Urban development planning adaptability evaluation method and system for urban drainage system

Abstract

The invention belongs to the technical field of urban planning, and discloses an urban development planning adaptability evaluation method and system for an urban drainage system, and the method comprises the steps: obtaining a performance reduction rule of a green infrastructure under a preset minimum maintenance condition, and carrying out the correction according to an expected maintenance input level, a long-term performance prediction curve which is more practical is generated; on the basis, the effective operation period of the green infrastructure is determined in combination with the prediction curve, namely, the time length when the performance of the green infrastructure is continuously kept above the lowest standard; and finally, inputting the dynamic performance data into the overall evaluation process of the urban drainage system, and obtaining a more accurate and more timeliness evaluation result of the adaptability of the urban drainage system to the urban development planning. According to the method, the problem of evaluation deviation caused by overestimation of green infrastructure performance in the prior art is effectively solved, and a more scientific and reliable decision basis is provided for urban planners, so that the toughness and sustainability of an urban drainage system are improved.

Description

Technical Field

[0001] This application relates to the field of urban planning technology, and more specifically, to a method and system for assessing the adaptability of urban drainage systems to urban development planning. Background Technology

[0002] In the process of rapid urban development, it is crucial to conduct urban development planning adaptability assessments of urban drainage systems to ensure sustainable urban operation. However, existing assessment methods generally suffer from a significant technical problem when dealing with the large-scale introduction of green infrastructure in urban development planning: they often treat the performance of green infrastructure as a static, idealized constant. This approach ignores the fact that, in actual operation, the rainwater infiltration, storage, and purification capacity of green infrastructure will continuously decline due to various real-world factors such as siltation, soil compaction, vegetation degradation, and inadequate maintenance and management.

[0003] The drawback of this static assessment is that it systematically overestimates the overall drainage system's adaptability to future urban development. This bias accumulates and amplifies over time, especially in medium- to long-term planning timescales. When urban construction is based on such overly optimistic assessments, even moderate rainfall can trigger unexpected flooding once the actual performance of green infrastructure deteriorates to a certain extent, posing a serious threat to urban safety and residents' lives. For example, in the development plan of a new urban area, the large-scale deployment of permeable pavement and sunken green spaces was initially given idealized runoff reduction capabilities. However, due to a lack of effective maintenance, the pores of the permeable pavement became clogged, and the soil in the sunken green spaces became compacted, resulting in actual performance far below design expectations. Ultimately, a moderate rainfall event triggered localized flooding, exposing the limitations of existing assessment methods in failing to adequately consider the dynamic degradation of green infrastructure performance.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for assessing the adaptability of urban drainage systems to urban development planning. This aims to solve the technical problem that existing urban drainage system assessment methods, when faced with green infrastructure, generally treat its performance as a static constant, leading to an overestimation of the drainage system's adaptability and causing the risk of urban flooding in medium- and long-term planning.

[0006] Firstly, this application provides a method for assessing the adaptability of urban drainage systems to urban development planning, comprising the following steps: A1. Obtain baseline performance degradation trend information of green infrastructure in urban development planning; the baseline performance degradation trend information indicates the performance decline pattern of the green infrastructure under preset minimum maintenance conditions; A2. Obtain the corresponding performance correction parameters based on the user-defined expected maintenance and management investment level for the green infrastructure; A3. Based on the baseline performance degradation trend information and the performance correction parameters, generate a long-term performance prediction curve for the green infrastructure; the long-term performance prediction curve represents the performance change trend of the green infrastructure under the expected maintenance and management input level; A4. Determine the effective operating period of the green infrastructure based on the long-term performance prediction curve; the effective operating period represents the length of time during which the performance of the green infrastructure remains above the minimum performance standard. A5. The long-term performance prediction curve and the effective operating period are used as dynamic performance data and input into the overall evaluation process of the urban drainage system to obtain the evaluation results of the urban drainage system's adaptability to urban development planning.

[0007] Secondly, this application provides an urban development planning adaptability assessment system for urban drainage systems, the system comprising: The basic information acquisition module is used to acquire baseline performance degradation trend information of green infrastructure in urban development planning; the baseline performance degradation trend information represents the performance decline pattern of the green infrastructure under preset minimum maintenance conditions; The parameter acquisition module is used to acquire corresponding performance correction parameters based on the user-defined expected maintenance and management investment level of the green infrastructure. The performance prediction module is used to generate a long-term performance prediction curve for the green infrastructure based on the baseline performance degradation trend information and the performance correction parameters; the long-term performance prediction curve represents the performance change trend of the green infrastructure under the expected maintenance and management input level. The validity period determination module is used to determine the effective operating period of the green infrastructure based on the long-term performance prediction curve; the effective operating period represents the length of time that the performance of the green infrastructure remains above the minimum performance standard. The adaptability assessment module is used to input the long-term performance prediction curve and the effective operating period as dynamic performance data into the overall assessment process of the urban drainage system to obtain the assessment results of the urban drainage system's adaptability to urban development planning.

[0008] Beneficial Effects: This application provides a method and system for assessing the adaptability of urban drainage systems to urban development planning. By acquiring the performance degradation patterns of green infrastructure under preset minimum maintenance conditions and correcting them according to expected maintenance investment levels, a more realistic long-term performance prediction curve is generated. Based on this, the effective operating period of the green infrastructure is determined by combining the prediction curve, i.e., the length of time its performance remains above the minimum standard. Finally, by inputting these dynamic performance data into the overall assessment process of the urban drainage system, a more accurate and timely assessment result of the adaptability of the urban drainage system to urban development planning can be obtained. This method effectively solves the assessment bias problem caused by overestimating the performance of green infrastructure in existing technologies, providing urban planners with a more scientific and reliable basis for decision-making, thereby helping to improve the resilience and sustainability of urban drainage systems. Attached Figure Description

[0009] Figure 1 A flowchart illustrating an urban development planning adaptability assessment method for an urban drainage system provided in this application.

[0010] Figure 2 A schematic diagram of an urban development planning adaptability assessment system for an urban drainage system provided in this application.

[0011] Labeling Explanation: 1. Basic Information Acquisition Module; 2. Correction Parameter Acquisition Module; 3. Performance Prediction Module; 4. Validity Period Determination Module; 5. Adaptability Evaluation Module; 301. Processor; 302. Memory; 303. Communication Bus. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0013] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0014] Please refer to Figure 1 , Figure 1 This application presents a method for assessing the urban development planning adaptability of an urban drainage system, as described in some embodiments, comprising the following steps: A1. Obtain baseline performance degradation trend information of green infrastructure in urban development planning; the baseline performance degradation trend information indicates the performance decline pattern of the green infrastructure under preset minimum maintenance conditions; A2. Obtain the corresponding performance correction parameters based on the user-defined expected maintenance and management investment level for the green infrastructure; A3. Based on the baseline performance degradation trend information and the performance correction parameters, generate a long-term performance prediction curve for the green infrastructure; the long-term performance prediction curve represents the performance change trend of the green infrastructure under the expected maintenance and management input level; A4. Determine the effective operating period of the green infrastructure based on the long-term performance prediction curve; the effective operating period represents the length of time during which the performance of the green infrastructure remains above the minimum performance standard. A5. The long-term performance prediction curve and the effective operating period are used as dynamic performance data and input into the overall evaluation process of the urban drainage system to obtain the evaluation results of the urban drainage system's adaptability to urban development planning.

[0015] This application introduces a dynamic degradation mechanism for the performance of green infrastructure and links it to the level of maintenance and management investment. This enables a more accurate prediction of the long-term performance of green infrastructure, thereby improving the accuracy and reliability of urban drainage system adaptability assessment and effectively avoiding the risk of urban flooding caused by overestimating the effectiveness of green infrastructure.

[0016] "Green infrastructure" refers to various facilities in urban development planning that aim to simulate natural hydrological processes, reduce runoff, purify water quality, and improve the ecological environment, such as rain gardens, permeable paving, sunken green spaces, and grassed swales.

[0017] The "baseline performance degradation trend information" refers to the pattern of performance decline over time under preset minimum maintenance conditions. This is typically modeled using historical data, experimental observations, or expert experience, and can be represented, for example, as a curve showing a linear or non-linear decline in performance over time.

[0018] Among them, "expected maintenance and management investment level" refers to the level of maintenance resource investment set by users for green infrastructure based on actual conditions or planning goals. For example, it can be divided into different levels such as "low investment", "medium investment" and "high investment".

[0019] Among them, the "performance correction parameter" is a coefficient or function that adjusts the baseline performance degradation trend based on the expected level of maintenance and management investment, and is used to reflect the impact of different maintenance levels on the performance degradation rate.

[0020] Among them, the "long-term performance prediction curve" is a curve that predicts the performance of green infrastructure over time at a specific level of maintenance and management input, after integrating baseline performance degradation trend information and performance correction parameters.

[0021] The “effective operating period” refers to the length of time that the performance of green infrastructure can be maintained above a preset or dynamically adjusted minimum performance standard.

[0022] Among them, "dynamic performance data" refers to the data that integrates long-term performance prediction curves and effective operating periods and is used to input into the overall assessment process of urban drainage systems. It reflects the dynamic characteristics of green infrastructure performance over time.

[0023] The “Urban Drainage System Overall Assessment Process” refers to a comprehensive simulation or analysis framework used to assess the adaptability of urban drainage systems to urban development planning, which typically includes hydrological and hydraulic models, risk assessment modules, etc.

[0024] The core of the urban development planning adaptability assessment method for urban drainage systems proposed in this application lies in incorporating the dynamic performance changes of green infrastructure into the assessment system.

[0025] In step A1, it is necessary to obtain baseline performance degradation trend information for green infrastructure in urban development planning. This information represents the performance degradation pattern of green infrastructure under preset minimum maintenance conditions. For example, this information can be obtained by consulting relevant literature, industry standards, or historical monitoring data. The baseline performance degradation trend may differ for different types of green infrastructure. For example, the permeability of permeable pavement may gradually decrease due to pore blockage, while the runoff reduction capacity of rain gardens may vary due to vegetation growth and soil compaction. This degradation trend can be described using empirical formulas, statistical models, or models based on physical processes. For example, an initial performance value can be set, and then, based on a preset degradation rate, the performance change over time under minimum maintenance conditions can be calculated.

[0026] In step A2, the corresponding performance correction parameters are obtained based on the user-defined expected maintenance and management investment level for the green infrastructure. Users can set different maintenance and management investment levels according to their actual budget, human resources, and management strategies. For example, users can set it to "regular dredging once a year," "inspection once a quarter," or "maintenance only when a significant fault occurs," etc. The system maps this specific setting to the corresponding level according to preset mapping rules (such as a mapping table). The performance correction parameters can be obtained through expert experience, historical data analysis, or experimental results. For example, a relationship table between maintenance investment level and performance degradation rate can be established, or a correction function can be obtained through regression analysis. When the maintenance investment level is high, the performance correction parameters will slow down the performance degradation rate; conversely, when the maintenance investment level is low, the performance degradation rate will accelerate.

[0027] In step A3, a long-term performance prediction curve for the green infrastructure is generated based on the baseline performance degradation trend information and performance correction parameters. This curve represents the performance change trend of the green infrastructure under the expected level of maintenance and management investment. Specifically, the performance correction parameters can be applied to the baseline performance degradation trend information to adjust its degradation rate or magnitude. For example, if the baseline performance degradation trend is a linear degradation model, the performance correction parameter can be a multiplier factor used to adjust the degradation slope. If the baseline performance degradation trend is a piecewise function, the performance correction parameter can be independently adjusted for the degradation characteristics of each segment. This results in a performance prediction curve that better reflects the actual level of maintenance and management investment.

[0028] In step A4, the effective operating period of the green infrastructure is determined based on the long-term performance prediction curve. This effective operating period represents the length of time the green infrastructure's performance consistently remains above a minimum performance standard. The minimum performance standard can be set based on urban flood control and drainage requirements, water quality targets, or ecological function needs. For example, for permeable pavement, the minimum performance standard could be that its permeability is not lower than a certain threshold; for rain gardens, it could be that its runoff reduction rate is not lower than a certain percentage. By comparing the long-term performance prediction curve with the minimum performance standard, the point in time when the performance curve first falls below the minimum performance standard can be determined; this point in time is the effective operating period.

[0029] In step A5, the long-term performance prediction curve and the effective operating period are used as dynamic performance data and input into the overall assessment process of the urban drainage system to obtain the assessment results of the urban drainage system's adaptability to urban development planning. Traditional assessment methods typically treat the performance of green infrastructure as a constant value, while this application incorporates dynamic changes in performance into the assessment. Specifically, the long-term performance prediction curve can be converted into a series of performance parameter values ​​at different time steps, and the effective operating period can be converted into a time-triggered event. During the simulation process of the overall assessment process of the urban drainage system, the drainage function contribution of green infrastructure can be updated in real time based on these dynamic performance data, thereby more accurately simulating the response of the urban drainage system. For example, in the early stages of the simulation, the performance of green infrastructure is high, and its runoff reduction effect is significant; as time goes on, the performance gradually declines, and its reduction effect weakens accordingly. Even after the effective operating period is exceeded, its functional contribution may be considered ineffective.

[0030] The core innovation of the assessment method proposed in this application lies in incorporating the dynamic performance degradation mechanism of green infrastructure into the adaptability assessment of urban drainage systems. Traditional methods often treat the performance of green infrastructure as static and unchanging, leading to a systematic overestimation of the drainage system's adaptability to future urban development. For example, in the development plan of a new urban area, the large-scale deployment of permeable pavement and sunken green spaces was initially given idealized runoff reduction capabilities. However, due to a lack of effective maintenance, the pores of the permeable pavement became clogged, and the soil in the sunken green spaces became compacted, resulting in their actual performance falling far short of design expectations. Ultimately, a moderate rainfall event triggered localized flooding, exposing the limitations of existing assessment methods in failing to adequately consider the dynamic performance degradation of green infrastructure.

[0031] This application, by introducing baseline performance degradation trend information and performance correction parameters, can generate long-term performance prediction curves that better reflect actual conditions based on different levels of maintenance and management investment. This allows for a more accurate determination of the effective operating life of green infrastructure, and these dynamic performance data can be input into the overall assessment process of urban drainage systems. This dynamic assessment method effectively avoids the risk of urban flooding caused by overestimating the effectiveness of green infrastructure, providing more reliable decision support for urban development planning. Compared to existing technologies, the assessment method of this application can more realistically reflect the actual effectiveness of green infrastructure in long-term operation, thereby significantly improving the accuracy and reliability of urban drainage system adaptability assessment and providing a solid technical guarantee for sustainable urban development.

[0032] In some implementations, step A1 includes: A101. Obtain information on the facility types and material characteristics of green infrastructure in urban development planning; A102. Based on the facility type information and material characteristics, query the preset green infrastructure type database, match and extract the corresponding baseline performance degradation trend information.

[0033] Specifically, in step A101, facility type information refers to the specific form or functional classification of green infrastructure, such as rain gardens, permeable paving, vegetated swales, constructed wetlands, and bioretention strips. Material characteristics refer to the physical or chemical properties of the key materials constituting green infrastructure, such as the porosity of permeable paving, the permeability coefficient of the soil medium, the type and coverage of vegetation, and the thickness of the filtration layer. This information is the basis for identifying the inherent performance degradation patterns of green infrastructure.

[0034] In step A102, the green infrastructure type database can be understood as a pre-established dataset containing various green infrastructure types and their performance degradation patterns under different material characteristics and preset minimum maintenance conditions. This database can be built based on historical data, experimental research, simulation analysis, or expert experience. Based on the facility type information and material characteristics obtained in step A101, the system will perform precise matching within this database to identify the baseline performance degradation trend information that best matches the current green infrastructure. The matching process can employ various algorithms, such as feature similarity-based matching, fuzzy matching, or rule-based matching. Once a match is successful, the corresponding baseline performance degradation trend information will be extracted for subsequent performance prediction.

[0035] The proposed solution first clarifies the facility type and material characteristics of green infrastructure, and then, based on these specific characteristics, queries and extracts corresponding baseline performance degradation trend information from a pre-set database. This ensures that the acquired baseline data is highly targeted and accurate. This systematic acquisition method avoids errors that may arise from subjective judgment or generalization, providing a solid data foundation for subsequent long-term performance prediction.

[0036] The above technical solutions ensure that the acquisition of baseline performance degradation trend information is more standardized and accurate, significantly improving the reliability of the assessment results. By utilizing a pre-defined database of green infrastructure types, performance degradation data matching specific green infrastructure can be obtained efficiently, thus providing a more accurate and scientific basis for assessing the adaptability of urban drainage systems to urban development planning.

[0037] In some implementations, step A2 includes: A201. Obtain the user-defined expected maintenance and management investment level for the green infrastructure; A202. Obtain facility type information of the green infrastructure, and regional characteristic information of the deployment area of ​​the green infrastructure; the regional characteristic information includes at least one of traffic load, surrounding land use type, and environmental pollution exposure level; A203. Based on the facility type information and the regional characteristic information, identify the maintenance efficiency category that matches the green infrastructure from a preset maintenance efficiency classification table; A204. Based on the maintenance efficiency category and the expected maintenance management input level, extract the corresponding performance correction parameters from the maintenance efficiency classification table.

[0038] Specifically, in step A201, the user-defined expected maintenance and management investment level for green infrastructure can be understood as the planned amount of resources to be invested in the maintenance and management of green infrastructure, such as maintenance budget, maintenance frequency, maintenance intensity, or maintenance personnel allocation (furthermore, the system can map these settings to level grades based on preset mapping rules). This can be achieved by the user inputting settings in the evaluation software interface, or by reading a configuration file containing the maintenance investment plan. The purpose is to quantify the future level of support for the maintenance of green infrastructure.

[0039] In step A202, regional characteristic information refers to the environmental characteristics of the area where the green infrastructure is deployed. For example, traffic load can refer to the vehicle or pedestrian flow in the area, which can be obtained by analyzing traffic monitoring data or using map service APIs; surrounding land use type can refer to whether the area is a residential area, commercial area, industrial area, or park / green space, which can be identified through Geographic Information System (GIS) data, satellite remote sensing imagery, or urban planning maps; environmental pollution exposure level can refer to the degree of air pollution, water pollution, or soil pollution faced by the area, which can be determined through environmental monitoring station data, pollution source distribution maps, or environmental impact assessment reports. This information is used to comprehensively describe the specific environmental conditions of the green infrastructure.

[0040] In practical applications, step A203, identifying the maintenance efficiency category matching green infrastructure from a pre-defined maintenance efficiency classification table, refers to searching for the corresponding category in a pre-established maintenance efficiency classification standard based on the acquired facility type information and regional characteristic information. The maintenance efficiency classification table is a database or lookup table containing different facility types, combinations of regional characteristics, and their corresponding maintenance efficiency categories. Specifically, it can be implemented using rule-based matching or decision tree algorithms. For example, if the facility type is "permeable pavement" and the regional characteristics are "high traffic load, high pollution exposure," then the category "low maintenance efficiency response" is matched.

[0041] In step A204, extracting the corresponding performance correction parameter from the maintenance effectiveness classification table refers to, after identifying the maintenance effectiveness category, obtaining a specific value or function from the maintenance effectiveness classification table based on that category and the user-defined expected maintenance management input level, to adjust the baseline performance degradation trend of green infrastructure. This can be achieved using a lookup table method or interpolation. For example, if the maintenance effectiveness category is "medium response" and the expected input level is "medium," then the corresponding performance correction parameter extracted would be 0.8.

[0042] This application's solution, by incorporating information on the facility type of green infrastructure and the regional characteristics of the deployment area, combined with the user-defined expected maintenance and management input level, can more precisely identify maintenance efficiency categories that match the actual situation of the green infrastructure. This is because different types of green infrastructure respond differently to maintenance input under different environmental conditions. For example, a permeable pavement located in a highly polluted area may require higher maintenance input to maintain its performance, while a vegetated swale located in a low-pollution area may achieve similar performance maintenance with lower maintenance input. In this way, the acquisition of performance correction parameters is no longer a single-dimensional process, but rather comprehensively considers the characteristics of the facility itself, the challenges of the external environment, and the expected maintenance efforts. This allows the acquired performance correction parameters to more accurately reflect the actual performance change trend of the green infrastructure under a specific maintenance and management input level.

[0043] Through the aforementioned technical solutions, the obtained performance correction parameters can more accurately reflect the actual performance change trends of green infrastructure under specific maintenance and management investment levels, avoiding prediction biases caused by parameter generalization. Consequently, the generated long-term performance prediction curves will be closer to reality, improving the reliability and accuracy of the assessment results of the urban drainage system's adaptability to urban development planning. This provides strong data support for urban managers to formulate more scientific and targeted green infrastructure maintenance strategies and urban development plans.

[0044] In some implementations, step A3 includes: A301. Obtain multiple decay stages defined in the baseline performance decay trend information and the decay model or decay characteristic parameters of each decay stage; A302. Based on the aforementioned performance correction parameters, the attenuation model or attenuation characteristic parameters for each attenuation stage shall be adjusted independently; A303. Combine the adjusted models or attenuation characteristic parameters of each stage to generate segmented long-term performance prediction curves.

[0045] Specifically, in step A301, the baseline performance degradation trend information can be obtained in advance through historical data analysis, expert experience, or simulation experiments. It typically defines the performance degradation pattern of green infrastructure under preset minimum maintenance conditions. This baseline performance degradation trend information is divided into multiple degradation stages, such as the initial break-in period, stable operation period, and accelerated degradation period. Each degradation stage corresponds to a specific degradation model or degradation characteristic parameter. The degradation model can be a linear model, exponential model, logarithmic model, or polynomial model, used to describe the performance change trend over time in that stage; the degradation characteristic parameter can be the degradation rate, half-life, performance threshold, etc., used to quantify the performance degradation characteristics of that stage.

[0046] Furthermore, in step A302, the performance correction parameters obtained based on the user-defined expected maintenance and management investment level for the green infrastructure are used to independently adjust the degradation model or degradation characteristic parameters for each degradation stage. This means that different levels of maintenance and management investment will have differentiated impacts on the performance degradation of the green infrastructure at different lifecycle stages, thus the obtained performance correction parameters can include the performance correction parameter values ​​corresponding to each degradation stage. For example, a high level of maintenance investment may extend the stable operation period or slow down the performance decline rate during the accelerated degradation phase. This independent adjustment ensures the granularity and accuracy of performance prediction, and can more realistically reflect the impact of maintenance and management strategies on the long-term performance of green infrastructure.

[0047] Therefore, in step A303, the adjusted models or attenuation characteristic parameters for each stage are combined to generate a segmented long-term performance prediction curve. This segmented curve consists of multiple consecutive curve segments, each corresponding to an attenuation stage, and is calculated using the adjusted attenuation model or characteristic parameters for that stage. This segmented combination method allows the generated long-term performance prediction curve to more accurately and flexibly reflect the performance change trend of green infrastructure throughout its entire lifecycle, avoiding prediction biases that may arise from a single model.

[0048] This application's solution overcomes the limitations of traditional single-model prediction by dividing the performance degradation process of green infrastructure into multiple discrete degradation stages and defining an independent degradation model or characteristic parameters for each stage. Specifically, performance correction parameters are finely applied to each degradation stage, enabling accurate quantification and reflection of the impact of maintenance and management inputs on different lifecycle stages. For example, during the initial break-in period, maintenance inputs may primarily affect performance stability and the speed at which design performance is achieved; during the stable operation period, maintenance inputs may primarily affect the level of performance maintenance and the duration of the stable period; and after entering the accelerated degradation phase, maintenance inputs may aim to delay the sharp decline in performance. This phased, independently adjusted mechanism allows the generated long-term performance prediction curves to more realistically simulate the actual performance evolution of green infrastructure under different levels of maintenance and management inputs, thereby providing more reliable dynamic performance data for subsequent determination of the effective operating period and overall assessment of urban drainage systems.

[0049] Through the aforementioned technical solution, this application can generate more accurate and detailed long-term performance prediction curves for green infrastructure. These curves not only reflect the performance changes of green infrastructure at different lifecycle stages but also allow for differentiated adjustments to performance degradation at each stage based on varying levels of maintenance and management investment. This significantly improves the accuracy and reliability of performance predictions, making the assessment of the adaptability of urban drainage systems to urban development planning more realistic and providing urban planners and managers with more instructive decision-making support. Furthermore, the segmented long-term performance prediction curves also enable more refined design and optimization of maintenance and management strategies for green infrastructure, thereby maximizing the utility of green infrastructure and extending its effective operational lifespan.

[0050] The following is a concrete example. Suppose a city's development plan includes rain gardens as green infrastructure, and their baseline performance degradation trend is defined as three degradation stages: Phase 1 (0-5 years): Initial break-in period, performance slowly decreases from the design value to a stable level, the decay model is linear decay, and the decay rate is R1; Phase 2 (5-20 years): Stable operation period, performance remains at a high level, the decay model is slow decay, and the decay rate is R2; The third stage (20 years or more): accelerated decline period, performance declines rapidly, the decay model is exponential decay, and the decay rate is R3; The user sets the expected maintenance and management input level as "medium maintenance". The performance correction parameters obtained in step A2 include: a correction factor C1 for the first-stage decay rate R1 (e.g., C1 = 0.8, indicating that medium maintenance can slow down the initial decay); a correction factor C2 for the second-stage decay rate R2 (e.g., C2 = 0.5, indicating that medium maintenance can significantly prolong the stabilization period and slow down the decay); and a correction factor C3 for the third-stage decay rate R3 (e.g., C3 = 0.9, indicating that medium maintenance can slightly delay the accelerated decay). In step A302, these performance correction parameters are applied independently to the decay model or characteristic parameters of each decay stage; for example, the adjusted decay rate of the first stage becomes R1 * C1; the adjusted decay rate of the second stage becomes R2 * C2; and the adjusted decay rate of the third stage becomes R3 * C3. Finally, in step A303, these adjusted degradation models or characteristic parameters for each stage are combined to generate a segmented long-term performance prediction curve. This curve uses the adjusted first-stage model for 0-5 years, the adjusted second-stage model for 5-20 years, and the adjusted third-stage model for over 20 years. Therefore, this segmented curve can more accurately reflect the performance change trend of rain gardens at different life stages under a "medium maintenance" level of investment; for example, the stable operating period is effectively extended, and the rate of performance degradation is controlled to a certain extent in each stage.

[0051] In some implementations, step A4 includes: A401. Obtain flood control level information and long-term climate change trend data for the urban areas where the green infrastructure is located; A402. Based on the flood control level information, determine the basic minimum performance standard from the preset flood control level-performance threshold correspondence rules; A403. Based on the long-term climate change trend data, obtain the performance threshold adjustment amount from the preset climate change-performance threshold adjustment rules; A404. Apply the performance threshold adjustment amount to the basic minimum performance standard to obtain the dynamically adjusted minimum performance standard; A405. Based on the long-term performance prediction curve and the dynamically adjusted minimum performance standard, determine the effective operating period of the green infrastructure, such that the effective operating period is equal to the length of time during which the performance of the green infrastructure remains above the dynamically adjusted minimum performance standard.

[0052] Flood control level information can be understood as the protection standards set for a city or specific area in the face of flood disasters, such as Level 1 flood control zone, Level 2 flood control zone, etc., reflecting the minimum requirements for drainage system performance in that area. Long-term climate change trend data refers to predicted data on climate model changes over a future period (e.g., the next 10, 20 years, or longer), such as changes in the frequency and intensity of extreme rainfall events, or sea-level rise trends, aiming to predict environmental factors that may impact the performance of green infrastructure in the future. The flood control level-performance threshold correspondence rule can be a pre-established database or algorithm model that associates different flood control levels with basic performance thresholds (e.g., runoff reduction rate, infiltration capacity, etc.) that green infrastructure should achieve, providing an initial performance benchmark for areas with different flood control requirements. The climate change-performance threshold adjustment rule can be a set of rules based on climate model predictions and engineering experience, calculating the necessary adjustments to the basic minimum performance standards based on climate change trends (e.g., the percentage increase in extreme rainfall), aiming to adapt performance standards to the challenges posed by future climate change. Applying performance threshold adjustments to the baseline minimum performance standard creates a more forward-looking and adaptive performance standard by overlaying additional performance requirements caused by climate change onto the baseline standard based on flood control levels. Consequently, the determination of the effective operating period is no longer based on a static threshold, but on a dynamic threshold that reflects future risks and changes in demand.

[0053] This application's solution addresses the problem of static minimum performance standards in traditional methods by incorporating flood control level information and long-term climate change trend data, and dynamically adjusting the minimum performance standards for green infrastructure accordingly. Specifically, the flood control level information and long-term climate change trend data obtained in step A401 provide crucial inputs for subsequent dynamic adjustments. The flood control level information, used in step A402, is used to determine the basic minimum performance standards, ensuring that the performance assessment of green infrastructure matches the flood control requirements of the region. Furthermore, the long-term climate change trend data, used in step A403, is used to obtain performance threshold adjustment amounts, allowing the minimum performance standards to anticipate the potential impacts of future climate change. Therefore, in step A404, applying the performance threshold adjustment amounts to the basic minimum performance standards yields a dynamically adjusted minimum performance standard that reflects not only current flood control needs but also considerations of future climate risks. Finally, in step A405, the effective operating period of the green infrastructure is determined based on this dynamically adjusted minimum performance standard, making the determined period more realistic and accurately reflecting the ability of green infrastructure to continue functioning in a complex and volatile future environment.

[0054] Through the above technical solution, this application provides a more accurate and forward-looking method for assessing the effective operating period of green infrastructure. This method incorporates urban flood control level information and long-term climate change trend data, enabling dynamic adjustment of minimum performance standards and avoiding assessment biases that may result from static standards. Therefore, the determined effective operating period more accurately reflects the ability of green infrastructure to address future climate change and meet evolving urban flood control needs, providing a more reliable basis for the formulation and adjustment of urban development plans, and significantly improving the accuracy and practicality of the assessment results regarding the adaptability of urban drainage systems to urban development plans.

[0055] The following is a concrete example. Suppose a city plans to build a new eco-friendly residential area, which includes a large amount of green infrastructure such as rain gardens and permeable paving. When assessing the effective lifespan of these green infrastructures: First, in step A401, it was found that the ecological residential area was designated as a level II urban flood control area, and according to long-term climate forecasts from the meteorological department, the frequency of extreme rainfall events in the area is expected to increase by 15% over the next 30 years; Next, in step A402, based on the flood control level II information, the basic minimum performance standard for the rain garden is determined by querying the preset flood control level-performance threshold correspondence rules, which is a runoff reduction rate of 75%. Then, in step A403, based on the long-term climate change trend data of a 15% increase in the frequency of extreme rainfall events, the minimum performance standard for runoff reduction rate needs to be adjusted by an additional 5% from the preset climate change-performance threshold adjustment rules. Subsequently, in step A404, this 5% performance threshold adjustment is applied to the basic minimum performance standard, resulting in a dynamically adjusted minimum performance standard of 75% + 5% = 80%. Finally, in step A405, the long-term performance prediction curve of the rain garden (e.g., a curve predicting a gradual decrease in its runoff reduction rate over time) is compared to this dynamically adjusted minimum performance standard of 80%. The effective operating life of the rain garden is determined by identifying the point at which the curve first falls below 80%. For example, if the effective operating life is 25 years under the static standard, it may be shortened to 20 years after dynamic adjustment. This more accurately reflects the actual time during which the rain garden can continuously meet flood control requirements in the context of future climate change.

[0056] In some implementations, step A5 includes: A501. Match the long-term performance prediction curve with the simulation time step of the overall assessment process of the urban drainage system, and sample the data of the long-term performance prediction curve to generate a performance parameter sequence. A502. Convert the effective operating period into a time-triggered event; the time-triggered event triggers the status update of performance parameters in the overall evaluation process of the urban drainage system when the simulation time reaches the effective operating period; A503. The performance parameter sequence and the time-triggered event are used as dynamic performance data and input into the overall evaluation process of the urban drainage system; A504. At each simulation time step in the overall assessment process of the urban drainage system, the performance parameters are updated according to the performance parameter sequence, and the performance status is adjusted according to the time-triggered events. Simulation calculations are performed to obtain the assessment results of the urban drainage system's adaptability to urban development planning.

[0057] Specifically, in step A501, the long-term performance prediction curves are typically continuous or generated at a high frequency, while the simulation time steps (e.g., hours, days, months, or years) of the overall urban drainage system assessment process are discrete. Therefore, it is necessary to match the data from the long-term performance prediction curves with the simulation time steps, for example, by interpolation, averaging, or direct sampling, to extract the performance values ​​corresponding to each simulation time step from the continuous long-term performance prediction curves, thereby generating a discrete sequence of performance parameters. This sequence of performance parameters represents the expected performance status of the green infrastructure at each simulation time point.

[0058] In step A502, the effective operating period is a time length representing the duration for which the performance of green infrastructure remains above the minimum standard. To effectively utilize this information in dynamic simulation, it is converted into a time-triggered event. This time-triggered event can be understood as a preset condition that triggers a status update related to the performance parameters of green infrastructure in the overall assessment process of the urban drainage system when the simulation time reaches or exceeds the effective operating period. For example, it could trigger a significant decline in performance parameters, a reduction in functional contribution, or failure.

[0059] In practical applications, step A503 uses the generated performance parameter sequence and time-triggered events as dynamic performance data, which is then uniformly input into the overall assessment process of the urban drainage system. This data will guide the assessment process on how to dynamically consider the performance changes of green infrastructure during simulation.

[0060] Furthermore, in step A504, within each simulation time step of the overall urban drainage system assessment process, the system extracts the green infrastructure performance parameters corresponding to the current time step based on the performance parameter sequence and applies them to the simulation calculation. Simultaneously, the system continuously monitors whether the simulation time has reached the effective operating period defined by the time-triggered event. Once reached, the system adjusts the performance status of the green infrastructure according to the time-triggered event, such as reducing or removing its drainage function contribution to reflect its performance degradation or failure. Through this dynamic updating and adjustment, the assessment results of the urban drainage system's adaptability to urban development planning can more accurately reflect the actual contribution of green infrastructure throughout its entire life cycle.

[0061] This application's solution discretizes continuous long-term performance prediction curves into a sequence of performance parameters matching the simulation time step, ensuring that dynamic performance data can be accurately utilized in the overall assessment process of urban drainage systems. Simultaneously, by converting the effective operating period into time-triggered events, the system can automatically and promptly adjust the performance status of green infrastructure during simulation, thereby avoiding static assumptions about green infrastructure performance during the assessment process and solving the problem that traditional assessment methods fail to fully consider the dynamic performance changes of green infrastructure. It is precisely because of this refined data processing and event-triggered mechanism that the assessment results of the urban drainage system's adaptability to urban development planning can more realistically and dynamically reflect the actual effectiveness of green infrastructure.

[0062] By employing the aforementioned technical solutions, the adaptability assessment results of urban drainage systems can more accurately reflect the actual performance contributions of green infrastructure at different life cycle stages, thereby improving the accuracy and reliability of the assessment. This dynamic assessment method helps urban planners and managers to formulate urban development plans and green infrastructure maintenance strategies more scientifically, optimize resource allocation, effectively address the challenges posed by climate change, and enhance the resilience of urban drainage systems.

[0063] Preferably, step A504 may include: B1. Within each simulation time step, extract the green infrastructure performance parameters corresponding to the current time step from the performance parameter sequence; B2. Determine whether the current simulation time has reached the valid running period defined by the time-triggered event; B3. If the target is met, adjust the drainage function contribution of the green infrastructure in the simulation as the effective drainage function contribution; otherwise, take the current drainage function contribution of the green infrastructure in the simulation as the effective drainage function contribution. B4. Input the extracted green infrastructure performance parameters and effective drainage function contributions into the hydrological and hydraulic model of the urban drainage system for simulation calculation, obtain the drainage system response at this time step, and based on the drainage system response, obtain the assessment results of the urban drainage system's adaptability to urban development planning.

[0064] The performance parameter sequence is generated by matching the long-term performance prediction curve with the simulation time step of the overall urban drainage system assessment process and sampling the data from the long-term performance prediction curve. It includes performance data of green infrastructure at different time points. Green infrastructure performance parameters may include, but are not limited to, permeability, water storage capacity, and runoff reduction efficiency. These parameters directly reflect the functional status of green infrastructure within a specific time step. Time-triggered events are a conversion of the effective operating period. They are used to trigger the status update of performance parameters in the overall urban drainage system assessment process when the simulation time reaches the effective operating period. Drainage function contribution refers to the role of green infrastructure in urban drainage systems, such as runoff control, rainwater retention, and pollutant reduction. When the effective operating period of green infrastructure expires, its performance may decline significantly. At this time, its drainage function contribution is adjusted, for example, by reducing its contribution ratio or setting its contribution to zero, to reflect the actual situation of its functional decline. The hydrological and hydraulic model of an urban drainage system is a computational tool used to simulate rainfall runoff processes and the hydraulic response of drainage networks within urban areas. It can calculate the drainage system response under specific rainfall events, such as waterlogging depth, total runoff, and network overflow, based on input performance parameters and drainage function contributions.

[0065] The proposed solution dynamically extracts green infrastructure performance parameters within each simulation time step and adjusts their drainage function contribution based on their effective operating life. This ensures that the overall assessment process of the urban drainage system accurately reflects the performance status of green infrastructure over time. Specifically, by determining whether the current simulation time has reached the effective operating life, the drainage function contribution of green infrastructure can be corrected in a timely manner, avoiding the bias of assessing green infrastructure based on its initial or undiminished state after its performance has deteriorated. Therefore, these dynamically adjusted parameters are input into a hydrological and hydraulic model for simulation calculations, enabling the simulation results to more realistically reflect the adaptability of the urban drainage system to urban development planning at different life stages.

[0066] The aforementioned technical solution enables more accurate and dynamic assessments of the adaptability of urban drainage systems. This approach considers the long-term performance degradation and effective operational lifespan of green infrastructure, ensuring that the assessment results not only reflect the current state but also predict system performance at different future points in time. This helps urban planners and managers gain a more comprehensive understanding of the long-term role of green infrastructure in urban drainage systems, thereby developing more forward-looking and adaptive urban development plans to effectively address the challenges posed by climate change and urbanization.

[0067] Furthermore, step B4 may include: B401. Divide the urban area into multiple spatial computing units; B402. Based on the spatial location of the green infrastructure, map the green infrastructure to the corresponding spatial computing unit; B403. Within each simulation time step, based on the extracted green infrastructure performance parameters and effective drainage function contribution, update the drainage function parameters of each mapped spatial computing unit, and maintain the drainage function parameters of unmapped spatial computing units. B404. In the hydrological and hydraulic model, the hydrological response contribution of each spatial calculation unit is calculated based on the latest drainage function parameters. B405. Integrate the hydrological response contributions of all spatial computing units to obtain the overall hydrological response of the urban drainage system, and use the overall hydrological response as the drainage system response at the time step. B406. Based on the drainage system response, obtain the assessment results of the urban drainage system's adaptability to urban development planning.

[0068] Specifically, in step B401, dividing the urban area into multiple spatial computing units refers to dividing the entire urban study area into several sub-regions with independent computing attributes according to certain rules or grid systems. These spatial computing units can be regular grids, such as square or hexagonal grids, or irregular polygonal areas based on geographical features, such as watersheds, administrative divisions, or land use types. The purpose is to provide a basic geospatial framework for the subsequent spatial positioning of green infrastructure and the calculation of local hydrological responses.

[0069] In step B402, the green infrastructure is mapped to corresponding spatial computing units based on its spatial location. This can be understood as identifying the specific geographic coordinates or extent of each green infrastructure entity, such as a rain garden, permeable paving, or green roof, within the urban area, and then performing spatial overlay analysis with the spatial computing units defined in step B401 to determine the spatial computing unit to which each green infrastructure belongs or is affected. For example, a rain garden may be entirely located within a single spatial computing unit or span the boundaries of multiple units; in this case, mapping can be performed based on its area proportion or main area of ​​influence. The purpose is to establish the relationship between green infrastructure and spatial computing units, providing a spatial basis for subsequent parameter updates.

[0070] In practical applications, in step B403, within each simulation time step, the drainage function parameters of each mapped spatial computing unit are updated based on the extracted green infrastructure performance parameters and effective drainage function contribution, while the drainage function parameters of unmapped spatial computing units are maintained. Specifically, for spatial computing units containing green infrastructure, their drainage function parameters, such as runoff coefficient, permeability, and retention capacity, are dynamically adjusted based on the current performance parameters and effective drainage function contribution of the green infrastructure within that unit. For example, if the performance of the green infrastructure declines, its runoff reduction capacity weakens, and the runoff coefficient of the corresponding spatial computing unit may increase. For spatial computing units not mapped to green infrastructure, their drainage function parameters remain unchanged or are updated based on other non-green infrastructure factors. The aim is to accurately reflect the dynamic performance changes and drainage function contribution of green infrastructure in the hydrological characteristics of the local spatial computing units in which it is located or affects.

[0071] Further, in step B404, within the hydrological and hydraulic model, the hydrological response contribution of each spatial computing unit is calculated based on the latest drainage function parameters. This means that at each simulation time step, the hydrological and hydraulic model (e.g., the SWMM model) utilizes the updated drainage function parameters of each spatial computing unit from step B403, combined with external conditions such as rainfall input, to independently calculate the hydrological response of each spatial computing unit within the current time step, such as runoff generation, infiltration, and surface retention. The purpose is to obtain the specific impact of green infrastructure on hydrological processes at a local scale.

[0072] Furthermore, in step B405, integrating the hydrological response contributions of all spatial computing units to obtain the overall hydrological response of the urban drainage system, and using this overall hydrological response as the drainage system response at the specified time step, means summarizing the local hydrological response contributions calculated by all spatial computing units or calculating the connectivity within the hydrological and hydraulic model to obtain the overall hydrological response of the entire urban drainage system at the current simulation time step, such as total runoff, drainage network flow, and urban flooding volume. The purpose is to derive the macroscopic system response from the local response.

[0073] Finally, in step B406, based on the drainage system response, the assessment result of the urban drainage system's adaptability to urban development planning is obtained. This refers to quantitatively assessing the adaptability of the urban drainage system under the current urban development plan based on the overall hydrological response data obtained in step B405, combined with preset assessment indicators and standards. Its purpose is to provide a final assessment conclusion to guide urban planning decisions.

[0074] This application's solution effectively addresses the shortcomings of traditional assessment methods in handling the spatial heterogeneity and local impacts of green infrastructure by introducing spatial computing units and a refined parameter update mechanism. Specifically, firstly, by dividing the urban area into multiple spatial computing units (B401), a basic framework is provided for the precise location of green infrastructure and the analysis of local effects. Secondly, mapping green infrastructure to corresponding spatial computing units (B402) ensures that the performance changes and drainage function contributions of each green infrastructure can be accurately correlated to its affected geographical area. Subsequently, within each simulation time step, the drainage function parameters (B403) of the mapped spatial computing units are updated based on the dynamic performance parameters and effective drainage function contributions of the green infrastructure, enabling the hydrological and hydraulic model to reflect the actual role of green infrastructure at different life cycle stages and maintenance levels in real time. Thus, the hydrological and hydraulic model can calculate the hydrological response contribution (B404) of each spatial computing unit based on these up-to-date, spatially differentiated drainage function parameters, thereby capturing the refined impact of green infrastructure on hydrological processes such as runoff and infiltration at a local scale. Finally, by integrating the hydrological response contributions (B405) of all spatial computing units, a more accurate and comprehensive overall hydrological response of the urban drainage system can be obtained. Based on this response, the assessment result (B406) of the urban drainage system's adaptability to urban development planning can be obtained. This bottom-up, local-to-global refined simulation approach enables the assessment results to more realistically reflect the actual effectiveness of green infrastructure in the urban drainage system.

[0075] Through the aforementioned technical solution, this application achieves a significant improvement in the assessment of the adaptability of urban drainage systems. Compared to traditional methods that treat green infrastructure as a whole or in a coarse manner, this application, by introducing spatial computing units and refined parameter updates, enables the assessment process to fully consider the spatial heterogeneity of green infrastructure distribution and its dynamic impact on local hydrological processes. As a result, the obtained urban drainage system response data is more refined and accurate, more realistically reflecting the actual contributions of green infrastructure in mitigating flooding and reducing runoff under different urban development planning scenarios. This high-precision assessment result helps urban planners to more scientifically optimize the layout, type, and maintenance strategies of green infrastructure, thereby improving the resilience and adaptability of urban drainage systems and effectively addressing the challenges brought about by climate change and urbanization.

[0076] Preferably, step B406 may include: Based on the drainage system response, calculate performance indicators reflecting the adaptability of the urban drainage system; the performance indicators include at least one of the following: waterlogging depth, waterlogging area, waterlogging duration, drainage network overflow frequency, drainage network overflow volume, total runoff reduction, and peak flow reduction. A comprehensive evaluation of the performance indicators yields an assessment of the urban drainage system's adaptability to urban development planning.

[0077] Specifically, the performance indicators refer to specific quantitative parameters used to quantify the operational status and coping capabilities of urban drainage systems under specific urban development plans and green infrastructure performance conditions. These indicators are designed to directly reflect the performance of urban drainage systems in the face of rainfall runoff, as well as their control effects on urban flooding, runoff pollution, and other problems.

[0078] The terms "waterlogging depth" and "flooding area" are defined as follows: Waterlogging depth refers to the maximum depth of surface water in a simulated urban area, reflecting the degree of flooding. Waterlogging area refers to the total area of ​​the flooded region, used to measure the extent of the flooding's impact. Waterlogging duration refers to the length of time the water depth in a specific area exceeds a preset threshold, indicating the sustained impact of the flooding event. Drainage network overflow frequency refers to the number of times or the percentage of time during which the flow in the drainage network exceeds its design capacity within the simulation period, reflecting the network's carrying capacity and overload risk. Drainage network overflow volume refers to the amount of water overflowing from the drainage network due to overload, directly related to increased urban surface runoff and potential pollution diffusion. Total runoff reduction refers to the total volume of runoff reduced by green infrastructure compared to the total runoff volume without green infrastructure or with degraded green infrastructure performance, reflecting the effectiveness of green infrastructure in controlling stormwater runoff. Peak flow reduction refers to the extent to which the maximum flow (peak flow) generated by the urban drainage system during a rainfall event is reduced by green infrastructure, which is crucial for alleviating pressure on downstream drainage facilities and ensuring flood control safety.

[0079] In practical applications, a comprehensive evaluation of these performance indicators typically involves weighted averaging, multi-objective decision analysis, or the establishment of a comprehensive scoring model for each or some of the aforementioned performance indicators. For example, different weights can be assigned to different performance indicators based on the priorities of urban planning (such as flood control priority, water quality improvement priority, etc.), and then a comprehensive score can be calculated. This comprehensive score represents the assessment result of the urban drainage system's adaptability to urban development planning. This comprehensive evaluation avoids the bias of a single indicator and provides a more comprehensive and objective assessment conclusion.

[0080] This application's solution transforms the drainage system response output from a hydrological and hydraulic model into specific, quantifiable performance indicators, and then comprehensively evaluates these indicators. This allows for a clear definition and acquisition of the assessment results regarding the adaptability of urban drainage systems to urban development planning. After simulating the urban drainage system using a hydrological and hydraulic model, a large amount of raw data such as flow rate, water level, and water depth is generated. By defining a series of performance indicators such as waterlogging depth, waterlogged area, and runoff reduction, these complex simulation results can be visualized into easily understood and comparable numerical values. Furthermore, by comprehensively evaluating these performance indicators, for example using multi-criteria decision analysis methods, different aspects of system performance can be integrated to form a unified and comprehensive adaptability assessment result. This method makes the assessment process more objective and scientific, and provides urban planners with clear decision-making basis.

[0081] Through the above technical solution, this application provides a more refined and quantitative method for assessing the adaptability of urban drainage systems. This method, by clearly defining a series of performance indicators, enables the specific performance of urban drainage systems under different urban development planning scenarios to be clearly quantified and presented. Furthermore, by comprehensively evaluating these multi-dimensional performance indicators, the one-sidedness of single-indicator assessments can be overcome, thus providing a more comprehensive, objective, and convincing assessment result. As a result, urban planners and managers can gain a deeper and more comprehensive understanding of the adaptability of urban drainage systems, thereby providing solid data support and decision-making basis for optimizing urban development planning and enhancing the resilience of cities to climate change and extreme rainfall events.

[0082] refer to Figure 2 This application provides an urban development planning adaptability assessment system for urban drainage systems, the system comprising: The basic information acquisition module 1 is used to acquire the baseline performance degradation trend information of green infrastructure in urban development planning; the baseline performance degradation trend information represents the performance degradation law of the green infrastructure under the preset minimum maintenance conditions (the specific process can be referred to step A1 above). The parameter acquisition module 2 is used to acquire the corresponding performance correction parameters based on the expected maintenance and management investment level of the green infrastructure set by the user (for details, please refer to step A2 above). The performance prediction module 3 is used to generate a long-term performance prediction curve for the green infrastructure based on the baseline performance degradation trend information and the performance correction parameters; the long-term performance prediction curve represents the performance change trend of the green infrastructure under the expected maintenance and management input level (the specific process can be referred to step A3 above). The validity period determination module 4 is used to determine the effective operating period of the green infrastructure based on the long-term performance prediction curve; the effective operating period represents the length of time that the performance of the green infrastructure remains above the minimum performance standard (for details, please refer to step A4 above). The adaptability assessment module 5 is used to input the long-term performance prediction curve and the effective operating period as dynamic performance data into the overall assessment process of the urban drainage system to obtain the assessment results of the adaptability of the urban drainage system to the urban development plan (the specific process can be referred to step A5 above).

[0083] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for assessing the adaptability of urban drainage systems to urban development planning, characterized in that, Includes the following steps: A1. Obtain baseline performance degradation trend information for green infrastructure in urban development planning; The baseline performance degradation trend information indicates the performance degradation pattern of the green infrastructure under preset minimum maintenance conditions; A2. Obtain the corresponding performance correction parameters based on the user-defined expected maintenance and management investment level for the green infrastructure; A3. Based on the baseline performance degradation trend information and the performance correction parameters, generate a long-term performance prediction curve for the green infrastructure; the long-term performance prediction curve represents the performance change trend of the green infrastructure under the expected maintenance and management input level; A4. Determine the effective operating period of the green infrastructure based on the long-term performance prediction curve; the effective operating period represents the length of time during which the performance of the green infrastructure remains above the minimum performance standard. A5. The long-term performance prediction curve and the effective operating period are used as dynamic performance data and input into the overall evaluation process of the urban drainage system to obtain the evaluation results of the urban drainage system's adaptability to urban development planning.

2. The method for assessing the adaptability of an urban drainage system to urban development planning according to claim 1, characterized in that, Step A1 includes: A101. Obtain information on the facility types and material characteristics of green infrastructure in urban development planning; A102. Based on the facility type information and material characteristics, query the preset green infrastructure type database, match and extract the corresponding baseline performance degradation trend information.

3. The method for assessing the adaptability of an urban drainage system to urban development planning according to claim 1, characterized in that, Step A2 includes: A201. Obtain the user-defined expected maintenance and management investment level for the green infrastructure; A202. Obtain facility type information of the green infrastructure, and regional characteristic information of the deployment area of ​​the green infrastructure; the regional characteristic information includes at least one of traffic load, surrounding land use type, and environmental pollution exposure level; A203. Based on the facility type information and the regional characteristic information, identify the maintenance efficiency category that matches the green infrastructure from a preset maintenance efficiency classification table; A204. Based on the maintenance efficiency category and the expected maintenance management input level, extract the corresponding performance correction parameters from the maintenance efficiency classification table.

4. The method for assessing the adaptability of an urban drainage system to urban development planning according to claim 1, characterized in that, Step A3 includes: A301. Obtain multiple decay stages defined in the baseline performance decay trend information and the decay model or decay characteristic parameters of each decay stage; A302. Based on the aforementioned performance correction parameters, the attenuation model or attenuation characteristic parameters for each attenuation stage shall be adjusted independently; A303. Combine the adjusted models or attenuation characteristic parameters of each stage to generate segmented long-term performance prediction curves.

5. The method for assessing the adaptability of an urban drainage system to urban development planning according to claim 1, characterized in that, Step A4 includes: A401. Obtain flood control level information and long-term climate change trend data for the urban areas where the green infrastructure is located; A402. Based on the flood control level information, determine the basic minimum performance standard from the preset flood control level-performance threshold correspondence rules; A403. Based on the long-term climate change trend data, obtain the performance threshold adjustment amount from the preset climate change-performance threshold adjustment rules; A404. Apply the performance threshold adjustment amount to the basic minimum performance standard to obtain the dynamically adjusted minimum performance standard; A405. Based on the long-term performance prediction curve and the dynamically adjusted minimum performance standard, determine the effective operating period of the green infrastructure, such that the effective operating period is equal to the length of time during which the performance of the green infrastructure remains above the dynamically adjusted minimum performance standard.

6. The method for assessing the adaptability of an urban drainage system to urban development planning according to claim 1, characterized in that, Step A5 includes: A501. Match the long-term performance prediction curve with the simulation time step of the overall assessment process of the urban drainage system, and sample the data of the long-term performance prediction curve to generate a performance parameter sequence. A502. Convert the effective operating period into a time-triggered event; the time-triggered event triggers the status update of performance parameters in the overall evaluation process of the urban drainage system when the simulation time reaches the effective operating period; A503. The performance parameter sequence and the time-triggered event are used as dynamic performance data and input into the overall evaluation process of the urban drainage system; A504. At each simulation time step in the overall assessment process of the urban drainage system, the performance parameters are updated according to the performance parameter sequence, and the performance status is adjusted according to the time-triggered events. Simulation calculations are performed to obtain the assessment results of the urban drainage system's adaptability to urban development planning.

7. The method for assessing the adaptability of an urban drainage system to urban development planning according to claim 6, characterized in that, Step A504 includes: B1. Within each simulation time step, extract the green infrastructure performance parameters corresponding to the current time step from the performance parameter sequence; B2. Determine whether the current simulation time has reached the valid running period defined by the time-triggered event; B3. If the target is met, adjust the drainage function contribution of the green infrastructure in the simulation as the effective drainage function contribution; otherwise, take the current drainage function contribution of the green infrastructure in the simulation as the effective drainage function contribution. B4. Input the extracted green infrastructure performance parameters and effective drainage function contributions into the hydrological and hydraulic model of the urban drainage system for simulation calculation, obtain the drainage system response at this time step, and based on the drainage system response, obtain the assessment results of the urban drainage system's adaptability to urban development planning.

8. The method for assessing the adaptability of an urban drainage system to urban development planning according to claim 7, characterized in that, Step B4 includes: B401. Divide the urban area into multiple spatial computing units; B402. Based on the spatial location of the green infrastructure, map the green infrastructure to the corresponding spatial computing unit; B403. Within each simulation time step, based on the extracted green infrastructure performance parameters and effective drainage function contribution, update the drainage function parameters of each mapped spatial computing unit, and maintain the drainage function parameters of unmapped spatial computing units. B404. In the hydrological and hydraulic model, the hydrological response contribution of each spatial calculation unit is calculated based on the latest drainage function parameters. B405. Integrate the hydrological response contributions of all spatial computing units to obtain the overall hydrological response of the urban drainage system, and use the overall hydrological response as the drainage system response at the time step. B406. Based on the drainage system response, obtain the assessment results of the urban drainage system's adaptability to urban development planning.

9. The method for assessing the adaptability of an urban drainage system to urban development planning according to claim 8, characterized in that, Step B406 includes: Based on the drainage system response, calculate performance indicators reflecting the adaptability of the urban drainage system; the performance indicators include at least one of the following: waterlogging depth, waterlogging area, waterlogging duration, drainage network overflow frequency, drainage network overflow volume, total runoff reduction, and peak flow reduction. A comprehensive evaluation of the performance indicators yields an assessment of the urban drainage system's adaptability to urban development planning.

10. A system for assessing the adaptability of urban drainage systems to urban development planning, characterized in that, The system includes: The basic information acquisition module is used to acquire baseline performance degradation trend information of green infrastructure in urban development planning; the baseline performance degradation trend information represents the performance decline pattern of the green infrastructure under preset minimum maintenance conditions; The parameter acquisition module is used to acquire corresponding performance correction parameters based on the user-defined expected maintenance and management investment level of the green infrastructure. The performance prediction module is used to generate a long-term performance prediction curve for the green infrastructure based on the baseline performance degradation trend information and the performance correction parameters; the long-term performance prediction curve represents the performance change trend of the green infrastructure under the expected maintenance and management input level. The validity period determination module is used to determine the effective operating period of the green infrastructure based on the long-term performance prediction curve; the effective operating period represents the length of time that the performance of the green infrastructure remains above the minimum performance standard. The adaptability assessment module is used to input the long-term performance prediction curve and the effective operating period as dynamic performance data into the overall assessment process of the urban drainage system to obtain the assessment results of the urban drainage system's adaptability to urban development planning.

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