CN-122022004-A - Global land comprehensive remediation coupling evaluation and decision optimization method, system, medium and product

CN122022004ACN 122022004 ACN122022004 ACN 122022004ACN-122022004-A

Abstract

A global land comprehensive renovation coupling evaluation and decision optimization method, system, medium and product relate to the field of land renovation evaluation. The method comprises the steps of extracting first data of each suitability index from basic data of comprehensive global land remediation to obtain a remediation suitability index, calculating the change rate of each benefit index according to second data before the remediation and third data after the remediation to obtain a remediation benefit index, calculating a coupling coordination schedule of a target geographic unit based on the remediation suitability index and the remediation benefit index, matching and generating a differentiation remediation optimization strategy aiming at the target geographic unit from a preset strategy knowledge base according to the coupling mismatch type of the target geographic unit when the target geographic unit needs to be optimized, and generating a resource allocation scheme with maximum expected comprehensive benefit under the condition of user-input remediation resource constraint. By implementing the technical scheme, the comprehensive improvement suitability and benefit of the global land can be scientifically evaluated.

Inventors

JIANG JING
CHEN GUODONG
BAI ANQI

Assignees

南京市市政设计研究院有限责任公司

Dates

Publication Date: 20260512
Application Date: 20251225

Claims (10)

1. The method for comprehensively improving coupling evaluation and decision optimization of the global land is characterized by comprising the following steps of: acquiring and fusing multi-source heterogeneous data for comprehensive remediation of a global land to obtain basic data, wherein the multi-source heterogeneous data comprises remote sensing data, geospatial data and ecological environment data; Extracting first data of each suitability index from the basic data, determining first weights of the suitability indexes by a combined evaluation method combining a hierarchical analysis method and an entropy weight method, and carrying out weighted summation operation on the first data and the first weights to obtain a remediation suitability index of a target geographic unit; Extracting second data before the improvement and third data after the improvement of each benefit index from the basic data, calculating the change rate of each benefit index according to the second data and the third data, determining the second weight of each change rate through the combined evaluation method, and obtaining the improvement benefit index of the target geographic unit through weighted summation operation of the change rate and the second weight; Calculating a coupling co-schedule of the target geographic unit based on the remediation suitability index and the remediation benefit index, and determining whether the target geographic unit needs to be optimized based on the remediation suitability index, the remediation benefit index and the coupling co-schedule; when the target geographic unit needs to be optimized, matching from a preset strategy knowledge base according to the coupling mismatch type of the target geographic unit, and generating a differentiation integer optimization strategy aiming at the target geographic unit; And responding to the user-input remediation resource constraint condition, sorting the remediation priorities of the plurality of target geographic units in the target area based on the differentiated remediation optimization strategy, and generating a resource allocation scheme with the maximum expected comprehensive benefit under the constraint condition.
2. The method for coupling evaluation and decision optimization of global land comprehensive remediation according to claim 1, wherein, Determining a first weight for each of the suitability indicators by: ; Wherein, the A first weight indicating a suitability index of a v-th item of the t-th evaluation period, Indicating subjective weight of the v-th suitability index of the t-th evaluation period obtained through analytic hierarchy process, The objective weight of the v-th suitability index of the t-th evaluation period obtained by entropy weight method treatment is represented, Representing the subjective weight coefficient(s), ∈[0,1]; Determining a second weight for each of the rates of change by: ; Wherein, the A second weight representing a variation rate of the ith evaluation period of the ith term, Subjective weight indicating the variation rate of the ith item in the ith evaluation period obtained by the analytic hierarchy process, An objective weight representing the variation rate of the ith item in the ith evaluation period obtained by the entropy weight method, Representing the subjective weight coefficient(s), ∈[0,1]。
3. The global land integrated remediation coupling evaluation and decision optimization method of claim 2, wherein the calculating the coupling coordination of the target geographic unit based on the remediation suitability index and the remediation benefit index includes: Calculating the coupling co-schedule of the target geographic unit by: ; Wherein D represents a coupling coordination schedule, C represents a coupling degree, and T represents a comprehensive coordination index; the degree of coupling is calculated by: ; wherein SI represents a remediation suitability index, and BI represents a remediation benefit index; the integrated coordination index is calculated by: T=α×SI+β×BI; wherein alpha and beta represent dynamic weight coefficients; the suitability index for remediation is calculated by the formula: ; Wherein, (S v , t) represents the v-th suitability index of the t-th evaluation period; Calculating the remediation benefit index by: ; Wherein, (B u , t) represents the nth evaluation period and the nth rate of change.
4. The global land comprehensive remediation coupling evaluation and decision optimization method of claim 1, further comprising case pushing based on the remediation suitability index, the remediation benefit index, and the coupling co-schedule before the matching from a preset policy knowledge base and generating a differential remediation optimization policy for the target geographic unit according to the coupling mismatch type to which the target geographic unit belongs, specifically comprising: Acquiring a plurality of characteristic attributes of the target geographic unit, and constructing a target characteristic vector, wherein the characteristic attributes comprise the improvement suitability index, the improvement benefit index, the coupling co-schedule and background indexes in ecological dimensions extracted from the basic data; Calculating the comprehensive similarity between the target feature vector and the feature vector of the historical case in a preset strategy knowledge base; And based on the comprehensive similarity, screening a preset number of historical cases with highest similarity from the strategy knowledge base to serve as recommended cases for pushing, so that the recommended cases and associated historical remediation strategies serve as reference bases for generating differentiated remediation optimization strategies aiming at the target geographic units.
5. The method of claim 4, wherein the calculating the integrated similarity between the target feature vector and the feature vector of the historical case in the preset policy knowledge base comprises: The integrated similarity is calculated by the following formula: ; Wherein X represents a target feature vector, Y represents a feature vector of a history case, X j represents a jth attribute in the target feature vector, Y j represents a jth attribute in the feature vector, sim (X, Y) represents a comprehensive similarity between the target feature vector and the feature vector, w j represents a weight of the jth attribute, d (X j ,y j ) represents a normalized euclidean distance between the target feature vector and the feature vector on the jth attribute, and M is a total number of feature attributes; Calculating the normalized Euclidean distance between the target feature vector and the feature vector on the j-th attribute by the following formula: ; Where max (y j ) represents the maximum value of all the historical cases in the policy repository on the jth attribute, and min (y j ) represents the minimum value of all the historical cases in the policy repository on the jth attribute.
6. The global land comprehensive remediation coupling evaluation and decision optimization method of claim 4, wherein the matching and generating a differential remediation optimization strategy for the target geographic unit from a preset strategy knowledge base according to the coupling mismatch type to which the target geographic unit belongs includes: dividing the target geographic unit into at least one coupling mismatch type of high fit-low benefit potential unreleased area, low fit-high benefit supernormal play area and low fit-low benefit low efficiency area by a preset threshold rule based on the repair suitability index, the repair benefit index and the coupling cooperative schedule; screening all associated primary remediation strategies from the strategy knowledge base according to the determined coupling mismatch type to form a first strategy set; Matching and weighting fusion are carried out on the history remediation strategies and the first strategy set to form a second strategy set, wherein the similarity of recommended cases is used as the fusion weight of the associated history remediation strategies; And adjusting the strategies in the second strategy set according to the real-time natural background conditions of the target geographic unit extracted from the basic data, and generating the differentiation treatment optimization strategy.
7. The method for global land integrated remediation coupling evaluation and decision optimization of claim 6, wherein the screening all associated primary remediation strategies from the strategy knowledge base according to the determined coupling mismatch type to form a first strategy set specifically includes: Based on the coupling mismatch type, one or more repairing failure modes are matched in a preset strategy failure mode library, and all repairing strategy nodes marked as avoiding the repairing failure modes are searched in a repairing strategy knowledge graph constructed by the strategy knowledge library to form an anti-risk strategy basic set by taking the matched repairing failure modes as starting points; For each strategy in the anti-risk strategy basic set, simulating a secondary failure chain derived from external interference or internal execution deviation in the implementation process, quantitatively deducting the occurrence probability of the secondary failure chain and the estimated negative influence degree of the overall treatment benefit index by using a risk propagation model trained by historical case data, and calculating to obtain the comprehensive risk exposure value of each strategy; Acquiring average benefit improvement data of each strategy in the anti-risk strategy basic set in historical application, constructing a risk-benefit balance function as a reference benefit expectation, and comprehensively calculating the comprehensive risk exposure value and the reference benefit expectation to obtain a primary screening priority score of each strategy; and sorting the strategies according to the preliminary screening priority score, and selecting a preset number of strategies with top ranks to form the first strategy set.
8. A computer system comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to carry out the steps of the method according to any one of claims 1-7.
9. A computer readable storage medium having stored thereon a computer program/instruction, which when executed by a processor, implements the steps of the method of any of claims 1-7.
10. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method of any of claims 1-7.

Description

Global land comprehensive remediation coupling evaluation and decision optimization method, system, medium and product Technical Field The application relates to the field of land remediation evaluation, in particular to a global land comprehensive remediation coupling evaluation and decision optimization method, system, medium and product. Background Along with the rapid development of socioeconomic and the acceleration of the urban process, the reasonable utilization and management of land resources become key factors for guaranteeing the sustainable development. The comprehensive land reclamation is taken as a systematic land treatment mode, and aims to integrate various land resources, improve land utilization efficiency, improve ecological environment and promote urban and rural overall development. In the prior art, the effect evaluation and decision making for global land comprehensive remediation mainly depend on a single data source and a traditional evaluation method. On the one hand, the partial evaluation is only based on a single type of data, such as only geographical space data or ecological environment data are considered, and the actual situation of comprehensive land remediation is difficult to comprehensively reflect. The limitation of the single data source makes the evaluation result inaccurate and comprehensive, and cannot provide sufficient basis for decision making. On the other hand, the conventional evaluation method is often focused on subjective judgment or a single mathematical model, and lacks comprehensive consideration on multiple factors. For example, the weight determination is carried out simply by means of analytic hierarchy process, and is easily affected by human factors, so that objectivity and scientificity of the evaluation result are questioned. Therefore, a more scientific and comprehensive evaluation and decision optimization method is urgently needed to improve the quality and efficiency of comprehensive global land remediation. Disclosure of Invention The application provides a method, a system, a medium and a product for comprehensively evaluating and optimizing global land comprehensive remediation, which can scientifically evaluate the suitability and benefit of the global land comprehensive remediation, reasonably determine the coupling cooperation schedule, precisely generate a differentiated comprehensive remediation optimization strategy and realize reasonable resource allocation so as to maximize the expected comprehensive benefit. In a first aspect, the present application provides a global land comprehensive remediation coupling evaluation and decision optimization method, the method comprising: acquiring and fusing multi-source heterogeneous data for comprehensive remediation of a global land to obtain basic data, wherein the multi-source heterogeneous data comprises remote sensing data, geospatial data and ecological environment data; Extracting first data of each suitability index from the basic data, determining first weights of the suitability indexes by a combined evaluation method combining a hierarchical analysis method and an entropy weight method, and carrying out weighted summation operation on the first data and the first weights to obtain a remediation suitability index of a target geographic unit; Extracting second data before the improvement and third data after the improvement of each benefit index from the basic data, calculating the change rate of each benefit index according to the second data and the third data, determining the second weight of each change rate through the combined evaluation method, and obtaining the improvement benefit index of the target geographic unit through weighted summation operation of the change rate and the second weight; Calculating a coupling co-schedule of the target geographic unit based on the remediation suitability index and the remediation benefit index, and determining whether the target geographic unit needs to be optimized based on the remediation suitability index, the remediation benefit index and the coupling co-schedule; when the target geographic unit needs to be optimized, matching from a preset strategy knowledge base according to the coupling mismatch type of the target geographic unit, and generating a differentiation integer optimization strategy aiming at the target geographic unit; And responding to the user-input remediation resource constraint condition, sorting the remediation priorities of the plurality of target geographic units in the target area based on the differentiated remediation optimization strategy, and generating a resource allocation scheme with the maximum expected comprehensive benefit under the constraint condition. By adopting the technical scheme, the multi-source heterogeneous data such as remote sensing, geographic space, ecological environment and the like are integrated, the improvement suitability index and the improvement benefit index are respectively c