Sponge facility layout optimization method based on adaptability evaluation

Abstract

A sponge facility layout optimization method based on adaptability evaluation comprises the following steps: constructing a layered index system, integrating soil substrate characteristics, natural conditions and region positioning factors, and dynamically distributing index weights by using an analytic hierarchy process; quantifying the effect variation of the sponge facility improvement measures on each subitem index and grading; based on the three-dimensional mapping relation between the index current situation level and the measure effect direction, generating an adaptability scoring matrix through an expert scoring mechanism; an adaptive priority coefficient is calculated in combination with weight weighting, and quantifiable decision parameters are generated after linear standardization processing. According to the method, spatial heterogeneity response, cost-benefit collaborative optimization and multi-objective decision are creatively fused, the dependence of a traditional evaluation method on fixed indexes is broken through, and scientific grading recommendation of facility improvement measures is realized; and quantifiable and extensible technical support is provided for differentiated layout of sponge facilities in an urban complex environment and optimal configuration under resource constraints.

Description

Technical Field

[0001] The present invention relates to the fields of urban stormwater management and green infrastructure assessment, and in particular to a sponge facility layout optimization method based on adaptability assessment. Background Art

[0002] With the acceleration of urbanization and the frequent occurrence of extreme weather events, urban flooding, non-point source pollution, and water shortages are becoming increasingly serious. Green infrastructure development has gradually become a key strategy for addressing urban water environment pressures. As a stormwater management concept promoted in my country, sponge city construction emphasizes the on-site retention, infiltration, and purification of rainwater and has been widely implemented in many cities. Sponge facilities, as the core components of this system, include various types, such as bioretention facilities, green roofs, and sunken green spaces.

[0003] In the actual implementation of projects, the deployment and renovation of sponge facilities according to local conditions has become a key link in improving construction efficiency. Especially in the context of urban stock space transformation, low-cost and high-efficiency improvements to existing facilities are of great significance. Currently, common methods of improving sponge facilities include adding different types of biochar, introducing earthworm activity, increasing fertilizer or litter, and other soil management measures. Although these measures are effective in improving soil properties, enhancing pollutant removal capacity, and increasing carbon storage capacity, their applicability has obvious regional differences.

[0004] In current practice, soil foundations, hydrological environments, and ecological needs vary significantly across different regions and types of green spaces, leading to disparate effects from the same improvement measures across different sites. For example, in some areas where soil nutrient content is already high, continued fertilization may be counterproductive; whereas densely populated areas may require a more comprehensive approach that balances landscape and environmental benefits. Therefore, a quantitative method is urgently needed to systematically evaluate the suitability of different improvement measures under diverse spatial conditions to scientifically guide facility deployment and renovation.

[0005] However, existing assessment methods often focus on engineering performance indicators of the facilities themselves, such as surface pollution reduction rate and runoff control efficiency, but lack a quantitative description of the adaptability of the measures. Furthermore, most decision-making methods employ fixed indicators or empirical weights, lacking the flexibility to respond to diverse regional data and factors, making them inadequate for meeting the demands of refined urban management. Furthermore, existing methods generally overlook the balance between implementation costs and ecological and environmental synergistic benefits, hindering optimal allocation within resource constraints.

[0006] Therefore, there is an urgent need for a structured and scalable evaluation method that can evaluate the spatial adaptability of various sponge facility improvement measures based on a comprehensive consideration of natural conditions, soil characteristics and regional economic and social background, and provide quantitative support for the optimal layout of facilities.

[0007] It should be noted that the information disclosed in the above background technology section is only used to understand the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0008] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology and provide a sponge facility layout optimization method based on adaptability evaluation.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] A sponge facility layout optimization method based on adaptability assessment includes the following steps:

[0011] S1. Construction of a hierarchical index system: Based on soil substrate characteristics, natural factors and regional location factors, a hierarchical sponge facility adaptability assessment index system is constructed, and the weight of each sub-index is determined through the hierarchical analysis method;

[0012] S2. Quantification of the effect of measures: For various sponge facility improvement measures, quantify the effect changes on each sub-indicator and divide them into preset levels;

[0013] S3. Three-dimensional adaptability assessment: Based on the current status of indicators at the research site and the effect direction of improvement measures, a scoring mechanism is used to determine the degree of adaptability recommendation and generate a sub-item priority scoring matrix;

[0014] S4. Priority coefficient calculation: Perform weighted calculation on the sub-item priority scoring matrix and the indicator weight to obtain the adaptability priority coefficient of each point for different improvement measures;

[0015] S5, coefficient standardization processing: linearly mapping the adaptive priority coefficient to a preset interval to generate a standardized priority coefficient;

[0016] S6. Layout optimization decision: Based on the standardized priority coefficient, combined with cost constraints and resource conditions, determine the layout priority and optimal configuration plan of sponge facility improvement measures.

[0017] Furthermore, step S1 specifically includes:

[0018] The soil base characteristics include soil organic carbon content, total nitrogen content, total phosphorus content and pH value;

[0019] The natural factors and conditions include rainfall, pollutant control capacity and hydrological environment parameters;

[0020] The regional positioning factors include economic cost adaptability and population density;

[0021] Each sub-indicator is assigned weights through the hierarchical analysis method and divided into three status levels: low, medium and high.

[0022] Furthermore, step S2 specifically includes:

[0023] The effect change is obtained through experimental measurement, field monitoring or literature research, and quantified as a percentage change;

[0024] The effect direction of the improvement measures includes positive improvement, negative reduction or maintaining the status quo, corresponding to the preset level range;

[0025] The preset levels are divided into seven levels, which are used to match the current status levels of indicators at different points.

[0026] Furthermore, step S3 specifically includes:

[0027] The three-dimensional adaptability assessment includes the logical mapping of the indicator status level, the direction of the measure effect and the degree of recommendation;

[0028] According to the unified scoring guidelines, quantitatively score the adaptability of each point to the improvement measures;

[0029] The scoring basis includes the consistency between the current status of indicators and the direction of the measures' effects, regional ecological needs and economic cost constraints.

[0030] In step S3, the three-dimensional adaptability evaluation is based on the logical correspondence between the indicator optimization direction and the measure effect, wherein:

[0031] Soil organic carbon content and plant biomass indicators are adapted to high-value optimization directions;

[0032] Pollutant control indicators are adapted to low-value optimization directions;

[0033] Optimization direction of the adaptation of soil total nitrogen content, total phosphorus content and pH value indicators;

[0034] According to the matching degree between the current status level of the indicators of the research points and the effect level of the improvement measures, combined with the optimization direction, the scoring criteria are determined and the sub-item priority scoring matrix is constructed.

[0035] Furthermore, step S4 specifically includes:

[0036] The adaptability priority coefficient is calculated by the weighted summation formula, which is: the sum of the product of the score value of each sub-indicator and its corresponding weight;

[0037] The coefficient reflects the comprehensive recommended priority of the improvement measures at a specific point, and a higher value indicates stronger adaptability.

[0038] Furthermore, step S6 specifically includes:

[0039] The standardized priority coefficient is used to correct the implementation cost of the improvement measures. The correction formula is: original cost divided by the standardized priority coefficient;

[0040] The revised cost is combined with spatial heterogeneity characteristics to generate a multi-objective optimization ranking scheme to support optimal layout decisions under resource constraints.

[0041] Furthermore, the method further comprises:

[0042] The method is embedded in the spatial information platform to realize the systematic process of data import, indicator calculation, adaptability evaluation, map visualization and result output;

[0043] The platform supports GIS or Web architecture, and dynamically adjusts the indicator system and evaluation algorithm through modular design to adapt to the refined planning needs of different regions.

[0044] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the sponge facility layout optimization method based on adaptability evaluation.

[0045] A computer program product includes a computer program, which implements the sponge facility layout optimization method based on adaptability evaluation when executed by a processor.

[0046] The present invention has the following beneficial effects:

[0047] This paper proposes a method for optimizing sponge facility layout based on adaptability assessment. By constructing a structured, modular hierarchical indicator system and a three-dimensional adaptability assessment mechanism, it innovatively transforms the complex problem of sponge facility layout optimization into a quantifiable and actionable assessment process. This method establishes a hierarchical indicator framework based on soil substrate characteristics, natural conditions, and regional location factors. Using the Analytic Hierarchy Process (AHP), it dynamically assigns weights. Integrating a scoring mechanism, it maps the current indicator level, the direction of the measure's effectiveness, and the degree of recommendation in three dimensions, forming a scientific and transparent adaptability scoring matrix. Its core advantage lies in transcending the traditional method's reliance on fixed indicators and empirical weights. By flexibly adjusting indicator settings and priority algorithms, it effectively responds to heterogeneous factors such as regional soil characteristics, hydrological environments, and economic costs. This significantly improves the spatial adaptability and decision-making accuracy of the assessment process, providing scalable technical support for the differentiated renovation of multiple types of sponge facilities. Furthermore, through weighted calculation and normalization of adaptability priority coefficients, the assessment results are converted into quantitative parameters that can be embedded in practical engineering decisions. The standardized priority coefficients are linearly mapped within a preset range, and combined with cost constraints and resource conditions, an optimal configuration plan is generated that balances efficiency and investment. This mechanism not only addresses the imbalance between environmental benefits and economic costs found in existing approaches, but also achieves optimal deployment decisions under resource constraints through a revised cost formula and multi-objective ranking strategy. The numerical output strengthens the practical application of evaluation results, providing a scientific and practical technical approach for refined urban management and maximizing investment returns. It effectively supports the dynamic optimization and sustainable operation of sponge facilities in complex urban environments.

[0048] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is an overall flow chart of the sponge facility layout optimization method based on adaptability evaluation of the present invention.

[0050] Figure 2 This is a technical roadmap for adaptability assessment of an embodiment of the present invention.

[0051] Figure 3 This is a grading chart of the research point indicators according to an embodiment of the present invention.

[0052] Figure 4 4 is a priority coefficient mapping relationship diagram of an embodiment of the present invention.

[0053] Figure 5 This is a distribution diagram of priority coefficients of improvement measures according to an embodiment of the present invention.

[0054] Figure 6 This is a diagram showing the normalization results of the priority coefficients according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope of the present invention and its application.

[0056] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0057] To address the problems of existing methods, such as a lack of spatial differentiation, inconsistent adaptability evaluation criteria, and difficulty balancing environmental benefits and economic constraints, this paper provides a sponge facility layout optimization method based on adaptability assessment, enabling the optimization and spatial layout of improvement measures tailored to local conditions and in a quantitatively controllable manner. This paper is an adaptability assessment method for the layout of sponge facility improvement measures. Based on a hierarchical indicator system, it constructs a clearly structured, scalable, and quantifiable evaluation framework suitable for promoting the optimization of sponge facility layout and the formulation of improvement strategies tailored to local conditions.

[0058] See Figure 1 , an embodiment of the present invention provides a sponge facility layout optimization method based on adaptability evaluation, comprising the following steps:

[0059] Step S1, constructing a hierarchical indicator system: Based on soil substrate characteristics, natural factors and regional positioning factors, a hierarchical sponge facility adaptability assessment index system is constructed, and the weight of each sub-indicator is determined by the hierarchical analysis method.

[0060] In some embodiments, step S1 specifically includes: the soil base characteristics include soil organic carbon content, total nitrogen content, total phosphorus content and pH value; the natural factor conditions include rainfall, pollutant control capacity and hydrological environment parameters; the regional positioning factors include economic cost adaptability (such as GDP as an indicator) and population density; each sub-indicator is assigned a weight through hierarchical analysis and divided into three status levels: low, medium and high.

[0061] Step S2, quantification of the effect of measures: for various sponge facility improvement measures, quantify the effect changes on each sub-indicator and divide them into preset levels.

[0062] In some embodiments, step S2 specifically includes: the effect change is obtained through experimental measurement, field monitoring or literature research, and quantified as a percentage change; the effect direction of the improvement measure includes positive improvement, negative reduction or maintaining the status quo, corresponding to a preset level interval; the preset level is divided into seven levels, which are used to match the status quo level of indicators at different points.

[0063] Step S3, three-dimensional adaptability assessment: Based on the current status of the indicators at the research site and the effect direction of the improvement measures, a scoring mechanism is used to determine the degree of adaptability recommendation and generate a sub-item priority scoring matrix;

[0064] In some embodiments, step S3 specifically includes: the three-dimensional adaptability assessment includes a logical mapping of the indicator status level, the measure effect direction and the recommendation degree; according to the unified scoring guidelines, the adaptability of each point to the improvement measures is quantitatively scored, such as 1 to 7 points; the scoring basis includes the consistency between the indicator status level and the measure effect direction, regional ecological needs and economic cost constraints.

[0065] In some embodiments, in step S3, the three-dimensional adaptability assessment is based on the logical correspondence between the indicator optimization direction and the measure effect, wherein: soil organic carbon content and plant biomass indicators adapt to the high value optimization direction; pollutant control indicators adapt to the low value optimization direction; soil total nitrogen content, total phosphorus content and pH value indicators adapt to the median optimization direction; according to the degree of matching between the current indicator level of the research point and the effect level of the improvement measures, combined with the optimization direction, the scoring criteria are determined and a sub-item priority scoring matrix is constructed.

[0066] Step S4, priority coefficient calculation: perform weighted calculation on the sub-item priority scoring matrix and the indicator weight to obtain the adaptability priority coefficient of each point for different improvement measures;

[0067] In some embodiments, step S4 specifically includes: the adaptability priority coefficient is calculated by a weighted summation formula, and the calculation formula is: the sum of the product of the score value of each sub-indicator and its corresponding weight; the coefficient reflects the comprehensive recommended priority of the improvement measures at a specific point, and the higher the value, the stronger the adaptability.

[0068] Step S5, coefficient normalization processing: linearly map the adaptive priority coefficient to a preset interval to generate a normalized priority coefficient.

[0069] Specifically, it can be mapped to the interval [0.8, 1.2] or other set ranges. By linearly mapping, the adaptability priority coefficient is adjusted to this preset interval to balance the implementation cost and benefit weights of improvement measures, enhancing the operability of the assessment results in engineering practice and the rationality of resource allocation.

[0070] Step S6, layout optimization decision: Based on the standardized priority coefficient, combined with cost constraints and resource conditions, determine the layout priority and optimization configuration plan of the sponge facility improvement measures.

[0071] In some embodiments, step S6 specifically includes: the standardized priority coefficient is used to correct the implementation cost of the improvement measures, and the correction formula is: the original cost divided by the standardized priority coefficient; the corrected cost is combined with the spatial heterogeneity characteristics to generate a multi-objective optimization sorting scheme to support the optimal layout decision under resource constraints.

[0072] In some embodiments, the sponge facility layout optimization method based on adaptability assessment further includes:

[0073] The method is embedded in the spatial information platform to realize the systematic process of data import, indicator calculation, adaptability evaluation, map visualization and result output;

[0074] The platform supports GIS or Web architecture, and dynamically adjusts the indicator system and evaluation algorithm through modular design to adapt to the refined planning needs of different regions.

[0075] The following further describes specific embodiments of the present invention and experimental verification.

[0076] A sponge facility layout optimization method based on adaptability assessment, the specific implementation process of which includes the following steps:

[0077] First, an indicator layer was constructed. Through literature review and expert consultation, a comprehensive indicator system was selected to represent the adaptability factors of sponge facilities. This indicator system is divided into three categories: soil substrate characteristics, natural factors, and regional positioning factors. Soil indicators reflect the facility's support for ecological carbon sequestration and nutrient cycling, natural factor indicators characterize the region's ability to control hydrological pollution, and regional positioning indicators measure the cost adaptability and landscape requirements of facility deployment. Each indicator is divided into several sub-indicators, and the indicator weights are calculated using the Analytic Hierarchy Process (AHP) to ensure the scientific and objective nature of the system.

[0078] Next, we constructed a measure-effect layer. We analyzed representative improvement measures (e.g., biochar addition, litter application, and earthworm activity) and collected their impact on each sub-indicator. This was quantified as a percentage change and categorized into seven levels (ranging from "substantially reduced" to "substantially improved"). Data for this layer can be derived from experimental measurements, field monitoring, or existing literature reviews, making it highly portable and updatable.

[0079] Third, conduct an adaptability assessment. This step evaluates the adaptability of each improvement measure to different research locations by constructing a three-dimensional scoring mechanism of "current status level - effect direction - matching recommendation". According to the current status level of the indicator (low, medium, high) and the effect direction of the measure (upward, downward or maintain), the degree of recommendation can be determined by expert scoring. The scoring range is 1 to 7 points, representing from "not recommended" to "strong recommendation". Expert scoring refers to unified scoring guidelines and typical cases to ensure the repeatability and cross-regional adaptability of the scoring process.

[0080] Fourth, the scoring results are weighted with the indicator weights to determine the "adaptability priority coefficient" for each location for different improvement measures. This coefficient, as a comprehensive scoring result, reflects the overall recommendation level for applying a specific improvement measure at that location under the given indicator system.

[0081] Fifth, coefficient normalization is performed, linearly mapping the priority coefficients to the interval [0.8, 1.2] or other set ranges to enhance the engineering applicability of the evaluation results. These normalized coefficients can be further used for investment cost correction, resource allocation prioritization, or as weighting factors in multi-objective optimization, expanding their application in real-world projects.

[0082] Sixth, we propose a corresponding implementation path for the evaluation system. This method can be embedded in a spatial information platform based on GIS or web architecture, systematically implementing functions such as data import, indicator calculation, score matching, map display, and result output, providing real-time auxiliary decision support for urban renewal and regional planning.

[0083] In order to further illustrate the implementation process and application effect of the adaptability evaluation method for sponge facility improvement measures proposed in this invention, Figures 2 to 6 The following content illustrates the application of the present invention's method in a typical urban area. This example, based on the Shenzhen City of Guangdong Province, covers a variety of typical green space scenarios, including street green spaces, community green spaces, and park green spaces. It is representative and applicable, reflecting the evaluation principles and practical effects of the present invention. The following example is illustrative and does not limit the present invention.

[0084] like Figure 2 As shown, the technical approach of the proposed method includes five core steps: constructing an indicator layer, establishing a measure effect layer, assessing adaptability scores, and calculating and standardizing priority coefficients. This process has a clear structured logic and can be widely applied as a general framework for adaptability evaluation.

[0085] First, if Figure 3As shown in the figure, a total of 53 green space sites were set up in Shenzhen. Through field sampling and data query, ten evaluation index data were obtained, covering soil SOC, TIN, TP, pH, underlying surface COD, TP, NH4 + -N, local rainfall, GDP per capita, and population density, etc. After normalization and grading, the above data are divided into three levels: low, medium, and high according to preset standards, thus forming an indicator sub-level map of the study area.

[0086] Based on this, combined with measured data and literature research, we determined the effect sizes of five typical improvement measures: adding herbaceous biochar, woody biochar, introducing earthworms, applying chemical fertilizers, and adding litter. The change in each measure across ten sub-indicators was quantified as a percentage and categorized into seven levels based on the direction of improvement, matching the current status of these indicators at different locations.

[0087] To construct the scoring rules, the present invention designs the following Figure 4 The priority coefficient mapping relationship model shown in the figure clarifies the logical correspondence between the optimization direction of different indicators and the effect of measures. Specifically, indicators such as SOC and plant biomass prefer high values; COD, TP, NH4 + Pollutant indicators such as -N favor low values, while TIN, TP, and pH prefer median values. Experts assign scores ranging from 1 to 7 based on the matching between the current status of each location and the effectiveness of the measures, forming a sub-item priority scoring matrix.

[0088] Next, according to Figure 5 The calculation results shown in the present invention combine the scoring matrix with the indicator weights, and use a weighted summation method to generate the adaptability priority coefficient of each improvement measure at each point. The higher the coefficient, the stronger the recommendation degree of the measure at that point. The results show that different measures show obvious differences in adaptability under different spatial conditions. For example, the introduction of earthworms has a higher priority in areas with high population density, while the addition of biochar and litter is more suitable in areas with low SOC content. Fertilizer measures have a relatively low priority because they may bring pollution risks.

[0089] Taking into account the resource constraints and cost sensitivity in actual projects, the present invention further introduces a standardized processing mechanism for priority coefficients, such as Figure 6 As shown, it is normalized to the range [0.8, 1.2]. The standardized priority coefficient can be used to adjust facility renovation costs, achieving cost flexibility through the formula "adjusted cost = original cost / priority coefficient." This method helps balance renovation benefits and capital investment efficiency, achieving optimal renovation prioritization within a fixed budget.

[0090] In summary, this example demonstrates the combined advantages of the proposed assessment method in terms of multi-source data integration, spatial heterogeneity response, expert experience integration, and engineering applicability. Through modular indicator design, standardized assessment processes, and result transformation mechanisms, the proposed method can be widely applied to the optimization and layout of sponge facilities across various urban areas, providing scientific support for the refined management and investment decision-making of green infrastructure.

[0091] Compared with the prior art, the present invention has the following advantages:

[0092] First, this paper establishes a structured, modular adaptive assessment framework that clearly delineates the indicator layer, the measure effectiveness layer, and the assessment and judgment mechanism. This transforms complex, locally tailored strategies into an actionable and quantifiable assessment process. This framework is highly scalable and adaptable, allowing for adjustments to indicators based on regional characteristics and the flexible replacement of assessment objects and priority algorithms. This overcomes the unclear structure and limited applicability of existing methods, making it applicable to sponge facility renovation tasks across multiple regions and types.

[0093] Secondly, this invention innovatively introduces a three-dimensional logical evaluation mechanism: "Current Indicator Level - Measure Effect Direction - Recommended Scoring." Based on the integration of empirical data, it establishes a mapping relationship between indicator level and measure response, and implements adaptive judgment through a standardized expert scoring method. This mechanism maintains consistency and transparency while enhancing the ability to express nonlinear matching relationships between complex indicators. It overcomes the problems of existing evaluation methods, such as reliance on empirical judgment, predominantly qualitative analysis, and ambiguous weighting between indicators, making the evaluation process more scientific, rigorous, and controllable.

[0094] Finally, the assessment results of this invention are output as "adaptability priority coefficients," which can be further standardized and embedded into practical engineering decision-making processes, such as cost correction, investment prioritization, and deployment optimization. This numerical, actionable output format facilitates integration with actual project costs and resource constraints, significantly improving the implementation and control flexibility of assessment results, and providing solid technical support for the refined management of urban sponge facilities and maximizing investment returns.

[0095] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0096] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0097] An embodiment of the present invention further provides a processor, which executes a computer program and at least performs the method described above.

[0098] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface memory, an optical disc or a read-only optical disc (CD-ROM); the magnetic surface memory can be a magnetic disk memory or a magnetic tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0099] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0100] The units described above as separate components may or may not be physically separated, and the components displayed 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.

[0101] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0102] Those skilled in the art will understand that all or part of the steps of the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. Various media that can store program codes.

[0103] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0104] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0105] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0106] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0107] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. Those skilled in the art will recognize that, without departing from the scope of the present invention, several equivalent substitutions or obvious variations can be made, and the performance or use of the same should be considered to fall within the scope of protection of the present invention.

Claims

1. A sponge facility layout optimization method based on adaptability evaluation, characterized in that: The following steps are involved: S1. Construction of a hierarchical index system: Based on soil substrate characteristics, natural factors and regional location factors, a hierarchical sponge facility adaptability assessment index system is constructed, and the weight of each sub-index is determined through the hierarchical analysis method; S2. Quantification of the effect of measures: For various sponge facility improvement measures, quantify the effect changes on each sub-indicator and divide them into preset levels; S3. Three-dimensional adaptability assessment: Based on the current status of indicators at the research site and the effect direction of improvement measures, a scoring mechanism is used to determine the degree of adaptability recommendation and generate a sub-item priority scoring matrix; S4. Priority coefficient calculation: Perform weighted calculation on the sub-item priority scoring matrix and the indicator weight to obtain the adaptability priority coefficient of each point for different improvement measures; S5, coefficient standardization processing: linearly mapping the adaptive priority coefficient to a preset interval to generate a standardized priority coefficient; S6. Layout optimization decision: Based on the standardized priority coefficient, combined with cost constraints and resource conditions, determine the layout priority and optimal configuration plan of sponge facility improvement measures.

2. The sponge facility layout optimization method based on adaptability evaluation according to claim 1, characterized in that: Step S1 specifically includes: The soil base characteristics include soil organic carbon content, total nitrogen content, total phosphorus content and pH value; The natural factors and conditions include rainfall, pollutant control capacity and hydrological environment parameters; The regional positioning factors include economic cost adaptability and population density; Each sub-indicator is assigned weights through the hierarchical analysis method and divided into three status levels: low, medium and high.

3. The sponge facility layout optimization method based on adaptability evaluation according to claim 1, characterized in that: Step S2 specifically includes: The effect change is obtained through experimental measurement, field monitoring or literature research, and quantified as a percentage change; The effect direction of the improvement measures includes positive improvement, negative reduction or maintaining the status quo, corresponding to the preset level range; The preset levels are divided into seven levels, which are used to match the current status levels of indicators at different points.

4. The sponge facility layout optimization method based on adaptability evaluation according to claim 1, characterized in that: Step S3 specifically includes: The three-dimensional adaptability assessment includes the logical mapping of the indicator status level, the direction of the measure effect and the degree of recommendation; According to the unified scoring guidelines, quantitatively score the adaptability of each point to the improvement measures; The scoring basis includes the consistency between the current status of indicators and the direction of the measures' effects, regional ecological needs and economic cost constraints.

5. The sponge facility layout optimization method based on adaptability evaluation according to claim 1, characterized in that: In step S3, the three-dimensional adaptability evaluation is based on the logical correspondence between the indicator optimization direction and the measure effect, wherein: Soil organic carbon content and plant biomass indicators are adapted to high-value optimization directions; Pollutant control indicators are adapted to low-value optimization directions; Optimization direction of the adaptation of soil total nitrogen content, total phosphorus content and pH value indicators; According to the matching degree between the current status level of the indicators of the research points and the effect level of the improvement measures, combined with the optimization direction, the scoring criteria are determined and the sub-item priority scoring matrix is constructed.

6. The sponge facility layout optimization method based on adaptability evaluation according to claim 1, characterized in that: Step S4 specifically includes: The adaptability priority coefficient is calculated by the weighted summation formula, which is: the sum of the product of the score value of each sub-indicator and its corresponding weight; The coefficient reflects the comprehensive recommended priority of the improvement measures at a specific point, and a higher value indicates stronger adaptability.

7. The sponge facility layout optimization method based on adaptability evaluation according to claim 1, characterized in that: Step S6 specifically includes: The standardized priority coefficient is used to correct the implementation cost of the improvement measures. The correction formula is: original cost divided by the standardized priority coefficient; The revised cost is combined with spatial heterogeneity characteristics to generate a multi-objective optimization ranking scheme to support optimal layout decisions under resource constraints.

8. The sponge facility layout optimization method based on adaptability evaluation according to any one of claims 1 to 7, characterized in that: Also includes: The method is embedded in the spatial information platform to realize the systematic process of data import, indicator calculation, adaptability evaluation, map visualization and result output; The platform supports GIS or Web architecture, and dynamically adjusts the indicator system and evaluation algorithm through modular design to adapt to the refined planning needs of different regions.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the sponge facility layout optimization method based on adaptability evaluation according to any one of claims 1 to 8 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the sponge facility layout optimization method based on adaptability evaluation according to any one of claims 1 to 8 is implemented.

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