Method, device and storage medium for constructing ecological security pattern integrating land subsidence

Abstract

The present invention discloses a method, device and storage medium for constructing an ecological security pattern integrating ground subsidence. The method includes: obtaining multi-source heterogeneous data to be processed; pre-processing the multi-source heterogeneous data to be processed to obtain target multi-source heterogeneous data; generating sampling points in the target research area; generating an ecological security pattern type probability map according to the target multi-source heterogeneous data and the sampling points; performing multi-layer fusion processing on multiple ecological security pattern type probability maps using a preset map fusion method to generate an ecological security pattern fusion distribution map; performing ground subsidence fusion processing on the ecological security pattern fusion distribution map according to ecological functional areas and subsidence levels to obtain a ground subsidence distribution map; performing subsidence level merging processing on the ground subsidence distribution map to obtain a target ecological security pattern distribution map. The present invention realizes the construction of an ecological security pattern and improves accuracy. The present invention can be widely used in the technical field of ecological security pattern construction.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological security pattern construction, and in particular to an ecological security pattern construction method, device and storage medium integrating ground subsidence. Background Art

[0002] Ecological security pattern refers to the spatial distribution and organization of ecosystems in a specific area, which can ensure the health, stability and sustainable development of the ecosystem. Building an ecological security pattern is crucial for urban planning and development. It can optimize the ecological space of the country, promote sustainable development, and achieve a balance between urban development and ecological protection. However, the process of urban development is usually affected by human engineering and economic activities, resulting in a complex geological environment caused by human interference. Traditional methods for constructing ecological security patterns mainly rely on land use type data and landscape ecology theory, which are difficult to accurately depict complex geological environments and have low accuracy.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and storage medium for constructing an ecological security pattern integrating land subsidence, which effectively improves the accuracy.

[0005] On the one hand, an embodiment of the present invention provides a method for constructing an ecological security pattern integrating land subsidence, comprising the following steps:

[0006] Acquire multi-source heterogeneous data to be processed, wherein the multi-source heterogeneous data to be processed includes historical ground subsidence data, geological data, topographic data, land use data, ecological environment data, climate data or humanities data;

[0007] Preprocessing the to-be-processed multi-source heterogeneous data to obtain target multi-source heterogeneous data;

[0008] Generate sampling points in the target study area, where the ecological security pattern types of the sampling points include non-ecologically suitable areas, ecological sources or ecological corridors;

[0009] Generate an ecological security pattern type probability map according to the target multi-source heterogeneous data and the sampling points, wherein the ecological security pattern type probability map includes a non-ecologically suitable area probability map, an ecological source probability map or an ecological corridor probability map;

[0010] Performing multi-layer fusion processing on the plurality of ecological security pattern type probability maps using a preset map fusion method to generate an ecological security pattern fusion distribution map, wherein the preset map fusion method includes a weighted average method, a principal component analysis method, an information entropy fusion method or a nonlinear fusion method;

[0011] According to the ecological function zones and the subsidence levels, the ecological security pattern fusion distribution map is subjected to ground subsidence fusion processing to obtain a ground subsidence distribution map, wherein the ecological function zones include construction areas, source areas, corridor areas or buffer areas, and the subsidence levels include non-subsidence, slight subsidence and moderate subsidence;

[0012] The ground subsidence distribution map is processed by merging the subsidence levels to obtain a target ecological security pattern distribution map.

[0013] In some embodiments, preprocessing the to-be-processed multi-source heterogeneous data to obtain target multi-source heterogeneous data includes:

[0014] Performing data cleaning processing on the multi-source heterogeneous data to be processed to obtain first multi-source heterogeneous data;

[0015] Performing standardization processing on the first multi-source heterogeneous data to obtain second multi-source heterogeneous data;

[0016] Data encoding is performed on the second multi-source heterogeneous data to obtain the target multi-source heterogeneous data.

[0017] In some embodiments, generating an ecological security pattern type probability map according to the target multi-source heterogeneous data and the sampling points includes:

[0018] Extracting ecological security pattern characteristics from the target multi-source heterogeneous data, wherein the ecological security pattern characteristics include geological characteristics, topographic characteristics, land use characteristics, ecological environment characteristics, climate characteristics or human characteristics;

[0019] Performing dimensionality reduction processing on the characteristics of the ecological security pattern;

[0020] According to the sampling points, acquiring sample point data from the target research area;

[0021] Inputting the ecological security pattern characteristics and the sample point data after dimensionality reduction processing into a preset deep learning model so that the preset deep learning model is trained to obtain an ecological security pattern prediction model;

[0022] The prediction data set of the target research area is input into the ecological security pattern prediction model to obtain the ecological security pattern type probability map.

[0023] In some embodiments, the structure construction process of the ecological security pattern prediction model includes:

[0024] Constructing an input layer, wherein the input layer includes 44 neurons;

[0025] After the input layer, construct a first hidden layer, the first hidden layer includes 64 neurons and a first activation function, and the dropout rate applied to the first hidden layer is 0.3;

[0026] After the first hidden layer, construct a second hidden layer, the second hidden layer includes 32 neurons and the first activation function, and the dropout rate applied to the second hidden layer is 0.3;

[0027] After the second hidden layer, a third hidden layer is constructed, wherein the third hidden layer includes 16 neurons and the first activation function;

[0028] After the third hidden layer, an output layer is constructed, and the output layer includes 3 neurons and a second activation function.

[0029] In some embodiments, the method of using a preset map fusion method to perform multi-layer fusion processing on a plurality of ecological security pattern type probability maps to generate an ecological security pattern fusion distribution map includes:

[0030] Extracting grid information from the ecological security pattern type probability map;

[0031] When the preset graph fusion method is the weighted average method, setting a first layer weight set;

[0032] According to the first layer weight set and the grid information, weighted average fusion processing is performed on the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map to generate a weighted average fusion distribution map;

[0033] When the preset map fusion method is the principal component analysis method, the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map are respectively subjected to array conversion processing to obtain the corresponding probability map. Figure 1 dimensional array;

[0034] For multiple probabilities Figure 1 Dimensional arrays are merged to obtain a multidimensional array of probability maps;

[0035] The probability graph multidimensional array is reshaped to obtain the probability Figure 2 dimensional array, the probability Figure 2 The first dimension of the dimensional array is used to represent the pixel, the probability Figure 2 The second dimension of the dimension array is used to represent the layer;

[0036] According to the proportion of explained variance, the probability Figure 2 Dimensional array to perform principal component analysis and obtain the principal component results;

[0037] Performing grid reshaping processing on the principal component results;

[0038] Generate a principal component fusion distribution map according to the principal component result after grid reshaping and the grid information;

[0039] When the preset map fusion method is the information entropy fusion method, information entropy calculation is performed on the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map respectively to obtain corresponding probability map information entropy;

[0040] Calculating the total information entropy based on the information entropies of the plurality of probability graphs;

[0041] Calculating a second layer weight set according to the plurality of probability map information entropies and the total information entropy;

[0042] According to the second layer weight set and the grid information, weighted fusion processing is performed on the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map to generate an information entropy fusion distribution map;

[0043] When the preset graph fusion method is the nonlinear fusion method, setting a third layer weight set;

[0044] According to the third layer weight set, the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map are input into a preset nonlinear activation function to generate a nonlinear fusion distribution map;

[0045] According to the characteristics of the preset map fusion method, a distribution map is selected from the fusion distribution maps to be selected as the ecological security pattern fusion distribution map, and the fusion distribution maps to be selected include the weighted average fusion distribution map, the principal component fusion distribution map, the information entropy fusion distribution map and the nonlinear fusion distribution map.

[0046] In some embodiments, the performing of land subsidence fusion processing on the ecological security pattern fusion distribution map according to the ecological functional zone and the subsidence level to obtain a land subsidence distribution map includes:

[0047] Access historical satellite radar data;

[0048] generating a ground subsidence rate map using synthetic aperture radar interferometry based on the historical satellite radar data and the subsidence level;

[0049] Construct a contingency table based on the sedimentation level and ecological safety level;

[0050] Based on the contingency table, calculate the expected frequency;

[0051] According to the expected frequency, chi-square statistics calculation and significance test are performed to calculate the correlation between the land subsidence and the fusion distribution map of ecological security pattern;

[0052] Spatially superimposing the land subsidence rate map and the ecological security pattern fusion distribution map to obtain a superimposed map;

[0053] According to the overlay map, calculating the settlement area ratio corresponding to each settlement level in the ecological function zone;

[0054] Calculate the impact of land subsidence on the ecological functional zone based on the proportion of the subsidence area;

[0055] The land subsidence distribution map is generated according to the correlation degree, the impact degree and the ecological security pattern fusion distribution map.

[0056] In some embodiments, the ground subsidence distribution map is subjected to subsidence grade merging processing to obtain a target ecological security pattern distribution map, including:

[0057] Extracting a plurality of settlement combinations between the ecological functional zones and the settlement grades from the land settlement distribution map;

[0058] Prioritizing the multiple sedimentation combinations to obtain a priority order;

[0059] According to the priority order and the number of types of the ecological functional zones, the multiple settlement combinations in the ground subsidence distribution map are classified and planned to obtain the target ecological security pattern distribution map.

[0060] In some embodiments, the method further comprises:

[0061] According to the target ecological security pattern distribution map, calculate the functional area ratio corresponding to each ecological functional zone;

[0062] Compare the area ratio of the functional area with the original area ratio before ecological protection to obtain the degree of ecological protection impact;

[0063] According to the land subsidence distribution map, identifying high-risk areas;

[0064] An ecological space layout strategy is generated based on the area ratio of the functional areas, the degree of ecological protection impact and the high-risk areas.

[0065] On the other hand, an embodiment of the present invention provides an ecological security pattern construction device integrating land subsidence, including:

[0066] The first module is used to obtain multi-source heterogeneous data to be processed, wherein the multi-source heterogeneous data to be processed includes historical ground subsidence data, geological data, topographic data, land use data, ecological environment data, climate data or humanities data;

[0067] The second module is used to preprocess the multi-source heterogeneous data to be processed to obtain target multi-source heterogeneous data;

[0068] The third module is used to generate sampling points in the target research area, and the ecological security pattern types of the sampling points include non-ecologically suitable areas, ecological sources or ecological corridors;

[0069] The fourth module is used to generate an ecological security pattern type probability map according to the target multi-source heterogeneous data and the sampling points, wherein the ecological security pattern type probability map includes a non-ecologically suitable area probability map, an ecological source probability map or an ecological corridor probability map;

[0070] The fifth module is used to perform multi-layer fusion processing on the plurality of ecological security pattern type probability maps using a preset map fusion method to generate an ecological security pattern fusion distribution map, wherein the preset map fusion method includes a weighted average method, a principal component analysis method, an information entropy fusion method or a nonlinear fusion method;

[0071] The sixth module is used to perform ground subsidence fusion processing on the ecological security pattern fusion distribution map according to ecological functional zones and subsidence levels to obtain a ground subsidence distribution map, wherein the ecological functional zones include construction areas, source areas, corridor areas or buffer areas, and the subsidence levels include non-subsidence, slight subsidence and moderate subsidence;

[0072] The seventh module is used to merge the settlement levels of the ground subsidence distribution map to obtain a target ecological security pattern distribution map.

[0073] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0074] The beneficial effects of the present invention are as follows:

[0075] The embodiment of the present invention first obtains the multi-source heterogeneous data to be processed, pre-processes the multi-source heterogeneous data to obtain the target multi-source heterogeneous data, then generates sampling points in the target study area, generates an ecological security pattern type probability map according to the target multi-source heterogeneous data and the sampling points, and then uses the preset map fusion method to perform multi-layer fusion processing on multiple ecological security pattern type probability maps to generate an ecological security pattern fusion distribution map, performs ground subsidence fusion processing on the ecological security pattern fusion distribution map according to the ecological functional area and the subsidence level to obtain a ground subsidence distribution map, and finally performs subsidence level merging processing on the ground subsidence distribution map to obtain a target ecological security pattern distribution map, thereby being able to integrate ground subsidence to realize ecological security pattern construction and improve accuracy.

[0076] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0078] Figure 1 A flowchart of a method for constructing an ecological security pattern integrating land subsidence according to an embodiment of the present invention;

[0079] Figure 2 This is an ecological source probability map according to an embodiment of the present invention;

[0080] Figure 3 An ecological corridor probability map according to an embodiment of the present invention;

[0081] Figure 4 A probability map of a non-ecologically suitable area according to an embodiment of the present invention;

[0082] Figure 5 A schematic diagram of an ecological security pattern prediction model structure according to an embodiment of the present invention;

[0083] Figure 6 A weighted average fusion distribution diagram according to an embodiment of the present invention;

[0084] Figure 7 This is a principal component fusion distribution diagram according to an embodiment of the present invention;

[0085] Figure 8 This is an information entropy fusion distribution diagram according to an embodiment of the present invention;

[0086] Fig. 9 A nonlinear fusion distribution diagram according to an embodiment of the present invention;

[0087] Fig.10 A land subsidence distribution map according to an embodiment of the present invention;

[0088] Fig.11 This is a target ecological security pattern distribution map of an embodiment of the present invention;

[0089] Fig.12 A schematic diagram of an overall process of generating an ecological space layout strategy according to an embodiment of the present invention;

[0090] Fig.13 It is a structural schematic diagram of a device for constructing an ecological security pattern integrating ground subsidence according to an embodiment of the present invention. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0092] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0093] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0094] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0095] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0096] Contingency table: It is a frequency table listed when the observed data is classified by two or more attributes (qualitative variables). It is a frequency distribution table of cross-classification of more than two variables.

[0097] Interferometric Synthetic Aperture Radar (InSAR): is a radar technology used in surveying and remote sensing. It uses synthetic aperture radar to perform coherent processing on two complex-valued images (images with both amplitude and phase) observed in the same area to obtain surface elevation information.

[0098] Land subsidence: also known as ground subsidence or ground subsidence. It is a local downward movement (or engineering geological phenomenon) in which the surface elevation of the earth's crust is lowered due to the consolidation and compression of loose underground strata under the influence of human engineering and economic activities.

[0099] In related technologies, ecological security pattern refers to the spatial distribution and organization of ecosystems in a specific area to ensure the health, stability and sustainable development of the ecosystem. Building an ecological security pattern is crucial for urban planning and development. It can optimize the national ecological space, promote sustainable development, and achieve a balance between urban development and ecological protection. Traditional ecological security pattern construction methods mainly rely on land use type data and landscape ecology theory. It is difficult to accurately depict complex geological environmental conditions and human activity interference, especially ignoring the dynamic impact of geological disasters such as ground subsidence on the regional ecological security pattern, and the accuracy is low.

[0100] In view of this, the present invention provides a method for constructing an ecological security pattern integrating land subsidence. The method can effectively integrate multi-source heterogeneous data and consider the impact of land subsidence on ecological security, thereby improving the accuracy and practicality of ecological security pattern construction.

[0101] The method for constructing an ecological security pattern integrating ground subsidence provided in the embodiment of the present application relates to the technical field of ecological security pattern construction. The method for constructing an ecological security pattern integrating ground subsidence provided in the embodiment of the present application can be applied to a terminal, can be applied to a server, or can be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The server can also be a node server in a blockchain network; the software can be an application that implements the method for constructing an ecological security pattern integrating ground subsidence, etc., but is not limited to the above forms.

[0102] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0103] The following is a detailed explanation of the embodiments of the present application in conjunction with the accompanying drawings:

[0104] Figure 1 is an optional flow chart of the method for constructing an ecological security pattern integrating land subsidence provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S107.

[0105] Step S101, obtaining multi-source heterogeneous data to be processed, where the multi-source heterogeneous data to be processed includes historical ground subsidence data, geological data, topographic data, land use data, ecological environment data, climate data or humanities data;

[0106] Step S102, preprocessing the multi-source heterogeneous data to be processed to obtain target multi-source heterogeneous data;

[0107] Step S103, generating sampling points in the target research area, where the ecological security pattern types of the sampling points include non-ecologically suitable areas, ecological sources or ecological corridors;

[0108] Step S104: Generate an ecological security pattern type probability map based on the target multi-source heterogeneous data and sampling points. The ecological security pattern type probability map includes a non-ecologically suitable area probability map, an ecological source probability map, or an ecological corridor probability map;

[0109] Step S105: using a preset graph fusion method to perform multi-layer fusion processing on multiple ecological security pattern type probability maps to generate an ecological security pattern fusion distribution map, the preset graph fusion method includes a weighted average method, a principal component analysis method, an information entropy fusion method or a nonlinear fusion method;

[0110] Step S106: According to the ecological function zones and the settlement levels, the ecological security pattern fusion distribution map is subjected to ground settlement fusion processing to obtain a ground settlement distribution map. The ecological function zones include construction zones, source zones, corridor zones or buffer zones. The settlement levels include non-settlement, slight settlement and moderate settlement.

[0111] Step S107: performing settlement grade merging processing on the ground subsidence distribution map to obtain a target ecological security pattern distribution map.

[0112] Steps S101 to S107 shown in the embodiment of the present application realize the construction of an ecological security pattern and improve accuracy.

[0113] In step S101 of some embodiments, the multi-source heterogeneous data to be processed can be obtained through a geographic information database. The multi-source heterogeneous data to be processed can also be obtained in other ways, not limited to this. Among them, the multi-source heterogeneous data to be processed may include historical ground subsidence data, geological data, topographic data, land use data, ecological environment data, climate data or humanities data. Geological data may include lithology, soil type or soft soil distribution; topographic data may include digital elevation model DEM, slope, aspect or curvature; ecological environment data may include ecological environment stability, ecological environment stress or environmental resource supply; climate data may include rainfall, temperature or wind speed; humanities data may include population density, distance from river or distance from road.

[0114] In some embodiments, in step S102, preprocessing the multi-source heterogeneous data to be processed to obtain target multi-source heterogeneous data may include but is not limited to the following steps:

[0115] Performing data cleaning on the multi-source heterogeneous data to be processed to obtain first multi-source heterogeneous data;

[0116] Performing standardization processing on the first multi-source heterogeneous data to obtain second multi-source heterogeneous data;

[0117] Data encoding is performed on the second multi-source heterogeneous data to obtain target multi-source heterogeneous data.

[0118] In some embodiments, the multi-source heterogeneous data to be processed may be first subjected to data cleaning processing to obtain the first multi-source heterogeneous data to remove outliers and erroneous data. The first multi-source heterogeneous data is then subjected to standardization processing to obtain the second multi-source heterogeneous data, wherein the standardization processing may include Z-score standardization. It is understood that the z-score, also called the standard score, is the process of dividing the difference between a number and the mean by the standard deviation. In statistics, the standard score is the number of symbols of the standard deviation of the mean value of an observation or data point that is higher than the observed value or measured value. Finally, the second multi-source heterogeneous data is subjected to data encoding to obtain the target multi-source heterogeneous data, wherein the data encoding may include unique hot encoding. It is understood that data encoding may unify the data format. Data encoding converts different types of categorical data into a unified numerical format so that subsequent feature extraction algorithms can process categorical variables more efficiently. At the same time, it can eliminate the artificial order between categories. For categorical variables (such as land use type, lithology, etc.), the use of One-Hot Encoding can eliminate the possible artificial order relationship between categories to ensure that no non-existent order bias is introduced in the subsequent feature extraction process.

[0119] In some embodiments, in step S103, sampling points may be randomly generated in the target study area, wherein the sampling points may be classified according to the existing land use and ecological environment characteristics, and the ecological security pattern type of the sampling points may include non-ecologically suitable areas, ecological sources, or ecological corridors. Non-ecologically suitable areas may include construction land; ecological sources may include nature reserves or forested land; and ecological corridors may include ecological networks or major river networks.

[0120] In some embodiments, in step S104, generating an ecological security pattern type probability map based on target multi-source heterogeneous data and sampling points may include but is not limited to the following steps:

[0121] Extract ecological security pattern characteristics from target multi-source heterogeneous data, where the ecological security pattern characteristics include geological characteristics, topographic characteristics, land use characteristics, ecological environment characteristics, climate characteristics or human characteristics;

[0122] Carry out dimensionality reduction processing on the characteristics of ecological security pattern;

[0123] According to the sampling points, sample point data are obtained from the target study area;

[0124] Inputting the ecological security pattern characteristics and sample point data after dimensionality reduction processing into a preset deep learning model so that the preset deep learning model is trained to obtain an ecological security pattern prediction model;

[0125] The prediction data set of the target study area is input into the ecological security pattern prediction model to obtain the ecological security pattern type probability map.

[0126] In some embodiments, the ecological security pattern characteristics can be first extracted from the target multi-source heterogeneous data, wherein the ecological security pattern characteristics can include geological characteristics, topographic characteristics, land use characteristics, ecological environment characteristics, climate characteristics or human characteristics. Then, the ecological security pattern characteristics are subjected to dimensionality reduction processing. For example, the principal component analysis (PCA) method can be used to reduce the dimension of the extracted ecological security pattern characteristics to reduce the data dimension and eliminate the multicollinearity between the characteristics. Then, according to the sampling points, the sample point data can be obtained from the target study area. For example, according to the position of each sampling point, the sample point data at the corresponding position can be obtained from the target study area. Then, the ecological security pattern characteristics and sample point data after dimensionality reduction processing are input into the preset deep learning model so that the preset deep learning model is trained to obtain the ecological security pattern prediction model, wherein the preset deep learning model can include a multi-layer perceptron (MLP) neural network model. During the training process, the model parameters can be optimized by cross-validation and other methods to improve the generalization ability of the model. Finally, the prediction data set of the target study area is input into the ecological security pattern prediction model, and the study area is predicted to obtain the ecological security pattern type probability map. Among them, the ecological security pattern type probability map shows the probability value of each grid unit belonging to a different ecological security pattern type, and the ecological security pattern type probability map includes a probability map of non-ecologically suitable areas, a probability map of ecological source areas, or a probability map of ecological corridors. For example, the ecological source area probability map is as follows: Figure 2 As shown in the figure, the probability map of ecological corridors is as follows Figure 3 The probability map of unsuitable ecological areas is shown in Figure 4 As shown in the figure, administrative boundaries and river systems are used to highlight the spatial distribution of the three types of ecological security patterns.

[0127] In some embodiments, the structure construction process of the ecological security pattern prediction model includes:

[0128] Construct the input layer, which includes 44 neurons;

[0129] After the input layer, the first hidden layer is constructed. The first hidden layer includes 64 neurons and the first activation function. The dropout rate applied to the first hidden layer is 0.3.

[0130] After the first hidden layer, a second hidden layer is constructed. The second hidden layer includes 32 neurons and the first activation function. The dropout rate applied to the second hidden layer is 0.3.

[0131] After the second hidden layer, a third hidden layer is constructed, and the third hidden layer includes 16 neurons and the first activation function;

[0132] After the third hidden layer, the output layer is constructed, which includes 3 neurons and the second activation function.

[0133] In some embodiments, in the construction of the structure of the ecological security pattern prediction model, such as Figure 5 As shown, an input layer can be constructed first to receive data input, wherein the input layer includes 44 neurons. After the input layer, a first hidden layer is constructed, wherein the first hidden layer includes 64 neurons and a first activation function, the discard rate applied to the first hidden layer can be set to 0.3, and the first activation function can include a ReLU function. It can be understood that the ReLU function is an activation function commonly used in artificial neural networks, usually referring to a nonlinear function represented by a ramp function and its variants. After the first hidden layer, a second hidden layer is constructed, wherein the second hidden layer includes 32 neurons and a first activation function, and the discard rate applied to the second hidden layer can be set to 0.3. After the second hidden layer, a third hidden layer is constructed, wherein the third hidden layer includes 16 neurons and a first activation function. After the third hidden layer, an output layer is constructed to obtain an output result, wherein the output layer includes 3 neurons and a second activation function, and the 3 neurons are used to output the predicted probabilities of 3 types of ecological security patterns, and the second activation function can include a Softmax function. It is understandable that in mathematics, especially probability theory and related fields, the Softmax function can also be called a normalized exponential function, which is a generalization of the logic function. It can "compress" a K-dimensional vector z containing any real number into another K-dimensional real vector σ(z), so that the range of each element is between (0,1) and the sum of all elements is 1. The Softmax function is mostly used in multi-classification problems.

[0134] In some embodiments, in step S105, a preset map fusion method is used to perform multi-layer fusion processing on multiple ecological security pattern type probability maps to generate an ecological security pattern fusion distribution map, which may include but is not limited to the following steps:

[0135] Extract raster information from ecological security pattern type probability map;

[0136] When the preset graph fusion method is weighted average method, set the weight set of the first layer;

[0137] According to the weight set of the first layer and the grid information, the probability map of the non-ecologically suitable area, the probability map of the ecological source area and the probability map of the ecological corridor are subjected to weighted average fusion processing to generate a weighted average fusion distribution map;

[0138] When the preset map fusion method is the principal component analysis method, the probability map of the non-ecologically suitable area, the probability map of the ecological source, and the probability map of the ecological corridor are respectively transformed into arrays to obtain the corresponding probability maps. Figure 1 dimensional array;

[0139] For multiple probabilities Figure 1 Dimensional arrays are merged to obtain a multidimensional array of probability maps;

[0140] Reshape the multidimensional array of probability graphs to obtain the probability Figure 2 dimensional array, probability Figure 2 The first dimension of the dimensional array is used to represent pixels, probabilities Figure 2 The second dimension of the dimension array is used to represent the layer;

[0141] According to the proportion of explained variance, the probability Figure 2 Dimensional array to perform principal component analysis and obtain the principal component results;

[0142] Perform grid reshaping on the principal component results;

[0143] Generate a principal component fusion distribution map based on the principal component results and grid information after grid reshaping;

[0144] When the preset map fusion method is the information entropy fusion method, the information entropy is calculated for the probability map of non-ecologically suitable areas, the probability map of ecological sources, and the probability map of ecological corridors, respectively, to obtain the corresponding probability map information entropy;

[0145] According to the information entropy of multiple probability graphs, the total information entropy is calculated;

[0146] Calculate the weight set of the second layer according to the information entropy of multiple probability maps and the total information entropy;

[0147] According to the second layer weight set and raster information, the probability map of non-ecologically suitable areas, the probability map of ecological source areas and the probability map of ecological corridors are weighted fused to generate an information entropy fusion distribution map.

[0148] When the preset graph fusion method is a nonlinear fusion method, set the third layer weight set;

[0149] According to the weight set of the third layer, the probability map of non-ecologically suitable areas, the probability map of ecological source areas and the probability map of ecological corridors are input into the preset nonlinear activation function to generate a nonlinear fusion distribution map;

[0150] According to the characteristics of the preset map fusion method, a distribution map is selected from the fusion distribution maps to be selected as the ecological security pattern fusion distribution map. The fusion distribution maps to be selected include weighted average fusion distribution map, principal component fusion distribution map, information entropy fusion distribution map and nonlinear fusion distribution map.

[0151] In some embodiments, a preset graph fusion method can be used to perform multi-layer fusion processing on multiple ecological security pattern type probability maps to generate an ecological security pattern fusion distribution map, wherein the preset graph fusion method may include a weighted average method, a principal component analysis method, an information entropy fusion method, or a nonlinear fusion method. Grid information can be first extracted from the ecological security pattern type probability map so as to provide input parameters in each preset graph fusion method later. Exemplarily, the program code can be expressed as: rows = layer1.height; cols = layer1.width; extent = layer1.extent. Where rows is a row, layer1 is a layer, height is a height, cols is a column, and width is a width.

[0152] When the preset map fusion method is the weighted average method, you can first set the first layer weight set, and then perform weighted average fusion processing on the non-ecologically suitable area probability map, ecological source probability map and ecological corridor probability map according to the first layer weight set and raster information to generate a weighted average fused distribution map. Exemplarily, the first layer weight set can be set to (0.5, 0.3, 0.2), and the program code for weighted average fusion can be expressed as: fused_raster_weighted_avg = (weight1*layer1) + (weight2*layer2) + (weight3*layer3). Among them, fused_raster_weighted_avg is the weighted average fused distribution map, weight1 is the weight, and layer1 is the layer. The generated weighted average fused distribution map is as follows. Figure 6 As shown in the figure, the buffer zone refers to the transition area between different functional areas of the ecosystem. Its main function is to slow down or absorb external interference and maintain the stability and connectivity of the ecosystem. The buffer zone is located between the construction area, corridor area and source area, reducing the negative impact of the construction area on the core ecological area, enhancing the ecological connectivity of the corridor area, and protecting the ecological function and biodiversity of the source area. Through the reasonable planning and management of the buffer zone, we can effectively balance the needs of ecological protection and economic development and promote the sustainable development of the ecosystem and regional ecological security.

[0153] When the preset map fusion method is the principal component analysis method, the probability map of the non-ecologically suitable area, the probability map of the ecological source, and the probability map of the ecological corridor can be transformed into arrays to obtain the corresponding probability maps. Figure 1 Dimensional array. For example, the program code can be expressed as: array1 = arcpy.RasterToNumPyArray (layer1), where array1 is the probability Figure 1 dimensional array, arcpy.RasterToNumPyArray is the array conversion process. Then multiple probabilities Figure 1 The program code can be expressed as: stacked_array=np.dstack((array1,array2,array3)). Among them, stacked_array is the multidimensional array of probability map, and np.dstack is the array merging process. Then the multidimensional array of probability map is reshaped to obtain the probability map. Figure 2 dimensional array, where the probability Figure 2 The first dimension of the dimensional array is used to represent pixels, probabilities Figure 2 The second dimension of the dimensional array is used to represent the layer. The program code can be expressed as: flat_array = stacked_array.reshape(-1,3). Among them, flat_array is the probability Figure 2 dimensional array, reshape is array reshaping. Then according to the explained variance ratio, select the principal component with the largest explained variance ratio, and Figure 2 dimensional array to perform principal component analysis and obtain the principal component results. The program code can be expressed as: pca = PCA (n_components = 1); pca_result = pca.fit_transform (flat_array). Among them, pca is the principal component with the largest proportion of explained variance, pca_result is the principal component result, and pca.fit_transform is the principal component analysis. Then the principal component results are reshaped into the original grid shape. The program code can be expressed as: fused_array_pca = pca_result.reshape (rows, cols). Among them, reshape is the grid reshaping process. Finally, according to the principal component results and grid information after grid reshaping, the principal component fusion distribution map is generated. The program code can be expressed as: lower_left = arcpy.Point(extent.XMin,extent.YMin); cell_size = layer1.meanCellWidth; fused_raster_pca = arcpy.NumPyArrayToRaster(fused_array_pca,lower_left,cell_size). Among them, fused_raster_pca is the principal component fusion distribution map. The generated principal component fusion distribution map is as follows Figure 7 shown.

[0154] When the preset graph fusion method is the information entropy fusion method, the information entropy of the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map can be calculated respectively to obtain the corresponding probability map information entropy. Exemplarily, the program code can be expressed as: entropy1 = calculate_entropy (array1). Among them, entropy1 is the probability map information entropy, and calculate_entropy is the information entropy calculation. It can be understood that a histogram can be created using the histogram function of numpy and used to calculate probability distribution and information entropy. The higher the information entropy, the richer the information contained in the layer. Then sum the information entropies of multiple probability maps to calculate the total information entropy, and then calculate the second layer weight set (weight1, weight2, weight3) based on the information entropies of multiple probability maps and the total information entropy. Among them, the second layer weight set includes the weights corresponding to the probability map of the non-ecologically suitable area, the probability map of the ecological source and the probability map of the ecological corridor. Finally, according to the weight set of the second layer and the grid information, the probability map of the non-ecologically suitable area, the probability map of the ecological source, and the probability map of the ecological corridor are weighted fused. Each probability map is multiplied by the corresponding weight, and then the results of multiple multiplications are added to generate an information entropy fusion distribution map, which enables the contribution of each layer in the fusion result to be adaptively adjusted according to the amount of information in each layer, effectively balancing the importance of different layers. The generated information entropy fusion distribution map is shown in the figure. Figure 8 shown.

[0155] When the preset map fusion method is a nonlinear fusion method, the third layer weight set can be set first. Exemplarily, the weight of each layer can be set to 1, indicating equal weight. Then, according to the third layer weight set, the probability map of the non-ecologically suitable area, the probability map of the ecological source, and the probability map of the ecological corridor are input into the preset nonlinear activation function to generate a nonlinear fusion distribution map. Among them, the preset nonlinear activation function may include a Sigmoid function. The program code can be expressed as: fused_raster_sigmoid = 1 / (1+Exp(-(weight1*layer1+weight2*layer2+weight3*layer3))). Mathematically expressed as: In the formula, F is the output nonlinear fusion distribution map, and x is the weighted sum of the layers. It can be understood that the nonlinear characteristics of the Sigmoid function are used for fusion. The Sigmoid function maps the input to between 0 and 1, has the special effects of compressing extreme values ​​and highlighting intermediate values, can effectively handle outliers, and produce smoother fusion results while maintaining the relative importance of each layer. The generated nonlinear fusion distribution map is shown in Fig. 9 shown.

[0156] In some embodiments, after generating the weighted average fusion distribution map, the principal component fusion distribution map, the information entropy fusion distribution map and the nonlinear fusion distribution map, a distribution map can be selected from the fusion distribution maps to be selected as the ecological security pattern fusion distribution map according to the characteristics of the preset map fusion method. Among them, the fusion distribution map to be selected includes the weighted average fusion distribution map, the principal component fusion distribution map, the information entropy fusion distribution map and the nonlinear fusion distribution map. Exemplarily, when selecting the final ecological security pattern fusion distribution map, the following characteristics can be considered: spatial continuity, boundary clarity, ecological function zone identification accuracy, consistency with field survey results, and sensitivity to the impact of ground subsidence. For example, if the result obtained by the weighted average method has good spatial continuity but fuzzy boundaries; and the result of the principal component analysis method has clear boundaries but may exaggerate the importance of certain areas, the fusion method based on information entropy (i.e., information entropy fusion method) can be selected as a compromise. In addition, the fusion results can be compared with expert knowledge and field survey data to evaluate their accuracy in identifying known ecologically important areas (such as nature reserves and key ecological corridors), so that these characteristics can be well balanced and the fusion results that are most sensitive to the impact of ground subsidence can be used as the final fusion distribution map of the ecological security pattern.

[0157] In some embodiments, in step S106, according to the ecological function zones and the subsidence levels, the ecological security pattern fusion distribution map is subjected to land subsidence fusion processing to obtain a land subsidence distribution map, which may include but is not limited to the following steps:

[0158] Access historical satellite radar data;

[0159] Based on historical satellite radar data and subsidence levels, ground subsidence rate maps were generated using synthetic aperture radar interferometry;

[0160] Contingency tables were constructed based on the sedimentation level and ecological safety level;

[0161] Based on the contingency table, calculate the expected frequency;

[0162] According to the expected frequency, chi-square statistics calculation and significance test were performed to calculate the correlation between land subsidence and the fusion distribution map of ecological security pattern;

[0163] The land subsidence rate map and the ecological security pattern fusion distribution map are spatially superimposed to obtain an overlay map;

[0164] According to the overlay map, calculate the proportion of subsidence area corresponding to each subsidence level in the ecological functional zone;

[0165] According to the proportion of subsidence area, calculate the impact of land subsidence on ecological functional areas;

[0166] The distribution map of land subsidence is generated by integrating the distribution maps according to the correlation, impact degree and ecological security pattern.

[0167] In some embodiments, in the calculation of the correlation, the historical satellite radar data can be obtained first, and then the synthetic aperture radar interferometry (InSAR technology) is used to generate a high-precision, large-scale ground settlement rate map based on the historical satellite radar data and the settlement level, and the settlement data is classified into multiple settlement levels, wherein the settlement level may include non-settlement, slight settlement and medium settlement. Then, according to the settlement level and the ecological safety level, a contingency table is constructed, and then the expected frequency is calculated according to the contingency table, and finally, according to the expected frequency, the chi-square statistic calculation and significance test are performed to calculate the correlation between the ground settlement and the ecological safety pattern fusion distribution map. In the calculation of the degree of influence, the ground settlement rate map and the ecological safety pattern fusion distribution map can be spatially superimposed to obtain an overlay map, and then the settlement area proportion corresponding to each settlement level in the ecological functional zone is calculated according to the overlay map, and then the degree of influence of the ground settlement on the ecological functional zone is calculated according to the settlement area proportion. Among them, the ecological functional zone may include a construction area, a source area, a corridor area or a buffer zone. After obtaining the correlation and impact, the distribution map can be integrated according to the correlation, impact and ecological security pattern, and the ecological security pattern type and land subsidence can be comprehensively considered to generate a land subsidence distribution map. Fig.10 As shown. It can be understood that the land subsidence distribution map includes both the correlation and the impact. Among them, the correlation refers to the relationship between the types of ecological functional zones (four categories) and the subsidence areas; the impact is the impact of the subsidence level (non-subsidence, slight subsidence and moderate subsidence) on the four types of ecological functional zones in space.

[0168] In some embodiments, in step S107, the ground subsidence distribution map is subjected to subsidence grade merging processing to obtain a target ecological security pattern distribution map, which may include but is not limited to the following steps:

[0169] Extract multiple settlement combinations between ecological functional zones and settlement levels from the land settlement distribution map;

[0170] Prioritize multiple settlement combinations to obtain a priority order;

[0171] According to the priority order and the number of types of ecological functional zones, multiple settlement combinations in the ground subsidence distribution map are classified and planned to obtain the target ecological security pattern distribution map.

[0172] In some embodiments, multiple settlement combinations between ecological functional areas and settlement levels can be first extracted from the ground settlement distribution map. Wherein, the settlement combination includes construction area-non-settlement area, construction area-slight settlement area, construction area-medium settlement area, corridor area-non-settlement area, corridor area-slight settlement area, corridor area-medium settlement area, buffer zone-non-settlement area, buffer zone-slight settlement area, buffer zone-medium settlement area, source area-non-settlement area, source area-slight settlement area or source area-medium settlement area. Then, the multiple settlement combinations are prioritized to obtain a priority order. Among them, the priority order can be: high priority: source area-non-subsidence area, source area-slight subsidence area, corridor area-non-subsidence area; medium priority: source area-moderate subsidence area, corridor area-slight subsidence area, buffer zone-non-subsidence area, buffer zone-slight subsidence area; low priority: corridor area-moderate subsidence area, buffer zone-moderate subsidence area, construction area-non-subsidence area, construction area-slight subsidence area, construction area-moderate subsidence area. Finally, according to the priority order and the number of types of ecological functional areas, the multiple settlement combinations in the ground subsidence distribution map are classified and planned to obtain the target ecological security pattern distribution map. Exemplarily, according to the priority order, the 12 combinations can be reclassified into four ecological functional area types to form an optimized target ecological security pattern distribution map. Among them, in the planning of the source area, all the original source areas are retained, and the corridor area-non-subsidence area in the high priority is upgraded to the source area. In the planning of the corridor area, the corridor area in the medium priority is retained. In the planning of buffer zones, the buffer zones in the medium priority are retained, and the corridor zones-medium settlement zones in the low priority are downgraded to buffer zones. Some high-risk construction zones can also be upgraded to buffer zones. In the planning of construction zones, low-risk construction zones are retained, while some high-risk construction zones can be replanned as buffer zones. The distribution map of the target ecological security pattern obtained after classified planning is shown in Figure 2. Fig.11 shown.

[0173] In some embodiments, the method further comprises:

[0174] According to the distribution map of the target ecological security pattern, calculate the functional area ratio corresponding to each ecological functional zone;

[0175] The proportion of the functional area area is compared with the original area proportion before ecological protection to obtain the degree of ecological protection impact;

[0176] Identify high-risk areas based on the land subsidence distribution map;

[0177] Generate an ecological space layout strategy based on the area ratio of functional areas, the degree of ecological protection impact and high-risk areas.

[0178] In some embodiments, after obtaining the target ecological security pattern distribution map, the result evaluation can be performed. The functional area ratio corresponding to each ecological functional area can be calculated according to the target ecological security pattern distribution map, and then the functional area ratio can be compared with the original area ratio before ecological protection to obtain the degree of ecological protection impact, so as to intuitively understand the degree of impact of the optimization measures. For example, the increase in the area of ​​the source area and the corridor area may indicate that ecological protection has been strengthened. Exemplarily, the area ratio data can be used to identify the problem of uneven distribution of four types of ecological functional areas and make adjustment suggestions. For example, if the proportion of the source area is too low, it can be suggested to increase the area of ​​the core protection area or strengthen ecological restoration. Differentiated management suggestions can be made according to the area ratio and characteristics of the four types of ecological functional areas. For example: for the "source area-high sedimentation impact" area, it is recommended to strengthen geological disaster prevention and control and ecosystem stability maintenance; for the "corridor area-low sedimentation impact" area, it is recommended to focus on connectivity protection and control human interference and construction activities. More importantly, remote sensing image comparison can be performed, and multi-temporal remote sensing images can be used to analyze the historical change trend of the optimization adjustment area and evaluate the rationality of the optimization plan. Ecological function assessment can be carried out: the functional integrity of the optimized pattern can be evaluated for key ecological processes (such as species migration and water conservation). Then, based on the land subsidence distribution map, high-risk areas can be identified based on the superposition analysis of land subsidence and four types of ecological functional zones, and the types of subsidence-sensitive areas that need priority attention can be determined using area ratio data. For example, for the "corridor area-high subsidence impact" area, it is recommended to implement special monitoring and protection measures to maintain ecological connectivity. Finally, based on the area ratio of functional areas, the degree of ecological protection impact and high-risk areas, an ecological space layout strategy is generated. For example, based on the distribution map of the four types of ecological functional zones and the degree of ecological protection impact, an ecological space layout strategy can be provided for the overall urban planning. Based on the area ratio data of functional areas, a quantitative basis can be provided for the delineation and adjustment of ecological red lines, especially the protection of source areas and corridor areas. The consistency assessment results can be used in combination with high-risk areas to propose targeted ecological monitoring and management strategies. For example, the monitoring frequency of buffer zones with low consistency can be increased and management measures can be optimized.

[0179] In some embodiments, the overall process of generating an ecological space layout strategy is as follows Fig.12As shown in the figure, we can first obtain multi-source heterogeneous data, and perform data cleaning, data standardization and data encoding. Then, we can randomly generate sampling points and classify them according to land use conditions. Then, we can extract the relevant characteristics of the ecological security pattern and perform principal component analysis. Then, we can build an MLP neural network model, train and optimize the model. Then, we can rasterize the study area, extract the characteristics of the grid units, and use the model for prediction. Then, we can use the weighted average method, principal component analysis method, information entropy-based fusion method (i.e., information entropy fusion method) or nonlinear fusion method to fuse multiple layers. Then, we can perform chi-square test and spatial overlay analysis. Then, we can generate an ecological security pattern distribution map, calculate the regional area ratio, and evaluate the consistency of the prediction results. Finally, we can put forward optimization suggestions, identify settlement-sensitive areas, and generate ecological space layout strategies to provide a scientific basis.

[0180] In some embodiments, this embodiment can effectively integrate multi-source heterogeneous data to improve the comprehensiveness and accuracy of the construction of the ecological security pattern. At the same time, the impact of ground subsidence is taken into account, and the practicality and reliability of the ecological security pattern are enhanced, especially for coastal cities and areas prone to geological disasters. This embodiment uses deep learning technology to improve the ability to capture complex ecosystem patterns and can more accurately predict the ecological security pattern. A variety of fusion methods are used and compared to provide flexible options for different application scenarios, and the optimal fusion solution can be selected according to actual needs. In addition, data-driven decision support is provided for urban planning and ecological management, which can assist in the formulation of more scientific and reasonable ecological protection and urban development plans.

[0181] The beneficial effects of implementing the embodiments of the present invention include: the embodiments of the present invention first obtain the multi-source heterogeneous data to be processed, pre-process the multi-source heterogeneous data to be processed, obtain the target multi-source heterogeneous data, then generate sampling points in the target study area, generate an ecological security pattern type probability map according to the target multi-source heterogeneous data and the sampling points, and then use the preset map fusion method to perform multi-layer fusion processing on multiple ecological security pattern type probability maps to generate an ecological security pattern fusion distribution map, perform ground subsidence fusion processing on the ecological security pattern fusion distribution map according to ecological functional areas and subsidence levels to obtain a ground subsidence distribution map, and finally perform subsidence level merging processing on the ground subsidence distribution map to obtain a target ecological security pattern distribution map, thereby being able to integrate ground subsidence to realize ecological security pattern construction and improve accuracy.

[0182] like Fig.13 As shown, the embodiment of the present invention also provides an ecological security pattern construction device integrating land subsidence, including:

[0183] The first module 801 is used to obtain multi-source heterogeneous data to be processed, where the multi-source heterogeneous data to be processed includes historical land subsidence data, geological data, topographic data, land use data, ecological environment data, climate data or humanities data;

[0184] The second module 802 is used to pre-process the multi-source heterogeneous data to be processed to obtain target multi-source heterogeneous data;

[0185] The third module 803 is used to generate sampling points in the target study area, and the ecological security pattern types of the sampling points include non-ecologically suitable areas, ecological sources or ecological corridors;

[0186] The fourth module 804 is used to generate an ecological security pattern type probability map based on the target multi-source heterogeneous data and sampling points, and the ecological security pattern type probability map includes a non-ecologically suitable area probability map, an ecological source probability map or an ecological corridor probability map;

[0187] The fifth module 805 is used to perform multi-layer fusion processing on multiple ecological security pattern type probability maps using a preset map fusion method to generate an ecological security pattern fusion distribution map. The preset map fusion method includes a weighted average method, a principal component analysis method, an information entropy fusion method or a nonlinear fusion method.

[0188] The sixth module 806 is used to perform ground subsidence fusion processing on the ecological security pattern fusion distribution map according to the ecological function zones and the subsidence levels to obtain a ground subsidence distribution map. The ecological function zones include construction zones, source zones, corridor zones or buffer zones. The subsidence levels include non-subsidence, slight subsidence and moderate subsidence.

[0189] The seventh module 807 is used to merge the ground subsidence distribution map according to the subsidence level to obtain the target ecological security pattern distribution map.

[0190] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0191] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, Figure 1 The method shown.

[0192] The contents of the above method embodiments are all applicable to the present storage medium embodiments. The functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0193] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for constructing an ecological security pattern integrating land subsidence, characterized in that: The following steps are involved: Acquire multi-source heterogeneous data to be processed, wherein the multi-source heterogeneous data to be processed includes historical ground subsidence data, geological data, topographic data, land use data, ecological environment data, climate data or humanities data; Preprocessing the to-be-processed multi-source heterogeneous data to obtain target multi-source heterogeneous data; Generate sampling points in the target study area, where the ecological security pattern types of the sampling points include non-ecologically suitable areas, ecological sources or ecological corridors; Generate an ecological security pattern type probability map according to the target multi-source heterogeneous data and the sampling points, wherein the ecological security pattern type probability map includes a non-ecologically suitable area probability map, an ecological source probability map or an ecological corridor probability map; Performing multi-layer fusion processing on the plurality of ecological security pattern type probability maps using a preset map fusion method to generate an ecological security pattern fusion distribution map, wherein the preset map fusion method includes a weighted average method, a principal component analysis method, an information entropy fusion method or a nonlinear fusion method; According to the ecological function zones and the subsidence levels, the ecological security pattern fusion distribution map is subjected to ground subsidence fusion processing to obtain a ground subsidence distribution map, wherein the ecological function zones include construction areas, source areas, corridor areas or buffer areas, and the subsidence levels include non-subsidence, slight subsidence and moderate subsidence; Performing settlement grade merging processing on the land subsidence distribution map to obtain a target ecological security pattern distribution map; Wherein, the ground subsidence fusion processing is performed on the ecological security pattern fusion distribution map according to the ecological functional zone and the subsidence level to obtain a ground subsidence distribution map, including: Access historical satellite radar data; generating a ground subsidence rate map using synthetic aperture radar interferometry based on the historical satellite radar data and the subsidence level; Construct a contingency table based on the sedimentation level and ecological safety level; Based on the contingency table, calculate the expected frequency; According to the expected frequency, chi-square statistics calculation and significance test are performed to calculate the correlation between the land subsidence and the fusion distribution map of ecological security pattern; Spatially superimposing the land subsidence rate map and the ecological security pattern fusion distribution map to obtain a superimposed map; According to the overlay map, calculating the settlement area ratio corresponding to each settlement level in the ecological function zone; Calculate the impact of land subsidence on the ecological functional zone based on the proportion of the subsidence area; The land subsidence distribution map is generated according to the correlation degree, the impact degree and the ecological security pattern fusion distribution map.

2. The method according to claim 1, characterized in that: The preprocessing of the to-be-processed multi-source heterogeneous data to obtain target multi-source heterogeneous data includes: Performing data cleaning processing on the multi-source heterogeneous data to be processed to obtain first multi-source heterogeneous data; Performing standardization processing on the first multi-source heterogeneous data to obtain second multi-source heterogeneous data; Data encoding is performed on the second multi-source heterogeneous data to obtain the target multi-source heterogeneous data.

3. The method according to claim 1, characterized in that The generating of an ecological security pattern type probability map according to the target multi-source heterogeneous data and the sampling points includes: Extracting ecological security pattern characteristics from the target multi-source heterogeneous data, wherein the ecological security pattern characteristics include geological characteristics, topographic characteristics, land use characteristics, ecological environment characteristics, climate characteristics or human characteristics; Performing dimensionality reduction processing on the characteristics of the ecological security pattern; According to the sampling points, acquiring sample point data from the target research area; Inputting the ecological security pattern characteristics and the sample point data after dimensionality reduction processing into a preset deep learning model so that the preset deep learning model is trained to obtain an ecological security pattern prediction model; The prediction data set of the target research area is input into the ecological security pattern prediction model to obtain the ecological security pattern type probability map.

4. The method according to claim 3, characterized in that The structural construction process of the ecological security pattern prediction model includes: Constructing an input layer, wherein the input layer includes 44 neurons; After the input layer, construct a first hidden layer, the first hidden layer includes 64 neurons and a first activation function, and the dropout rate applied to the first hidden layer is 0.3; After the first hidden layer, construct a second hidden layer, the second hidden layer includes 32 neurons and the first activation function, and the dropout rate applied to the second hidden layer is 0.3; After the second hidden layer, a third hidden layer is constructed, wherein the third hidden layer includes 16 neurons and the first activation function; After the third hidden layer, an output layer is constructed, and the output layer includes 3 neurons and a second activation function.

5. The method according to claim 1, characterized in that The method of using a preset map fusion method to perform multi-layer fusion processing on the plurality of ecological security pattern type probability maps to generate an ecological security pattern fusion distribution map includes: Extracting grid information from the ecological security pattern type probability map; When the preset graph fusion method is the weighted average method, setting a first layer weight set; According to the first layer weight set and the grid information, weighted average fusion processing is performed on the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map to generate a weighted average fusion distribution map; When the preset map fusion method is the principal component analysis method, array conversion processing is performed on the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map respectively to obtain a corresponding one-dimensional array of probability maps; Performing array merging processing on a plurality of one-dimensional arrays of probability maps to obtain a multi-dimensional array of probability maps; Performing array reshaping processing on the probability map multidimensional array to obtain a probability map two-dimensional array, wherein the first dimension of the probability map two-dimensional array is used to represent pixels, and the second dimension of the probability map two-dimensional array is used to represent layers; According to the explained variance ratio, principal component analysis is performed on the two-dimensional array of the probability map to obtain a principal component result; Performing grid reshaping processing on the principal component results; Generate a principal component fusion distribution map according to the principal component result after grid reshaping and the grid information; When the preset map fusion method is the information entropy fusion method, information entropy calculation is performed on the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map respectively to obtain corresponding probability map information entropy; Calculating the total information entropy based on the information entropies of the plurality of probability graphs; Calculating a second layer weight set according to the plurality of probability map information entropies and the total information entropy; According to the second layer weight set and the grid information, weighted fusion processing is performed on the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map to generate an information entropy fusion distribution map; When the preset graph fusion method is the nonlinear fusion method, setting a third layer weight set; According to the third layer weight set, the non-ecologically suitable area probability map, the ecological source probability map and the ecological corridor probability map are input into a preset nonlinear activation function to generate a nonlinear fusion distribution map; According to the characteristics of the preset map fusion method, a distribution map is selected from the fusion distribution maps to be selected as the ecological security pattern fusion distribution map, and the fusion distribution maps to be selected include the weighted average fusion distribution map, the principal component fusion distribution map, the information entropy fusion distribution map and the nonlinear fusion distribution map.

6. The method according to claim 1, characterized in that The ground subsidence distribution map is subjected to subsidence grade merging processing to obtain a target ecological security pattern distribution map, including: Extracting a plurality of settlement combinations between the ecological functional zones and the settlement grades from the land settlement distribution map; Prioritizing the multiple sedimentation combinations to obtain a priority order; According to the priority order and the number of types of the ecological functional zones, the multiple settlement combinations in the ground subsidence distribution map are classified and planned to obtain the target ecological security pattern distribution map.

7. The method according to claim 1, characterized in that The method further comprises: According to the target ecological security pattern distribution map, calculate the functional area ratio corresponding to each ecological functional zone; Compare the area ratio of the functional area with the original area ratio before ecological protection to obtain the degree of ecological protection impact; According to the land subsidence distribution map, identifying high-risk areas; An ecological space layout strategy is generated based on the area ratio of the functional areas, the degree of ecological protection impact and the high-risk areas.

8. The device for constructing an ecological security pattern integrating land subsidence is characterized by: include: The first module is used to obtain multi-source heterogeneous data to be processed, wherein the multi-source heterogeneous data to be processed includes historical ground subsidence data, geological data, topographic data, land use data, ecological environment data, climate data or humanities data; The second module is used to preprocess the multi-source heterogeneous data to be processed to obtain target multi-source heterogeneous data; The third module is used to generate sampling points in the target research area, and the ecological security pattern types of the sampling points include non-ecologically suitable areas, ecological sources or ecological corridors; The fourth module is used to generate an ecological security pattern type probability map according to the target multi-source heterogeneous data and the sampling points, wherein the ecological security pattern type probability map includes a non-ecologically suitable area probability map, an ecological source probability map or an ecological corridor probability map; The fifth module is used to perform multi-layer fusion processing on the plurality of ecological security pattern type probability maps using a preset map fusion method to generate an ecological security pattern fusion distribution map, wherein the preset map fusion method includes a weighted average method, a principal component analysis method, an information entropy fusion method or a nonlinear fusion method; The sixth module is used to perform ground subsidence fusion processing on the ecological security pattern fusion distribution map according to ecological functional zones and subsidence levels to obtain a ground subsidence distribution map, wherein the ecological functional zones include construction areas, source areas, corridor areas or buffer areas, and the subsidence levels include non-subsidence, slight subsidence and moderate subsidence; The seventh module is used to merge the land subsidence distribution map according to the subsidence grade to obtain the target ecological security pattern distribution map; Wherein, the ground subsidence fusion processing is performed on the ecological security pattern fusion distribution map according to the ecological functional zone and the subsidence level to obtain a ground subsidence distribution map, including: Access historical satellite radar data; generating a ground subsidence rate map using synthetic aperture radar interferometry based on the historical satellite radar data and the subsidence level; Construct a contingency table based on the sedimentation level and ecological safety level; Based on the contingency table, calculate the expected frequency; According to the expected frequency, chi-square statistics calculation and significance test are performed to calculate the correlation between the land subsidence and the fusion distribution map of ecological security pattern; Spatially superimposing the land subsidence rate map and the ecological security pattern fusion distribution map to obtain a superimposed map; According to the overlay map, calculating the settlement area ratio corresponding to each settlement level in the ecological function zone; Calculate the impact of land subsidence on the ecological functional zone based on the proportion of the subsidence area; The land subsidence distribution map is generated according to the correlation degree, the impact degree and the ecological security pattern fusion distribution map.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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