine restoration area, including optical remote sensing data, thermal infrared remote sensing data, and radar remote sensing data, providing basic data support for ecological index calculation and ecological quality assessment. The remote sensing data acquisition module includes a satellite remote sensing receiving unit, a UAV remote sensing unit, and a data storage unit, wherein: The satellite remote sensing receiving unit is used to receive remote sensing data transmitted by satellites such as Landsat and Sentinel-2, covering the visible light band, near-infrared band, mid-infrared band and thermal infrared band, with a data resolution of no less than 10m, and can obtain basic ecological information such as vegetation cover, soil moisture and surface temperature in the mine restoration area. The UAV remote sensing unit is used to conduct high-precision remote sensing photography of key areas in the mine restoration area (such as slope restoration area, vegetation planting area, and soil erosion prone area), and to acquire high-resolution (not less than 0.5m) optical image data to make up for the lack of resolution of satellite remote sensing data and achieve accurate monitoring of key areas. The data storage unit uses a combination of solid-state drives and cloud storage to store acquired multi-source remote sensing data, preprocessed data, calculation results and evaluation reports. The storage capacity is no less than 1TB, supporting long-term data preservation and fast retrieval, and also has a data backup function to prevent data loss.

[0019] The data preprocessing module is used to preprocess the multi-source remote sensing data acquired by the remote sensing data acquisition module, eliminating data noise, correcting data biases, and ensuring data accuracy and consistency, thus providing a high-quality data foundation for subsequent ecological index calculations. The data preprocessing module includes a radiometric correction unit, a geometric correction unit, an image fusion unit, a cloud removal and noise reduction unit, and a data normalization unit. The specific functions of each unit are as follows: The radiometric correction unit is used to perform radiometric correction on satellite and UAV remote sensing data, eliminating the effects of atmospheric absorption, scattering, and sensor errors on the remote sensing data. It converts the grayscale values ​​of the remote sensing image into the true surface reflectance. The radiometric correction formula is as follows:

[0020] in, This represents the corrected surface radiance. The original grayscale values ​​of the remote sensing image. , These are the minimum and maximum gray values ​​for this band, respectively. , These are the minimum and maximum radiance for that band, respectively, which can be obtained from the satellite sensor parameter manual.

[0021] The geometric correction unit is used to perform geometric correction on remote sensing images, eliminating geometric distortions caused by factors such as satellite attitude, orbital offset, and terrain undulation. It adopts a quadratic polynomial correction method, combined with ground control points (GCPs) in the mine restoration area, to correct the remote sensing images to a unified geographic coordinate system (WGS84 coordinate system). The correction error does not exceed 1 pixel, ensuring spatial consistency of remote sensing data from different periods and sources.

[0022] The image fusion unit is used to fuse satellite remote sensing data and UAV remote sensing data. It employs a wavelet transform fusion algorithm to combine the wide coverage advantage of satellite remote sensing data with the high resolution advantage of UAV remote sensing data, generating a fused image that combines both wide coverage and high resolution. The fusion formula is as follows:

[0023] in, The pixel values ​​of the merged image. Let be the weighting coefficient for the i-th type of remote sensing image. Let be the pixel value of the i-th type of remote sensing image at position (x,y), and n be the number of types of remote sensing images (n=2 in this device, i.e., satellite remote sensing images and UAV remote sensing images). The weighting coefficient is calculated by the entropy weighting method to ensure the clarity and information integrity of the fused images.

[0024] The cloud removal and noise reduction unit is used to eliminate clouds, fog, shadows, and random noise in remote sensing images. It employs a multi-band thresholding method to remove cloud-covered areas and a median filtering algorithm to remove random noise. The median filter window size is set to 3×3, and the calculation formula is as follows:

[0025] in, Let be the pixel value at position (x,y) in the denoised image. The pixel value at position (xi, yj) in the original image is denoted by 'median', which represents the median value of the pixels within the window. This algorithm can effectively remove image noise while preserving the edge information of the image.

[0026] The data normalization unit is used to normalize the preprocessed remote sensing data of each band, eliminate the dimensional differences between different bands, and unify the data value range to [0,1]. This facilitates subsequent ecological index calculation and multi-indicator fusion. The normalization adopts the minimum-maximum normalization method, and the formula is as follows:

[0027] Where NI is the normalized index value, and I is the original index value. and These are the maximum and minimum values ​​of the indicator, respectively. Through normalization, the comparability of each ecological indicator is ensured.

[0028] The ecological index calculation module is used to calculate the Remote Sensing Ecological Index (RSEI) of the mine restoration area based on preprocessed remote sensing data. This index integrates four core ecological indicators: greenness, humidity, aridity, and heat, to comprehensively characterize the ecological quality of the mine restoration area. The ecological index calculation module includes a greenness index calculation unit with the following functions: The greenness index calculation unit is used to calculate the greenness index of the mine restoration area. It uses the Normalized Difference Vegetation Index (NDVI) as the characterization parameter for the greenness index. NDVI effectively reflects the vegetation coverage and growth status and is a core indicator for measuring the vegetation restoration effect in the mine restoration area. The calculation formula is as follows:

[0029] in, The surface reflectance is in the near-infrared band. NDVI represents the surface reflectance in the red light band, with a value ranging from -1 to 1. A higher value indicates higher vegetation cover and better ecological conditions. When NDVI < 0, the area is mainly water bodies, bare rock, or construction land. When 0 ≤ NDVI < 0.3, the area is low vegetation cover. When 0.3 ≤ NDVI < 0.6, the area is medium vegetation cover. When NDVI ≥ 0.6, the area is high vegetation cover.

[0030] The humidity index calculation unit is used to calculate the humidity index of the mine restoration area. It uses the Humidity Index (WET) as the characterization parameter. WET reflects the humidity status of surface soil and vegetation and is closely related to soil erosion and soil degradation in the mine restoration area. The calculation formula is as follows:

[0031] in, , These are the surface reflectances for the blue and green light bands, respectively. , The values ​​are the surface reflectance of the first and second shortwave infrared bands, respectively. The range of WET values ​​is [-1, 1]. The larger the value, the higher the surface humidity and the lower the risk of soil erosion.

[0032] The dryness index calculation unit is used to calculate the dryness index of the mine restoration area. It uses the Normalized Bare Soil Index (NDBSI) as the characterization parameter. NDBSI reflects the distribution of bare soil and built-up land, and is an important indicator for measuring soil degradation and insufficient vegetation cover in the mine restoration area. The calculation formula is as follows:

[0033] The meaning of reflectivity for each band is consistent with the above formula. The range of NDBSI is [-1, 1]. The larger the value, the higher the proportion of bare soil and built-up land, and the worse the ecological condition.

[0034] The thermal index calculation unit is used to calculate the thermal index of the mine restoration area, using land surface temperature (LST) as the characterization parameter. LST reflects the heat distribution on the land surface and is closely related to factors such as vegetation cover and soil moisture. It is an important indicator for measuring the stability of the ecosystem in the mine restoration area. The land surface temperature is calculated using thermal infrared remote sensing data, and the calculation formula is as follows:

[0035] in, For surface temperature, , These are the calibration coefficients for the thermal infrared sensor, which can be obtained from satellite sensor parameter manuals. This represents the surface radiance in the thermal infrared band.

[0036] The RSEI integrated calculation unit is used to calculate the Remote Sensing Ecological Index (RSEI) of the mine restoration area based on the four core ecological indicators mentioned above, using principal component analysis (PCA) combined with the Dung Beetle Optimization (DBO) algorithm, thus achieving a quantitative representation of ecological quality. Compared with the traditional principal component analysis method, the introduction of the Dung Beetle Optimization (DBO) algorithm can optimize the weight allocation of each principal component, improve the calculation accuracy of the RSEI index, and adapt to the special ecological environment of the mine restoration area. The specific calculation steps are as follows: Step 1: Construct an ecological index matrix X from the normalized NDVI, WET, NDBSI, and LST data. The matrix has dimensions m×n, where m is the number of pixels in the remote sensing image and n is the number of ecological indicators (n=4). Step 2: Perform principal component analysis on the index matrix X to calculate the covariance matrix C. The formula for calculating the covariance matrix is ​​as follows:

[0037] in, Let be the ecological indicator vector of the i-th pixel. This is the mean vector of ecological indicators for all pixels; Step 3: Solve for the eigenvalues ​​of the covariance matrix C (sorted from largest to smallest) and their corresponding feature vectors The larger the eigenvalue, the higher the contribution rate of the corresponding principal component; Step 4: Calculate the contribution rate of each principal component:

[0038] Principal components with a cumulative contribution rate of over 85% are used as principal components for RSEI index calculation; Step 5: Use the Dung Beetle Optimization (DBO) algorithm to optimize the contribution rate of each principal component, and obtain the optimized weight coefficients. The optimization objective is to minimize the evaluation error, and the fitness function is as follows:

[0039] in, Let be the predicted RSEI value of the i-th pixel. Let W be the actual ecological quality score of the i-th pixel (obtained through manual sampling verification), and let W be the weight coefficient vector. Step 6: Based on the optimized weighting coefficients, calculate the RSEI index using the following formula:

[0040] Where t is the number of principal components. The RSEI score is the score of the k-th principal component. The RSEI value ranges from [0,1]. The larger the value, the better the ecological quality of the mine restoration area. Step 7: Normalize the calculated RSEI index to ensure that its value range is strictly between [0,1]. The normalization formula is formula (3) to obtain the final RSEI index value.

[0041] The weighting module is used to assign weights to the four core ecological indicators—greenness, humidity, dryness, and heat—and their principal components, ensuring the scientific rigor and relevance of the RSEI index calculation. Considering the ecological characteristics of the mine restoration area (with vegetation restoration and soil and water conservation as core objectives), a combined weighting method using the Analytic Hierarchy Process (AHP) and entropy weighting is adopted to avoid the limitations of a single weighting method. The specific steps are as follows: Step 1: Construct a hierarchical model. The target layer is the ecological quality assessment of the mine restoration area, the criteria layer consists of four ecological indicators: greenness (NDVI), humidity (WET), dryness (NDBSI), and heat (LST), and the scheme layer consists of each assessment unit. Step 2: Use the Analytic Hierarchy Process (AHP) to determine subjective weights. By inviting experts in fields such as ecological environment monitoring and mine restoration, the importance of each indicator in the criterion layer is compared pairwise to construct a judgment matrix A. The value of the judgment matrix adopts the 1-9 scale (1 indicates that the two indicators are equally important, and 9 indicates that one indicator is extremely more important than the other). Step 3: Perform a consistency check on the judgment matrix A, and calculate the consistency index CI and the consistency ratio CR, where CI = (λ_max - n) / (n - 1), λ_max is the largest eigenvalue of the judgment matrix, and n is the number of indices (n = 4); CR = CI / RI, RI is the average random consistency index (RI = 0.90 when n = 4); when CR < 0.1, the judgment matrix meets the consistency requirements, otherwise the judgment matrix needs to be readjusted. Step 4: Determine objective weights using the entropy weight method. Based on the preprocessed ecological indicator data, calculate the entropy value of each indicator. and coefficient of difference The formula for calculating entropy is as follows:

[0042] in, , The normalized value of the j-th index for the i-th pixel, where m is the number of pixels; the difference coefficient. The larger the difference coefficient, the higher the discrimination of the indicator and the greater its weight. Step 5: Calculate the combined weights. The subjective weights (AHP weights) and objective weights (entropy weights) are linearly combined. The formula for calculating the combined weights is as follows:

[0043] in, Let the combined weight of the j-th indicator be , Let j be the AHP weight of the j-th indicator. Let α be the entropy weight of the j-th indicator, and α be the weight coefficient (α=0.5) to ensure the balance between subjective and objective weights. Step 6: Input the combined weights into the ecological index calculation module for the optimized calculation of the RSEI index, and store the combined weights in the data storage unit for easy subsequent querying and adjustment.

[0044] The comprehensive assessment module is used to conduct a multi-dimensional comprehensive assessment of ecological quality based on the RSEI index calculation results and the actual ecological conditions of the mine restoration area, classify ecological quality levels, and analyze the rationality and effectiveness of the assessment results. The comprehensive assessment module includes a level classification unit, a deviation analysis unit, and an effectiveness assessment unit. The specific functions of each unit are as follows: The grading unit is used to classify the ecological quality of the mine restoration area into five levels based on the RSEI index value: excellent, good, medium, poor, and very poor, with specific grading criteria.

[0045] The deviation analysis unit is used to compare and analyze the evaluation results calculated from the RSEI index with manually sampled verification data, calculate the evaluation deviation, and verify the accuracy of the evaluation results. The evaluation deviation is characterized by the root mean square error (RMSE), and the calculation formula is as follows:

[0046] Where n is the number of validation samples. Let i be the RSEI evaluation value of the i-th validation sample. The actual value of the manually sampled i-th validation sample; when RMSE < 0.05, the evaluation result meets the accuracy requirements; when 0.05 ≤ RMSE < 0.1, the weight coefficients are fine-tuned; when RMSE ≥ 0.1, data preprocessing and RSEI index calculation are performed again.

[0047] The effectiveness evaluation unit is used to analyze the dynamic trend of the RSEI index by combining information such as the implementation time and remediation measures of the mine restoration project, and to evaluate the effectiveness of the mine restoration project. By calculating the mean and rate of change of the RSEI index at different periods (before restoration, during restoration, and after restoration), the improvement of ecological quality is determined. The formula for calculating the rate of change is as follows:

[0048] in, The rate of change of the RSEI index. This represents the average RSEI index for subsequent periods. The average RSEI index for the previous period; when When 0 < 0, it indicates that the repair effect is significant; when 0 < ..., it indicates that the repair effect is significant. When the percentage is ≤10%, it indicates a good repair effect; when When, it indicates that the repair was ineffective; when This indicates ecological degradation, necessitating adjustments to the restoration plan.

[0049] The results output module is used to output ecological index calculation results, comprehensive assessment results, dynamic monitoring results, etc., in various formats, providing staff with intuitive and clear assessment information. The results output module includes a display unit, a printing unit, a data export unit, and a remote transmission unit. The specific functions of each unit are as follows: The display unit uses a high-definition touch screen (resolution not less than 1920×1080) to display RSEI index distribution map, ecological quality level classification map, dynamic change trend map, abnormal early warning information, etc. in real time. It supports zooming, panning, querying and other operations, making it easy for staff to view the assessment results intuitively. The printing unit, connected to the display unit, supports the printing of evaluation reports, index distribution charts, grade classification charts, etc., with a printing resolution of no less than 300dpi, meeting the needs of archiving and reporting. The data export unit supports exporting RSEI index data, evaluation result data, remote sensing data, etc., to Excel, Shp, TIF and other formats, which facilitates subsequent data processing and analysis. The remote transmission unit uses 5G / 4G wireless communication or wired Ethernet communication to remotely transmit assessment results and monitoring data to the mine ecological environment supervision platform, realizing data sharing and remote supervision, and supporting viewing on multiple terminals (computers, mobile phones).

[0050] For data export units supporting multiple formats such as Excel, Shp, and TIF, differentiated encryption strategies are implemented: Structured data encryption: For RSEI index data and evaluation results in Excel format, file-level encryption using the SM4 algorithm is employed, while also supporting individual encryption of sensitive fields at the cell level. A complex password is mandatory during export, with a minimum length of 12 characters, including uppercase and lowercase letters, numbers, and special characters. Hierarchical export permissions are supported; ordinary users can only export anonymized statistical data, while administrators can export the complete original data. Spatial data encryption: For Shp format vector data, a comprehensive packaging encryption method is used. Related files such as Shp, DBF, and SHX are merged into an encrypted compressed package. Sensitive attributes in the DBF attribute table (such as mine coordinates and mining area) are stored separately with encrypted encryption, requiring user permission verification for decryption. Image data encryption: For TIF format remote sensing images, block encryption technology is used, dividing the image into 256×256 pixel blocks for independent encryption. On-demand decryption and viewing are supported; users can only decrypt image areas within their authorized scope, preventing full data leakage. Export Audit Traceability: All export operations generate immutable audit logs, recording information such as the exporter, export time, file name, format, size, purpose, and IP address. Logs are retained for at least 6 months and support querying and exporting audit reports based on specific conditions.

[0051] For remote transmission units using 5G / 4G wireless communication or wired Ethernet, a multi-layered transmission encryption system is constructed: Transmission channel encryption: All remote transmissions establish secure channels using the TLS 1.3 protocol and employ the national cryptographic algorithm SM2 for two-way authentication to prevent man-in-the-middle attacks. 5G / 4G wireless transmissions additionally utilize a dedicated operator-provided APN network, physically isolated from the public network; wired Ethernet transmissions deploy IPSec VPN tunnels to achieve end-to-end encryption. Application layer data encryption: Evaluation results and monitoring data transmitted are further encrypted at the application layer using the SM4 algorithm, ensuring that even if the transmission channel is compromised, the data cannot be decrypted. Sensitive data employs end-to-end encryption, with encryption and decryption operations performed only at the sender and receiver; intermediate nodes cannot access plaintext data.

[0052] Transmission Integrity Protection: An SM3 hash checksum is appended to all transmitted data packets. The receiver verifies the checksum's consistency before processing. If data tampering is detected, the data packet is immediately discarded and an alarm is triggered. Secure Multi-Terminal Access: Digital certificate authentication is used on the computer side, and a mobile security SDK encrypted with national cryptographic algorithms is used on the mobile phone side, supporting biometric authentication such as fingerprint and facial recognition. All terminal access requires device registration and binding; unauthorized devices cannot access the system. Remote erasure of cached data on lost or stolen terminals is supported.

[0053] A unified key management system is established, employing a hierarchical key architecture: the root key is stored in the Hardware Security Module (HSM), the master key is derived from the root key, and the data keys are derived from the master key. Keys are automatically rotated periodically, with a rotation cycle not exceeding 90 days. Key destruction employs a secure erasure method to ensure irrecoverability.

[0054] The main control module is the core control unit of the entire device. It uses an embedded processor (such as ARM Cortex-A9) and runs on a Linux operating system. It controls the working timing, parameter settings, and data interaction of each module to ensure the stable and efficient operation of the device. The main control module has a built-in control program that can automate the processes of data acquisition, preprocessing, calculation, evaluation, and output. It also supports a manual operation mode, allowing staff to manually set parameters and trigger relevant functions as needed. In addition, the main control module has a fault diagnosis function that can monitor the working status of each module in real time. When a module malfunctions, it automatically issues a fault prompt and records the fault information for easy repair and maintenance.

[0055] The ecological quality assessment device for mine restoration areas based on remote sensing ecological indices of this invention has the following workflow and specific steps: Step 1: The device is started, and the main control module initializes the parameters of each module, sets the remote sensing data acquisition cycle, ecological index calculation parameters, assessment level standards, early warning thresholds, etc. Step 2: The remote sensing data acquisition module acquires multi-source remote sensing data (optical remote sensing data, thermal infrared remote sensing data, and radar remote sensing data) of the mine restoration area through the satellite remote sensing receiving unit and the UAV remote sensing unit, and stores it in the data storage unit; Step 3: The data preprocessing module performs radiometric correction, geometric correction, image fusion, cloud removal and noise reduction, and data normalization on the acquired remote sensing data to generate high-quality preprocessed data, which is then transmitted to the ecological index calculation module. Step 4: The weight assignment module uses the Analytic Hierarchy Process (AHP) combined with the entropy weight method to calculate the combined weights of the four ecological indicators: greenness, humidity, dryness, and heat, and then transmits them to the ecological index calculation module. Step 5: The ecological index calculation module calculates four core ecological indicators—NDVI, WET, NDBSI, and LST—based on preprocessed data and combined weights. Then, it calculates the RSEI index of the mine restoration area through principal component analysis (PCA) combined with the dung beetle optimization algorithm (DBO) and transmits it to the comprehensive evaluation module. Step 6: The comprehensive assessment module classifies the ecological quality level based on the RSEI index value, performs deviation analysis with the artificial sampling data to verify the assessment accuracy, and evaluates the restoration effectiveness in conjunction with the restoration project information, generates a comprehensive assessment report, and transmits it to the dynamic monitoring module and the result output module. Step 7: The dynamic monitoring module analyzes the dynamic trend of ecological quality based on RSEI index data from different periods, monitors for any abnormalities, and if an abnormality occurs, triggers an early warning signal and transmits it to the result output module. Step 8: The results output module outputs the RSEI index, assessment report, dynamic trends, and early warning information through the display unit, printing unit, data export unit, and remote transmission unit, for staff to view, archive, and monitor. Step 9: Repeat steps 2-8 according to the preset cycle to achieve dynamic monitoring and continuous assessment of the ecological quality of the mine restoration area. If staff find any deviation in the assessment results, they can manually adjust the parameters through the main control module and recalculate and assess.

[0056] This invention addresses the shortcomings of existing ecological quality assessment technologies for mine restoration areas. Combining the technological advantages of remote sensing ecological indices, it proposes a multi-source remote sensing data fusion solution. This solution integrates satellite remote sensing and UAV remote sensing data, leveraging the advantages of satellite remote sensing's wide coverage and the high resolution of UAV remote sensing. It solves the technical problems of insufficient accuracy in large-scale monitoring and limited range of high-precision monitoring in traditional remote sensing assessments. This achieves a combination of full coverage of the mine restoration area and precise monitoring of key areas, improving the completeness and accuracy of the data.

[0057] Comprehensive assessment: It integrates four core ecological indicators—greenness, humidity, dryness, and heat—to construct a remote sensing ecological index (RSEI), which can comprehensively characterize the ecosystem status of the mine restoration area. It overcomes the limitations of traditional single-indicator assessments, achieves a holistic assessment of ecological quality, and covers the entire mine restoration area, ensuring the representativeness of the assessment results.

[0058] High assessment accuracy: The multi-source remote sensing data fusion technology is adopted to improve the resolution and completeness of the data; the RSEI index calculation algorithm is optimized, and the dung beetle optimization algorithm and combined weight method are introduced to solve the problems of unreasonable weight allocation and poor adaptability of traditional algorithms. At the same time, the accuracy of the assessment results is ensured through manual sampling verification and deviation analysis. The assessment accuracy is improved by more than 30% compared with traditional methods, and it can accurately reflect the ecological quality status of the mine restoration area.

[0059] Strong dynamic monitoring capability: It realizes regular updates of remote sensing data and real-time updates of assessment results, can capture the dynamic trend of ecological quality changes in mine restoration areas, and sets up anomaly early warning function to promptly detect problems in the restoration process, providing timely and reliable basis for the optimization and adjustment of restoration plans, and overcoming the shortcomings of traditional static assessment.

[0060] High degree of automation: It realizes the automated operation of data acquisition, preprocessing, index calculation, evaluation and output processes without human intervention, which greatly reduces the workload of staff, shortens the evaluation cycle and improves the evaluation efficiency. The work that traditional manual evaluation takes 15-30 days to complete can be completed in 1-3 days by this device, improving efficiency by more than 80%.

[0061] Wide applicability: Designed specifically for the unique ecological environment of mine restoration areas, it is suitable for the ecological quality assessment needs of various mines (open-pit mines and underground mines). It also supports parameter adjustment, allowing for adjustments to the weight of assessment indicators, grading standards, and early warning thresholds based on the restoration goals and ecological characteristics of different mines, making it widely applicable.

[0062] Highly practical: It has multiple output formats, supports display, printing, data export and remote transmission, which makes it easy for staff to view, archive and report. At the same time, it realizes the sharing of assessment data and remote supervision, providing reliable technical support for the effectiveness verification, scheme optimization and supervision of mine ecological restoration projects. It has important engineering application value and promotion prospects.

[0063] Cost-controllable: Utilizing mature remote sensing technology and embedded hardware, the device has a simple structure, stable operation, and low maintenance costs. At the same time, it reduces the workload of manual sampling and laboratory testing, thereby lowering the evaluation cost. Compared with traditional manual evaluation methods, it can reduce the evaluation cost by more than 50%.

[0064] To further verify the practical application effect, stability, and assessment accuracy of the device of this invention, a typical open-pit coal mine restoration area in North my country was selected as a case study. This restoration area covers a total area of ​​12.6 km², has a mining history of over 20 years, and suffers from typical mine ecological problems such as vegetation destruction, exposed soil, soil erosion, and abnormal surface thermal environment. An ecological restoration project was initiated in March 2022, employing comprehensive restoration measures including vegetation replanting, soil improvement, slope stabilization, and drainage management. From June 2023 to June 2024, the device of this invention was used to conduct a 12-month dynamic ecological quality assessment and monitoring of the restoration area. Simultaneously, a comparative test was conducted using traditional manual sampling combined with a single remote sensing assessment method to verify the device's practical application performance.

[0065] Case test parameter settings: In this application test, the core parameters of the device were set as follows: satellite remote sensing data was Sentinel-2 satellite data with a resolution of 10m; UAV remote sensing images had a resolution of 0.5m, focusing on core areas such as slope restoration areas and vegetation replanting areas; the data monitoring and update cycle was 1 month; the ecological indicator weights were combined using the AHP-entropy weight method, with α=0.5; the ecological quality early warning threshold was set to 0.6 (good standard); a total of 80 sets of manual sampling verification samples were evenly distributed throughout the restoration area, covering different restoration stages and different landform areas.

[0066] The measured data of the core indicators were statistically analyzed. Through the automated calculation of this device, the average values ​​of the four core ecological indicators and the average RSEI index of the mine restoration area before, during and after restoration were obtained. The specific measured data are shown in the table below:

[0067] As shown in the table above, after two years of ecological restoration and management, the vegetation coverage of the mine restoration area has significantly improved, soil moisture has continued to increase, the proportion of bare soil has been greatly reduced, the surface thermal environment has continued to improve, the RSEI index has increased from 0.196 to 0.785, and the ecological quality has improved from "poor" to "good". The overall restoration effect is significant, with an overall change rate of RSEI index ΔR=300.5%, which meets the standard for significant restoration effect.

[0068] This device has been applied in various types of mine restoration areas, covering open-pit coal mines, underground coal mine subsidence areas, rare earth tailings ponds, and alpine grassland metal mining areas, and has achieved good application results.

[0069] Assessment of the restoration of a subsidence area in a high-water-level underground coal mine in East China. Basic information about the restoration area: Located in the East Third Mining Area of ​​Pan'er Mine in Huainan City, Anhui Province, the area covers 8.7 km². A restoration project was initiated in 2021, operating while mining resumed. The project adopted a zoned management model: "leveled farmland - soil and water conservation on slopes - aquatic vegetation in shallow water areas - ecological fisheries in deep water areas." Before restoration, the area suffered from large-scale waterlogging due to coal mining subsidence, resulting in severe damage to farmland and extremely poor ecological environment quality.

[0070] Device parameter settings: Sentinel-2 satellite 10m resolution data + UAV 0.3m resolution data, plus SBAS-InSAR radar data to monitor surface subsidence and deformation, monitoring period set to 15 days, RSEI early warning threshold set to 0.55.

[0071] Changes in key metrics: Before repair (2021): RSEI mean = 0.327, NDVI = 0.315, WET = 0.223, NDBSI = 0.689, LST = 301.72K; After repair (2024): RSEI mean = 0.652, NDVI = 0.587, WET = 0.514, NDBSI = 0.387, LST = 296.45K; Assessment results: The ecological quality of the restoration area improved from "poor" to "good", and the land restoration rate increased from 78.8% to 91.7%. By integrating subsidence deformation data with ecological quality data, this device accurately identified three areas of vegetation degradation caused by subsidence, guiding the construction team to adjust the restoration plan in a timely manner and avoiding secondary damage.

[0072] Assessment of the remediation of an ion-adsorption rare earth tailings dam in South China. Basic information of the remediation area: Located in the Zudong rare earth mine tailings dam area of ​​Longnan County, Ganzhou City, Jiangxi Province, the area covers 3.2 km². A soil improvement + Miscanthus sinensis + Napier grass composite vegetation remediation project was launched in 2020, primarily addressing soil acidification and heavy metal pollution. Before remediation, the soil pH in this area was only 4.2, the vegetation coverage was less than 20%, and soil erosion was severe.

[0073] Device parameter settings: Based on conventional multispectral data, UAV hyperspectral remote sensing data is added to retrieve soil pH and rare earth element content. The monitoring cycle is set to 1 month, and the RSEI early warning threshold is set to 0.5.

[0074] Changes in key metrics: Before remediation (2020): RSEI mean = 0.218, NDVI = 0.187, NDBSI = 0.823, soil pH = 4.2; After remediation (2024): RSEI mean = 0.546, NDVI = 0.492, NDBSI = 0.472, soil pH = 5.8; Assessment Results: This study resolved the problem of traditional methods failing to differentiate vegetation health, accurately identifying 12 areas with severe soil acidification. It guided the construction team in implementing targeted lime amendments and organic fertilizer application, increasing the vegetation survival rate from 32% to 78%. Furthermore, the device monitored the enrichment effect of *Dictamnus dasycarpus* on rare earth elements using hyperspectral data, providing data support for the recovery of rare earth resources from plants.

[0075] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A device for evaluating the ecological quality of a mine restoration area based on remote sensing ecological indices, characterized in that, include: The device comprises a remote sensing data acquisition module, a data preprocessing module, an ecological index calculation module, a weight assignment module, a comprehensive evaluation module, a dynamic monitoring module, a result output module, and a main control module. These modules are connected via a data bus to enable bidirectional data transmission and collaborative operation. The main control module is electrically connected to each module to control the working sequence and parameter settings of each module, ensuring stable operation of the device.

2. The ecological quality assessment device for mine restoration areas based on remote sensing ecological indices as described in claim 1, characterized in that, The remote sensing data acquisition module is used to acquire multi-source remote sensing data of the mine restoration area, including optical remote sensing data, thermal infrared remote sensing data, and radar remote sensing data, providing basic data support for ecological index calculation and ecological quality assessment. The remote sensing data acquisition module includes a satellite remote sensing receiving unit, an UAV remote sensing unit, and a data storage unit. The satellite remote sensing receiving unit is used to receive remote sensing data transmitted by Landsat and Sentinel-2 satellites, covering the visible light band, near-infrared band, mid-infrared band, and thermal infrared band, with a data resolution of not less than 10m, and can acquire basic ecological information such as vegetation cover, soil moisture, and surface temperature of the mine restoration area.

3. The ecological quality assessment device for mine restoration areas based on remote sensing ecological indices as described in claim 1, characterized in that, The UAV remote sensing unit is used to conduct high-precision remote sensing images of key areas in the mine restoration area, acquire high-resolution optical image data, make up for the lack of resolution of satellite remote sensing data, and achieve accurate monitoring of key areas. The data storage unit uses a combination of solid-state drives and cloud storage to store acquired multi-source remote sensing data, preprocessed data, calculation results and evaluation reports. The storage capacity is no less than 1TB, supporting long-term data preservation and fast retrieval, and also has a data backup function to prevent data loss. The data preprocessing module is used to preprocess the multi-source remote sensing data acquired by the remote sensing data acquisition module, eliminate data noise, correct data bias, ensure data accuracy and consistency, and provide a high-quality data foundation for subsequent ecological index calculations.

4. The ecological quality assessment device for mine restoration areas based on remote sensing ecological indices as described in claim 1, characterized in that, The data preprocessing module includes a radiometric correction unit, a geometric correction unit, an image fusion unit, a cloud removal and noise reduction unit, and a data normalization unit. The specific functions of each unit are as follows: The radiometric correction unit is used to perform radiometric correction on satellite and UAV remote sensing data, eliminating the effects of atmospheric absorption, scattering, and sensor errors on the remote sensing data. It converts the grayscale values ​​of the remote sensing image into the true surface reflectance. The radiometric correction formula is as follows: in, This represents the corrected surface radiance. The original grayscale values ​​of the remote sensing image. , These are the minimum and maximum gray values ​​for this band, respectively. , These are the minimum and maximum radiance for that band, respectively, which can be obtained from the satellite sensor parameter manual.

5. The ecological quality assessment device for mine restoration areas based on remote sensing ecological indices as described in claim 1, characterized in that, The geometric correction unit is used to perform geometric correction on remote sensing images, eliminating geometric distortions caused by satellite attitude, orbital offset, and terrain undulation. It adopts a quadratic polynomial correction method, combined with ground control points in the mine restoration area, to correct the remote sensing images to a unified geographic coordinate system. The correction error does not exceed 1 pixel, ensuring spatial consistency of remote sensing data from different periods and sources.

6. The ecological quality assessment device for mine restoration areas based on remote sensing ecological indices as described in claim 1, characterized in that, The image fusion unit is used to fuse satellite remote sensing data and UAV remote sensing data. It employs a wavelet transform fusion algorithm to combine the wide coverage advantage of satellite remote sensing data with the high resolution advantage of UAV remote sensing data, generating a fused image that combines both wide coverage and high resolution. The fusion formula is as follows: in, The pixel values ​​of the merged image. Let be the weighting coefficient for the i-th type of remote sensing image. Let be the pixel value of the i-th type of remote sensing image at position (x,y), and n be the number of remote sensing image types. The weighting coefficient is calculated using the entropy weighting method to ensure the clarity and information integrity of the fused image.

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