Method and system for constructing monitoring database for ecological restoration

Abstract

The present invention discloses a method and system for constructing a monitoring database for ecological restoration, which belongs to the technical field of mine ecological restoration. The method for constructing a monitoring database for ecological restoration of the present invention includes the following steps: Step 1: Collect historical data and remote sensing images of mine ecological restoration monitoring; Step 2: Establish a monitoring database; Step 3: Obtain real-time monitoring data of mine ecological restoration; Step 4: Establish a monitoring database update mechanism; Step 5: Issue an early warning signal when there is an anomaly in the data in the monitoring database. The present invention solves the problem that the data acquisition path of the prior art is relatively complicated, and the data in the database cannot be discovered in time when an anomaly occurs, which affects the integrity and reliability of the data. The present invention can more accurately evaluate the effect and progress of mine ecological restoration, and data acquisition is more efficient and convenient, effectively ensuring the security of data, and improving the success rate and quality of ecological restoration.

Description

Technical Field

[0001] The present invention relates to the technical field of mine ecological restoration, and in particular to a method and system for constructing a monitoring database for ecological restoration. Background Art

[0002] When mining mineral resources in mines, it is usually accompanied by environmental problems such as land destruction, vegetation destruction and water body destruction, and it will also lead to the depletion of mineral resources. Therefore, it is necessary to carry out ecological restoration of the mines after mining so that the damaged vegetation, land and water bodies can be repaired. In order to timely and accurately grasp the dynamic changes of vegetation, land and water bodies in mines, it is necessary to establish an ecological restoration monitoring database.

[0003] The Chinese patent publication number CN116383222A discloses a method for constructing a core database for grassland ecological restoration monitoring, which is divided into four steps: data collection, data processing and storage, data integration and transformation, and database construction. A dynamic database is established by combining historical data and real-time data on grassland ecological restoration. Data with short change cycles and high frequencies, such as meteorological and vegetation changes, are kept frequently used and updated in the database, making it convenient for staff to provide the latest grassland ecological data for subsequent proposals and on-site restoration. The method also combines DEM data, drone detailed photography, dynamic online maps and other methods to present grassland ecological restoration details in terms of spatial and temporal changes, contributing to the grid management system of grassland ecological resources.

[0004] In actual use, the above-mentioned patent obtains ecological restoration monitoring-related data in the following ways: historical data query, remote sensing imagery, real-time acquisition of data sites, manual surveys, and online maps. The data acquisition path is relatively complex, and abnormalities in the data in the database cannot be discovered in time, affecting the integrity and reliability of the data. Therefore, it does not meet existing needs. In this regard, we propose a method and system for constructing a monitoring database for ecological restoration. Summary of the Invention

[0005] The purpose of the present invention is to provide a monitoring database construction method and system for ecological restoration, which can more accurately evaluate the effect and progress of mine ecological restoration, obtain data more efficiently and conveniently, monitor database access and operation records in real time, detect abnormal behavior in time, effectively ensure data security, reduce the impact of security incidents on the database, and ensure the security and integrity of data in the monitoring database. By constructing a mine ecological monitoring database, the latest status of the mine ecological environment can be quickly grasped, the effectiveness of restoration work can be evaluated, the success rate and quality of ecological restoration can be improved, and the risk of environmental damage and safety accidents can be reduced, thereby ensuring the health and stability of the ecological environment around the mine, and solving the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing a monitoring database for ecological restoration, comprising the following steps:

[0007] Step 1: Collect historical data on mine ecological restoration monitoring and remote sensing images of mine ecological restoration;

[0008] Step 2: Establish multiple sub-databases of different types of data based on the historical data of ecological restoration in the mining area, and obtain a monitoring database based on the sub-databases of different types of data;

[0009] Step 3: Process the remote sensing images of mine ecological restoration and identify ground objects. Classify the mine ecological restoration data based on the identification results to obtain real-time monitoring data of mine ecological restoration.

[0010] Step 4: Associate the classified real-time monitoring data of mine ecological restoration with the data in the monitoring database and establish a monitoring database update mechanism;

[0011] Step 5: Monitor the data in the monitoring database in real time and monitor the intrusion behavior in the monitoring database in real time, and issue an early warning signal when there is an abnormality in the data in the monitoring database.

[0012] Preferably, the method of establishing a plurality of sub-databases of different types of data based on the historical data of ecological restoration in mining areas, and obtaining a monitoring database based on the sub-databases of different types of data, specifically includes:

[0013] Classify historical data on ecological restoration in mining areas and establish multiple sub-databases of different types of monitoring data based on the classification results;

[0014] Assigning index hierarchical identifiers corresponding to different types of monitoring data to sub-databases of different types of data, where the index hierarchical identifiers are obtained by mixing at least two of the spatiotemporal index, inverted index, and multi-level index;

[0015] According to the index hierarchical identification, multiple groups of different types of monitoring data are hierarchically stored to obtain a monitoring database.

[0016] Preferably, the processing of remote sensing images of mine ecological restoration and ground feature identification specifically includes:

[0017] Establish a ground feature recognition model and use the data set to train the ground feature recognition model. After the training, the ground feature recognition model of the mine ecological restoration area is obtained.

[0018] Preprocessing, geometric correction and image enhancement are performed on the remote sensing images of the mine ecological restoration area to obtain the processed mine restoration remote sensing images;

[0019] The remote sensing image of the mine ecological restoration area to be identified is input into the mine ecological restoration area feature identification model to obtain the feature identification result.

[0020] Preferably, the image enhancement processing of the remote sensing image of the mine ecological restoration area specifically includes:

[0021] Extracting remote sensing images of the mine ecological restoration area;

[0022] The Canny edge detection algorithm is used to obtain the edge image area of ​​the remote sensing image of the mine ecological restoration area;

[0023] Extracting the grayscale values ​​corresponding to the pixels contained in the edge image area;

[0024] Obtaining a grayscale coefficient using grayscale values ​​corresponding to pixels contained in the edge image area;

[0025] The grayscale coefficient is obtained by the following formula:

[0026]

[0027] Among them, Y represents the grayscale coefficient; n represents the number of pixels contained in the edge image area; S i Indicates the grayscale value corresponding to the i-th pixel in the edge image area; S p Represents the grayscale average value corresponding to n pixels in the edge image area; S max and S min Indicates the maximum and minimum grayscale values ​​corresponding to n pixels in the edge image area; S fz Represents the central grayscale value of the non-edge image area;

[0028] comparing the gamma coefficient with a preset gamma coefficient threshold;

[0029] When the gamma coefficient exceeds a preset gamma coefficient threshold, the brightness of the edge image area is adjusted.

[0030] Preferably, when the gamma coefficient exceeds a preset gamma coefficient threshold, the brightness of the edge image area is adjusted, specifically including:

[0031] When the grayscale coefficient does not exceed a preset grayscale coefficient threshold, retrieving a non-edge image area of ​​a remote sensing image of a mine ecological restoration area;

[0032] Extracting the grayscale values ​​of the pixels contained in the non-edge image area of ​​the remote sensing image of the mine ecological restoration area;

[0033] Obtaining a brightness adjustment coefficient using the grayscale values ​​corresponding to the pixels included in the edge image area and the grayscale values ​​of the pixels included in the non-edge image area;

[0034] The brightness adjustment coefficient is obtained by the following formula:

[0035]

[0036] Where r represents the brightness adjustment coefficient; n represents the number of pixels contained in the edge image area; m represents the number of pixels contained in the non-edge image area; S i Indicates the grayscale value corresponding to the i-th pixel in the edge image area; S j Indicates the grayscale value corresponding to the jth pixel in the non-edge image area; Y represents the grayscale coefficient; S fz Represents the central grayscale value of the non-edge image area; S yz Indicates the central grayscale value of the edge image area; S fmax Indicates the maximum grayscale value of the non-edge image area;

[0037] The brightness of the edge image area is adjusted using the brightness adjustment coefficient, and the brightness value after brightness adjustment is obtained by the following formula:

[0038]

[0039] Wherein, L represents the brightness value after brightness adjustment; L0 represents the brightness value before brightness adjustment; r represents the brightness adjustment coefficient; and Y represents the grayscale coefficient.

[0040] The ecological restoration monitoring database construction system is applied in the ecological restoration monitoring database construction method, including:

[0041] Data acquisition module, used to obtain historical data of mine ecological restoration and real-time remote sensing images of mine ecological restoration, and to process, identify and classify the real-time remote sensing images of mine ecological restoration;

[0042] Monitoring database, used to establish an ecological restoration monitoring database, establish a monitoring database update mechanism for the monitoring database, and encrypt and update the data in the monitoring database;

[0043] The monitoring and early warning module is used to monitor the data in the monitoring database in real time and determine whether there are any anomalies in the data in the monitoring database. If there are any anomalies, an early warning signal will be issued.

[0044] Preferably, the data acquisition module includes:

[0045] The data processing module is used to obtain real-time remote sensing images of mine ecological restoration and historical data of mine ecological restoration monitoring, and to perform preprocessing, geometric correction and image enhancement on remote sensing images of mine ecological restoration areas.

[0046] The feature recognition module is used to establish, train, and optimize the feature recognition model. The remote sensing image of the mine ecological restoration area to be identified is input into the mine ecological restoration area feature recognition model to obtain the feature recognition results.

[0047] The classification module is used to classify the monitoring data of mine ecological restoration according to the recognition results of the ground feature recognition module.

[0048] Preferably, the monitoring database includes:

[0049] The database establishment module is used to establish multiple sub-databases of different types of data based on the historical data of ecological restoration in the mining area, and store multiple groups of monitoring data in layers to obtain a monitoring database;

[0050] The data update module is used to match the real-time collected data related to mine ecological restoration monitoring with the data in the monitoring database, extract and convert the monitoring data in the monitoring database according to the matching results, update the data in the monitoring database, fill in the missing data, and clear the invalid data.

[0051] The data encryption module is used to encrypt the transmitted mine restoration monitoring data using homomorphic encryption, generate chain encryption access logs and dynamic permission configuration files, and generate corresponding keys.

[0052] Preferably, the data encryption module includes:

[0053] An encryption execution module, which is used to encrypt the transmitted mine restoration monitoring data using homomorphic encryption and generate chained encryption access logs and dynamic permission configuration files;

[0054] The decryption module is used to obtain a secret key corresponding to the dynamic rights configuration file based on the dynamic rights configuration file, and decrypt the dynamic rights configuration file according to the corresponding secret key.

[0055] Preferably, the monitoring and early warning module includes:

[0056] Data monitoring module, used to monitor the data in the monitoring database and monitor the intrusion behavior in the monitoring database in real time;

[0057] The early warning module is used to issue corresponding early warning signals when there are abnormalities in the data in the monitoring database.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] The present invention can timely understand the progress and effect of mine restoration, can more accurately evaluate the difficulty and cost of restoration work, significantly improve monitoring efficiency, avoid interference and errors from human factors, and can more accurately evaluate the effect and progress of mine ecological restoration. Data acquisition is more efficient and convenient, and the cost is low. It can monitor database access and operation records in real time, discover abnormal behavior in time, effectively ensure data security, reduce the impact of security incidents on the database, and ensure the security and integrity of data in the monitoring database. By constructing a mine ecological monitoring database, the latest status of the mine ecological environment can be quickly grasped, the effectiveness of restoration work can be evaluated, the success rate and quality of ecological restoration can be improved, and the risk of environmental damage and safety accidents can be reduced, thereby ensuring the health and stability of the ecological environment around the mine. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 Schematic diagram of the method for constructing a monitoring database for ecological restoration according to the present invention;

[0061] Figure 2 Schematic diagram of the monitoring database construction system for ecological restoration of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0063] In order to solve the problem that the existing patent data acquisition channels are relatively complex and anomalies in the database cannot be discovered in time, affecting the integrity and reliability of the data, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0064] A method for constructing a monitoring database for ecological restoration includes the following steps:

[0065] Step 1: Collect historical data on mine ecological restoration monitoring and remote sensing images of mine ecological restoration;

[0066] Step 2: Establish multiple sub-databases of different types of data based on the historical data of ecological restoration in the mining area, and obtain a monitoring database based on the sub-databases of different types of data;

[0067] Step 3: Process the remote sensing images of mine ecological restoration and identify ground objects. Classify the mine ecological restoration data based on the identification results to obtain real-time monitoring data of mine ecological restoration.

[0068] Step 4: Associate the classified real-time monitoring data of mine ecological restoration with the data in the monitoring database and establish a monitoring database update mechanism;

[0069] Step 5: Monitor the data in the monitoring database in real time and monitor the intrusion behavior in the monitoring database in real time, and issue an early warning signal when there is an abnormality in the data in the monitoring database.

[0070] Based on the historical data of ecological restoration in mining areas, multiple sub-databases of different types of data are established, and a monitoring database is obtained based on the sub-databases of different types of data, including:

[0071] Classify historical data on ecological restoration in mining areas and establish multiple sub-databases of different types of monitoring data based on the classification results;

[0072] Assigning index hierarchical identifiers corresponding to different types of monitoring data to sub-databases of different types of data, where the index hierarchical identifiers are obtained by mixing at least two of the spatiotemporal index, inverted index, and multi-level index;

[0073] According to the index hierarchical identification, multiple groups of different types of monitoring data are hierarchically stored to obtain a monitoring database.

[0074] Processing of remote sensing images and ground feature identification for mine ecological restoration, including:

[0075] Establish a ground feature recognition model and use the data set to train the ground feature recognition model. After the training, the ground feature recognition model of the mine ecological restoration area is obtained.

[0076] Preprocessing, geometric correction and image enhancement are performed on the remote sensing images of the mine ecological restoration area to obtain the processed mine restoration remote sensing images;

[0077] Input the remote sensing image of the mine ecological restoration area to be identified into the mine ecological restoration area feature identification model to obtain the feature identification result;

[0078] By identifying the features of mine ecological restoration through the mine ecological restoration area feature identification model, we can timely understand the progress and effect of mine restoration, provide a scientific basis for restoration work, help determine the key areas and methods of restoration, thereby optimizing the restoration plan and improving restoration efficiency. It can more accurately assess the difficulty and cost of restoration work, provide a basis for decision-making, analyze the changes in mine features, timely discover and solve problems, and improve management efficiency. At the same time, by directly identifying features on mine ecological restoration remote sensing images, relevant data of mine ecological restoration can be obtained. Remote sensing images can cover a large range of surface areas, realize comprehensive and rapid monitoring of mine environment, and significantly improve monitoring efficiency. Feature identification is based on objective data and information, avoiding interference and errors caused by human factors, and can more accurately evaluate the effect and progress of mine ecological restoration. By comparing remote sensing images of different time periods, dynamic monitoring of mine restoration can be achieved, and changes in mine environment over time can be observed and analyzed, providing scientific guidance for subsequent restoration work. Data acquisition is more efficient and convenient, and the cost is low, which can greatly save manpower, material and time costs.

[0079] Specifically, the image enhancement processing of the remote sensing image of the mine ecological restoration area specifically includes:

[0080] Extracting remote sensing images of the mine ecological restoration area;

[0081] The Canny edge detection algorithm is used to obtain the edge image area of ​​the remote sensing image of the mine ecological restoration area;

[0082] Extracting the grayscale values ​​corresponding to the pixels contained in the edge image area;

[0083] Obtaining a grayscale coefficient using grayscale values ​​corresponding to pixels contained in the edge image area;

[0084] The grayscale coefficient is obtained by the following formula:

[0085]

[0086] Among them, Y represents the grayscale coefficient; n represents the number of pixels contained in the edge image area; S i Indicates the grayscale value corresponding to the i-th pixel in the edge image area; S p Represents the grayscale average value corresponding to n pixels in the edge image area; S max and S min Indicates the maximum and minimum grayscale values ​​corresponding to n pixels in the edge image area; S fz Represents the central grayscale value of the non-edge image area;

[0087] comparing the gamma coefficient with a preset gamma coefficient threshold;

[0088] When the gamma coefficient exceeds a preset gamma coefficient threshold, the brightness of the edge image area is adjusted.

[0089] The technical solution described above achieves the following: By employing the Canny edge detection algorithm, it can accurately extract edge image regions from remote sensing images of mine ecological restoration areas. The Canny algorithm is a multi-stage edge detection algorithm with low false positive and false negative rates. It effectively identifies edge information in images, providing an accurate basis for subsequent processing. Grayscale values ​​of pixels in edge image regions are extracted, and the gamma coefficient is calculated based on these values, enabling quantitative analysis of the image's grayscale characteristics. This step provides a deeper understanding of the image's grayscale distribution characteristics, providing a basis for subsequent judgment and processing. By comparing the calculated gamma coefficient with a preset gamma coefficient threshold, it is determined whether brightness adjustment is required in edge image regions. If the gamma coefficient exceeds the threshold, it indicates that the brightness of the edge region may be too high or too low, requiring adjustment to improve image visualization. This adaptive brightness adjustment method helps maintain overall image brightness and contrast, improving image quality. By adjusting the brightness of edge image regions, the visual quality of the image can be further improved, making the features of the mine ecological restoration area more distinct. This helps improve the accuracy of image recognition and analysis, providing strong support for subsequent mine ecological restoration efforts. By extracting and analyzing edge information and grayscale features from remote sensing images, this technical solution provides more objective and accurate data support for mine ecological restoration. This data can help decision-makers better understand the actual conditions in mine ecological restoration areas, leading to more scientific and reasonable restoration plans.

[0090] In summary, this technical solution improves the recognition and analysis accuracy of remote sensing images in mine ecological restoration areas through precise edge detection, quantitative analysis of grayscale features, and adaptive brightness adjustment, providing strong technical support for mine ecological restoration work.

[0091] Specifically, when the gamma coefficient exceeds a preset gamma coefficient threshold, the brightness of the edge image area is adjusted, which specifically includes:

[0092] When the grayscale coefficient does not exceed a preset grayscale coefficient threshold, retrieving a non-edge image area of ​​a remote sensing image of a mine ecological restoration area;

[0093] Extracting the grayscale values ​​of the pixels contained in the non-edge image area of ​​the remote sensing image of the mine ecological restoration area;

[0094] Obtaining a brightness adjustment coefficient using the grayscale values ​​corresponding to the pixels included in the edge image area and the grayscale values ​​of the pixels included in the non-edge image area;

[0095] The brightness adjustment coefficient is obtained by the following formula:

[0096]

[0097] Where r represents the brightness adjustment coefficient; n represents the number of pixels contained in the edge image area; m represents the number of pixels contained in the non-edge image area; S i Indicates the grayscale value corresponding to the i-th pixel in the edge image area; S j Indicates the grayscale value corresponding to the jth pixel in the non-edge image area; Y represents the grayscale coefficient; S fz Represents the central grayscale value of the non-edge image area; S yz表示 The center gray value of the edge image area; S fmax Indicates the maximum grayscale value of the non-edge image area;

[0098] The brightness of the edge image area is adjusted using the brightness adjustment coefficient, and the brightness value after brightness adjustment is obtained by the following formula:

[0099]

[0100] Wherein, L represents the brightness value after brightness adjustment; L0 represents the brightness value before brightness adjustment; r represents the brightness adjustment coefficient; and Y represents the grayscale coefficient.

[0101] The technical effect of the above-mentioned technical solution is that, when the gamma coefficient does not exceed the preset threshold, the solution not only considers the grayscale characteristics of the edge image area but also incorporates the grayscale information of the non-edge image area. By calculating the brightness adjustment coefficient, adaptive adjustment of the brightness of the edge image area is achieved. This adjustment method can more precisely handle brightness differences in the image, avoiding over-adjustment or under-adjustment. By incorporating the grayscale information of the non-edge image area, the solution can more comprehensively understand the grayscale distribution of the entire remote sensing image. This helps maintain the overall consistency and naturalness of the image when adjusting the brightness of the edge image area. The adjusted image improves brightness, contrast, and clarity, thereby enhancing the visual quality and readability. Edge image areas often contain important feature information, such as terrain changes and vegetation distribution. By adjusting the brightness of edge image areas, their features can be more distinct, thereby enhancing image recognition. This is of great significance for subsequent monitoring, assessment, and planning of mine ecological restoration. The brightness adjustment coefficient calculation formula in the solution comprehensively considers the grayscale information of both edge and non-edge image areas, as well as the influence of the gamma coefficient. This makes the processing process more scientific and rational, and facilitates automation and intelligent implementation. Remote sensing images after brightness adjustment can more accurately reflect the actual conditions in the mine ecological restoration area, providing more reliable data support for decision makers. This helps decision makers develop more scientific and reasonable restoration plans, improving restoration effectiveness and efficiency. Compared to traditional manual brightness adjustment methods, this solution automatically adjusts brightness by calculating a brightness adjustment coefficient. This significantly reduces the need for manual intervention and improves processing efficiency and accuracy.

[0102] The ecological restoration monitoring database construction system is applied in the ecological restoration monitoring database construction method, including:

[0103] Data acquisition module, used to obtain historical data of mine ecological restoration and real-time remote sensing images of mine ecological restoration, and to process, identify and classify the real-time remote sensing images of mine ecological restoration;

[0104] Monitoring database, used to establish an ecological restoration monitoring database, establish a monitoring database update mechanism for the monitoring database, and encrypt and update the data in the monitoring database;

[0105] The monitoring and early warning module is used to monitor the data in the monitoring database in real time and determine whether there are any anomalies in the data in the monitoring database. If there are any anomalies, an early warning signal will be issued.

[0106] Data acquisition module, including:

[0107] The data processing module is used to obtain real-time remote sensing images of mine ecological restoration and historical data of mine ecological restoration monitoring, and preprocess, geometrically correct and enhance the remote sensing images of the mine ecological restoration area. Among them, preprocessing includes: eliminating or reducing periodic noise to ensure image quality, reducing thin clouds that appear on remote sensing images due to weather reasons, improving image clarity, using the ratio method to eliminate mountain shadows caused by the solar altitude angle, and ensuring the accuracy of image information. Geometric correction includes: aligning the geographic coordinates of different data sources for overlay display and mathematical calculations, correcting through image-to-image, image-to-map or image-to-known coordinate points to ensure that remote sensing data is accurately located in a specific geographic coordinate system, using geographic reference data and digital elevation model data to correct the original remote sensing images and eliminate image deformation caused by terrain undulations. Image enhancement includes: using color synthesis methods to process multispectral images to obtain color images, improve the readability of ground feature information, adjust the histogram to a normal distribution, improve image quality, enhance contrast, and classify grayscale images and assign different colors to convert the original grayscale images into pseudo-color images.

[0108] Preprocessing can remove noise and interference information in remote sensing images, improve image clarity and accuracy, and provide a reliable data basis for subsequent analysis and evaluation. Geometric correction can correct the geometric distortion caused by sensor position, angle and other factors during the acquisition of remote sensing images, ensure that the spatial information in the image is consistent with the actual situation, and improve the accuracy of spatial positioning. Image enhancement processing adjusts the image contrast, brightness, color and other parameters to make the key features in the image more prominent, which facilitates the identification and extraction of relevant information in the mine ecological restoration area, such as vegetation recovery and soil quality changes. Remote sensing images that have undergone preprocessing, geometric correction and image enhancement can be analyzed and interpreted more quickly, improving the efficiency and accuracy of mine ecological restoration effect evaluation, and providing strong support for the planning, implementation and monitoring of restoration work. Preprocessing, geometric correction and image enhancement of remote sensing images in mine ecological restoration areas are of great significance for improving data quality, ensuring spatial accuracy, enhancing image features and improving analysis efficiency.

[0109] The feature recognition module is used to establish, train, and optimize the feature recognition model. The remote sensing image of the mine ecological restoration area to be identified is input into the mine ecological restoration area feature recognition model to obtain the feature recognition results.

[0110] Land feature identification results include remote sensing images, which can clearly observe the mining boundaries, mining areas, and the progress of mining activities. Remote sensing images can display the characteristics of the mine's geological environment, such as topography and geological structure, helping to identify potential geological disaster risk areas such as landslides and collapses. Vegetation coverage and recovery can be assessed in mine ecological restoration areas. Remote sensing images can show the distribution, density, and growth of vegetation, providing an important basis for monitoring vegetation restoration results. Remote sensing images can be used to assess the extent of land damage caused by mining, such as land exposure and soil erosion. At the same time, by comparing remote sensing images before and after restoration, the effectiveness of restoration work, including land reclamation and vegetation reconstruction, can be quantitatively evaluated.

[0111] The classification module is used to classify the monitoring data of mine ecological restoration according to the recognition results of the ground feature recognition module.

[0112] Monitoring database, including:

[0113] The database establishment module is used to establish multiple sub-libraries of different types of data based on the historical data of ecological restoration in the mining area, and assign index hierarchical identifiers corresponding to multiple groups of monitoring data according to the sub-libraries of different types of data. The index hierarchical identifiers are obtained by mixing at least two of the spatiotemporal index, inverted index, and multi-level index; according to the index hierarchical identifiers, multiple groups of monitoring data are hierarchically stored to obtain a monitoring database. The historical data of mine ecological restoration monitoring include: mining scope and progress, geological environment conditions, vegetation coverage and restoration, and land damage and restoration effects.

[0114] The data update module is used to match the real-time collected mine ecological restoration monitoring related data with the data in the monitoring database, extract and convert the monitoring data in the monitoring database according to the matching results, update the data in the monitoring database, fill in the missing data, and clear the invalid data.

[0115] The data encryption module is used to encrypt the transmitted mine restoration monitoring data using homomorphic encryption, generate chain encryption access logs and dynamic permission configuration files, and generate corresponding keys.

[0116] Data encryption module, including:

[0117] An encryption execution module, which is used to encrypt the transmitted mine restoration monitoring data using homomorphic encryption and generate chained encryption access logs and dynamic permission configuration files;

[0118] The decryption module is used to obtain a secret key corresponding to the dynamic rights configuration file based on the dynamic rights configuration file, and decrypt the dynamic rights configuration file according to the corresponding secret key.

[0119] Monitoring and early warning module, including:

[0120] The data monitoring module is used to monitor the data in the monitoring database in real time and monitor the intrusion behavior of the monitoring database in real time, including: detecting whether there are network vulnerabilities through vulnerability detection programs, detecting whether there are network intrusions through intrusion detection programs, and detecting whether there are network viruses through virus detection programs;

[0121] The early warning module is used to issue corresponding early warning signals when there are abnormalities in the data in the monitoring database.

[0122] By real-time monitoring of the data in the monitoring database, it is possible to monitor the access and operation records of the database in real time, and promptly detect abnormal behaviors, such as frequent data queries or modifications, and quickly discover and prevent potential security threats, effectively ensuring the security of data. It is possible to pre-discover potential problems in the database, such as performance degradation, insufficient resources, etc., so as to make timely adjustments and optimizations to avoid these problems leading to more serious failures. By understanding the resource usage of the database, such as CPU, memory, etc., it is possible to ensure the rational use of resources, avoid resource waste, and improve the efficiency and performance of the database. By real-time monitoring of intrusion behaviors in the monitoring database, it is possible to promptly detect and defend against potential network attacks. The early warning module can issue early warnings for abnormal behaviors, effectively reducing the impact of security incidents on the database, and ensuring the security and integrity of the data in the monitoring database.

[0123] In summary, the present invention identifies the land features of mine ecological restoration through the mine ecological restoration area land feature identification model, can timely understand the progress and effect of mine restoration, can more accurately evaluate the difficulty and cost of restoration work, realize comprehensive and rapid monitoring of mine environment, and significantly improve monitoring efficiency. Land feature identification is based on objective data and information, avoids interference and errors of human factors, can more accurately evaluate the effect and progress of mine ecological restoration, and can realize dynamic monitoring of mine restoration by comparing remote sensing images of different time periods, observe and analyze changes in mine environment over time, and provide scientific guidance for subsequent restoration work. Data acquisition is more efficient and convenient, and the cost is low, which can greatly save manpower, material and time costs. By real-time monitoring of data in the monitoring database, it is possible to monitor database access and operation records in real time, and timely discover Abnormal behavior, effectively ensure the security of data, avoid waste of resources, improve the efficiency and performance of the database, can timely detect and defend against potential network attacks, effectively reduce the impact of security incidents on the database, and ensure the security and integrity of the data in the monitoring database. By building a mine ecological monitoring database, we can quickly grasp the latest status of the mine ecological environment, timely discover environmental anomalies and potential problems, so that we can quickly take measures to intervene and repair, and prevent environmental problems from further deteriorating. We can also track the progress and effects of ecological restoration projects, and evaluate the effectiveness of restoration work by comparing data before and after restoration, which will help to adjust restoration strategies and methods in a timely manner, improve the success rate and quality of ecological restoration, and timely discover and solve mine ecological and environmental problems, reduce the risk of environmental damage and safety accidents, and thus ensure the health and stability of the ecological environment around mines.

[0124] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0125] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for constructing a monitoring database for ecological restoration, characterized in that: The following steps are involved: Step 1: Collect historical data on mine ecological restoration monitoring and remote sensing images of mine ecological restoration; Step 2: Establish multiple sub-databases of different types of data based on the historical data of ecological restoration in the mining area, and obtain a monitoring database based on the sub-databases of different types of data; Step 3: Process the remote sensing images of mine ecological restoration and identify ground objects. Classify the mine ecological restoration data based on the identification results to obtain real-time monitoring data of mine ecological restoration. The processing of remote sensing images and ground feature identification for mine ecological restoration specifically includes: Establish a ground feature recognition model and use the data set to train the ground feature recognition model. After the training, the ground feature recognition model of the mine ecological restoration area is obtained. Preprocessing, geometric correction and image enhancement are performed on the remote sensing images of the mine ecological restoration area to obtain the processed mine restoration remote sensing images; Input the remote sensing image of the mine ecological restoration area to be identified into the mine ecological restoration area feature identification model to obtain the feature identification result; Image enhancement processing is performed on remote sensing images of mine ecological restoration areas, specifically including: Extracting remote sensing images of the mine ecological restoration area; The Canny edge detection algorithm is used to obtain the edge image area of ​​the remote sensing image of the mine ecological restoration area; Extracting the grayscale values ​​corresponding to the pixels contained in the edge image area; Obtaining a grayscale coefficient using grayscale values ​​corresponding to pixels contained in the edge image area; The grayscale coefficient is obtained by the following formula: Among them, Y represents the grayscale coefficient; n represents the number of pixels contained in the edge image area; S i Indicates the grayscale value corresponding to the i-th pixel in the edge image area; S p Represents the grayscale average value corresponding to n pixels in the edge image area; S max and S min Indicates the maximum and minimum grayscale values ​​corresponding to n pixels in the edge image area; S fz Represents the central grayscale value of the non-edge image area; comparing the gamma coefficient with a preset gamma coefficient threshold; When the gamma coefficient exceeds a preset gamma coefficient threshold, the brightness of the edge image area is adjusted; When the gamma coefficient exceeds a preset gamma coefficient threshold, the brightness of the edge image area is adjusted, specifically including: When the grayscale coefficient does not exceed a preset grayscale coefficient threshold, retrieving a non-edge image area of ​​a remote sensing image of a mine ecological restoration area; Extracting the grayscale values ​​of the pixels contained in the non-edge image area of ​​the remote sensing image of the mine ecological restoration area; Obtaining a brightness adjustment coefficient using the grayscale values ​​corresponding to the pixels included in the edge image area and the grayscale values ​​of the pixels included in the non-edge image area; The brightness adjustment coefficient is obtained by the following formula: Where r represents the brightness adjustment coefficient; n represents the number of pixels contained in the edge image area; m represents the number of pixels contained in the non-edge image area; S i Indicates the grayscale value corresponding to the i-th pixel in the edge image area; S j Indicates the grayscale value corresponding to the jth pixel in the non-edge image area; Y represents the grayscale coefficient; S fz Represents the central grayscale value of the non-edge image area; S yz Indicates the central grayscale value of the edge image area; S fmax Indicates the maximum grayscale value of the non-edge image area; The brightness of the edge image area is adjusted using the brightness adjustment coefficient, and the brightness value after brightness adjustment is obtained by the following formula: Wherein, L represents the brightness value after brightness adjustment; L0 represents the brightness value before brightness adjustment; r represents the brightness adjustment coefficient; Y represents the grayscale coefficient; Step 4: Associate the classified real-time monitoring data of mine ecological restoration with the data in the monitoring database and establish a monitoring database update mechanism; Step 5: Monitor the data in the monitoring database in real time and monitor the intrusion behavior in the monitoring database in real time, and issue an early warning signal when there is an abnormality in the data in the monitoring database.

2. The method for constructing a monitoring database for ecological restoration according to claim 1, wherein: The method of establishing multiple sub-databases of different types of data based on historical data of ecological restoration in mining areas and obtaining a monitoring database based on the sub-databases of different types of data specifically includes: Classify historical data on ecological restoration in mining areas and establish multiple sub-databases of different types of monitoring data based on the classification results; Assigning index hierarchical identifiers corresponding to different types of monitoring data to sub-databases of different types of data, where the index hierarchical identifiers are obtained by mixing at least two of the spatiotemporal index, inverted index, and multi-level index; According to the index hierarchical identification, multiple groups of different types of monitoring data are hierarchically stored to obtain a monitoring database.

3. A system for constructing a monitoring database for ecological restoration, as used in the method for constructing a monitoring database for ecological restoration according to claim 2, characterized in that: include: Data acquisition module, used to obtain historical data of mine ecological restoration and real-time remote sensing images of mine ecological restoration, and to process, identify and classify the real-time remote sensing images of mine ecological restoration; Monitoring database, used to establish an ecological restoration monitoring database, establish a monitoring database update mechanism for the monitoring database, and encrypt and update the data in the monitoring database; The monitoring and early warning module is used to monitor the data in the monitoring database in real time and determine whether there are any anomalies in the data in the monitoring database. If there are any anomalies, an early warning signal will be issued.

4. The monitoring database construction system for ecological restoration according to claim 3, characterized in that: The data acquisition module includes: The data processing module is used to obtain real-time remote sensing images of mine ecological restoration and historical data of mine ecological restoration monitoring, and to perform preprocessing, geometric correction and image enhancement on remote sensing images of mine ecological restoration areas; The feature recognition module is used to establish, train, and optimize the feature recognition model. The remote sensing image of the mine ecological restoration area to be identified is input into the mine ecological restoration area feature recognition model to obtain the feature recognition results. The classification module is used to classify the monitoring data of mine ecological restoration according to the recognition results of the ground feature recognition module.

5. The monitoring database construction system for ecological restoration according to claim 3, characterized in that: The monitoring database includes: The database establishment module is used to establish multiple sub-databases of different types of data based on the historical data of ecological restoration in the mining area, and store multiple groups of monitoring data in layers to obtain a monitoring database; The data update module is used to match the real-time collected data related to mine ecological restoration monitoring with the data in the monitoring database, extract and convert the monitoring data in the monitoring database according to the matching results, update the data in the monitoring database, fill in the missing data, and clear the invalid data; The data encryption module is used to encrypt the transmitted mine restoration monitoring data using homomorphic encryption, generate chain encryption access logs and dynamic permission configuration files, and generate corresponding keys.

6. The monitoring database construction system for ecological restoration according to claim 5, characterized in that: The data encryption module includes: An encryption execution module, which is used to encrypt the transmitted mine restoration monitoring data using homomorphic encryption and generate chained encryption access logs and dynamic permission configuration files; The decryption module is used to obtain a secret key corresponding to the dynamic rights configuration file based on the dynamic rights configuration file, and decrypt the dynamic rights configuration file according to the corresponding secret key.

7. The monitoring database construction system for ecological restoration according to claim 3, characterized in that: The monitoring and early warning module includes: Data monitoring module, used to monitor the data in the monitoring database and monitor the intrusion behavior in the monitoring database in real time; The early warning module is used to issue corresponding early warning signals when there are abnormalities in the data in the monitoring database.

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