Municipal drainage project investment evaluation system

Abstract

The invention relates to the technical field of municipal drainage project investment assessment, in particular to a municipal drainage project investment assessment system which comprises a multi-source data acquisition module, a multi-source data integration module, a multi-dimensional dynamic assessment and risk assessment module and a cost benefit analysis module. The multi-source data acquisition module comprises a real-time acquisition unit, a weather access unit, a geographic information unit and a BIM cost unit, and the real-time acquisition unit acquires health state data of a pipe network in real time through an Internet of Things monitoring terminal and analyzes a deposition rate and a corrosion rate according to an AI image recognition technology; according to the method, through multi-source data fusion, an intelligent algorithm and a dynamic cost-benefit model, the problems of data islands, poor climate adaptability, risk early warning lag and other pain points in traditional assessment are solved, conversion from passive restoration to active prevention and control is achieved, a scientific decision tool is provided for urban drainage project investment, and complete-cycle scientific management and control from construction to operation and maintenance are achieved.

Description

Technical Field

[0001] This invention relates to the field of investment evaluation technology for municipal drainage projects, and specifically to an investment evaluation system for municipal drainage projects. Background Technology

[0002] Investment assessment for municipal drainage projects is a comprehensive evaluation of the economic, social, and technical feasibility of urban drainage system construction or renovation projects. Its aim is to optimize resource allocation, reduce urban flooding risks, and improve environmental quality. Its core components include investment cost estimation and funding plans.

[0003] For example, application number CN201110452617.3, with an authorization announcement date of 20131030, describes a GIS-based urban drainage network design system, including modules for network element drawing, sewage volume prediction, and network optimization design. This invention also provides an operation method for the GIS-based urban drainage network design system. By fully utilizing the mapping, editing, data management, and storage functions of the geographic information system, it performs pipeline layout drawing and adjustment, conducts corresponding hydraulic calculations and pipe diameter optimization for the pipelines in the design scheme, and facilitates the scientific and rational design of drainage network planning schemes.

[0004] Existing technologies suffer from multi-dimensional systemic defects. They rely excessively on data from a single sensor at the data integration level and lack the ability to conduct collaborative analysis of multi-modal sensors such as acoustic waves, sonar, and corrosion. This results in insufficient accuracy in detecting pipe cracks and poor dynamic adaptability, making it difficult to respond promptly to extreme weather changes. Therefore, there is an urgent need to design an investment assessment system for municipal drainage projects to solve these problems. Summary of the Invention

[0005] The purpose of this invention is to provide an investment evaluation system for municipal drainage projects to address the aforementioned shortcomings of the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A municipal drainage engineering investment evaluation system includes a multi-source data acquisition module, a multi-source data integration module, a multi-dimensional dynamic evaluation and risk assessment module, and a cost-benefit analysis module; The multi-source data acquisition module includes a real-time acquisition unit, a meteorological access unit, a geographic information unit, and a BIM cost estimation unit. The real-time acquisition unit collects pipeline health status data in real time through IoT monitoring terminals, and also analyzes siltation rate and corrosion rate based on AI image recognition technology. The meteorological access unit dynamically connects to the meteorological early warning system and integrates the ECMWF climate model to output extreme precipitation probability and 24-hour rainstorm intensity prediction. The geographic information unit is connected to the GIS geographic information system and loaded with the pipeline network 3D model and geological data; The BIM cost unit is connected to the BIM cost database, synchronizing material price fluctuation data and dynamic labor cost ratio parameters. It should be noted that the real-time acquisition unit integrates multimodal sensing technology, including: An acoustic sensor array uses a time-difference method to locate the damage point and generate a pipeline health rating. The sonar detection module detects defects with internal cracks ≥2mm in depth in the pipeline and marks high-risk areas in conjunction with the BIM model; The BIM cost unit includes: Dynamic price early warning mechanism: When steel prices fluctuate by more than ±5%, a cost recalculation is triggered, and the Monte Carlo simulation is used to update the cost curve; Pipe life prediction model: The replacement cycle of HDPE pipes and concrete pipes is dynamically updated based on corrosion rate.

[0007] The geographic information unit loads multi-source geological data: Soil permeability coefficient and groundwater level dynamic correction drainage capacity; The fusion of UAV aerial survey data and ground-penetrating radar data generates a 3D model of the pipeline network, with an accuracy rate of ≥95% for identifying rainwater and sewage mixing points.

[0008] The multi-source data integration module includes a data cleaning unit, a 3D modeling unit, and a parameter association unit; The data cleaning unit uses machine learning algorithms to correct historical data, identify and calibrate records where the estimation error of the inner diameter of old pipes is greater than 15%. The three-dimensional modeling unit integrates sensor data and GIS coordinates to establish a three-dimensional health record of the pipeline network, supporting the identification of mixed rainwater and sewage connections. It should be noted that the data cleaning unit employs a hybrid algorithm: The inner diameter error was calibrated using the random forest algorithm on complete historical data; The KNN algorithm was used to fill in missing records, with input parameters including soil corrosivity and service life.

[0009] The parameter association unit dynamically associates with the socio-economic parameter database; The multi-dimensional dynamic assessment and risk assessment module includes a climate resilience algorithm unit, a pipeline network model unit, a social benefit unit, and a risk quantification unit. The climate resilience algorithm unit dynamically adjusts the design return period: Where α is the regional vulnerability coefficient, and ΔP is the rate of change in precipitation over the past 10 years; The bearing capacity of the pipeline model unit is calculated iteratively according to the pipe material properties, where the aging coefficient of concrete pipe is k=0.85 and the smoothing coefficient of HDPE pipe is k=1.12. The social benefit unit quantifies the waterlogging loss as ∑ (inundated area × GDP per unit area × water retention time) and the environmental benefit as the equivalent value of pollutant interception × ecological restoration cost. The risk quantification unit integrates Monte Carlo simulation to output cost probability distribution, constructs a sensitivity spider diagram to locate key risk factors, and triggers circuit breaker alarms. It should be noted that the climate resilience algorithm unit integrates: IPCC precipitation prediction model dynamically corrects the formula for rainfall intensity. k in cc coefficients, where k cc =f(Regional precipitation trend under PCCRCP8.5 scenario); The sponge city facility adjustment coefficient β∈(0.6-1.4), adjusted according to the permeable pavement rate, results in: α adj =α×β×k cc .

[0010] The risk quantification unit is equipped with a phased circuit breaker mechanism: Deviation > 10%, Level 1 Warning: Suspend non-critical fund payments; Deviation > 15%, Level 2 Circuit Breaker: Freeze budget and initiate scheme reconstruction; BCR < 1.0, Level 3 Termination: Terminate the investment by generating an audit report.

[0011] The cost-benefit analysis module includes an LCC calculation unit, a benefit visualization unit, and a scheme optimization unit; The LCC calculation unit calculates the total lifecycle cost: C build For the construction cost generated by the BIM real-time cost library, C maintain Annual maintenance costs based on pipe corrosion rate; The benefit visualization unit generates a benefit-cost ratio (BCR) heatmap, dynamically mapping the flooding loss avoidance value and environmental benefits. The optimization unit automatically compares and selects construction techniques and outputs the optimal solution for 20-year net present value (NPV).

[0012] It should be noted that the scheme optimization unit has a built-in construction comparison matrix, and the comparison parameters include: Pipe jacking solution: Reduces road repair costs by 30%, increases machinery costs by 25%, and extends construction period by 15%; Open-cut method: earthwork costs increase by 40%, construction period is shortened by 20%, and cost parameters are automatically matched with the pipe material unit price library in the "China Municipal Engineering Cost Yearbook".

[0013] The LCC computing unit integrates green benefits and policy factors: γ is the regional carbon sink coefficient, ranging from 0.65 to 0.85, and η is the local subsidy ratio, ranging from 5% to 20%.

[0014] The benefit visualization unit generates a PPP financing decision map, which is then overlaid and displayed. High-return zone: BCR > 1.2, red heat value; High-risk areas: Flooding losses exceeding 1 million yuan per hectare, blue alert issued; Priority renovation area: Environmental benefits improved by more than 20% and land value appreciation premium of more than 15%, green label.

[0015] In the above technical solution, the municipal drainage engineering investment evaluation system provided by the present invention has the following beneficial effects: (1) This invention solves the pain points of data silos, poor climate adaptability and delayed risk warning in traditional assessment by multi-source data fusion, intelligent algorithms and dynamic cost-benefit model, realizes the transformation from passive repair to active prevention and control, provides scientific decision-making tools for urban drainage engineering investment, and realizes scientific management and control of the entire cycle from construction to operation and maintenance.

[0016] (2) This invention integrates high-precision acoustic positioning, sonar crack detection and corrosion sensing technologies to construct a pipeline health grade assessment system, which significantly improves the accuracy of rainwater and sewage mixing identification. The system dynamically accesses meteorological prediction models, geographic information systems and three-dimensional modeling data, combines sponge city adjustment parameters to optimize drainage design standards in real time, and uses machine learning algorithms to correct historical data errors to establish a high-precision three-dimensional health record of the pipeline network.

[0017] (3) This invention introduces a climate elasticity algorithm to dynamically adjust the design parameters of the drainage system according to the regional environmental characteristics, thereby achieving climate adaptability optimization. It constructs a cost risk distribution model through probability simulation, locates key risk factors by combining sensitivity analysis, and establishes a graded early warning and circuit breaker mechanism. The system has a built-in intelligent comparison function for construction schemes, comprehensively compares the economic efficiency and construction period impact of different processes, and outputs the optimal decision scheme for the entire cycle.

[0018] (4) This invention establishes a dynamic full life cycle cost accounting model, integrates material price fluctuations, pipe life prediction and green benefit factors, and achieves dual optimization of economic efficiency and sustainability. By quantifying the economic losses of waterlogging and the benefits of environmental governance, it provides a scientific basis for investment priorities and generates a visual decision map to intuitively display high-return areas, risk areas and priority renovation areas, assisting the government and investors in making efficient decisions.

[0019] (5) This invention realizes the automated synchronization and cross-validation of multi-source data such as meteorology, geology, and cost, supports the rapid generation of emergency plans under sudden extreme weather events, dynamically triggers early warnings and pushes optimization strategies by real-time monitoring of pipeline status and external environmental changes, enhances the proactive prevention and control capabilities of urban drainage systems, and has a built-in carbon emission reduction and ecological restoration benefit accounting model to automatically match local environmental protection policies and subsidy standards, generate investment plans that meet regional sustainable development goals, guide funds to high environmental value projects by dynamically adjusting green benefit weights, and the system also adopts a modular design to support the rapid access of new sensors and algorithms, is compatible with the personalized needs of cities of different sizes, provides standardized data interfaces, and achieves seamless connection with multiple platforms such as smart city platforms and financial management systems, expanding decision support scenarios. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0021] Figure 1 This is a schematic diagram of the framework of an embodiment of the municipal drainage engineering investment evaluation system of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0023] like Figure 1 As shown in the figure, the municipal drainage engineering investment evaluation system provided by the present invention includes a multi-source data acquisition module, a multi-source data integration module, a multi-dimensional dynamic evaluation and risk assessment module, and a cost-benefit analysis module; The multi-source data acquisition module includes a real-time acquisition unit, a meteorological access unit, a geographic information unit, and a BIM cost estimation unit. The real-time acquisition unit collects pipeline health status data in real time through IoT monitoring terminals, and also analyzes siltation rate and corrosion rate based on AI image recognition technology; The meteorological access unit dynamically connects to the meteorological early warning system and integrates the ECMWF climate model to output extreme precipitation probability and 24-hour heavy rainfall intensity forecast. Geographic information units are connected to the GIS geographic information system, and the 3D model of the pipeline network and geological data are loaded. BIM cost units are connected to the BIM cost database to synchronize material price fluctuation data and dynamic labor cost ratio parameters. It should be noted that the real-time acquisition unit integrates multimodal sensing technology, including: An acoustic sensor array uses a time-difference method to locate the damage point and generate a pipeline health rating. The sonar detection module detects defects with internal cracks ≥2mm in depth in the pipeline and marks high-risk areas in conjunction with the BIM model; BIM cost units include: Dynamic price early warning mechanism: When steel prices fluctuate by more than ±5%, a cost recalculation is triggered, and the Monte Carlo simulation is used to update the cost curve; Pipe life prediction model: The replacement cycle of HDPE pipes and concrete pipes is dynamically updated based on corrosion rate.

[0024] Geographic Information Unit (GIS) loading multi-source geological data: Soil permeability coefficient and groundwater level dynamic correction drainage capacity; The fusion of UAV aerial survey data and ground-penetrating radar data generates a 3D model of the pipeline network, with an accuracy rate of ≥95% for identifying rainwater and sewage mixing points.

[0025] The multi-source data integration module includes a data cleaning unit, a 3D modeling unit, and a parameter correlation unit; The data cleaning unit uses machine learning algorithms to correct historical data, identify and calibrate records where the estimation error of the inner diameter of old pipes is greater than 15%; The 3D modeling unit integrates sensor data and GIS coordinates to establish a 3D health record of the pipeline network, supporting the identification of mixed rainwater and sewage connections. It should be noted that the data cleaning unit uses a hybrid algorithm: The inner diameter error was calibrated using the random forest algorithm on complete historical data; The KNN algorithm was used to fill in missing records, with input parameters including soil corrosivity and service life.

[0026] The parameter association unit dynamically associates with the socio-economic parameter database; The multi-dimensional dynamic assessment and risk assessment module includes a climate resilience algorithm unit, a pipeline network model unit, a social benefit unit, and a risk quantification unit; Climate resilience algorithm unit dynamic adjustment design return period: Where α is the regional vulnerability coefficient, and ΔP is the rate of change in precipitation over the past 10 years; The bearing capacity of the pipeline network model unit is calculated iteratively according to the pipe material properties, where the aging coefficient of concrete pipe is k=0.85 and the smoothing coefficient of HDPE pipe is k=1.12. The social benefit unit quantifies the waterlogging loss = ∑ (inundated area × GDP per unit area × water retention time) and the environmental benefit = pollutant interception amount × equivalent value of ecological restoration cost; The risk quantification unit integrates Monte Carlo simulation to output the cost probability distribution, constructs a sensitivity spider diagram to locate key risk factors, and triggers circuit breaker alarms. It should be noted that the climate resilience algorithm unit is integrated as follows: IPCC precipitation prediction model dynamically corrects the formula for rainfall intensity. k in cc coefficients, where k cc =f(Regional precipitation trend under PCCRCP8.5 scenario); The sponge city facility adjustment coefficient β∈(0.6-1.4), adjusted according to the permeable pavement rate, results in: α adj =α×β×k cc .

[0027] The risk quantification unit sets up a phased circuit breaker mechanism: Deviation > 10%, Level 1 Warning: Suspend non-critical fund payments; Deviation > 15%, Level 2 Circuit Breaker: Freeze budget and initiate scheme reconstruction; BCR < 1.0, Level 3 Termination: Terminate the investment by generating an audit report.

[0028] The cost-benefit analysis module includes an LCC calculation unit, a benefit visualization unit, and a solution optimization unit; LCC computing unit accounting for the entire lifecycle cost: C build For the construction cost generated by the BIM real-time cost library, C maintain Annual maintenance costs based on pipe corrosion rate; The benefit visualization unit generates a benefit-cost ratio (BCR) heatmap, dynamically mapping the flooding loss avoidance value and environmental benefits. The scheme optimization unit automatically compares and selects construction technology and outputs the optimal solution for 20-year net present value (NPV).

[0029] It should be noted that the scheme optimization unit has a built-in construction comparison matrix, and the comparison parameters include: Pipe jacking solution: Reduces road repair costs by 30%, increases machinery costs by 25%, and extends construction period by 15%; Open-cut method: earthwork costs increase by 40%, construction period is shortened by 20%, and cost parameters are automatically matched with the pipe material unit price library in the "China Municipal Engineering Cost Yearbook".

[0030] LCC computing unit integrates green benefits and policy factors: γ is the regional carbon sink coefficient, ranging from 0.65 to 0.85, and η is the local subsidy ratio, ranging from 5% to 20%.

[0031] The benefit visualization unit generates a PPP financing decision map, which is then overlaid and displayed. High-return zone: BCR > 1.2, red heat value; High-risk areas: Flooding losses exceeding 1 million yuan per hectare, blue alert issued; Priority renovation area: Environmental benefits improved by more than 20% and land value appreciation premium of more than 15%, green label.

[0032] Working Principle: This invention's municipal drainage engineering investment assessment system is based on a closed-loop framework of multi-source data fusion, dynamic modeling, and intelligent decision-making. It collects real-time pipeline health data through IoT sensors (flow rate, pressure, corrosion rate), and combines this with acoustic wave positioning of damage points, sonar crack detection, and AI image recognition technology to construct a pipeline health grading system. Simultaneously, it integrates meteorological data (ECMWF climate model, rainstorm forecast), geographic data (GIS 3D model, soil permeability coefficient), and economic data (BIM dynamic cost, material price fluctuations). It uses a random forest algorithm to calibrate historical data errors and a KNN algorithm to fill in missing records, establishing a high-precision 3D pipeline health profile. The system dynamically adjusts drainage design parameters (based on regional vulnerability coefficient, precipitation change rate, and sponge city adjustment coefficient) through a climate elasticity algorithm, iteratively calculates pipe material bearing capacity and predicts failure risks, and combines Monte Carlo simulation to generate cost probability distribution and sensitivity analysis, triggering a tiered circuit breaker early warning mechanism (budget deviation > 10% warning, > 15% circuit breaker). The Life Cycle Cost (LCC) system integrates dynamic cost estimates, pipe life predictions, and green benefit factors (carbon sink coefficient, local subsidies) to quantify flooding losses (inundated area × GDP per unit area × water retention time) and environmental benefits (pollutant retention × ecological restoration costs). It optimizes pipe jacking and open-cut methods through a construction comparison matrix, generating the optimal 20-year net present value solution, a BCR heat map, and a PPP financing decision map, marking high-return areas, risk areas, and priority remediation areas. The system supports real-time data cross-validation to trigger emergency strategies and expands sensor access and multi-platform integration through a modular architecture, achieving full-cycle scientific management from "data acquisition → modeling calibration → dynamic evaluation → cost optimization → decision output," thereby improving the adaptability, economy, and sustainability of urban drainage projects.

[0033] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A municipal drainage engineering investment evaluation system, comprising a multi-source data acquisition module, a multi-source data integration module, a multi-dimensional dynamic evaluation and risk assessment module, and a cost-benefit analysis module, characterized in that: The multi-source data acquisition module includes a real-time acquisition unit, a meteorological access unit, a geographic information unit, and a BIM cost estimation unit. The real-time acquisition unit collects pipeline health status data in real time through IoT monitoring terminals, and also analyzes siltation rate and corrosion rate based on AI image recognition technology. The meteorological access unit dynamically connects to the meteorological early warning system and integrates the ECMWF climate model to output extreme precipitation probability and 24-hour rainstorm intensity prediction. The geographic information unit is connected to the GIS geographic information system and loaded with the pipeline network 3D model and geological data; The BIM cost unit is connected to the BIM cost database, synchronizing material price fluctuation data and dynamic labor cost ratio parameters. The multi-source data integration module includes a data cleaning unit, a 3D modeling unit, and a parameter association unit; The data cleaning unit uses machine learning algorithms to correct historical data, identify and calibrate records where the estimation error of the inner diameter of old pipes is greater than 15%. The three-dimensional modeling unit integrates sensor data and GIS coordinates to establish a three-dimensional health record of the pipeline network, supporting the identification of mixed rainwater and sewage connections. The parameter association unit dynamically associates with the socio-economic parameter database; The multi-dimensional dynamic assessment and risk assessment module includes a climate resilience algorithm unit, a pipeline network model unit, a social benefit unit, and a risk quantification unit. The climate resilience algorithm unit dynamically adjusts the design return period: ； Where α is the regional vulnerability coefficient, and ΔP is the rate of change in precipitation over the past 10 years; The bearing capacity of the pipeline model unit is calculated iteratively according to the pipe material properties, where the aging coefficient of concrete pipe is k=0.85 and the smoothing coefficient of HDPE pipe is k=1.

12. The social benefit unit quantifies the waterlogging loss as ∑ (inundated area × GDP per unit area × water retention time) and the environmental benefit as the equivalent value of pollutant interception × ecological restoration cost. The risk quantification unit integrates Monte Carlo simulation to output cost probability distribution, constructs a sensitivity spider diagram to locate key risk factors, and triggers circuit breaker alarms. The cost-benefit analysis module includes an LCC calculation unit, a benefit visualization unit, and a scheme optimization unit; The LCC calculation unit calculates the total lifecycle cost: ； C build For the construction cost generated by the BIM real-time cost library, C maintain Annual maintenance costs based on pipe corrosion rate; The benefit visualization unit generates a benefit-cost ratio (BCR) heatmap, dynamically mapping the flooding loss avoidance value and environmental benefits. The optimization unit automatically compares and selects construction techniques and outputs the optimal solution for 20-year net present value (NPV).

2. The municipal drainage engineering investment evaluation system according to claim 1, characterized in that, The real-time acquisition unit integrates multimodal sensing technology, including: An acoustic sensor array uses a time-difference method to locate the damage point and generate a pipeline health rating. The sonar detection module detects defects with internal cracks ≥2mm in depth in the pipeline and marks high-risk areas in conjunction with the BIM model.

3. The municipal drainage engineering investment evaluation system according to claim 1, characterized in that, The BIM cost unit includes: Dynamic price early warning mechanism: When steel prices fluctuate by more than ±5%, a cost recalculation is triggered, and the Monte Carlo simulation is used to update the cost curve; Pipe life prediction model: The replacement cycle of HDPE pipes and concrete pipes is dynamically updated based on corrosion rate.

4. The municipal drainage engineering investment evaluation system according to claim 1, characterized in that, The data cleaning unit employs a hybrid algorithm: The inner diameter error was calibrated using the random forest algorithm on complete historical data; The KNN algorithm was used to fill in missing records, with input parameters including soil corrosivity and service life.

5. The municipal drainage engineering investment evaluation system according to claim 1, characterized in that, The climate resilience algorithm unit integrates: IPCC precipitation prediction model dynamically corrects the formula for rainfall intensity. k in cc coefficients, where k cc =f(Regional precipitation trend under PCCRCP8.5 scenario); The sponge city facility adjustment coefficient β∈(0.6-1.4), adjusted according to the permeable pavement rate, results in: α adj =α×β×k cc 。 6. The municipal drainage engineering investment evaluation system according to claim 1, characterized in that, The geographic information unit loads multi-source geological data: Soil permeability coefficient and groundwater level dynamic correction drainage capacity; The fusion of UAV aerial survey data and ground-penetrating radar data generates a 3D model of the pipeline network, with an accuracy rate of ≥95% for identifying rainwater and sewage mixing points.

7. The municipal drainage engineering investment evaluation system according to claim 1, characterized in that, The risk quantification unit is equipped with a phased circuit breaker mechanism: Deviation > 10%, Level 1 Warning: Suspend non-critical fund payments; Deviation > 15%, Level 2 Circuit Breaker: Freeze budget and initiate scheme reconstruction; BCR < 1.0, Level 3 Termination: Terminate the investment by generating an audit report.

8. The municipal drainage engineering investment evaluation system according to claim 1, characterized in that, The scheme optimization unit has a built-in construction comparison matrix, and the comparison parameters include: Pipe jacking solution: Reduces road repair costs by 30%, increases machinery costs by 25%, and extends construction period by 15%; Open-cut method: earthwork costs increase by 40%, construction period is shortened by 20%, and cost parameters are automatically matched with the pipe material unit price library in the "China Municipal Engineering Cost Yearbook".

9. The municipal drainage engineering investment evaluation system according to claim 1, characterized in that, The LCC computing unit integrates green benefits and policy factors: ； γ is the regional carbon sink coefficient, ranging from 0.65 to 0.85, and η is the local subsidy ratio, ranging from 5% to 20%.

10. The municipal drainage engineering investment evaluation system according to claim 1, characterized in that, The benefit visualization unit generates a PPP financing decision map, which is then overlaid and displayed. High-return zone: BCR > 1.2, red heat value; High-risk areas: Flooding losses exceeding 1 million yuan per hectare, blue alert issued; Priority renovation area: Environmental benefits improved by more than 20% and land value appreciation premium of more than 15%, green label.

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