Regional ecological restoration decision-making method and device, electronic device and storage medium

Abstract

The embodiment of the present application provides a method and device for generating regional ecological restoration decisions, an electronic device and a storage medium, and belongs to the field of ecological restoration technology. The method includes: obtaining spatial ecological data of the target plate; wherein the target plate includes: primary area, intermediate area and advanced area, and the spatial ecological data includes: ecological damage data, regional development data, green space distribution data; generating restoration decisions according to ecological damage data and primary decision-making construction rules to obtain primary ecological restoration decisions; generating restoration decisions according to regional development data and intermediate decision-making construction rules to obtain intermediate ecological restoration decisions; generating restoration decisions according to green space distribution data and advanced decision-making construction rules to obtain advanced ecological restoration decisions; constructing a restoration strategy network architecture diagram based on primary ecological restoration decisions, intermediate ecological restoration decisions and advanced ecological restoration decisions. The embodiment of the present application can provide a more comprehensive ecological restoration decision.

Description

Technical Field

[0001] The present application relates to the field of ecological restoration technology, and in particular to a method and device for generating decisions on regional ecological restoration, an electronic device, and a storage medium. Background Art

[0002] In recent years, with the rapid economic growth, ecological disasters, ecosystem function degradation, resource degradation and environmental problems have become increasingly frequent. In this context, scholars have reached a consensus on the issue of ecological restoration: to promote the recovery of ecosystem functions through ecological restoration, and at the same time promote the development of human-land relations. However, most of the current ecological restoration-related projects are aimed at repairing a single ecosystem or a single scale of ecological problems. These single-target ecological restoration measures have achieved results in solving ecological problems in a small range, but they still cannot reverse the trend of ecological degradation on a large scale. In addition, in terms of ecological restoration, there is a lack of unified planning, confusion in site selection, inconsistent goals or conflicting goals.

[0003] Ecological restoration is of great significance to alleviating global ecological degradation. In order to achieve the macro-goal of ecological restoration, it is urgent to solve the problem of unclear implementation path due to the lack of relevant practical experience and scientific research guidance. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a method and device for generating regional ecological restoration decisions, an electronic device and a storage medium, aiming to improve the scientific nature of the ecological restoration method.

[0005] To achieve the above-mentioned purpose, the first aspect of the embodiment of the present application proposes a method for generating a regional ecological restoration decision, the method comprising:

[0006] Acquire spatial ecological data of the target sector; wherein the target sector includes: primary area, intermediate area and advanced area, the intermediate area includes at least one primary area, the advanced area includes at least one intermediate area, and the spatial ecological data includes: ecological damage data, regional development data, and green space distribution data;

[0007] Generating a restoration decision based on the ecological damage data and a preset primary decision-making rule to obtain a primary ecological restoration decision for the primary area;

[0008] Generating restoration decisions based on the regional development data and preset intermediate decision-making construction rules to obtain intermediate ecological restoration decisions for the intermediate region;

[0009] Generating restoration decisions based on the green space distribution data and preset advanced decision-making construction rules to obtain advanced ecological restoration decisions for the advanced areas;

[0010] A restoration strategy network architecture diagram is constructed based on the primary ecological restoration decision, the intermediate ecological restoration decision and the advanced ecological restoration decision.

[0011] In some embodiments, the regional development data is evaluated according to preset evaluation indicators to obtain evaluation data of each evaluation indicator;

[0012] Determining the indicator grade data of each of the evaluation indicators according to the evaluation data and the preset range of each grade;

[0013] Constructing cloud map data of each evaluation indicator according to the indicator level data of a plurality of the intermediate areas;

[0014] Performing clustering processing on the intermediate regions according to the cloud map data to obtain cluster regions;

[0015] An intermediate restoration decision is generated based on the preset intermediate decision-making construction rules of the group area to obtain the intermediate ecological restoration decision.

[0016] In some embodiments, an indicator set is constructed for the evaluation indicator; wherein the indicator set includes at least one indicator;

[0017] Inputting the level data of the indicator set into a preset forward cloud generator, outputting expected data, entropy data, super entropy data, and preliminary cloud droplet data;

[0018] Constructing a normal cloud membership function according to a preset normal cloud model function, the expected data, the entropy data and the super entropy data;

[0019] The preliminary cloud droplet data is updated according to a normal cloud membership function to obtain target cloud droplet data;

[0020] Constructing a fuzzy subset matrix according to the target cloud droplet data and the indicator set;

[0021] extracting an evaluation value from the fuzzy subset matrix according to the target cloud droplet data;

[0022] The evaluation value is plotted in a preset coordinate system to obtain the cloud map data.

[0023] In some embodiments, entropy weight processing is performed on the indicator set to obtain a weight set;

[0024] The product between the target cloud droplet data and the weight set is obtained to obtain a fuzzy subset matrix.

[0025] In some embodiments, the maximum value subscript of each evaluation dimension is obtained according to the target cloud droplet data to obtain the maximum membership subscript;

[0026] The evaluation value is extracted from the fuzzy subset matrix according to the maximum membership index.

[0027] In some embodiments, hot spot analysis is performed on the ecological damage data to obtain primary ecological aggregation data;

[0028] According to the primary ecological aggregation data, the primary area is partitioned to obtain primary ecological restoration partition data;

[0029] A restoration decision is generated according to the primary ecological restoration zone to which each primary area belongs and the primary decision-making construction rules to obtain a primary ecological restoration decision for the primary area.

[0030] In some embodiments, a green space patch area is identified from the green space distribution data by a preset regional green space assessment model; wherein the green space patch area includes: regional patch data and bridge patch data;

[0031] Determining importance data of each green patch area according to the greening attributes in each patch area; wherein the importance data represents the importance of the green patch area as a stepping stone patch;

[0032] Classifying each of the green space patch areas according to the importance data to obtain a green space grade for each of the green space patch areas;

[0033] Screening out high-level patch areas from the green patch areas according to the green patch level;

[0034] A restoration decision is generated according to each of the high-level patch areas and the high-level decision construction rules to obtain the high-level ecological restoration decision for the high-level area.

[0035] To achieve the above-mentioned purpose, the second aspect of the embodiment of the present application proposes a regional ecological restoration decision-making generation device, the device comprising:

[0036] An acquisition module is used to acquire spatial ecological data of a target sector; wherein the target sector includes: primary areas, intermediate areas and advanced areas, the intermediate areas include at least one primary area, the advanced areas include at least one intermediate area, and the spatial ecological data include: ecological damage data, regional development data, and green space distribution data;

[0037] A primary decision-making module, used to generate a restoration decision based on the ecological damage data and a preset primary decision-making construction rule, and obtain a primary ecological restoration decision for the primary area;

[0038] An intermediate decision-making module is used to generate restoration decisions based on the regional development data and preset intermediate decision-making construction rules to obtain intermediate ecological restoration decisions for the intermediate region;

[0039] An advanced decision-making module, used to generate restoration decisions based on the green space distribution data and preset advanced decision-making construction rules, and obtain advanced ecological restoration decisions for the advanced areas;

[0040] An architecture module is used to construct a restoration strategy network architecture diagram based on the primary ecological restoration decision, the intermediate ecological restoration decision and the advanced ecological restoration decision.

[0041] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0042] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes 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 in the first aspect is implemented.

[0043] The regional ecological restoration decision generation method and device, electronic device and storage medium proposed in this application can obtain comprehensive information about the ecological status of the region by acquiring the spatial ecological data of the target plate, that is, obtaining the corresponding ecological damage data, regional development data and green space distribution data of the primary area, intermediate area and advanced area. Based on the acquired ecological damage data, the restoration decision is generated using the preset primary decision construction rules to obtain the primary ecological restoration decision of the primary area. Targeted solutions are provided for the ecological damage problems in the primary area to provide suggestions for improving the ecological environment in the primary area. At the same time, by using regional development data and preset intermediate decision construction rules, intermediate ecological restoration decisions are generated to provide accurate and feasible ecological restoration solutions for intermediate areas. Further, using green space distribution data and preset advanced decision construction rules, advanced ecological restoration decisions are generated to provide decisions to solve ecological problems in advanced areas. Generation helps to comprehensively repair ecosystems in a larger range and improve the ecological quality of the entire region. Finally, by integrating primary ecological restoration decisions, intermediate ecological restoration decisions and advanced ecological restoration decisions, a restoration strategy network architecture diagram is constructed. The network architecture diagram shows the relationship between decisions at all levels, forming a complete set of ecological restoration strategies, and providing systematic guidance for achieving sustainable ecological protection and development. Therefore, the entire process from data acquisition to decision generation to the construction of the restoration strategy network forms an ecological management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of the method for generating regional ecological restoration decisions provided in an embodiment of the present application;

[0045] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.

[0046] Figure 3 yes Figure 1 Flow chart of step S105 in FIG.

[0047] Figure 4 yes Figure 3 Flow chart of step S302 in FIG.

[0048] Figure 5 yes Figure 3 Flow chart of step S304 in FIG.

[0049] Figure 6 yes Figure 5 Flow chart of step S502 in FIG.

[0050] Figure 7 yes Figure 1 Flow chart of step S106 in FIG.

[0051] Figure 8 It is a structural schematic diagram of a regional ecological restoration decision-making generation device provided in an embodiment of the present application;

[0052] Fig. 9 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application;

[0053] Fig.10 It is an optional cloud droplet data graph provided in an embodiment of the present application.

[0054] Fig.11 (a) is an optional box-type data diagram provided in an embodiment of the present application.

[0055] Fig.11 (b) is an optional box-and-whisker data diagram provided in an embodiment of the present application.

[0056] Fig.12 It is an optional bar graph provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0059] 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.

[0060] First, some nouns involved in this application are analyzed:

[0061] Clustering: is a data analysis method that belongs to the field of machine learning and statistics. Its main purpose is to group similar items in the data set into clusters, so that items in the same cluster are more similar to each other, and items in different clusters are more different from each other. This helps to reveal the inherent structure in the data set and provide organization and understanding of the data. Clustering is widely used in various fields, such as customer segmentation in marketing, gene expression pattern analysis in biology, and user group discovery in social networks. For example, in the field of medicine, cluster analysis can be applied to patient data to group patients according to similar symptoms and physiological characteristics, so as to better personalize treatment plans. This clustering of large-scale medical data helps doctors understand the characteristics of patient groups more accurately and provides strong support for the formulation of personalized medical plans.

[0062] Normal Cloud Model: It is a fuzzy mathematical theory belonging to the field of information science and engineering. Based on the normal distribution, the model models the uncertainty of random variables as interval random variables of normal distribution, while considering the fusion of randomness, fuzziness and certainty. The normal cloud model has advantages in dealing with uncertainty problems, especially suitable for modeling and analysis of complex systems with fuzziness and randomness. In practical applications, the normal cloud model can be used in risk assessment, decision support and control systems. For example, in financial risk management, the normal cloud model can be used to more comprehensively describe the uncertainty of asset prices and improve the accuracy of risk assessment. This model has also been applied in many fields such as environmental monitoring and engineering management to help deal with uncertainty in complex systems and improve the robustness of decision-making.

[0063] Cloud Diagram: It is an information visualization method, mainly used in the field of data analysis and presentation. This technology presents the key information in the data in a visual way, and shows the importance or frequency distribution of the data through cloud-like patterns of different sizes, colors or shapes. Cloud diagrams are usually used to display keywords or tags in text data, where the words with higher frequency are displayed in larger fonts or areas, thereby intuitively showing the key features of the data. Cloud diagrams are widely used in fields such as information retrieval, social media analysis, and text mining. Through cloud diagrams, users can quickly identify key information in the data and understand the themes, trends or hot spots of the data. For example, in social media analysis, cloud diagrams can be used to display the most frequently appearing keywords in user comments, helping analysts quickly understand topics and user concerns on social media, thereby guiding marketing strategies or public opinion management.

[0064] Fuzzy Subset Matrix: refers to a matrix whose elements represent the membership between elements in a fuzzy set. In the study of fuzzy logic and fuzzy systems, this matrix is ​​often used to represent fuzzy relations or fuzzy rules. Specifically, the rows and columns of the fuzzy subset matrix usually correspond to the elements in the fuzzy set, and the elements in the matrix represent the membership or similarity between the corresponding elements. This matrix can be used to describe fuzzy relations, for example, in control systems, to describe fuzzy rules between input and output. Such matrices have applications in fuzzy logic, fuzzy control, fuzzy association analysis and other fields. Through this matrix, fuzzy properties can be better understood and modeled, which is helpful for the design and analysis of fuzzy systems.

[0065] Hotspot Analysis: It is a method of identifying important topics, events or areas that have attracted attention, discussion or activities at present or in a specific time period by deeply mining data. This analysis can be applied in many fields, including social media, news reports, market trends, scientific research, etc. In social media, hotspot analysis usually identifies popular topics by monitoring the frequency of keywords, topics or tags. This helps to understand the focus of public attention, guide marketing strategies, or conduct public opinion monitoring. In news reports, hotspot analysis can be used to discover topics of current social concern and support the decision-making of news editors. Technically, hotspot analysis may involve methods such as text mining, data mining, and natural language processing to extract key information from large-scale data. This analysis helps to quickly identify and understand the current focus in an environment of information overload, and provide targeted information to decision makers.

[0066] Based on this, the embodiments of the present application provide a method and device for generating regional ecological restoration decisions, an electronic device and a storage medium, aiming to provide a more comprehensive ecological restoration decision.

[0067] The regional ecological restoration decision-making method and device, electronic device and storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the regional ecological restoration decision-making method in the embodiments of the present application is described.

[0068] The regional ecological restoration decision-making method provided in the embodiment of the present application relates to the field of ecological restoration technology. The regional ecological restoration decision-making method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as 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; the software can be an application that implements the regional ecological restoration decision-making method, etc., but is not limited to the above forms.

[0069] 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.

[0070] Figure 1 is an optional flow chart of the method for generating regional ecological restoration decisions provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S105.

[0071] Step S101, obtaining spatial ecological data of a target sector; wherein the target sector includes: primary areas, intermediate areas and advanced areas, the intermediate areas include at least one primary area, the advanced areas include at least one intermediate area, and the spatial ecological data include: ecological damage data, regional development data, and green space distribution data;

[0072] Step S102, generating a restoration decision based on the ecological damage data and a preset primary decision-making rule to obtain a primary ecological restoration decision for the primary area;

[0073] Step S103, generating restoration decisions based on regional development data and preset intermediate decision-making construction rules to obtain intermediate ecological restoration decisions for intermediate regions;

[0074] Step S104, generating restoration decisions based on green space distribution data and preset advanced decision-making rules to obtain advanced ecological restoration decisions for advanced areas;

[0075] Step S105, constructing a restoration strategy network architecture diagram based on primary ecological restoration decisions, intermediate ecological restoration decisions and advanced ecological restoration decisions.

[0076] Steps S101 to S106 shown in the embodiment of the present application can obtain comprehensive information about the ecological status of the region by acquiring the spatial ecological data of the target plate, that is, obtaining the corresponding ecological damage data, regional development data and green space distribution data of the primary area, the intermediate area and the advanced area. Based on the acquired ecological damage data, the restoration decision is generated using the preset primary decision construction rules to obtain the primary ecological restoration decision of the primary area. Targeted solutions are provided for the ecological damage problems in the primary area to provide suggestions for improving the ecological environment in the primary area. At the same time, by using regional development data and preset intermediate decision construction rules, intermediate ecological restoration decisions are generated to provide accurate and feasible ecological restoration solutions for the intermediate area. Further, using green space distribution data and preset advanced decision construction rules, advanced ecological restoration decisions are generated to provide decisions to solve ecological problems in advanced areas. Generation helps to comprehensively repair ecosystems in a larger range and improve the ecological quality of the entire region. Finally, by integrating primary ecological restoration decisions, intermediate ecological restoration decisions and advanced ecological restoration decisions, a restoration strategy network architecture diagram is constructed. The network architecture diagram shows the relationship between decisions at all levels, forming a complete set of ecological restoration strategies, and providing systematic guidance for achieving sustainable ecological protection and development. Therefore, the entire process from data acquisition to decision generation to the construction of the restoration strategy network forms an ecological management system.

[0077] See also Figure 2 In some embodiments, step S103 may include but is not limited to steps S201 to S205:

[0078] Step S201, evaluating the regional development data according to the preset evaluation index to obtain evaluation data of each evaluation index;

[0079] Step S202, determining the indicator level data of each evaluation indicator according to the evaluation data and the preset range of each level;

[0080] Step S203, constructing cloud map data of each evaluation index according to the index level data of multiple intermediate areas;

[0081] Step S204, clustering the intermediate regions according to the cloud map data to obtain cluster regions;

[0082] Step S205, generating an intermediate restoration decision based on the preset intermediate decision-making construction rules according to the group area to obtain an intermediate ecological restoration decision.

[0083] In step S201 of some embodiments, the preset evaluation indicators include, but are not limited to, population growth rate, GDP growth rate, industrial output value growth rate, ecological pressure index, ecological connectivity index, ecosystem service function index, basic farmland ratio, ecological red line ratio, and main functional zoning. By estimating regional development data through preset evaluation indicators, it is possible to comprehensively evaluate regions at all levels and obtain regional development levels at all levels, which is helpful for subsequent ecological restoration decisions by scientific analysis methods.

[0084] For example, Fig.11 and Fig.12 As shown, Fig.11 (a) shows the evaluation data of 8 evaluation indicators in 50 areas. Fig.11 From (a) in the figure, we can know the evaluation data of each evaluation index in the region, and P4, P3, P2 and S2 have the highest evaluation data in the study area, among which P3 has the most significant difference among all the evaluation indicators in the intermediate region; in addition, according to Fig.11 (b) in the above formula can propose specific optimization directions for each region. For example, in the ecological restoration classification, Group 1 has the lowest comprehensive score, indicating that its ecological environment has the greatest pressure, the worst state, and the weakest response, so the most stringent ecological protection and restoration should be implemented. Fig.12 The comprehensive scores and grades of spatial ecological restoration in each intermediate region are shown, and the results can provide a reference for ecological restoration grading in intermediate regions with the same regional spatial ecological restoration assessment data.

[0085] In step S202 of some embodiments, the indicator level data is divided into five levels, and in this embodiment, the indicators are graded using an arithmetic difference grading method.

[0086] It should be noted that in other embodiments, the indicator level data can be set to other values, and this embodiment does not make specific restrictions. In step S203 of some embodiments, the indicator level data is the corresponding level of the intermediate area under the corresponding indicator. By constructing cloud map data for each evaluation indicator based on the indicator level data of multiple intermediate areas, it is helpful to comprehensively consider the situation of each indicator. The visualization effect of the cloud map data makes the information easier to understand, facilitates the identification of areas that perform better or worse on certain indicators, and helps to form a reasonable repair strategy.

[0087] In step S204 of some embodiments, the intermediate regions are clustered according to the cloud map data to obtain the group regions. The cloud map data provides information on multiple evaluation indicators of the intermediate regions. By clustering these cloud map data, the relationship between different indicators can be comprehensively considered to form a more comprehensive regional feature, which can help discover the similarities and differences between the intermediate regions, help to better understand the commonalities and differences between the intermediate regions, and provide targeted suggestions for subsequent decision-making. By obtaining the group regions, intermediate ecological restoration decisions can be made more targetedly, and different restoration strategies can be adopted to respond to their specific ecological needs and problems.

[0088] For example, K-means is used to cluster the cloud map data, and the clusters obtained after K-means clustering are used as cluster areas.

[0089] It should be noted that in other embodiments, the indicator clustering method can be set to other clustering methods, and this embodiment does not make any specific limitation.

[0090] In the steps S201 to S204 shown in the present embodiment, the regional development data is comprehensively evaluated by the preset evaluation indexes to obtain the detailed evaluation data of each evaluation index. Then, according to these evaluation data and the preset level range, the specific index level data of each evaluation index is determined. The comprehensive performance of the intermediate region on different evaluation indicators is understood more accurately. Using the index level data, the cloud map data of each evaluation index is further constructed, and the data distribution of different intermediate regions on multiple indicators is displayed in a visual way. The cloud map data provides a clear understanding of the data distribution of the intermediate region, and also reflects the ambiguity and uncertainty of the data. By clustering the cloud map data, the group area is obtained, that is, the intermediate regions similar to multiple evaluation indicators are classified into the same group. The result of this clustering provides a basis for subsequent decision-making, and different ecological restoration strategies can be adopted for different groups in a more targeted manner. Finally, according to the group area, the preset intermediate decision construction rules are used to generate the intermediate ecological restoration decision. This series of steps forms a smooth and systematic process through the organic connection of the conjunctions. From data acquisition to decision-making, each step provides deeper and more specific information based on the previous step, providing comprehensive support for ecological restoration decisions, improving the scientific nature, pertinence and operability of decisions, and making the ecological restoration process more effective and sustainable.

[0091] See also Figure 3 In some embodiments, step S203 may include but is not limited to steps S301 to S307:

[0092] Step S301, constructing an indicator set for the evaluation indicators; wherein the indicator set includes at least one indicator;

[0093] Step S302, input the level data of the indicator set into a preset forward cloud generator, and output expected data, entropy data, super entropy data, and preliminary cloud droplet data;

[0094] Step S303, constructing a normal cloud membership function according to a preset normal cloud model function, expected data, entropy data and super entropy data;

[0095] Step S304, updating the preliminary cloud droplet data according to the normal cloud membership function to obtain target cloud droplet data;

[0096] Step S305, constructing a fuzzy subset matrix according to the target cloud droplet data and the index set;

[0097] Step S306, extracting an evaluation value from the fuzzy subset matrix according to the target cloud droplet data;

[0098] Step S307, plotting the evaluation values ​​in a preset coordinate system to obtain cloud map data.

[0099] In step S301 of some embodiments, the indicator set is constructed by constructing the indicator data of all intermediate areas under the evaluation indicators into an indicator set.

[0100] In step S302 of some embodiments, the forward cloud generator in the normal cloud model is an algorithm for generating a normal cloud. Based on the given normal distribution parameters, it is used to construct the membership function of the normal cloud, thereby describing the possibility of a certain value in the normal distribution. The preliminary cloud droplet data is used for subsequent continuous updates to generate target cloud droplet data. By inputting a preset indicator set into the forward cloud generator to generate expected data, entropy data, super entropy data, and preliminary cloud droplet data, these data together constitute a comprehensive description, covering all aspects of the fuzzy set, from central tendency to uncertainty measurement to high-order information. Through these data, the normal cloud model can handle fuzzy and uncertain information more comprehensively and flexibly.

[0101] In step S303 of some embodiments, in the normal cloud model, the membership function is used to represent the membership of an element to a fuzzy set. The membership function in the normal cloud model usually adopts a normal distribution function. The normal cloud membership function is constructed by using the preset normal cloud model function, the upper data, and the super entropy data to obtain the membership of each element in the fuzzy set, thereby mining the uncertain information between the elements and quantifying the uncertain information for subsequent updating and iteration of the cloud droplet data.

[0102] In step S304 of some embodiments, the preliminary cloud droplet data is calculated using the normal cloud membership function, and the target cloud droplet data is obtained by continuously updating and iterating, thereby discovering the undiscovered connections between different indicators, so that the subsequent clustering can be classified according to the hidden commonalities. The target cloud droplet data is obtained by repeatedly calculating and iterating the preliminary cloud droplet data using the normal cloud membership function, and the implicit information between the data in the indicator set is continuously calculated and mined using the normal cloud membership function to obtain the target cloud droplet data.

[0103] In step S307 of some embodiments, the evaluation values ​​are plotted on a preset coordinate system to obtain a cloud map, such as Fig.10 As shown in the figure, visualizing the data makes it easier to intuitively understand the comprehensive situation of the data and improve the comprehensiveness and accuracy of the evaluation.

[0104] In the steps S301 to S307 shown in this embodiment, by constructing an index set, the level data of the constructed index set is input into the preset forward cloud generator, and the expected data, entropy data, super entropy data and preliminary cloud drop data are generated through the output of the forward cloud generator. Through these data, the normal cloud model can process fuzzy and uncertain information more comprehensively and flexibly, thereby comprehensively mining the internal connection in the index set data. Subsequently, a normal cloud membership function is constructed according to the preset normal cloud model function, expected data, entropy data and super entropy data, and the membership of each element in the fuzzy set is obtained, thereby mining the uncertain information between the elements, and quantifying the uncertain information for subsequent update and iteration of the cloud drop data. Furthermore, the preliminary cloud droplet data is updated using the constructed normal cloud membership function to obtain the target cloud droplet data, and the mined fuzzy information and internal connections can be iterated to the target cloud droplet data. Next, a fuzzy subset matrix is ​​constructed based on the target cloud droplet data and the constructed indicator set. Then, the evaluation value is extracted from the constructed fuzzy subset matrix. Finally, the extracted evaluation value is plotted in a preset coordinate system to obtain cloud map data. Through the visualization of cloud map data, the comprehensive evaluation of the object can be more intuitively understood, thereby providing a more intuitive reference for decision-making.

[0105] See also Figure 4 In some embodiments, step S305 may include but is not limited to steps S401 to S402:

[0106] Step S401, performing entropy weight processing on the indicator set to obtain a weight set;

[0107] Step S402, obtaining the product between the target cloud droplet data and the weight set to obtain a fuzzy subset matrix.

[0108] In step S401 of some embodiments, entropy weight processing is a method for determining weights, and the weight of each indicator in the indicator set is confirmed by performing entropy weight processing on the indicator set, thereby obtaining a weight set.

[0109] In step S402 of some embodiments, the fuzzy subset matrix is ​​used to represent the degree of influence of each evaluation index on regions of different grades at different levels, and the matrix reflects the fuzzy membership relationship of each index under different conditions.

[0110] In steps S401 to S402 shown in this embodiment, entropy weight processing is performed on the indicator set, and the weight of each indicator is obtained by calculating the entropy of each indicator, and then a weight set is obtained. Matrix multiplication calculation is then performed based on the weight set and cloud droplet data to obtain a fuzzy subset matrix, which more comprehensively reflects the fuzzy relationship between each evaluation indicator under different conditions, and provides a data basis for subsequent comprehensive evaluation.

[0111] See also Figure 5 In some embodiments, step S306 may include but is not limited to steps S501 to S502:

[0112] Step S501, obtaining the maximum value subscript of each evaluation dimension according to the target cloud droplet data to obtain the maximum membership subscript;

[0113] Step S502: extracting evaluation values ​​from the fuzzy subset matrix according to the maximum membership index.

[0114] In step S501 of some embodiments, the target cloud droplet data is a matrix consisting of multiple evaluation dimensions corresponding to multiple evaluation values, one evaluation dimension corresponds to a row in the matrix, and according to one evaluation dimension, the maximum value of a row in the matrix is ​​obtained, and then the subscript of the maximum value is obtained to obtain the maximum membership subscript.

[0115] In step S502 of some embodiments, the value of the corresponding subscript of the corresponding row of the fuzzy subset matrix is ​​obtained according to the maximum membership subscript as the evaluation value.

[0116] In steps S501 to S502 shown in this embodiment, the maximum value subscript corresponding to the target cloud droplet data is obtained, and the evaluation value of each evaluation dimension is determined from the fuzzy subset matrix according to the maximum value subscript, so as to find the evaluation value corresponding to the corresponding evaluation dimension in the fuzzy subset matrix, and then obtain the most contributing fuzzy data in the fuzzy subset matrix, so as to explore the correlation between each evaluation index.

[0117] See also Figure 6 In some embodiments, step S102 includes but is not limited to steps S601 to S603:

[0118] Step S601, performing hot spot analysis on ecological damage data to obtain primary ecological aggregation data;

[0119] Step S602, partitioning the primary area according to the primary ecological aggregation data to obtain primary ecological restoration partition data;

[0120] Step S603, generating a restoration decision according to the primary ecological restoration zone to which each primary area belongs and the primary decision-making construction rules, and obtaining a primary ecological restoration decision for the primary area.

[0121] In step S601 of some embodiments, Getis-Ord Gi* hotspot analysis in ARCmap10.7 spatial analysis is used to analyze the ecological sensitivity, ecological vulnerability, and ecological damage degree in the ecological damage data from three perspectives, identify local hot spots and cold spot clusters in the ecological damage data, and obtain primary ecological cluster data, which serves as a data basis for the subsequent classification of primary areas.

[0122] It should be noted that the specific method of hot spot analysis in other embodiments may be set to other methods, and this embodiment does not make any specific limitation.

[0123] It should be noted that in other embodiments, hot spot analysis may be performed on ecological damage data from other angles, and this embodiment does not impose any specific limitation.

[0124] In step S602 of some embodiments, the primary ecological aggregation data obtained after the hotspot data analysis is partitioned. In some embodiments, 7 partitions can be obtained based on the ecological aggregation data, where level 7 represents the least ecological pressure and level 1 represents the greatest ecological data pressure. Levels 7 and 6 can be considered as ecological pressure cold spot areas, and mainly implement spatial control ecological protection policies to minimize human interference. Levels 5, 4, and 3 represent the need to implement a method that combines ecological restoration and ecological protection, and tend to favor ecological restoration policies based on natural solutions. Levels 2 and 1 indicate that the original ecological environment is severely sloped and requires human intervention for ecological restoration, while cooperating with the most stringent ecological protection policies and spatial control measures.

[0125] In steps S601 to S602 shown in this embodiment, hot spot analysis is performed on the ecological damage data, hot spot areas are identified, and primary ecological aggregation data are obtained. Based on the results of the hot spot analysis, the area is further partitioned according to the primary ecological aggregation data. Primary ecological restoration partition data is generated based on the characteristics of each primary area and the primary ecological aggregation data. Primary ecological restoration decisions are generated based on the primary ecological restoration partitions to which each primary area belongs and the primary decision-making construction rules. Through hot spot analysis, cold spots and hot spots in the ecological damage data are identified, hot spots and cold spots are classified, and different decision analyses are carried out for different classified data, so as to make scientific and appropriate strategies based on their ecological pressure decisions.

[0126] See also Figure 7 In some embodiments, step S104 may include but is not limited to steps S701 to S705:

[0127] Step S701, identifying green patch areas from green distribution data using a preset regional green space assessment model; wherein the green patch areas include: regional patch data and bridge patch data;

[0128] Step S702, determining the importance data of each green patch area according to the greening attributes in each patch area; wherein the importance data represents the importance of the green patch area as a stepping stone patch;

[0129] Step S703, classifying each green space patch area according to the importance degree data to obtain the green space grade of each green space patch area;

[0130] Step S704, screening out high-level plaque areas from the green space plaque areas according to the green space level;

[0131] Step S705, generating a restoration decision according to each high-level patch area and high-level decision-making construction rules, and obtaining a high-level ecological restoration decision for the high-level area.

[0132] In step S701 of some embodiments, the preset regional green space assessment model includes a landscape graph model and morphological spatial pattern analysis (MSPA), and the regional patch data generally includes the location, area, shape, land cover type and other information of each patch. The bridging patch data includes the location, width, length and other information of the connecting patches. By combining the landscape graph model and the morphological spatial pattern to analyze the green space distribution data, the regional patch data and the bridging patch data are obtained, which can provide a more comprehensive perspective, help understand the landscape dynamics and changes in the geographic space, and provide a scientific basis for sustainable development and resource management.

[0133] It should be noted that the preset regional green space assessment model may be other models, and this embodiment does not impose any specific limitation thereto.

[0134] In step S702 of some embodiments, the greening attribute is the greening value of the patch area, and the greening value of the patch area is obtained by calculating the dPCcon value of each patch as its importance data.

[0135] It should be noted that the calculation method for calculating the greening attribute may be other methods, and this embodiment does not impose any specific limitation.

[0136] In step S703 of some embodiments, the green patch area is divided into three levels: extremely important, important, and general, using the natural breakpoint method according to the importance degree data. The natural breakpoint method refers to a method commonly used in statistics and data analysis to determine the natural dividing points in the data. This method does not rely on subjective human intervention, but determines the dividing points by the distribution characteristics of the data itself, so it is called the natural breakpoint method. The natural breakpoint method arranges the data, visualizes the data, and attempts to identify natural breakpoints in the data. Natural breakpoints usually correspond to inflection points in the data or locations where the distribution changes greatly. According to the natural breakpoints, the data is divided into different levels. Here, the core patches and bridging classes may be divided into three levels: extremely important, important, and general, so that the subsequent ecological restoration priorities can be determined.

[0137] It should be noted that other methods may be used to divide the plaque area, and the levels of division may be other levels, which are not specifically limited in this embodiment.

[0138] In step S704 of some embodiments, the MCR model is used to screen out high-level green space patch areas according to the green space level.

[0139] In steps S701 to S705 shown in this embodiment, green patch areas are identified from green space distribution data through a preset regional green space assessment model. On the basis of determining the greening attributes in each patch area, the importance data of each green patch area is calculated, and the green space level of each green patch area is obtained by grading the importance data, which helps to classify different areas and better understand their role in the ecosystem. Based on the green space level, high-level patch areas are further screened out, and finally, high-level ecological restoration decisions are generated based on the construction rules of high-level patch areas and high-level decisions. This process combines the ecological attributes and restoration needs of specific areas, which is beneficial to the formulation of ecological restoration strategies with high feasibility and good results to promote the recovery and sustainable development of the overall ecosystem. From data identification to decision generation, a systematic approach is provided for ecological restoration.

[0140] The embodiment of the present application can obtain comprehensive information about the ecological status of the region by acquiring the spatial ecological data of the target plate, that is, obtaining the corresponding ecological damage data, regional development data and green space distribution data of the primary area, intermediate area and advanced area. Based on the acquired ecological damage data, the restoration decision is generated using the preset primary decision construction rules to obtain the primary ecological restoration decision of the primary area. Targeted solutions are provided for the ecological damage problems in the primary area to provide suggestions for improving the ecological environment in the primary area. At the same time, by using regional development data and preset intermediate decision construction rules, intermediate ecological restoration decisions are generated to provide accurate and feasible ecological restoration solutions for the intermediate area. Further, using green space distribution data and preset advanced decision construction rules, advanced ecological restoration decisions are generated to provide decisions to solve ecological problems in advanced areas. Generation helps to comprehensively repair ecosystems in a larger range and improve the ecological quality of the entire region. Finally, by integrating primary ecological restoration decisions, intermediate ecological restoration decisions and advanced ecological restoration decisions, a restoration strategy network architecture diagram is constructed. The network architecture diagram shows the relationship between decisions at all levels, forming a complete set of ecological restoration strategies, and providing systematic guidance for achieving sustainable ecological protection and development. Therefore, the entire process from data acquisition to decision generation and then to the construction of a restoration strategy network forms an ecological management system.

[0141] See also Figure 8 The embodiment of the present application also provides a regional ecological restoration decision-making generation device, which can implement the above-mentioned regional ecological restoration decision-making generation method, and the device includes:

[0142] An acquisition module is used to acquire spatial ecological data of a target sector; wherein the target sector includes: primary area, intermediate area and advanced area, the intermediate area includes at least one primary area, the advanced area includes at least one intermediate area, and the spatial ecological data includes: ecological damage data, regional development data, and green space distribution data;

[0143] The primary decision-making module is used to generate restoration decisions based on ecological damage data and preset primary decision-making construction rules to obtain primary ecological restoration decisions for primary areas;

[0144] The intermediate decision-making module is used to generate restoration decisions based on regional development data and preset intermediate decision-making construction rules to obtain intermediate ecological restoration decisions for intermediate regions;

[0145] The advanced decision-making module is used to generate restoration decisions based on green space distribution data and preset advanced decision-making construction rules to obtain advanced ecological restoration decisions for advanced areas;

[0146] The architecture module is used to construct a restoration strategy network architecture diagram based on primary ecological restoration decisions, intermediate ecological restoration decisions, and advanced ecological restoration decisions.

[0147] The specific implementation of the regional ecological restoration decision-making device is basically the same as the specific implementation example of the above-mentioned regional ecological restoration decision-making method, and will not be repeated here.

[0148] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned regional ecological restoration decision-making generation method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, a car computer, etc.

[0149] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0150] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0151] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the regional ecological restoration decision-making generation method of the embodiment of this application;

[0152] Input / output interface 903, used to implement information input and output;

[0153] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0154] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0155] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0156] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned regional ecological restoration decision-making method is implemented.

[0157] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0158] The regional ecological restoration decision generation method, regional ecological restoration decision generation device, electronic device and storage medium provided in the embodiment of the present application can obtain comprehensive information about the ecological status of the region by acquiring the spatial ecological data of the target plate, that is, obtaining the corresponding ecological damage data, regional development data and green space distribution data of the primary area, intermediate area and advanced area. Based on the acquired ecological damage data, the restoration decision is generated using the preset primary decision construction rules to obtain the primary ecological restoration decision of the primary area. Targeted solutions are provided for the ecological damage problems in the primary area to provide suggestions for improving the ecological environment in the primary area. At the same time, by using regional development data and preset intermediate decision construction rules, intermediate ecological restoration decisions are generated to provide accurate and feasible ecological restoration solutions for intermediate areas. Further, using green space distribution data and preset advanced decision construction rules, advanced ecological restoration decisions are generated to provide decisions to solve ecological problems in advanced areas. Generation helps to comprehensively repair ecosystems in a larger range and improve the ecological quality of the entire region. Finally, by integrating primary ecological restoration decisions, intermediate ecological restoration decisions and advanced ecological restoration decisions, a restoration strategy network architecture diagram is constructed. The network architecture diagram shows the relationship between decisions at all levels, forming a complete set of ecological restoration strategies, and providing systematic guidance for achieving sustainable ecological protection and development. Therefore, the entire process from data acquisition to decision generation to the construction of the restoration strategy network forms an ecological management system.

[0159] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0160] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0161] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0162] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0163] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0164] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0165] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0166] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0168] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0169] 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 generating regional ecological restoration decisions, characterized in that: The method comprises: Acquire spatial ecological data of the target sector; wherein the target sector includes: primary area, intermediate area and advanced area, the intermediate area includes at least one primary area, the advanced area includes at least one intermediate area, and the spatial ecological data includes: ecological damage data, regional development data, and green space distribution data; A restoration decision is generated based on the ecological damage data and the preset primary decision-making rules to obtain a primary ecological restoration decision for the primary area, specifically including: Conduct hot spot analysis on the ecological damage data to obtain primary ecological aggregation data; According to the primary ecological aggregation data, the primary area is partitioned to obtain primary ecological restoration partition data; Generate a restoration decision according to the primary ecological restoration zone to which each primary area belongs and the primary decision-making construction rule to obtain the primary ecological restoration decision; The restoration decision is generated according to the regional development data and the preset intermediate decision-making construction rules to obtain the intermediate ecological restoration decision of the intermediate region, which specifically includes: Evaluate the regional development data according to preset evaluation indicators to obtain evaluation data of each evaluation indicator; Determining the indicator grade data of each of the evaluation indicators according to the evaluation data and the preset range of each grade; Constructing cloud map data of each evaluation indicator according to the indicator level data of a plurality of the intermediate areas; Performing clustering processing on the intermediate regions according to the cloud map data to obtain cluster regions; Generating an intermediate restoration decision for the preset intermediate decision-making construction rules according to the group area to obtain the intermediate ecological restoration decision; A restoration decision is generated according to the green space distribution data and the preset advanced decision-making construction rules to obtain an advanced ecological restoration decision for the advanced area, specifically including: Identifying a green patch area from the green distribution data using a preset regional green space assessment model; wherein the green patch area includes: regional patch data and bridge patch data; Determining importance data of each green patch area according to the greening attributes in each patch area; wherein the importance data represents the importance of the green patch area as a stepping stone patch; Classifying each of the green space patch areas according to the importance data to obtain a green space grade for each of the green space patch areas; Screening out high-level patch areas from the green patch areas according to the green patch level; Generate a restoration decision according to each of the advanced patch areas and the advanced decision-making construction rules to obtain the advanced ecological restoration decision; A restoration strategy network architecture diagram is constructed based on the primary ecological restoration decision, the intermediate ecological restoration decision and the advanced ecological restoration decision.

2. The method according to claim 1, characterized in that The step of constructing cloud map data of each evaluation indicator according to the indicator level data of the plurality of intermediate areas includes: Constructing an indicator set for the evaluation indicator; wherein the indicator set includes at least one indicator; Inputting the level data of the indicator set into a preset forward cloud generator, outputting expected data, entropy data, super entropy data, and preliminary cloud droplet data; Constructing a normal cloud membership function according to a preset normal cloud model function, the expected data, the entropy data and the super entropy data; The preliminary cloud droplet data is updated according to a normal cloud membership function to obtain target cloud droplet data; Constructing a fuzzy subset matrix according to the target cloud droplet data and the indicator set; extracting an evaluation value from the fuzzy subset matrix according to the target cloud droplet data; The evaluation value is plotted in a preset coordinate system to obtain the cloud map data.

3. The method according to claim 2, characterized in that The step of constructing a fuzzy subset matrix according to the target cloud droplet data and the indicator set includes: Performing entropy weight processing on the indicator set to obtain a weight set; The product between the target cloud droplet data and the weight set is obtained to obtain a fuzzy subset matrix.

4. The method according to claim 2, characterized in that: The target cloud droplet data includes at least one evaluation dimension; and extracting the evaluation value from the fuzzy subset matrix according to the target cloud droplet data includes: According to the target cloud droplet data, the maximum value subscript of each evaluation dimension is obtained to obtain the maximum membership subscript; The evaluation value is extracted from the fuzzy subset matrix according to the maximum membership index.

5. A regional ecological restoration decision-making device, characterized in that: The device comprises: An acquisition module is used to acquire spatial ecological data of a target sector; wherein the target sector includes: primary areas, intermediate areas and advanced areas, the intermediate areas include at least one primary area, the advanced areas include at least one intermediate area, and the spatial ecological data include: ecological damage data, regional development data, and green space distribution data; The primary decision-making module is used to generate a restoration decision based on the ecological damage data and the preset primary decision-making construction rules to obtain the primary ecological restoration decision of the primary area, specifically including: Conduct hot spot analysis on the ecological damage data to obtain primary ecological aggregation data; According to the primary ecological aggregation data, the primary area is partitioned to obtain primary ecological restoration partition data; Generate a restoration decision according to the primary ecological restoration zone to which each primary area belongs and the primary decision-making construction rule to obtain the primary ecological restoration decision; The intermediate decision-making module is used to generate restoration decisions based on the regional development data and preset intermediate decision-making construction rules to obtain intermediate ecological restoration decisions for the intermediate region, specifically including: Evaluate the regional development data according to preset evaluation indicators to obtain evaluation data of each evaluation indicator; Determining the indicator grade data of each of the evaluation indicators according to the evaluation data and the preset range of each grade; Constructing cloud map data of each evaluation indicator according to the indicator level data of a plurality of the intermediate areas; Performing clustering processing on the intermediate regions according to the cloud map data to obtain cluster regions; Generating an intermediate restoration decision for the preset intermediate decision-making construction rules according to the group area to obtain the intermediate ecological restoration decision; The advanced decision module is used to generate restoration decisions based on the green space distribution data and preset advanced decision building rules to obtain advanced ecological restoration decisions for the advanced areas, specifically including: Identifying a green patch area from the green distribution data using a preset regional green space assessment model; wherein the green patch area includes: regional patch data and bridge patch data; Determining importance data of each green patch area according to the greening attributes in each patch area; wherein the importance data represents the importance of the green patch area as a stepping stone patch; Classifying each of the green space patch areas according to the importance data to obtain a green space grade for each of the green space patch areas; Screening out high-level patch areas from the green patch areas according to the green patch level; Generate a restoration decision according to each of the advanced patch areas and the advanced decision-making construction rules to obtain the advanced ecological restoration decision; An architecture module is used to construct a restoration strategy network architecture diagram based on the primary ecological restoration decision, the intermediate ecological restoration decision and the advanced ecological restoration decision.

6. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the regional ecological restoration decision-making method described in any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for generating regional ecological restoration decisions according to any one of claims 1 to 4 is implemented.

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