Monitoring database construction method and system for ecological restoration

Abstract

The invention discloses a monitoring database construction method and system for ecological restoration, and belongs to the technical field of mine ecological restoration. The invention discloses a monitoring database construction method for ecological restoration. The method comprises the following steps: step 1, collecting historical data and remote sensing images of mine ecological restoration monitoring; 2, establishing a monitoring database; 3, obtaining real-time monitoring data of mine ecological restoration; 4, establishing a monitoring database updating mechanism; and 5, when the data in the monitoring database is abnormal, sending out an early warning signal. The problems that in the prior art, a data acquisition way is complex, abnormity of data in a database cannot be found in time, and the integrity and reliability of the data are affected are solved, the effect and progress of mine ecological restoration can be evaluated more accurately, data acquisition is more efficient and convenient, the data safety is effectively guaranteed, and the method is suitable for popularization and application. The success rate and quality of ecological restoration are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mine ecological restoration, and in particular to a monitoring database construction method and system 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 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] A Chinese patent with 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 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, thus contributing to the grid management system of grassland ecological resources.

[0004] In actual use, the above patent obtains ecological restoration monitoring-related data in the following ways: historical data query, remote sensing images, real-time acquisition of data sites, manual surveys, and online maps. The data acquisition methods are relatively complicated, 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 method and system for constructing a monitoring database for ecological restoration, which can more accurately evaluate the effect and progress of mine ecological restoration, make data acquisition more efficient and convenient, monitor database access and operation records in real time, promptly discover abnormal behavior, 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-mentioned background technology.

[0006] To achieve the above object, 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 mining areas, 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 according to the identification results, and 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 of mining areas, and obtaining a monitoring database based on the sub-databases of different types of data, specifically includes:

[0013] Classify the historical data of 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, the inverted index, and the multi-level index;

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

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

[0017] Establish a land feature recognition model, use the data set to train the land feature recognition model, and obtain the land feature recognition model of the mine ecological restoration area after the training;

[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 land feature identification model to obtain the land 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 grayscale values ​​corresponding to pixels contained in the edge image area;

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

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

[0026]

[0027] Where Y represents the grayscale coefficient; n represents the number of pixels contained in the edge image area; S i Represents the gray 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 Represents 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 grayscale coefficient with a preset grayscale coefficient threshold;

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

[0030] Preferably, when the grayscale coefficient exceeds a preset grayscale 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 Represents the gray value corresponding to the i-th pixel in the edge image area; S j represents the gray 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 gray 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 the brightness adjustment is obtained by the following formula:

[0038]

[0039] Wherein, L represents the brightness value after brightness adjustment; L 0 It represents the brightness value before brightness adjustment; r represents the brightness adjustment coefficient; 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] The data acquisition module is 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 abnormalities in the data in the monitoring database. If there are any abnormalities, 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 preprocess, geometrically correct and enhance the remote sensing images of the mine ecological restoration area.

[0046] The ground object recognition module is used to establish a ground object recognition model and perform training and optimization, input the remote sensing image of the mine ecological restoration area to be identified into the ground object recognition model of the mine ecological restoration area, and obtain the ground object recognition result;

[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] A 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 comprises:

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

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

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

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

[0057] The early warning module is used to send out 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 caused by human factors, can more accurately evaluate the effect and progress of mine ecological restoration, and obtain data more efficiently and conveniently, and at a lower cost. 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 A schematic diagram of a method for constructing a monitoring database for ecological restoration according to the present invention;

[0061] Figure 2 A schematic diagram of a monitoring database construction system for ecological restoration according to the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work 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 abnormal data 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] The 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 mining areas, 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 according to the identification results, and 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 monitoring databases are obtained based on the sub-databases of different types of data, including:

[0071] Classify the historical data of 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, the inverted index, and the multi-level index;

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

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

[0075] Establish a land feature recognition model, use the data set to train the land feature recognition model, and obtain the land feature recognition model of the mine ecological restoration area after the training;

[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 evaluate the difficulty and cost of restoration work and provide a basis for decision-making. It can analyze the changes in mine features, timely discover and solve problems, and improve management efficiency. At the same time, by directly identifying the features of mine ecological restoration remote sensing images, we can obtain relevant data of mine ecological restoration. 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 realized, 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 grayscale values ​​corresponding to pixels contained in the edge image area;

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

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

[0085]

[0086] Where Y represents the grayscale coefficient; n represents the number of pixels contained in the edge image area; S i Represents the gray 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 Represents 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 grayscale coefficient with a preset grayscale coefficient threshold;

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

[0089] The technical effect of the above technical solution is: by adopting the Canny edge detection algorithm, the technical solution can accurately extract the edge image area of ​​the remote sensing image of the mine ecological restoration area. The Canny algorithm is a multi-level edge detection algorithm with the advantages of low false alarm rate and low false alarm rate, which can effectively identify the edge information in the image and provide an accurate basis for subsequent processing. The grayscale values ​​of the pixels in the edge image area are extracted, and the grayscale coefficient is calculated based on these grayscale values, so as to realize the quantitative analysis of the grayscale characteristics of the image. This step helps to understand the grayscale distribution characteristics of the image more deeply and provide a basis for subsequent judgment and processing. By comparing the calculated grayscale coefficient with the preset grayscale coefficient threshold, it can be judged whether the brightness of the edge image area needs to be adjusted. When the grayscale coefficient exceeds the threshold, it means that the brightness of the edge area may be too high or too low, and it needs to be adjusted to improve the visualization effect of the image. This adaptive brightness adjustment method helps to maintain the overall brightness and contrast of the image and improve the quality of the image. By adjusting the brightness of the edge image area, the visual effect of the image can be further improved, making the characteristics of the mine ecological restoration area more obvious. This helps improve the accuracy of image recognition and analysis, and provides strong support for subsequent mine ecological restoration work. This technical solution provides more objective and accurate data support for mine ecological restoration by extracting and analyzing edge information and grayscale features in remote sensing images. These data can help decision makers better understand the actual situation of mine ecological restoration areas, so as to formulate 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 grayscale coefficient exceeds a preset grayscale coefficient threshold, the brightness of the edge image area is adjusted, specifically including:

[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 Represents the gray value corresponding to the i-th pixel in the edge image area; S j represents the gray 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 central 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 the brightness adjustment is obtained by the following formula:

[0099]

[0100] Wherein, L represents the brightness value after brightness adjustment; L 0 It represents the brightness value before brightness adjustment; r represents the brightness adjustment coefficient; Y represents the grayscale coefficient.

[0101] The technical effect of the above technical solution is: when the grayscale coefficient does not exceed the preset threshold, the solution not only considers the grayscale characteristics of the edge image area, but also introduces 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 finely handle the brightness difference in the image and avoid the problem of over-adjustment or under-adjustment. By introducing 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 to maintain the overall consistency and naturalness of the image when adjusting the brightness of the edge image area. The adjusted image will be improved in brightness, contrast and clarity, thereby improving the visual effect and readability of the image. The edge image area usually contains important feature information, such as terrain changes, vegetation distribution, etc. By adjusting the brightness of the edge image area, its features can be more obvious, thereby enhancing the recognition ability of the image. This is of great significance for subsequent mine ecological restoration monitoring, evaluation and planning. The brightness adjustment coefficient calculation formula in the scheme comprehensively considers the grayscale information of the edge image area and the non-edge image area, as well as the influence of the grayscale coefficient. This makes the processing flow more scientific and reasonable, and easy to automate and intelligentize. The remote sensing images after brightness adjustment can more accurately reflect the actual situation of the mine ecological restoration area and provide more reliable data support for decision makers. This helps decision makers to formulate more scientific and reasonable restoration plans and improve restoration effects and efficiency. Compared with the traditional manual brightness adjustment method, this solution realizes automatic adjustment by calculating the brightness adjustment coefficient. This greatly 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] The data acquisition module is 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 abnormalities in the data in the monitoring database. If there are any abnormalities, 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, the preprocessing includes: eliminating or reducing periodic noise to ensure image quality, reducing thin clouds appearing 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. The geometric correction includes: aligning the geographic coordinates of different data sources for superimposed display and mathematical calculations, correcting by image to image, image to map or image to known coordinate points to ensure that remote sensing data is accurately located to a specific geographic coordinate system, using geographic reference data and digital elevation model data to correct the original remote sensing image, and eliminating 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 object 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 is convenient for identifying and extracting relevant information in the mine ecological restoration area, such as vegetation restoration, soil quality changes, etc. Remote sensing images that have been preprocessed, geometrically corrected and image enhanced 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 ground object recognition module is used to establish a ground object recognition model and perform training and optimization, input the remote sensing image of the mine ecological restoration area to be identified into the ground object recognition model of the mine ecological restoration area, and obtain the ground object recognition result;

[0110] The results of ground feature identification include that through remote sensing images, the mining boundaries, mining areas and progress of mining activities can be clearly observed. Remote sensing images can show the characteristics of the geological environment of the mine, such as topography, geological structure, etc., which are helpful to identify potential geological disaster risk areas, such as landslides and collapses. Vegetation coverage and recovery can evaluate the vegetation coverage and recovery of the ecological restoration area of ​​the mine. Remote sensing images can show the distribution, density and growth of vegetation, providing an important basis for monitoring the effectiveness of vegetation restoration. Remote sensing images can be used to evaluate the degree of damage caused by mining to the land, 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 can be quantitatively evaluated, including the effects of land reclamation and vegetation reconstruction.

[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] A database establishment module is used to establish multiple sub-databases of different types of data based on the historical data of ecological restoration in mining areas, and to assign index hierarchical identifiers corresponding to multiple groups of monitoring data according to the sub-databases of different types of data. The index hierarchical identifiers are obtained by mixing at least two of the spatiotemporal index, the inverted index, and the multi-level index; according to the index hierarchical identifiers, the 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 encrypted access logs and dynamic permission profiles;

[0118] The decryption module is used to obtain a secret key corresponding to the dynamic authority configuration file based on the dynamic authority configuration file, and decrypt the dynamic authority 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 send out 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 detect and prevent potential security threats, effectively ensuring the security of the data. It is possible to pre-detect potential problems in the database, such as performance degradation, insufficient resources, etc., so as to make timely adjustments and optimizations to avoid these problems from causing 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 waste of resources, 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 features of mine ecological restoration through the mine ecological restoration area 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. Feature identification is based on objective data and information, avoids interference and errors from 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 to provide scientific guidance for subsequent restoration work. Data acquisition is more efficient, convenient, and low-cost, 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 can effectively ensure the security of data, avoid waste of resources, improve the efficiency and performance of the database, 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 the 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 article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0125] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations 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 mining areas, 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 according to the identification results, and obtain real-time monitoring data of mine ecological restoration; 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, characterized in that: 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: Classify the historical data of 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, the inverted index, and the multi-level index; According to the index hierarchical identification, multiple groups of different types of monitoring data are stored in layers to obtain a monitoring database.

3. The method for constructing a monitoring database for ecological restoration according to claim 1, characterized in that: The processing of remote sensing images and identification of ground objects for mine ecological restoration specifically include: Establish a land feature recognition model, use the data set to train the land feature recognition model, and obtain the land feature recognition model of the mine ecological restoration area after the training; 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; The remote sensing image of the mine ecological restoration area to be identified is input into the mine ecological restoration area land feature identification model to obtain the land feature identification result.

4. The method for constructing a monitoring database for ecological restoration according to claim 3, characterized in that: The image enhancement processing of the remote sensing image of the mine ecological restoration area specifically includes: 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 grayscale values ​​corresponding to pixels contained in the edge image area; Obtaining a grayscale coefficient using grayscale values ​​corresponding to pixels included in the edge image area; The grayscale coefficient is obtained by the following formula: Where Y represents the grayscale coefficient; n represents the number of pixels contained in the edge image area; S i Represents the gray 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 Represents 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 grayscale coefficient with a preset grayscale coefficient threshold; When the grayscale coefficient exceeds a preset grayscale coefficient threshold, the brightness of the edge image area is adjusted.

5. The method for constructing a monitoring database for ecological restoration according to claim 4, characterized in that: When the grayscale coefficient exceeds a preset grayscale 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 Represents the gray value corresponding to the i-th pixel in the edge image area; S j represents the gray 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 gray 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 the 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; and Y represents the grayscale coefficient.

6. A system for constructing a monitoring database for ecological restoration, applied in the method for constructing a monitoring database for ecological restoration as claimed in claim 5, characterized in that: include: The data acquisition module is 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 abnormalities in the data in the monitoring database. If there are any abnormalities, an early warning signal will be issued.

7. The monitoring database construction system for ecological restoration according to claim 6 is characterized in that: The data acquisition module comprises: 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 ground object recognition module is used to establish a ground object recognition model and perform training and optimization, input the remote sensing image of the mine ecological restoration area to be identified into the ground object recognition model of the mine ecological restoration area, and obtain the ground object recognition result; 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.

8. The monitoring database construction system for ecological restoration according to claim 6 is characterized in that: The monitoring database comprises: A 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.

9. The monitoring database construction system for ecological restoration according to claim 6, characterized in that: The data encryption module comprises: 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.

10. The monitoring database construction system for ecological restoration according to claim 6, characterized in that: The monitoring and early warning module comprises: Data monitoring module, used to monitor the data in the monitoring database and the intrusion behavior in the monitoring database in real time; The early warning module is used to send out corresponding early warning signals when there are abnormalities in the data in the monitoring database.

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