CN116415843A - Multi-mode remote sensing auxiliary mine ecological environment evaluation method for weak network environment - Google Patents
Multi-mode remote sensing auxiliary mine ecological environment evaluation method for weak network environment Download PDFInfo
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Abstract
The invention discloses a multi-mode remote sensing auxiliary mine ecological environment evaluation method for a weak network environment, which belongs to the technical field of mine environment evaluation and comprises the steps of creating a goaf three-dimensional digital information model, acquiring vegetation coverage information in a multi-mode remote sensing auxiliary image and goaf settlement data of a mine, acquiring ecological environment monitoring data, evaluating mine ecological environment, acquiring a target ecological environment evaluation image three-dimensional label and comprehensively evaluating ecological environment. According to the invention, when the laser radar is used for scanning the three-dimensional information of the goaf in the goaf weak network environment, all information of the multi-mode remote sensing auxiliary image is obtained, and the correlation between vegetation coverage condition and goaf settlement condition of the mine environment and the three-dimensional information of the land, the water body, the atmosphere and the goaf, which are analyzed by the multi-mode remote sensing auxiliary image, is combined, so that potential unit bodies with all evaluation indexes and comprehensive indexes reaching standards are obtained and removed, and an analyzed data base is provided for indicating key potential areas of mine treatment.
Description
Technical Field
The invention relates to a multi-mode remote sensing auxiliary mine ecological environment evaluation method for a weak network environment, and belongs to the technical field of mine environment evaluation.
Background
When evaluating the ecological environment of a mine, a goaf in the mine can be subjected to three-dimensional scanning by adopting a laser radar, so that a three-dimensional digital model of the goaf is formed, and the mine environment cannot be well evaluated by single goaf three-dimensional imaging information. The existing mine ecological environment evaluation system is mainly characterized in that an attribute hierarchy method and a fuzzy comprehensive evaluation method are adopted to analyze the ecological environment, environmental impact caused by mine reconstruction engineering is analyzed, factors suitable for evaluation are screened, an evaluation index system and a grading standard of the ecological environment quality of a mining area are constructed, the weight of each index is determined by the attribute hierarchy method, the ecological environment quality is evaluated by the fuzzy comprehensive method, the opinion of a plurality of evaluation subjects is integrated, and the ambiguity problem in the evaluation process is effectively solved. The evaluation method only analyzes the numerical value, the qualitative grade and the quantitative grade of each index in a multi-directional and dispersive manner, can not acquire all information of the multi-mode remote sensing auxiliary image when the laser radar is used for scanning the three-dimensional information of the goaf under the weak network environment of the goaf, can not combine the vegetation coverage condition and the subsidence condition of the goaf of the mine environment analyzed by the multi-mode remote sensing auxiliary image, has correlation with the three-dimensional information of the land, the water body, the atmosphere and the goaf, and can not provide targeted ecological environment treatment measures for the treatment of the mine ecological environment. Therefore, the invention provides a multi-mode remote sensing auxiliary mine ecological environment evaluation method for a weak network environment.
Disclosure of Invention
In order to solve the problems of the background technology, the invention provides a multi-mode remote sensing auxiliary mine ecological environment evaluation method for a weak network environment.
The aim of the invention can be achieved by adopting the following technical scheme:
the multi-mode remote sensing auxiliary mine ecological environment evaluation method for the weak network environment comprises the following steps of:
s1, creating a goaf three-dimensional digital information model: performing three-dimensional information scanning on the goaf by using a laser radar technology, creating a goaf point cloud by using the scanned three-dimensional information, generating a three-dimensional information digital model of the goaf by using point cloud data, and acquiring goaf volume information;
s2, acquiring vegetation coverage information in the multi-mode remote sensing auxiliary image and goaf settlement data of the mine;
s3, acquiring ecological environment monitoring data: the ecological environment monitoring information management system of the combined mine goaf acquires the soil organic matter level change quantity, the soil PH change value and the soil heavy metal content change value in the mine goaf research period, takes the data as basic data of soil evaluation, acquires the biomass change and the total biological category number change in the mine environment research period, takes the data as basic data of biological evaluation in the ecological environment, acquires the water quality change, the atmosphere quality change and the precipitation change in the mine environment research period, takes the data as basic data of hydrologic evaluation, acquires the water loss and soil loss intensity change and the soil utilization rate change in the mine ecological environment research period, and combines the data acquired by S1 and S2 to be taken as basic data of topography evaluation;
s4, evaluating the ecological environment of the mine: acquiring a soil evaluation index by using an evaluation mechanism of the soil evaluation index, acquiring a biological evaluation index by using an evaluation mechanism of the biological evaluation index, acquiring a hydrologic evaluation index by using an evaluation mechanism of the hydrologic evaluation index, and acquiring a topography evaluation index by combining the evaluation mechanism of the topography evaluation index;
s5, acquiring a three-dimensional label of the target ecological environment evaluation image: training the spatial information labels of the multi-mode remote sensing auxiliary image by using a deep learning framework, carrying out multi-level classification by using the values of each evaluation index, grabbing the image element labels of the target level classification, and forming a target level evaluation image label data set of each index;
s6, comprehensively evaluating the ecological environment: transmitting the target grade evaluation image tag data set of each index obtained in the step S5 to a processing unit outside the goaf, constructing a target grade evaluation image of each index, obtaining an ecological comprehensive evaluation index by using the weighted sum of each evaluation index, constructing a mine three-dimensional model by using the multi-mode remote sensing image and the goaf three-dimensional digital information model, carrying out three-dimensional segmentation on the mine three-dimensional model according to a preset grid, grabbing a unit body with the standard of the comprehensive index in the segmented unit body, displaying a potential unit body with the standard of each evaluation index and the comprehensive index removed, and displaying three-dimensional coordinate information of the potential unit body.
Further, the soil evaluation index evaluation mechanism involved in S4 is as follows:
wherein: i TR K is the soil evaluation index yjz To study the change of organic matter level in time period, K PH To study the pH change value of soil in time period, K zjs And the method is used for researching the change value of the heavy metal content in the soil in a period.
Further, the biological evaluation index evaluation mechanism involved in S4 is as follows:
wherein: i sw Biological evaluation index, N sw To study the amount of change in total biomass over a period of time, M wz To study the variation of biological species over time.
Further, the hydrologic evaluation index evaluation mechanism involved in S4 is as follows:
I ww ＝0.1S st +0.5S dq +0.6S js ；
wherein: i ww S is a hydrologic evaluation index st To study the change of water quality in time period, S dq To study the change in atmospheric mass over time, S js To investigate the variation in precipitation over time.
Further, the water mass change in the research period is inversely related to the COD change amount of the water, and is positively related to the ammonia nitrogen change amount, and the formula of the water mass change is as follows:
wherein: k (K) COD To study the COD change value of water in a period of time, K AD To study the ammonia nitrogen content change of water in a period;
the change of the atmosphere quality in the research period is inversely related to the sulfur dioxide content in the atmosphere and positively related to the TSP in the atmosphere, and the formula of the change of the atmosphere quality is as follows:
wherein: r is R SO2 Is SO in the atmosphere 2 Content variation, R TSP Is the TSP variation in the atmosphere.
Further, the topography evaluation index evaluation mechanism related in S4 is as follows:
wherein: v (V) ck To change the volume of the goaf, H min And H max Respectively representing a sedimentation minimum value and a sedimentation maximum value, S st The change of the water and soil loss intensity is represented, and Q is the change value of precipitation.
Further, the process of creating the three-dimensional digital information model of the goaf S1 is as follows:
A1. a laser light source: placing a laser radar on the ground of a goaf, wherein the laser radar generates a laser light source;
A2. shooting a depth picture: the laser radar acquires a light source reflected by the measured object, continuously shoots pictures containing depth information, and transmits the depth pictures to the microcomputer processing module through a transmission unit on the radar;
A3. preprocessing point cloud data: filtering Gaussian noise in the data by using a Gaussian filtering technology, and removing outliers and outliers according to Euclidean distances of adjacent points of each point in the point cloud;
A4. reconstructing three-dimensional data information: performing voxel-based downsampling by using a point cloud image, creating a 3D voxel grid, wherein each voxel comprises three points with the same axis and the same interval, downsampling the points belonging to the same voxel, and replacing the points with the mass centers of the points;
A5. constructing a three-dimensional information digital model: performing surface smoothing processing by utilizing the point cloud data to form a three-dimensional model of the goaf, and calculating the voxel volume by combining each voxel and the goaf height;
A6. constructing a goaf historical initial three-dimensional information data model: and (3) acquiring a goaf history initial three-dimensional model in a history initial period according to laser radar scanning data of the goaf history by utilizing the steps A1-A5, acquiring voxel volume information of the goaf, and performing volume difference values by utilizing corresponding points of A5 and A6 to acquire goaf volume change in a research period.
Further, in the step S2, the vegetation coverage information in the multi-mode remote sensing auxiliary image is obtained, the multi-mode remote sensing auxiliary image is subjected to feature learning through a deep learning classifier, and then the vegetation coverage graphic elements in the multi-mode remote sensing auxiliary image are extracted and classified to obtain vegetation coverage information of the mine goaf, and the vegetation coverage of the mine goaf in the initial period of the history is obtained by combining the historical multi-mode remote sensing auxiliary image.
Further, the method for acquiring the subsidence condition of the mine goaf in the multi-mode remote sensing auxiliary image in S2 is to acquire a curved cloud image of the goaf ground by using depth information in the multi-mode remote sensing auxiliary image, and acquire a subsidence minimum value and a subsidence maximum value in the subsidence height by using a difference value between the height information of each grid point in the curved cloud image and the height of each grid point in the historical curved grid cloud image.
Compared with the prior art, the invention has the following advantages:
the evaluation method can acquire all information of the multi-mode remote sensing auxiliary image when the laser radar is used for scanning the three-dimensional information of the goaf under the weak network environment of the goaf, and simultaneously, the numerical value, the qualitative and quantitative grades of each index are analyzed in a multi-directional and decentralized manner, and the correlation between vegetation coverage conditions and goaf settlement conditions of the mine environment and three-dimensional information of the goaf, which are analyzed by combining the multi-mode remote sensing auxiliary image, and the three-dimensional information of the land, the water body, the atmosphere and the goaf, is achieved, potential unit bodies which are used for removing all the evaluation indexes and the comprehensive indexes and reach standards are acquired, an analyzed data basis is provided for the indication of key potential areas of mine treatment, a targeted ecological environment treatment measure is provided for the treatment of the mine ecological environment, and the defect that the three-dimensional information of the goaf cannot be combined for the mine ecological environment is combined by analyzing the ecological environment by adopting a fuzzy comprehensive evaluation method based on attribute hierarchy is overcome.
Drawings
Fig. 1 is a flowchart of a multi-modal remote sensing assisted mine ecological environment assessment method for a weak network environment according to the present invention.
Detailed Description
In order to make the technical solution of the present invention more clear and obvious to those skilled in the art, the present invention will be described in further detail with reference to examples and drawings, but the embodiments of the present invention are not limited thereto.
As shown in fig. 1, the multi-mode remote sensing auxiliary mine ecological environment evaluation method for the weak network environment provided by the embodiment includes the following steps:
s1, creating a goaf three-dimensional digital information model: performing three-dimensional information scanning on the goaf by using a laser radar technology, creating a goaf point cloud by using the scanned three-dimensional information, generating a three-dimensional information digital model of the goaf by using point cloud data, and acquiring goaf volume information;
s2, acquiring vegetation coverage information in the multi-mode remote sensing auxiliary image and goaf settlement data of the mine;
s3, acquiring ecological environment monitoring data: the ecological environment monitoring information management system of the combined mine goaf acquires the soil organic matter level change quantity, the soil PH change value and the soil heavy metal content change value in the mine goaf research period, takes the data as basic data of soil evaluation, acquires the biomass change and the total biological category number change in the mine environment research period, takes the data as basic data of biological evaluation in the ecological environment, acquires the water quality change, the atmosphere quality change and the precipitation change in the mine environment research period, takes the data as basic data of hydrologic evaluation, acquires the water loss and soil loss intensity change and the soil utilization rate change in the mine ecological environment research period, and combines the data acquired by S1 and S2 to be taken as basic data of topography evaluation;
s4, evaluating the ecological environment of the mine: acquiring a soil evaluation index by using an evaluation mechanism of the soil evaluation index, acquiring a biological evaluation index by using an evaluation mechanism of the biological evaluation index, acquiring a hydrologic evaluation index by using an evaluation mechanism of the hydrologic evaluation index, and acquiring a topography evaluation index by combining the evaluation mechanism of the topography evaluation index;
s5, acquiring a three-dimensional label of the target ecological environment evaluation image: training the spatial information labels of the multi-mode remote sensing auxiliary image by using a deep learning framework, carrying out multi-level classification by using the values of each evaluation index, grabbing the image element labels of the target level classification, and forming a target level evaluation image label data set of each index;
s6, comprehensively evaluating the ecological environment: transmitting the target grade evaluation image tag data set of each index obtained in the step S5 to a processing unit outside the goaf, constructing a target grade evaluation image of each index, obtaining an ecological comprehensive evaluation index by using the weighted sum of each evaluation index, constructing a mine three-dimensional model by using the multi-mode remote sensing image and the goaf three-dimensional digital information model, carrying out three-dimensional segmentation on the mine three-dimensional model according to a preset grid, grabbing a unit body with the standard of the comprehensive index in the segmented unit body, displaying a potential unit body with the standard of each evaluation index and the comprehensive index removed, and displaying three-dimensional coordinate information of the potential unit body.
Compared with the prior art, the invention has the following advantages:
the evaluation method can acquire all information of the multi-mode remote sensing auxiliary image when the laser radar is used for scanning the three-dimensional information of the goaf under the weak network environment of the goaf, and simultaneously, the numerical value, the qualitative and quantitative grades of each index are analyzed in a multi-directional and decentralized manner, and the correlation between vegetation coverage conditions and goaf settlement conditions of the mine environment and three-dimensional information of the goaf, which are analyzed by combining the multi-mode remote sensing auxiliary image, and the three-dimensional information of the land, the water body, the atmosphere and the goaf, is achieved, potential unit bodies which are used for removing all the evaluation indexes and the comprehensive indexes and reach standards are acquired, an analyzed data basis is provided for the indication of key potential areas of mine treatment, a targeted ecological environment treatment measure is provided for the treatment of the mine ecological environment, and the defect that the three-dimensional information of the goaf cannot be combined for the mine ecological environment is combined by analyzing the ecological environment by adopting a fuzzy comprehensive evaluation method based on attribute hierarchy is overcome.
The soil evaluation index evaluation mechanism involved in the step S4 is as follows:
wherein: i TR K is the soil evaluation index yjz To study the change of organic matter level in time period, K PH To study the pH change value of soil in time period, K zjs And the method is used for researching the change value of the heavy metal content in the soil in a period.
Through a soil evaluation index evaluation mechanism, the soil evaluation index can be positively correlated with the organic matter level change quantity, the PH change value and the heavy metal content change value in the soil in a research period, so that the trend of growth occurs along with the enlargement of each evaluation factor, the comprehensive reflection of the change of a plurality of factors on the soil evaluation index is facilitated, and a data foundation is laid for searching potential unit voxels.
The biological evaluation index evaluation mechanism involved in S4 is as follows:
wherein: i sw Biological evaluation index, N sw To study the amount of change in total biomass over a period of time, M wz To study the variation of biological species over time.
By means of a biological evaluation index evaluation mechanism, the estimated value obtained by sampling and collecting species number information in the goaf and species type information can be reflected on the biological evaluation index, and the variation of biological diversity in the goaf is represented by a positively-correlated variation trend.
The hydrologic evaluation index evaluation mechanism involved in the step S4 is as follows:
I ww ＝0.1S st +0.5S dq +0.6S js ；
wherein: i ww S is a hydrologic evaluation index st To study the change of water quality in time period, S dq To study the change in atmospheric mass over time, S js To investigate the variation in precipitation over time.
Through the evaluation mechanism of hydrologic evaluation mechanism index, the water quality change, the atmospheric quality change and the precipitation change in the research period can be linearly combined, and the multi-element comprehensive consideration can be conveniently realized by multi-element combined regression.
The water mass change in the research period is inversely related to the COD change amount of water and positively related to the ammonia nitrogen change amount, and the formula of the water mass change is as follows:
wherein: k (K) COD To study the COD change value of water in a period of time, K AD To study the ammonia nitrogen content change of water in a period;
the change of the atmosphere quality in the research period is inversely related to the sulfur dioxide content in the atmosphere and positively related to the TSP in the atmosphere, and the formula of the change of the atmosphere quality is as follows:
wherein:
is SO in the atmosphere 2 Content variation, R TSP Is the TSP variation in the atmosphere.
Through the water quality change and the atmospheric quality change evaluation mechanism, the COD and ammonia nitrogen content which directly affect the water quality, the sulfur dioxide content which directly affect the atmospheric quality and the TSP can be subjected to data diversification equalization, so that good harmony of data is maintained, and the numerical model convergence and regression speed improvement are conveniently realized.
The terrain evaluation index evaluation mechanism involved in the step S4 is as follows:
wherein: v (V) ck To change the volume of the goaf, H min And H max Respectively representing a sedimentation minimum value and a sedimentation maximum value, S st The change of the water and soil loss intensity is represented, and Q is the change value of precipitation.
The three-dimensional information of the goaf, the subsidence of the ground surface of the goaf, the water and soil loss intensity and the precipitation amount change can be combined through the terrain evaluation index evaluation mechanism to form multi-element combined analysis on multiple dimensions, so that the difference between data values is weakened, the terrain evaluation index value change caused by each factor change can be analyzed in a balanced mode, and the situation that trend change is not obvious or extremely obvious due to different influence degrees of each factor on the terrain evaluation index is avoided.
The S1 goaf three-dimensional digital information model creation process comprises the following steps:
A1. a laser light source: placing a laser radar on the ground of a goaf, wherein the laser radar generates a laser light source;
A2. shooting a depth picture: the laser radar acquires a light source reflected by the measured object, continuously shoots pictures containing depth information, and transmits the depth pictures to the microcomputer processing module through a transmission unit on the radar;
A3. preprocessing point cloud data: filtering Gaussian noise in the data by using a Gaussian filtering technology, and removing outliers and outliers according to Euclidean distances of adjacent points of each point in the point cloud;
A4. reconstructing three-dimensional data information: performing voxel-based downsampling by using a point cloud image, creating a 3D voxel grid, wherein each voxel comprises three points with the same axis and the same interval, downsampling the points belonging to the same voxel, and replacing the points with the mass centers of the points;
A5. constructing a three-dimensional information digital model: performing surface smoothing processing by utilizing the point cloud data to form a three-dimensional model of the goaf, and calculating the voxel volume by combining each voxel and the goaf height;
A6. constructing a goaf historical initial three-dimensional information data model: and (3) acquiring a goaf history initial three-dimensional model in a history initial period according to laser radar scanning data of the goaf history by utilizing the steps A1-A5, acquiring voxel volume information of the goaf, and performing volume difference values by utilizing corresponding points of A5 and A6 to acquire goaf volume change in a research period.
And S2, acquiring vegetation coverage information in the multi-mode remote sensing auxiliary image, performing feature learning on the multi-mode remote sensing auxiliary image through a deep learning classifier, extracting and classifying vegetation coverage primitives in the multi-mode remote sensing auxiliary image to acquire vegetation coverage information of the mine goaf, and acquiring the vegetation coverage of the mine goaf in the initial period of the history by combining the historical multi-mode remote sensing auxiliary image.
The method for acquiring the subsidence condition of the mine goaf in the multi-mode remote sensing auxiliary image in the S2 is to acquire a curved cloud image of the goaf ground by utilizing depth information in the multi-mode remote sensing auxiliary image, and acquire a subsidence minimum value and a subsidence maximum value in the subsidence height by utilizing difference values between the height information of each grid point in the curved cloud image and the height of each grid point in the historical curved grid cloud image.
Through a soil evaluation index evaluation mechanism, the soil evaluation index can be positively correlated with the organic matter level change quantity, the PH change value and the heavy metal content change value in the soil in a research period, so that the trend of growth occurs along with the enlargement of each evaluation factor, the comprehensive reflection of the change of a plurality of factors on the soil evaluation index is facilitated, and a data foundation is laid for searching potential unit voxels.
The biological evaluation index evaluation mechanism involved in S4 is as follows:
wherein: i sw Biological evaluation index, N sw To study the amount of change in total biomass over a period of time, M wz To study the variation of biological species over time.
By means of a biological evaluation index evaluation mechanism, the estimated value obtained by sampling and collecting species number information in the goaf and species type information can be reflected on the biological evaluation index, and the variation of biological diversity in the goaf is represented by a positively-correlated variation trend.
The hydrologic evaluation index evaluation mechanism involved in the step S4 is as follows:
I ww ＝0.1S st +0.5S dq +0.6S js ；
wherein: i ww S is a hydrologic evaluation index st To study the change of water quality in time period, S dq To study the change in atmospheric mass over time, S js To investigate the variation in precipitation over time.
Through the evaluation mechanism of hydrologic evaluation mechanism index, the water quality change, the atmospheric quality change and the precipitation change in the research period can be linearly combined, and the multi-element comprehensive consideration can be conveniently realized by multi-element combined regression.
The water mass change in the research period is inversely related to the COD change amount of water and positively related to the ammonia nitrogen change amount, and the formula of the water mass change is as follows:
wherein: k (K) COD To study the COD change value of water in a period of time, K AD To study the ammonia nitrogen content change of water in a period;
the change of the atmosphere quality in the research period is inversely related to the sulfur dioxide content in the atmosphere and positively related to the TSP in the atmosphere, and the formula of the change of the atmosphere quality is as follows:
wherein:
is SO in the atmosphere 2 Content variation, R TSP Is the TSP variation in the atmosphere.
Through the water quality change and the atmospheric quality change evaluation mechanism, the COD and ammonia nitrogen content which directly affect the water quality, the sulfur dioxide content which directly affect the atmospheric quality and the TSP can be subjected to data diversification equalization, so that good harmony of data is maintained, and the numerical model convergence and regression speed improvement are conveniently realized.
The terrain evaluation index evaluation mechanism involved in the step S4 is as follows:
wherein: v (V) ck To change the volume of the goaf, H min And H max Respectively representing a sedimentation minimum value and a sedimentation maximum value, S st The change of the water and soil loss intensity is represented, and Q is the change value of precipitation.
The three-dimensional information of the goaf, the subsidence of the ground surface of the goaf, the water and soil loss intensity and the precipitation amount change can be combined through the terrain evaluation index evaluation mechanism to form multi-element combined analysis on multiple dimensions, so that the difference between data values is weakened, the terrain evaluation index value change caused by each factor change can be analyzed in a balanced mode, and the situation that trend change is not obvious or extremely obvious due to different influence degrees of each factor on the terrain evaluation index is avoided.
In summary, in this embodiment, according to the multi-mode remote sensing auxiliary mine ecological environment evaluation method for a weak network environment of this embodiment, by using the soil evaluation index evaluation mechanism, the soil evaluation index can be positively correlated with the organic matter level change amount, the PH change value and the heavy metal content change value of the soil in the research period, so that the trend of growth occurs with the increase of each evaluation factor, and the change of multiple factors is helped to be comprehensively reflected on the soil evaluation index, so as to lay a data foundation for searching potential unit voxels. By means of a biological evaluation index evaluation mechanism, the estimated value obtained by sampling and collecting species number information in the goaf and species type information can be reflected on the biological evaluation index, and the variation of biological diversity in the goaf is represented by a positively-correlated variation trend. Through the evaluation mechanism of hydrologic evaluation mechanism index, the water quality change, the atmospheric quality change and the precipitation change in the research period can be linearly combined, and the multi-element comprehensive consideration can be conveniently realized by multi-element combined regression. Through the water quality change and the atmospheric quality change evaluation mechanism, the COD and ammonia nitrogen content which directly affect the water quality, the sulfur dioxide content which directly affect the atmospheric quality and the TSP can be subjected to data diversification equalization, so that good harmony of data is maintained, and the numerical model convergence and regression speed improvement are conveniently realized. The three-dimensional information of the goaf, the subsidence of the ground surface of the goaf, the water and soil loss intensity and the precipitation amount change can be combined through the terrain evaluation index evaluation mechanism to form multi-element combined analysis on multiple dimensions, so that the difference between data values is weakened, the terrain evaluation index value change caused by each factor change can be analyzed in a balanced mode, and the situation that trend change is not obvious or extremely obvious due to different influence degrees of each factor on the terrain evaluation index is avoided.
The above description is merely a further embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art will be able to apply equivalents and modifications according to the technical solution and the concept of the present invention within the scope of the present invention disclosed in the present invention.
Claims (9)
1. The multi-mode remote sensing auxiliary mine ecological environment evaluation method for the weak network environment is characterized by comprising the following steps of:
s1, creating a goaf three-dimensional digital information model: performing three-dimensional information scanning on the goaf by using a laser radar technology, creating a goaf point cloud by using the scanned three-dimensional information, generating a three-dimensional information digital model of the goaf by using point cloud data, and acquiring goaf volume information;
s2, acquiring vegetation coverage information in the multi-mode remote sensing auxiliary image and goaf settlement data of the mine;
s3, acquiring ecological environment monitoring data: the ecological environment monitoring information management system of the combined mine goaf acquires the soil organic matter level change quantity, the soil PH change value and the soil heavy metal content change value in the mine goaf research period, takes the data as basic data of soil evaluation, acquires the biomass change and the total biological category number change in the mine environment research period, takes the data as basic data of biological evaluation in the ecological environment, acquires the water quality change, the atmosphere quality change and the precipitation change in the mine environment research period, takes the data as basic data of hydrologic evaluation, acquires the water loss and soil loss intensity change and the soil utilization rate change in the mine ecological environment research period, and combines the data acquired by S1 and S2 to be taken as basic data of topography evaluation;
s4, evaluating the ecological environment of the mine: acquiring a soil evaluation index by using an evaluation mechanism of the soil evaluation index, acquiring a biological evaluation index by using an evaluation mechanism of the biological evaluation index, acquiring a hydrologic evaluation index by using an evaluation mechanism of the hydrologic evaluation index, and acquiring a topography evaluation index by combining the evaluation mechanism of the topography evaluation index;
s5, acquiring a three-dimensional label of the target ecological environment evaluation image: training the spatial information labels of the multi-mode remote sensing auxiliary image by using a deep learning framework, carrying out multi-level classification by using the values of each evaluation index, grabbing the image element labels of the target level classification, and forming a target level evaluation image label data set of each index;
s6, comprehensively evaluating the ecological environment: transmitting the target grade evaluation image tag data set of each index obtained in the step S5 to a processing unit outside the goaf, constructing a target grade evaluation image of each index, obtaining an ecological comprehensive evaluation index by using the weighted sum of each evaluation index, constructing a mine three-dimensional model by using the multi-mode remote sensing image and the goaf three-dimensional digital information model, carrying out three-dimensional segmentation on the mine three-dimensional model according to a preset grid, grabbing a unit body with the standard of the comprehensive index in the segmented unit body, displaying a potential unit body with the standard of each evaluation index and the comprehensive index removed, and displaying three-dimensional coordinate information of the potential unit body.
2. The method for evaluating the ecological environment of the multimode remote sensing auxiliary mine for the weak network environment according to claim 1, wherein the soil evaluation index evaluation mechanism involved in S4 is as follows:
wherein: i TR K is the soil evaluation index yjz To study the change of organic matter level in time period, K PH To study the pH change value of soil in time period, K zjs And the method is used for researching the change value of the heavy metal content in the soil in a period.
3. The method for evaluating the ecological environment of the multi-modal remote sensing auxiliary mine for the weak network environment according to claim 1, wherein the biological evaluation index evaluation mechanism involved in S4 is as follows:
wherein: i sw Biological evaluation index, N sw To study the amount of change in total biomass over a period of time, M wz To study the variation of biological species over time.
4. The method for evaluating the ecological environment of the multimode remote sensing auxiliary mine for the weak network environment according to claim 1, wherein the hydrologic evaluation index evaluation mechanism involved in S4 is as follows:
I ww ＝0.1S st +0.5S dq +0.6S js ；
wherein: i ww S is a hydrologic evaluation index st To study the change of water quality in time period, S dq To study the change in atmospheric mass over time, S js To investigate the variation in precipitation over time.
5. The method for evaluating the ecological environment of the multi-mode remote sensing auxiliary mine for the weak network environment according to claim 4, wherein the mass change of the water body in the research period is inversely related to the COD change amount of the water, is positively related to the ammonia nitrogen change amount, and has the formula:
wherein: k (K) COD To study the COD change value of water in a period of time, K AD To study the ammonia nitrogen content change of water in a period;
the change of the atmosphere quality in the research period is inversely related to the sulfur dioxide content in the atmosphere and positively related to the TSP in the atmosphere, and the formula of the change of the atmosphere quality is as follows:
wherein:
is SO in the atmosphere 2 Content variation, R TSP Is the TSP variation in the atmosphere.
6. The method for evaluating the ecological environment of the multimode remote sensing auxiliary mine for the weak network environment according to claim 1, wherein the topography evaluation index evaluation mechanism involved in S4 is as follows:
wherein: v (V) ck To change the volume of the goaf, H min And H max Respectively representing a sedimentation minimum value and a sedimentation maximum value, S st The change of the water and soil loss intensity is represented, and Q is the change value of precipitation.
7. The multi-mode remote sensing auxiliary mine ecological environment evaluation method for the weak network environment according to claim 1, wherein the process of creating the three-dimensional digital information model of the S1 goaf is as follows:
A1. a laser light source: placing a laser radar on the ground of a goaf, wherein the laser radar generates a laser light source;
A2. shooting a depth picture: the laser radar acquires a light source reflected by the measured object, continuously shoots pictures containing depth information, and transmits the depth pictures to the microcomputer processing module through a transmission unit on the radar;
A3. preprocessing point cloud data: filtering Gaussian noise in the data by using a Gaussian filtering technology, and removing outliers and outliers according to Euclidean distances of adjacent points of each point in the point cloud;
A4. reconstructing three-dimensional data information: performing voxel-based downsampling by using a point cloud image, creating a 3D voxel grid, wherein each voxel comprises three points with the same axis and the same interval, downsampling the points belonging to the same voxel, and replacing the points with the mass centers of the points;
A5. constructing a three-dimensional information digital model: performing surface smoothing processing by utilizing the point cloud data to form a three-dimensional model of the goaf, and calculating the voxel volume by combining each voxel and the goaf height;
A6. constructing a goaf historical initial three-dimensional information data model: and (3) acquiring a goaf history initial three-dimensional model in a history initial period according to laser radar scanning data of the goaf history by utilizing the steps A1-A5, acquiring voxel volume information of the goaf, and performing volume difference values by utilizing corresponding points of A5 and A6 to acquire goaf volume change in a research period.
8. The method for evaluating the ecological environment of a multi-mode remote sensing auxiliary mine for a weak network environment according to claim 1, wherein the step S2 is characterized in that the vegetation coverage information in the multi-mode remote sensing auxiliary image is obtained by performing feature learning on the multi-mode remote sensing auxiliary image through a deep learning classifier, extracting and classifying the vegetation coverage graphic elements in the multi-mode remote sensing auxiliary image to obtain vegetation coverage information of a mine goaf, and the vegetation coverage of the mine goaf in an initial period of history is obtained by combining the historical multi-mode remote sensing auxiliary image.
9. The method for evaluating the ecological environment of the multi-mode remote sensing auxiliary mine for the weak network environment according to claim 1, wherein the mode of acquiring the subsidence condition of the mine goaf in the multi-mode remote sensing auxiliary image in the step S2 is to acquire a curved cloud image of the goaf ground by using depth information in the multi-mode remote sensing auxiliary image, and acquire a subsidence minimum value and a subsidence maximum value in the subsidence height by using difference between the height information of each grid point in the curved cloud image and the height of each grid point in the historical curved grid cloud image.
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| CN117113830A (en) * | 2023-08-21 | 2023-11-24 | 山东省鲁南地质工程勘察院(山东省地质矿产勘查开发局第二地质大队) | Mountain restoration evaluation prediction system based on data analysis |
| CN117474212A (en) * | 2023-12-25 | 2024-01-30 | 中国地质科学院水文地质环境地质研究所 | Underground water resource evaluation method based on remote sensing technology |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117113830A (en) * | 2023-08-21 | 2023-11-24 | 山东省鲁南地质工程勘察院(山东省地质矿产勘查开发局第二地质大队) | Mountain restoration evaluation prediction system based on data analysis |
| CN117113830B (en) * | 2023-08-21 | 2024-04-19 | 山东省鲁南地质工程勘察院(山东省地质矿产勘查开发局第二地质大队) | Mountain restoration evaluation prediction system based on data analysis |
| CN117474212A (en) * | 2023-12-25 | 2024-01-30 | 中国地质科学院水文地质环境地质研究所 | Underground water resource evaluation method based on remote sensing technology |
| CN117474212B (en) * | 2023-12-25 | 2024-04-09 | 中国地质科学院水文地质环境地质研究所 | Underground water resource evaluation method based on remote sensing technology |
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