Data detection system and method applied to environmental ecological restoration

Abstract

The invention discloses a data detection system and method applied to environmental ecological restoration, and relates to the technical field of environmental data detection, the system comprises a regional data processing module, a restoration effect analysis module and a restoration strategy optimization module; the regional data processing module acquires information of an environmental ecological restoration region, performs grid unit division on the environmental ecological restoration region, performs attribute labeling on each grid unit, and corrects a grid unit division result; the restoration effect analysis module is used for sampling data at adjacent sampling time points, carrying out evolution analysis through an environment evolution model, respectively obtaining environment ecological restoration influence factor prediction data of each grid unit at a future time point, and calculating an environment ecological restoration gain of each grid unit through environment ecological restoration evaluation; and the restoration strategy optimization module is used for dividing graded regions, calculating boundary gradients between adjacent regions, and performing optimization feedback on an environmental ecological restoration strategy of each region.

Description

Technical Field

[0001] This invention relates to the field of environmental data detection technology, specifically a data detection system and method for environmental ecological restoration. Background Technology

[0002] With the development of ecological environment restoration technologies, data-driven environmental monitoring methods have gradually become an important means of evaluating and optimizing restoration measures;

[0003] However, due to the long overall time span, high volatility of short-term effects, and regional differences in environmental and ecological restoration, existing technologies have the following shortcomings: First, traditional methods of sampling, evaluating, and analyzing environmental parameters lack a holistic consideration of the long-term effects of environmental and ecological restoration, causing environmental restoration measures to often lag behind ecological changes, resulting in passive and delayed decision-making. Second, evolutionary analysis of environmental parameters neglects the reasonable assessment of the current stage of environmental restoration effectiveness, leading to a lack of comparative analysis between predicted and actual environmental restoration effectiveness. Furthermore, when evaluating the effectiveness of environmental restoration, the lack of analysis of local differences in effectiveness within the region to be restored makes it difficult to identify uneven local restoration effects, further exacerbating the lag in adjusting and optimizing local restoration strategies.

[0004] Therefore, a data detection system and method for environmental ecological restoration are needed to address the aforementioned technical deficiencies. Summary of the Invention

[0005] The purpose of this invention is to provide a data detection system and method for environmental ecological restoration, so as to solve the problems raised in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A data detection method for environmental ecological restoration, the method comprising the following steps:

[0008] Step S100: Obtain information on the environmental and ecological restoration area, divide the environmental and ecological restoration area into grid units, analyze the influencing factors of environmental and ecological restoration, label the attributes of each grid unit, and correct the grid unit division results based on the attribute labeling results.

[0009] Step S200: Time series sampling of environmental and ecological restoration influencing factors for each grid unit is performed. Data from adjacent sampling time points are used to conduct evolutionary analysis through an environmental evolution model to obtain predicted data of environmental and ecological restoration influencing factors for each grid unit at future time points.

[0010] Step S300: Based on the predicted data of environmental ecological restoration influencing factors obtained from the evolution of sampling data in each grid cell at adjacent sampling time points, conduct environmental ecological restoration assessments respectively, and calculate the environmental ecological restoration gain of each grid cell.

[0011] Step S400: Set gain breakpoints, classify each grid cell according to the environmental ecological restoration gain of each grid cell, divide the grid cells with the same environmental ecological restoration effectiveness level and adjacent geographical orientations into the same level area, and mark the boundaries of each level area.

[0012] Step S500: Calculate the boundary gradient between adjacent regions, and based on the calculation result of the boundary gradient, optimize and feedback the environmental ecological restoration strategies of each region.

[0013] In the above technical solution, the step S100 includes the following steps:

[0014] Step S101: Obtain the information of the environmental ecological restoration area, and divide the environmental ecological restoration area into grid cells; select any set of orthogonal directions in the horizontal plane, and set equally spaced parallel lines at intervals of step respectively to divide the environmental ecological restoration area into grid cells.

[0015] Step S102: Analyze the environmental ecological restoration influencing factors and conduct grid cell attribute annotation.

[0016] For any grid cell g, the attribute annotation is: g[F_eri(g), R_a(g)]; where, F_eri(g) is the set of environmental ecological restoration influencing factors of grid cell g, and R_a(g) is the proportion of the area of the environmental ecological restoration area in grid cell g.

[0017] Step S103: Correct the grid cell division result according to the proportion of the area of the environmental ecological restoration area in each grid cell.

[0018] Set the minimum occupancy ratio threshold R_0 of the grid cell. If R_a(g) < R_0, measure the length of the common side between grid cell g and each adjacent grid cell in the environmental ecological restoration area respectively, calculate the ratio of the measurement result to step as the regional association degree between grid cell g and each adjacent grid cell, select the adjacent grid cell g_tar with the highest regional association degree with grid cell g, add the proportion of the environmental ecological restoration area in grid cell g and grid cell g_tar as the weight coefficient, correct the environmental ecological restoration influencing factors of grid cell g to the attribute annotation result of grid cell g_tar, add the proportion of the area of the environmental ecological restoration area in grid cell g and grid cell g_tar, and at the same time cancel the setting of grid cell g.

[0019] Repeat step S103 until the area ratio of the environmental ecological restoration area in all grids is greater than or equal to the minimum occupancy ratio threshold R_0 of the grid cell.

[0020] The environmental ecological restoration area is managed in a fine-grained discretized manner by adopting a grid division method. Combined with influencing factor analysis and attribute labeling, the restoration area can be accurately described. In addition, the grid division results are corrected based on attribute labeling to further improve the monitoring efficiency of the restoration area.

[0021] In the above technical solution, step S200 includes the following steps:

[0022] Step S201: Perform time-series sampling of environmental and ecological restoration influencing factors for each grid unit, and clean the sampled data;

[0023] Step S202: Use the environmental and ecological restoration influencing factors of each grid cell to sample data at adjacent sampling time points, and perform evolutionary analysis through the environmental evolution model to obtain the predicted data of environmental and ecological restoration influencing factors obtained from the evolution of the sampled data of each grid cell at adjacent sampling time points;

[0024] Using sampling data from adjacent sampling time points for evolutionary analysis and prediction ensures the effectiveness of environmental restoration effectiveness analysis over long periods, while enabling reasonable comparative analysis of changes in environmental restoration effectiveness over short periods, thus more accurately reflecting the trend changes in environmental and ecological restoration effectiveness.

[0025] In the above technical solution, step S300 includes the following steps:

[0026] Step S301: Set the effectiveness evaluation time point in the future time period, and extract the prediction data of the effectiveness evaluation time point from the environmental and ecological restoration impact factor prediction data obtained by the evolution of sampling data of each grid unit at adjacent sampling time points.

[0027] For any grid cell g, the extracted data are denoted as F_eri(g,t-1) and F_eri(g,t), where F_eri(g,t-1) is the predicted data of environmental and ecological restoration influencing factors at the effectiveness evaluation time point obtained from the evolution of the sampling data at the previous time point in adjacent sampling time points, and F_eri(g,t) is the predicted data of environmental and ecological restoration influencing factors at the effectiveness evaluation time point obtained from the evolution of the sampling data at the next time point in adjacent sampling time points.

[0028] Step S302: Perform environmental and ecological restoration assessments on the predicted data of the effectiveness evaluation time points extracted from adjacent time points of each grid cell, and calculate the environmental and ecological restoration gain of each grid cell at the effectiveness evaluation time point based on the environmental and ecological restoration assessment results; for any grid cell g, calculate according to the formula:

[0029] G(g,t) = A_eri(g,t) - A_eri(g,t - 1);

[0030] Where, G(g,t) is the environmental ecological restoration gain of grid cell g at time point t, A_eri(g,t) is the environmental ecological restoration evaluation result of the predicted data of the environmental ecological restoration influencing factors obtained by the evolution of the sampling data of grid cell g at time point t, and A_eri(g,t - 1) is the environmental ecological restoration evaluation result of the predicted data of the environmental ecological restoration influencing factors obtained by the evolution of the sampling data of grid cell g at time point t - 1;

[0031] By calculating the changes in the effectiveness of environmental ecological restoration of each grid cell after evolutionary analysis, it intuitively reflects the impact of changes in short-term environmental ecological restoration influencing factors on the long-term effectiveness of environmental restoration, weakens the lag of restoration strategy adjustment, and improves the effectiveness of restoration strategy adjustment.

[0032] In the above technical solution, step S400 includes the following steps:

[0033] Step S401: Set gain breakpoints G_1, G_2,..., G_n at equal intervals, and classify each grid cell according to the environmental ecological restoration gain of each grid cell;

[0034] For any grid cell g, if G_a ≤ G(g,t) < G_(a + 1), then the grid cell g at time point t is classified into category a;

[0035] Step S402: Divide grid cells with the same environmental ecological restoration effectiveness level and adjacent geographical positions into the same level area, and mark the boundaries of each level area;

[0036] When marking the boundary, only mark the boundary line where there are grid cells on both sides in the level area;

[0037] For any level area x divided at any time point t, the boundary marking result is: x_t[lev_x,{(G_1(t),G_1^'(t)),(G_2(t),G_2^'(t)),(G_3(t),G_3^'(t)),...}]; where, lev_x is the level category to which the level area x belongs at time point t, G_1(t), G_2(t), G_3(t) are the environmental ecological restoration gains of the grid cells belonging to the level area x at the boundary of the level area x at time point t, and G_1^'(t), G_2^'(t), G_3^'(t) are the environmental ecological restoration gains of the grid cells belonging to the adjacent level area of the level area x at the boundary of the level area x at time point t;

[0038] In the above technical solution, step S500 includes the following:

[0039] Obtain the boundary marking results for each level of region, calculate the boundary gradient between adjacent level regions. For any level region x divided at any time point t, the boundary gradient Grad(x,t) is calculated as follows:

[0040] Grad(x,t)=1 / N_border×∑_(i=1)^(N_border)(G_i(t)-G_i^'(t))

[0041] Where N_border is the number of line segments contained in the boundary line of the grade region x divided at time point t, i is the line segment number of the boundary line of the grade region x divided at time point t, G_i(t) is the environmental ecological restoration gain of the grid cell belonging to grade region x at the i-th boundary line of the grade region x divided at time point t at time point t, and G_i^'(t) is the environmental ecological restoration gain of the grid cell belonging to the grade region adjacent to grade region x at the i-th boundary line of the grade region x divided at time point t at time point t.

[0042] Set a boundary gradient window [-Grad_th, Grad_th] and sort the regions of each level from largest to smallest according to the boundary gradient. For any level region x divided at any time point t, if Grad(x,t)≥-Grad_th and Grad(x,t)≤Grad_th, the environmental restoration effect of level region x is determined to be in line with the overall level, and no environmental ecological restoration strategy optimization feedback is made. If Grad(x,t)<-Grad_th, the environmental restoration effect of level region x is determined to be lower than the overall level, and an abnormal feedback on the environmental ecological restoration strategy is sent to the management personnel. All grid cells in the level region are synchronously fed back in order of environmental ecological restoration gain from smallest to largest at time point t. If Grad(x,t)>Grad_th, the environmental restoration effect of level region x is determined to be higher than the overall level, and an environmental ecological restoration strategy optimization feedback is sent to the management personnel. Resource balancing optimization is performed on the environmental ecological restoration strategy in the current level region.

[0043] By employing a gain breakpoint partitioning method, grid cells with similar environmental remediation effects and geographical proximity are grouped into the same level of region and their boundaries are marked to achieve hierarchical management of remediation effectiveness. Furthermore, the boundary gradient between regions of each level is calculated to accurately identify regions with significant differences in remediation effects. This helps to optimize environmental remediation strategies in a hierarchical manner and improve the rationality and accuracy of resource allocation.

[0044] A data detection system for environmental ecological restoration, which utilizes the data detection method for environmental ecological restoration described above, includes: a regional data processing module, a restoration effectiveness analysis module, and a restoration strategy optimization module.

[0045] The regional data processing module acquires information about the environmental and ecological restoration area, divides the area into grid cells, labels the attributes of each grid cell, and corrects the grid cell division results. The restoration effectiveness analysis module uses sampling data from adjacent sampling time points, performs evolutionary analysis through an environmental evolution model, obtains predicted data of environmental and ecological restoration influencing factors for each grid cell at future time points, and then calculates the environmental and ecological restoration gain of each grid cell through environmental and ecological restoration assessment. The restoration strategy optimization module is used to divide the area into different levels, calculate the boundary gradient between adjacent areas, and provide feedback on the optimization of environmental and ecological restoration strategies for each level of area.

[0046] In the above technical solution, the regional data processing module includes: a data acquisition unit, a regional division unit, and a regional correction unit;

[0047] The data acquisition unit is used to acquire information on the environmental ecological restoration area and the influencing factors of environmental ecological restoration; the area division unit is used to divide the environmental ecological restoration area into grid units; the area correction unit corrects the grid unit division results by annotating the attributes of each grid unit.

[0048] In the above technical solution, the repair effectiveness analysis module includes: an evolution analysis unit and a repair effectiveness analysis unit;

[0049] The evolutionary analysis unit uses sampling data from adjacent sampling time points and performs evolutionary analysis through an environmental evolution model to obtain predicted data on environmental and ecological restoration influencing factors for each grid cell at future time points; the restoration effectiveness analysis unit calculates the environmental and ecological restoration gain of each grid cell through environmental and ecological restoration assessment.

[0050] In the above technical solution, the repair strategy optimization module includes: a level region division unit, a boundary gradient calculation unit, and a strategy optimization feedback unit;

[0051] The hierarchical region division unit divides the region into hierarchical regions based on the environmental and ecological restoration gain of each grid cell; the boundary gradient calculation unit calculates the boundary gradient of each hierarchical region based on the environmental and ecological restoration gain of the grid cells at the boundary of each hierarchical region; and the strategy optimization feedback unit performs adaptive environmental and ecological restoration strategy optimization feedback for each hierarchical region based on the boundary gradient calculation results.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] In this invention, by performing evolutionary prediction on short-term sampling data, the gain of environmental and ecological restoration effectiveness is calculated. Taking into account the characteristics of long time span and small short-term changes in environmental and ecological restoration, and balancing the analysis of short-term changes and long-term effects, the scientific nature and timeliness of adjusting environmental and ecological restoration strategies are ensured.

[0054] In this invention, by equally dividing the gains in environmental and ecological restoration effectiveness, the environmental and ecological restoration area is divided into different level areas, thereby realizing hierarchical management of areas with different restoration effectiveness and improving the efficiency and accuracy of restoration strategy optimization.

[0055] In this invention, by dividing the region into graded areas and calculating the boundary gradient between each graded area, areas with significant differences in restoration effectiveness are accurately identified, thereby further improving the accuracy of identifying areas with abnormal restoration effectiveness, effectively improving the efficiency of restoration strategy optimization in areas with good restoration effectiveness, and improving the overall efficiency of environmental and ecological restoration. Attached Figure Description

[0056] Figure 1 This is a flowchart of a data detection method for environmental ecological restoration according to the present invention;

[0057] Figure 2 This is an organizational structure diagram of a data detection system for environmental ecological restoration according to the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example: Please refer to Figures 1-2 The present invention provides the following technical solution:

[0060] like Figure 1 As shown, the present invention provides a data detection method for environmental ecological restoration, the method comprising the following steps:

[0061] Step S100: Obtain information on the environmental and ecological restoration area, divide the environmental and ecological restoration area into grid units, analyze the influencing factors of environmental and ecological restoration, label the attributes of each grid unit, and correct the grid unit division results based on the attribute labeling results.

[0062] Step S200: Time series sampling of environmental and ecological restoration influencing factors for each grid unit is performed. Data from adjacent sampling time points are used to conduct evolutionary analysis through an environmental evolution model to obtain predicted data of environmental and ecological restoration influencing factors for each grid unit at future time points.

[0063] Step S300: Based on the environmental and ecological restoration influencing factor prediction data obtained from the evolution of sampling data of each grid cell at adjacent sampling time points, conduct environmental and ecological restoration assessments respectively, and calculate the environmental and ecological restoration gain of each grid cell.

[0064] Step S400: Set gain breakpoints, classify each grid cell according to the environmental ecological restoration gain of each grid cell, divide grid cells with the same environmental ecological restoration effectiveness level and geographically adjacent into the same level area, and mark the boundaries of each level area.

[0065] Step S500: Calculate the boundary gradient between adjacent regions, and optimize the environmental and ecological restoration strategy for each region based on the boundary gradient calculation results.

[0066] Step S100 includes the following steps:

[0067] Step S101: Obtain information on the environmental ecological restoration area and divide the environmental ecological restoration area into grid units; select any set of orthogonal directions in the horizontal plane, set equally spaced parallel lines with a step size as the interval, and divide the environmental ecological restoration area into grid units.

[0068] Step S102: Analyze the influencing factors of environmental and ecological restoration and label the raster unit attributes;

[0069] For any raster cell g, the attribute is labeled as: g[F_eri(g),R_a(g)]; where F_eri(g) is the set of environmental and ecological restoration influencing factors of raster cell g, and R_a(g) is the proportion of the area occupied by the environmental and ecological restoration area in raster cell g;

[0070] Step S103: Correct the grid cell division results according to the area ratio of the environmental ecological restoration area in each grid cell;

[0071] Set the minimum occupancy ratio threshold \(R_0\) of the grid cell. If \(R_a(g)<R_0\), measure the length of the common side between the grid cell \(g\) and each adjacent grid cell that is in the environmental ecological restoration area respectively, calculate the ratio of the measurement result to the step size step as the regional correlation degree between the grid cell \(g\) and each adjacent grid cell, select the adjacent grid cell \(g_{tar}\) with the highest regional correlation degree with the grid cell \(g\), add the proportion of the environmental ecological restoration area in the grid cell \(g\) and the grid cell \(g_{tar}\) as the weight coefficient, correct the environmental ecological restoration influencing factors of the grid cell \(g\) to the attribute annotation result of the grid cell \(g_{tar}\), and add the proportion of the area of the environmental ecological restoration area in the grid cell \(g\) and the grid cell \(g_{tar}\), and at the same time cancel the setting of the grid cell \(g\);

[0072] Repeat the operation in step S103 until the proportion of the area of the environmental ecological restoration area in all grid cells is greater than or equal to the minimum occupancy ratio threshold \(R_0\) of the grid cell;

[0073] In specific implementation, collect the geographical information of the polluted area by means of remote sensing images, GIS, etc., set the grid division step size, and perform periodic sampling on the environmental ecological restoration index parameters in each grid cell;

[0074] Due to the irregularity of the environmental restoration area, the situation that the grid cells in the boundary area are not fully occupied during grid division occurs. For grid cells with small occupancy, if all are set as independent grid cells, it will cause greater supervision and optimization pressure on the overall system. Therefore, cancel the setting of grid cells with small area occupancy ratio and merge the data therein into adjacent grid cells;

[0075] When selecting grid cells to be merged, compare the lengths of the actual restoration areas occupied on the adjacent boundary lines between each adjacent grid cell and the grid cell to be merged. According to common sense, for grid cells with a larger ratio of the occupied length to the step size step, their adjacency is closer. Therefore, select the grid cell with the largest ratio of the occupied length to the step size step as the merge target, and merge the attribute annotation results by weighted calculation based on the area ratio.

[0076] The step S200 includes the following steps:

[0077] Step S201: Perform time series sampling on the environmental ecological restoration influencing factors of each grid cell, and clean the sampling data;

[0078] Step S202: Use the sampling data of the environmental ecological restoration influencing factors of each grid cell at adjacent sampling time points, and perform evolutionary analysis through the environmental evolution model respectively to obtain the predicted data of the environmental ecological restoration influencing factors obtained by the evolution of the sampling data of each grid cell at adjacent sampling time points;

[0079] In practical implementation, environmental evolution analysis is used to predict and analyze the influencing factors of environmental and ecological restoration. Taking pollutants in the environment as an example, pollutant fate migration models can be used for analysis and prediction, or time series prediction models can be used to predict and analyze the concentration of pollutants in each grid cell.

[0080] Step S300 includes the following steps:

[0081] Step S301: Set the effectiveness evaluation time point in the future time period, and extract the prediction data of the effectiveness evaluation time point from the environmental and ecological restoration impact factor prediction data obtained by the evolution of sampling data of each grid unit at adjacent sampling time points.

[0082] For any grid cell g, the extracted data are denoted as F_eri(g,t-1) and F_eri(g,t), where F_eri(g,t-1) is the predicted data of environmental and ecological restoration influencing factors at the effectiveness evaluation time point obtained from the evolution of the sampling data at the previous time point in adjacent sampling time points, and F_eri(g,t) is the predicted data of environmental and ecological restoration influencing factors at the effectiveness evaluation time point obtained from the evolution of the sampling data at the next time point in adjacent sampling time points.

[0083] Step S302: Perform environmental and ecological restoration assessments on the predicted data of the effectiveness evaluation time points extracted from adjacent time points of each grid cell, and calculate the environmental and ecological restoration gain of each grid cell at the effectiveness evaluation time point based on the environmental and ecological restoration assessment results; for any grid cell g, calculate according to the formula:

[0084] G(g,t)=A_eri(g,t)-A_eri(g,t-1);

[0085] Wherein, G(g,t) is the environmental and ecological restoration gain of grid cell g at time point t, A_eri(g,t) is the environmental and ecological restoration assessment result of the environmental and ecological restoration influencing factor prediction data obtained from the evolution of the sampling data of grid cell g at time point t, and A_eri(g,t-1) is the environmental and ecological restoration assessment result of the environmental and ecological restoration influencing factor prediction data obtained from the evolution of the sampling data of grid cell g at time point t-1.

[0086] In practical implementation, the method relies on the impact of short-term environmental changes on the long-term effectiveness of environmental restoration. However, the technical requirements and analytical accuracy of this method are difficult to meet the needs of practical use. Therefore, we use the sampling data at adjacent sampling time points to perform evolutionary analysis and prediction, and use the predicted data to evaluate and calculate the environmental ecological restoration gain, thereby accurately reflecting the impact of short-term environmental changes on the long-term restoration effectiveness.

[0087] Step S400 includes the following steps:

[0088] Step S401: Set gain breakpoints G_1, G_2, …, G_n at equal intervals, and classify each grid cell according to the environmental ecological restoration gain of each grid cell;

[0089] For any grid cell g, if G_a ≤ G(g, t) < G_(a + 1), then classify the grid cell g at time point t into category a;

[0090] Step S402: Divide grid cells with the same environmental ecological restoration effectiveness level and adjacent geographical orientations into the same level area, and mark the boundaries of each level area;

[0091] When marking the boundary, only mark the boundary line where there are grid cells on both sides in the level area;

[0092] For any level area x divided at any time point t, the boundary marking result is: x_t[lev_x,{(G_1(t), G_1^'(t)),(G_2(t), G_2^'(t)),(G_3(t), G_3^'(t)),…}]; where lev_x is the level category to which the level area x belongs at time point t, G_1(t), G_2(t), G_3(t) are the environmental ecological restoration gains of the grid cells belonging to the level area x at the boundary of the level area x at time point t, and G_1^'(t), G_2^'(t), G_3^'(t) are the environmental ecological restoration gains of the grid cells belonging to the adjacent level area of the level area x at the boundary of the level area x at time point t;

[0093] In specific implementation, classify and categorize each grid cell according to the environmental ecological restoration gain. According to the environmental ecological restoration gain calculation formula, it can be seen that for grid cells with higher environmental ecological restoration gain calculation results, the environmental restoration effectiveness improvement speed is faster in the time period from (t - 1) to t, while for grid cells with lower calculation results, the environmental restoration effectiveness improvement speed is slower in the time period from (t - 1) to t. Classify the grid cells according to the calculation results to achieve personalized strategy management for grid cells of different level types;

[0094] For grid cells with the same level type and adjacent geographical locations, uniformly divide them into the same level area, and then analyze the boundaries of each level area to reduce the operation pressure of the system for analyzing each grid cell separately.

[0095] The said step S500 includes the following contents:

[0096] Obtain the boundary marking results for each level of region, calculate the boundary gradient between adjacent level regions. For any level region x divided at any time point t, the boundary gradient Grad(x,t) is calculated as follows:

[0097] Grad(x,t)=1 / N_border×∑_(i=1)^(N_border)(G_i(t)-G_i^'(t))

[0098] Where N_border is the number of line segments contained in the boundary line of the grade region x divided at time point t, i is the line segment number of the boundary line of the grade region x divided at time point t, G_i(t) is the environmental ecological restoration gain of the grid cell belonging to grade region x at the i-th boundary line of the grade region x divided at time point t at time point t, and G_i^'(t) is the environmental ecological restoration gain of the grid cell belonging to the grade region adjacent to grade region x at the i-th boundary line of the grade region x divided at time point t at time point t.

[0099] Set a boundary gradient window [-Grad_th, Grad_th] and sort the regions of each level from largest to smallest according to the boundary gradient. For any level region x divided at any time point t, if Grad(x,t)≥-Grad_th and Grad(x,t)≤Grad_th, the environmental restoration effect of level region x is determined to be in line with the overall level, and no environmental ecological restoration strategy optimization feedback is made. If Grad(x,t)<-Grad_th, the environmental restoration effect of level region x is determined to be lower than the overall level, and an abnormal feedback on the environmental ecological restoration strategy is sent to the management personnel. All grid cells in the level region are synchronously fed back in order of environmental ecological restoration gain from smallest to largest at time point t. If Grad(x,t)>Grad_th, the environmental restoration effect of level region x is determined to be higher than the overall level, and an environmental ecological restoration strategy optimization feedback is sent to the management personnel. Resource balancing optimization is performed on the environmental ecological restoration strategy in the current level region.

[0100] In practical implementation, it is assumed that there are repair areas divided by the following raster at time t, which are represented in the form of a two-dimensional array as: [1,2,2; 1,1,2; 1,3,3]; where each element of the matrix represents the level type of the level area to which the raster unit belongs.

[0101] If we compare and analyze the grid cells of the boundary region, such as the grid cell numbered (2,2) in the array, there are 3 other level grid cells adjacent to it. When performing boundary marking and boundary gradient calculation, the data storage complexity is high. However, based on the grid division, the boundary lines between different level regions are broken lines composed of different line segments. Therefore, we only need to analyze the grid cells on both sides of the line segments in the boundary to intuitively and efficiently reflect the differences between the boundaries of each level region.

[0102] Taking the repair area represented by the above matrix as an example, assuming that the environmental repair gain of each grid cell is as follows, represented by a two-dimensional array: [1.10,-1.52,-1.38; 1.05,1.13,-1.46; 1.08,3.23,3.02];

[0103] According to the boundary gradient calculation formula, the boundary gradient Grad(1,t) of the grade type 1 region at time t is:

[0104] Grad(1,t)=1 / 5[(1.10+1.52)+(1.13+1.52)+(1.13+1.46)+(1.13-3.23)+(1.08-3.23)]=3.61;

[0105] The gradient Grad(2,t) of the boundary region of level type 2 at time point t is:

[0106] Grad(2,t)=1 / 4[(-1.52-1.10)+(-1.52-1.13)+(-1.46-1.13)+(-1.46-3.02)]=-3.085;

[0107] The gradient Grad(3,t) of the boundary region of level type 3 at time point t is:

[0108] Grad(3,t)=1 / 3[(3.23-1.08)+(3.23-1.13)+(3.02+1.46)]=2.91;

[0109] It is evident that the boundary gradient of the level 2 area is relatively low, and the overall restoration effect deviates significantly from that of the surrounding area. Therefore, it is necessary to conduct environmental and ecological restoration strategy optimization feedback to ensure timely adjustment of the restoration strategy and guarantee the overall restoration effect of the environmental restoration area.

[0110] like Figure 2 As shown, the present invention also provides a data detection system for environmental ecological restoration, the system comprising: a regional data processing module, a restoration effectiveness analysis module, and a restoration strategy optimization module;

[0111] The regional data processing module acquires information about the environmental and ecological restoration area, divides the area into grid cells, labels the attributes of each grid cell, and corrects the grid cell division results. The restoration effectiveness analysis module uses sampling data from adjacent sampling time points, performs evolutionary analysis through an environmental evolution model, obtains predicted data of environmental and ecological restoration influencing factors for each grid cell at future time points, and then calculates the environmental and ecological restoration gain of each grid cell through environmental and ecological restoration assessment. The restoration strategy optimization module is used to divide the area into different levels, calculate the boundary gradient between adjacent areas, and provide feedback on the optimization of environmental and ecological restoration strategies for each level of area.

[0112] The regional data processing module includes: a data acquisition unit, a regional division unit, and a regional correction unit;

[0113] The data acquisition unit is used to acquire information on the environmental ecological restoration area and the influencing factors of environmental ecological restoration; the area division unit is used to divide the environmental ecological restoration area into grid units; the area correction unit corrects the grid unit division results by annotating the attributes of each grid unit.

[0114] The repair effectiveness analysis module includes: an evolution analysis unit and a repair effectiveness analysis unit;

[0115] The evolutionary analysis unit uses sampling data from adjacent sampling time points and performs evolutionary analysis through an environmental evolution model to obtain predicted data on environmental and ecological restoration influencing factors for each grid cell at future time points; the restoration effectiveness analysis unit calculates the environmental and ecological restoration gain of each grid cell through environmental and ecological restoration assessment.

[0116] The repair strategy optimization module includes: a level region division unit, a boundary gradient calculation unit, and a strategy optimization feedback unit;

[0117] The hierarchical region division unit divides the region into hierarchical regions based on the environmental and ecological restoration gain of each grid cell; the boundary gradient calculation unit calculates the boundary gradient of each hierarchical region based on the environmental and ecological restoration gain of the grid cells at the boundary of each hierarchical region; and the strategy optimization feedback unit performs adaptive environmental and ecological restoration strategy optimization feedback for each hierarchical region based on the boundary gradient calculation results.

[0118] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A data detection method for environmental ecological restoration, characterized in that... , The method includes the following steps: Step S100: Obtain the information of the environmental ecological restoration area, divide the environmental ecological restoration area into grid cells, analyze the influencing factors of environmental ecological restoration, perform attribute annotation on each grid cell, and correct the grid cell division result according to the attribute annotation result; Step S200: Conduct time series sampling on the influencing factors of environmental ecological restoration for each grid cell, use the sampling data at adjacent sampling time points, and perform evolutionary analysis through the environmental evolution model to obtain the predicted data of the influencing factors of environmental ecological restoration at future time points for each grid cell respectively; Step S300: Conduct environmental ecological restoration assessment respectively according to the predicted data of the influencing factors of environmental ecological restoration obtained by the evolution of the sampling data at adjacent sampling time points for each grid cell, and calculate the environmental ecological restoration gain of each grid cell; Step S400: Set the gain breakpoint, divide each grid cell into levels according to the environmental ecological restoration gain of each grid cell, divide the grid cells with the same environmental ecological restoration effect level and adjacent geographical positions into the same level area, and mark the boundaries of each level area; 2. The data detection method for environmental ecological restoration according to claim 1, characterized in that, Step S500: Calculate the boundary gradient between adjacent regions, and optimize and feedback the environmental ecological restoration strategy for each region according to the calculation result of the boundary gradient. The said step S100 includes the following steps: Step S101: Obtain the information of the environmental ecological restoration area, and divide the environmental ecological restoration area into grid cells; select any set of orthogonal directions in the horizontal plane, set equally spaced parallel lines at intervals of step respectively, and divide the environmental ecological restoration area into grid cells; Step S102: Analyze the influencing factors of environmental ecological restoration and perform grid cell attribute annotation; For any grid cell g, the attribute annotation is: g[F_eri(g), R_a(g)]; where, F_eri(g) is the set of influencing factors of environmental ecological restoration of grid cell g, and R_a(g) is the proportion of the area of the environmental ecological restoration area in grid cell g; Step S103: Correct the grid cell division result according to the proportion of the area of the environmental ecological restoration area in each grid cell; Set the minimum occupancy ratio threshold R_0 of the grid cell. If R_a(g) < R_0, measure the length of the common side between grid cell g and each adjacent grid cell in the environmental ecological restoration area respectively, calculate the ratio of the measurement result to step as the regional correlation degree between grid cell g and each adjacent grid cell, select the adjacent grid cell g_tar with the highest regional correlation degree with grid cell g, add the proportion of the environmental ecological restoration area in grid cell g and grid cell g_tar as the weight coefficient, correct the influencing factors of environmental ecological restoration of grid cell g to the attribute annotation result of grid cell g_tar, add the proportion of the area of the environmental ecological restoration area in grid cell g and grid cell g_tar, and cancel the setting of grid cell g at the same time; Repeat the operation in step S103 until the proportion of the area of the environmental ecological restoration area in all grid cells is greater than or equal to the minimum occupancy ratio threshold R_0 of the grid cell.

3. The data detection method for environmental ecological restoration according to claim 1, characterized in that, The step S200 includes the following steps: Step S201: Conduct time-series sampling on the environmental ecological restoration influencing factors of each grid cell, and perform data cleaning on the sampled data; Step S202: Use the sampled data of the environmental ecological restoration influencing factors of each grid cell at adjacent sampling time points, and perform evolutionary analysis through the environmental evolution model respectively to obtain the predicted data of the environmental ecological restoration influencing factors obtained by the evolution of the sampled data of each grid cell at adjacent sampling time points.

4. The data detection method for environmental ecological restoration according to claim 1, characterized in that, The step S300 includes the following steps: Step S301: Set the effectiveness evaluation time points in the future time period, and extract the predicted data of the effectiveness evaluation time points respectively from the predicted data of the environmental ecological restoration influencing factors obtained by the evolution of the sampled data of each grid cell at adjacent sampling time points; For any grid cell g, the extracted data are respectively denoted as: F_eri(g,t - 1) and; in F_eri(g,t), F_eri(g,t - 1) is the predicted data of the environmental ecological restoration influencing factors at the effectiveness evaluation time point obtained by the evolution of the sampled data at the previous time point in the adjacent sampling time points, and F_eri(g,t) is the predicted data of the environmental ecological restoration influencing factors at the effectiveness evaluation time point obtained by the evolution of the sampled data at the later time point in the adjacent sampling time points; Step S302: Conduct environmental ecological restoration evaluations on the predicted data of the effectiveness evaluation time points extracted for each grid cell at adjacent time points respectively, and calculate the environmental ecological restoration gain of each grid cell at the effectiveness evaluation time point according to the environmental ecological restoration evaluation results; for any grid cell g, calculate according to the formula: G(g,t) = A_eri(g,t) - A_eri(g,t - 1); where, G(g,t) is the environmental ecological restoration gain of grid cell g at time point t, A_eri(g,t) is the environmental ecological restoration evaluation result of the predicted data of the environmental ecological restoration influencing factors obtained by the evolution of the sampled data of grid cell g at time point t, and A_eri(g,t - 1) is the environmental ecological restoration evaluation result of the predicted data of the environmental ecological restoration influencing factors obtained by the evolution of the sampled data of grid cell g at time point t - 1.

5. The data detection method for environmental ecological restoration according to claim 1, characterized in that, The step S400 includes the following steps: Step S401: Set gain breakpoints G_1, G_2,..., G_n at equal intervals, and classify each grid cell according to the environmental ecological restoration gain of each grid cell; For any grid cell g, if G_a ≤ G(g,t) < G_(a + 1), then classify grid cell g at time point t into category a; Step S402: Divide the grid cells with the same environmental ecological restoration effectiveness level and adjacent geographical positions into the same level area, and mark the boundaries of each level area; When marking the boundaries, only mark the boundary lines where there are grid cells on both sides in the level area; For any level region x divided at any time point t, the boundary labeling result is: x_t[lev_x,{(G_1(t),G_1^'(t)),(G_2(t),G_2^'(t)),(G_3(t),G_3^'(t)),…}]; where lev_x is the level category to which level region x belongs at time point t, G_1(t),G_2(t),G_3(t) are the environmental and ecological restoration gains of the raster cells belonging to level region x at the boundary of level region x at time point t, and G_1^'(t),G_2^'(t),G_3^'(t) are the environmental and ecological restoration gains of the raster cells belonging to the adjacent level region x at the boundary of level region x at time point t.

6. The data detection method for environmental ecological restoration according to claim 5, characterized in that, Step S500 includes the following: Obtain the boundary marking results for each level of region, calculate the boundary gradient between adjacent level regions. For any level region x divided at any time point t, the boundary gradient Grad(x,t) is calculated as follows: Grad(x,t)=1 / N_border×∑_(i=1)^(N_border)(G_i(t)-G_i^'(t)) Where N_border is the number of line segments contained in the boundary line of the grade region x divided at time point t, i is the line segment number of the boundary line of the grade region x divided at time point t, G_i(t) is the environmental ecological restoration gain of the grid cell belonging to grade region x at the i-th boundary line of the grade region x divided at time point t at time point t, and G_i^'(t) is the environmental ecological restoration gain of the grid cell belonging to the grade region adjacent to grade region x at the i-th boundary line of the grade region x divided at time point t at time point t. Set a boundary gradient window [-Grad_th, Grad_th] and sort the regions of each level from largest to smallest according to the boundary gradient. For any level region x divided at any time point t, if Grad(x,t)≥-Grad_th and Grad(x,t)≤Grad_th, the environmental restoration effect of level region x is determined to be in line with the overall level, and no environmental ecological restoration strategy optimization feedback is made. If Grad(x,t)<-Grad_th, the environmental restoration effect of level region x is determined to be lower than the overall level, and an abnormal feedback on the environmental ecological restoration strategy is sent to the management personnel. All grid cells in the level region are synchronously fed back in ascending order of environmental ecological restoration gain at time point t. If Grad(x,t)>Grad_th, the environmental restoration effect of level region x is determined to be higher than the overall level, and an environmental ecological restoration strategy optimization feedback is sent to the management personnel. Resource balancing optimization is performed on the environmental ecological restoration strategy in the current level region.

7. A data detection system for environmental ecological restoration, employing the data detection method for environmental ecological restoration as described in any one of claims 1-6, characterized in that, The system includes: a regional data processing module, a repair effectiveness analysis module, and a repair strategy optimization module; The regional data processing module acquires information about the environmental and ecological restoration area, divides the area into grid cells, labels the attributes of each grid cell, and corrects the grid cell division results. The restoration effectiveness analysis module uses sampling data from adjacent sampling time points, performs evolutionary analysis through an environmental evolution model, obtains predicted data of environmental and ecological restoration influencing factors for each grid cell at future time points, and then calculates the environmental and ecological restoration gain of each grid cell through environmental and ecological restoration assessment. The restoration strategy optimization module is used to divide the area into different levels, calculate the boundary gradient between adjacent areas, and provide feedback on the optimization of environmental and ecological restoration strategies for each level of area.

8. A data detection system for environmental ecological restoration according to claim 7, characterized in that, The regional data processing module includes: a data acquisition unit, a regional division unit, and a regional correction unit; The data acquisition unit is used to acquire information on the environmental ecological restoration area and the influencing factors of environmental ecological restoration; the area division unit is used to divide the environmental ecological restoration area into grid units; the area correction unit corrects the grid unit division results by annotating the attributes of each grid unit.

9. A data detection system for environmental ecological restoration according to claim 7, characterized in that, The repair effectiveness analysis module includes: an evolution analysis unit and a repair effectiveness analysis unit; The evolutionary analysis unit uses sampling data from adjacent sampling time points and performs evolutionary analysis through an environmental evolution model to obtain predicted data on environmental and ecological restoration influencing factors for each grid cell at future time points; the restoration effectiveness analysis unit calculates the environmental and ecological restoration gain of each grid cell through environmental and ecological restoration assessment.

10. A data detection system for environmental ecological restoration according to claim 7, characterized in that, The repair strategy optimization module includes: a level region division unit, a boundary gradient calculation unit, and a strategy optimization feedback unit; The hierarchical region division unit divides the region into hierarchical regions based on the environmental and ecological restoration gain of each grid cell; the boundary gradient calculation unit calculates the boundary gradient of each hierarchical region based on the environmental and ecological restoration gain of the grid cells at the boundary of each hierarchical region; and the strategy optimization feedback unit performs adaptive environmental and ecological restoration strategy optimization feedback for each hierarchical region based on the boundary gradient calculation results.

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