Water-based city method and system

Abstract

The application discloses a kind of with water fixed city method and system, it is related to the cross data processing technical field of city planning and water resources management, comprising: obtaining the water system present situation background data of target area, city water supply system topological data, legal planning parameter set and stock update type data;According to the rigid constraint index extracted from present situation background data and is converted into the upper limit list of update bearing total amount;Combined with pipe network topology and update type data, the upper limit list is decomposed into the water pipe control double-control threshold of each partition difference;Introduce legal planning parameter set, and map the double-control threshold into the pre-constraint condition in planning parameter space;Water constraint feasible development boundary is constructed in parameter space by using the constraint condition;Generate each partition update core index set in boundary and summarize as initial scheme;Coupling simulation verification outputs update scheme.The application realizes the positive deduction of spatial form parameter, and improves the objectivity and physical landing of planning index.

Description

Technical Field

[0001] This invention relates to the field of cross-data processing technology of urban planning and water resource management, and in particular to a method and system for determining urban development based on water resources. Background Technology

[0002] Urban renewal involves the reconstruction of the physical properties of large areas of the underlying surface, directly altering regional runoff generation and runoff characteristics and the distribution patterns of non-point source pollution. During this construction process, configuring development capacity and spatial morphology indicators for micro-zoning helps maintain the stability of the hydrological cycle and ensures the overall operational efficiency of the urban water system.

[0003] Existing urban planning indicator verification schemes typically adopt a sequential reverse evaluation model, which involves setting spatial planning parameters such as building density and floor area ratio based on experience, and then importing hydrological calculation models to calculate their one-way impact on regional runoff and hydrodynamic state.

[0004] In complex urban redevelopment scenarios with limited space, existing technologies struggle to establish precise quantitative relationships between multidimensional water environment carrying capacity and microscopic spatial morphology, relying heavily on trial and error. Therefore, further research and innovation are needed to address these issues with existing technologies. Summary of the Invention

[0005] Purpose of the invention: In view of the above-mentioned problems of the prior art, this application provides a method and system for determining the location of a city by water.

[0006] Technical solution: According to one aspect of this application, a method for determining a city's location by water features includes:

[0007] Acquire baseline data on the current status of the water system in the target area, topological data of the urban water system in the area, the set of statutory planning parameters for the area, and the stock update type data for each sub-area;

[0008] Based on the baseline data of the current status of the water system in the area, rigid constraint indicators of the water system in the area are extracted and transformed into a list of the upper limit of the total carrying capacity of the area for renewal.

[0009] By combining the urban water system topology data and existing stock update type data of the area, the list of the upper limit of the total update capacity of the area is decomposed into the water management dual control thresholds of each zone;

[0010] By introducing a set of statutory planning parameters for the area, the water management dual control thresholds are mapped as pre-constraint conditions within the planning parameter space;

[0011] Based on this (preconditions), construct the water-constrained feasible development boundaries for each zone in the planning parameter space in a forward manner.

[0012] Within the water-constrained feasible development boundary, generate updated core indicator sets for each zone, and summarize them to form an initial plan for the area's renewal;

[0013] Perform water system coupling simulation verification on it (the initial area update scheme) and output the final area update scheme that passes the verification.

[0014] Furthermore, a water-based city planning system includes:

[0015] At least one processor;

[0016] And at least one memory storing program instructions that, when executed by at least one processor, cause at least one processor to perform the method described in any one of the embodiments of this application.

[0017] Beneficial effects: By employing a path-weighted bottleneck margin algorithm based on a directed acyclic graph and a dual-control consistency verification mechanism, the area-sharing mode is transformed into a dynamic capacity allocation constrained by the physical network topology. The related technical effects will be described in detail below with reference to specific embodiments. Attached Figure Description

[0018] Figure 1 A flowchart of a method for determining a city's location based on water flow, provided as an embodiment of this application.

[0019] Figure 2 This document provides a flowchart for extracting rigid constraint indicators of the regional water system and converting them into a list of the upper limit of the regional updated carrying capacity.

[0020] Figure 3 The flowchart provided in this application embodiment combines the topological data of the urban water system in the area with the data on the type of existing water updates to decompose the list of the total upper limit of the area's update capacity into differentiated water management and control thresholds for each zone.

[0021] Figure 4 This is a flowchart illustrating how, in accordance with embodiments of this application, a priority matrix for different control indicators is determined by combining existing update type data with the differentiated allocation matrix for various update partitions.

[0022] Figure 5 This is a flowchart illustrating the process of generating updated core indicator sets for each partition within the feasible development boundary of water constraints, as provided in this embodiment of the application.

[0023] Figure 6 A flowchart illustrating cross-cycle offline dynamic feedback provided in an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a predetermined order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] To address the aforementioned issues, the applicant conducted in-depth searches and analyses, and discovered:

[0027] Existing methods rely on proportional area conversion when decomposing water resource carrying capacity indicators, ignoring the capacity bottleneck effect of the physical topology of drainage networks, and making it difficult to uniformly handle hydrological characteristics of different mass dimensions such as runoff and pollution.

[0028] Furthermore, given the limited construction conditions in existing infrastructure renovations, the current parameter verification process lacks a mathematical mapping mechanism, which can easily lead to a cycle of repeated manual calculations and modifications, resulting in local pipeline overload or the inability of indicators to be physically implemented in the physical space.

[0029] To solve these problems, combined with Figures 1 to 6 The present invention will be specifically described through the following embodiments.

[0030] In some embodiments, a water-based city determination method is described, particularly the forward generation of pre-construction water constraints and the classification and transformation of rigid red lines. Specifically, it can be implemented by performing the following steps:

[0031] Step 101: Obtain the baseline data of the current status of the water system in the target area, the topological data of the urban water system in the area, the set of statutory planning parameters for the area, and the data on the stock update type of each sub-district;

[0032] Among them, the baseline data of the current water system in the area includes the average annual rainfall of the target area, the current underlying surface analysis data, and hydrogeological parameters.

[0033] In some scenarios, the topological data of urban water systems in a region can be abstracted into a directed acyclic graph to represent the direction of gravity convergence of water flow, which includes the pipeline connection relationship, the design flow rate of each pipe segment and the current base flow.

[0034] The set of statutory planning parameters for the area is a rigid development boundary issued by the urban planning management department, covering the upper and lower limits of plot ratio, the lower limit of green space ratio, and the building height limit for each zone.

[0035] Existing data update type is used to identify the transformation methods of each partition within the target area.

[0036] Furthermore, based on the degree of preservation of the building foundations within the zones, the zones are divided into three types:

[0037] Demolition and reconstruction zones involve large-scale reconstruction of the underlying surface.

[0038] The improvement and upgrading zones only involve partial permeable paving or greening renovations;

[0039] The protected zones are designated as non-alterable in terms of their building foundations and existing pipeline connections.

[0040] It should be understood that obtaining the above-mentioned data can provide data boundaries for subsequent positive indicator extrapolation and avoid the failure of reverse trial calculations caused by data silos in the planning process.

[0041] Step 102: Extract rigid constraint indicators of the water system in the area based on the baseline data of the current status of the water system in the area, and convert them (rigid constraint indicators of the water system in the area) into a list of the upper limit of the total carrying capacity of the area for renewal.

[0042] This step specifically includes:

[0043] The rigid constraint indicators of the regional water system are divided into spatially decomposable indicators and system-level constraint indicators.

[0044] Generate a list of the maximum carrying capacity of an area by using spatially decomposable indicators;

[0045] The system-level constraint indicators are transformed into zonal infiltration intensity constraints, which are then passed on to the subsequent water management dual-control threshold verification and water constraint mapping, serving as the input basis for the intensity management benchmark and infiltration constraint construction.

[0046] After extracting the rigid constraint indicators of the regional water system, these indicators are strictly divided into spatially decomposable indicators and system-level constraint indicators. Specifically, indicators such as total runoff, pollution load, and total water consumption exhibit spatial additivity, meaning that the sum of the values ​​of these indicators generated by each zone equals the overall value of the region. Therefore, they are classified as spatially decomposable indicators.

[0047] Furthermore, a list of the total capacity limit for area updates can be directly generated using spatially decomposable indicators, which can be used as the capacity limit for subsequent zone-level allocation.

[0048] Conversely, ecological flows possess system-level constraints and lack the mathematical conditions for direct addition and allocation across different sub-regions. Therefore, it is necessary to transform system-level constraint indicators into intensity control baselines for each sub-region, i.e., into sub-regional infiltration intensity constraints.

[0049] By setting this constraint, the total baseflow replenishment provided by green spaces and permeable pavements in each zone is ensured to be no less than the minimum limit for maintaining the ecological flow of the river channel. During this conversion process, the lower limit of the baseline can be calculated using the following formula:

[0050] F _inf_min =Q _eco / (A _total ×C _conv );

[0051] In the formula, F _inf_min Q is the lower limit benchmark value for the zoned infiltration intensity constraint. _eco A represents the minimum ecological flow during the dry season for the target river section. _total C represents the total physical area of ​​the target region. _conv This is the unit conversion constant used to convert per-second flow rate to daily penetration rate.

[0052] Based on this, the zonal infiltration intensity constraint can be used as a rigid input for subsequent determination of intensity control benchmarks.

[0053] Step 103: Combining the urban water system topology data and existing update type data of the area, the list of the upper limit of the total update carrying capacity of the area is decomposed into the water management dual control thresholds of each zone.

[0054] The sensitive location of each zone in the drainage network is quantified by using the topology data of the urban water system in the area, and the potential for new water impact in different zones is distinguished by using the data of existing update types.

[0055] In summary, through the coupling of these two methods, the list of the total capacity limit for area updates is divided into indicators at the partition level.

[0056] Among them, the water management dual control threshold is composed of a total amount control threshold and an intensity control upper limit.

[0057] Through this step, the total budget at the district level is transformed into emission quotas and emission concentration limits that each district must independently adhere to.

[0058] Step 104: Introduce the statutory planning parameter set for the area and map the water management dual control thresholds into pre-constraint conditions within the planning parameter space;

[0059] Among them, the planning parameter space refers to a multi-dimensional mathematical space with urban morphology variables such as building density, green space ratio, and hardened proportion as coordinate axes.

[0060] Optionally, this step can employ an algebraic mapping method to translate the dual control thresholds for water management into algebraic inequalities between urban morphology variables. Since extreme value constraints from the set of statutory planning parameters for the area are also introduced, the pre-constraints constitute a set of feasibility boundary equations under the dual laws of water resources and urban planning.

[0061] Step 105: Using the pre-existing constraints, construct the water-constrained feasible development boundaries for each partition in the planning parameter space in a forward manner.

[0062] After establishing the system of equations, a closed convex polyhedron is defined in the planning parameter space based on the geometric envelope rule in operations research. The surface and internal space of this convex polyhedron constitute the water-constrained feasible development boundary. Any set of coordinate points falling within this boundary represents a building layout scheme that satisfies local statutory planning requirements without violating the rigid red line of the water system.

[0063] Furthermore, this mechanism can reduce the rework rate of planning schemes.

[0064] Step 106: Generate the updated core indicator set for each zone within the water-constrained feasible development boundary, and summarize them to form the initial plan for the area update.

[0065] After locking in the feasible development boundary constrained by water, an optimization algorithm is used to find a better coordinate solution that meets the predetermined planning objective at the vertices or boundaries of the convex polyhedron. The volume ratio value and underlying surface ratio parameters corresponding to the better coordinate solution are extracted and assembled to form an updated core index set.

[0066] Furthermore, the core update indicator sets of all sub-regions within the target area are combined and aggregated to output an initial update plan for the area from a global perspective.

[0067] Step 107: Perform water system coupling simulation verification on the initial area update scheme and output the final area update scheme that passes the verification.

[0068] Accordingly, a one-dimensional or two-dimensional hydrodynamic model can be used to perform global calculations on the initial scheme for area updates, simulating the liquid level changes and discharge flow rates at pipeline nodes.

[0069] In some scenarios, this step can also be implemented in the following way:

[0070] The initial plan for the area renewal is verified by water system coupling simulation. When the verification is successful, the final area renewal plan is output and encapsulated into a digital control boundary model for guiding physical space construction and hydraulic flood control scheduling.

[0071] When the verification fails, a parameter callback to correct the initial scheme for updating the area is triggered.

[0072] In other scenarios, when the simulation results do not trigger overflow warnings across the entire region and meet water quality assessment requirements, the verification is deemed successful, and the final area update plan is output.

[0073] In other scenarios, for the spatial decomposition problem of updating the total carrying capacity limit list of a region, a decomposition algorithm based on graph theory and the coupling of existing features can also be adopted.

[0074] In some embodiments, the hydrological topology decomposition and differential allocation priority determination mechanism based on directed acyclic graphs can specifically perform the following steps:

[0075] Step 201: Calculate the hydrological topological influence coefficient of each zone based on the urban water system topology data of the area, specifically including:

[0076] Optionally, the topological data of the urban water system in the area can be abstracted into a directed acyclic graph of the pipe network;

[0077] In this step, the pipeline connections in the water infrastructure are obtained, and the drainage network is converted into a graph theory representation.

[0078] Next, we define the directed acyclic graph of the pipeline network as including the set of pipe segment nodes and the set of directed pipe segments.

[0079] For example, the set of pipe segment nodes represents the drainage access points and pipe network intersections of a zone, and the set of directed pipe segments represents underground drainage pipelines.

[0080] In another example, the water flow direction is constrained to flow from the upstream node to the downstream node along a directional pipe segment, eliminating non-existent annular dead water areas.

[0081] Optionally, the capacity margin of each pipe segment can be obtained by calculating the percentage difference between the design flow and the current base flow recorded in the directed acyclic diagram of the pipe network based on the topological data of the urban water system in the area.

[0082] Accordingly, the design full-pipe flow rate parameters of the pipe section and the current baseflow parameters during the non-rainfall period collected by online monitoring equipment are extracted. The difference between the design flow rate and the current baseflow is calculated, and the ratio of this difference to the design flow rate is obtained. The pipe section capacity margin is output, which reflects the proportion of the remaining usable space of the pipe section to the total design capacity.

[0083] For example, u _e =(Q _design -Q _base ) / Q _design ;

[0084] In the formula, u _eQ represents the capacity margin of the pipeline section. _design Q is the design flow rate of the pipe section. _base The current base current of the pipe section.

[0085] When u _e The smaller the value, the tighter the remaining capacity of the pipe section.

[0086] Optionally, the confluence path from each zone to the area's drainage outlet can be extracted;

[0087] Accordingly, breadth-first search or depth-first search algorithms in graph theory can be used to trace the flow along the directed pipe segment from the starting node representing the drainage access point of the target zone in the directed acyclic graph of the pipeline network until the zone drainage outlet node representing the final discharged water body is reached.

[0088] In this process, all directional pipe segments encountered during the tracking process are extracted and recorded in an orderly manner to form a unique confluence path for the predetermined partition.

[0089] Optionally, the path-weighted bottleneck margin can be calculated using the pipe segment capacity margin of each pipe segment on the confluence path and its physical length.

[0090] In this step, a weighted average algorithm is used to weight and fuse the capacity margin of all pipe segments along the confluence path with their physical length, i.e.:

[0091] u _w_i =Σ(u _e ×L _e ) / Σ(L _e );

[0092] Among them, u _w_i For path-weighted bottleneck margin, u _e L represents the capacity margin of a predetermined pipe segment along the confluence path. _e The physical length of the predetermined pipe segment is Σ, which represents the summation operation performed on all pipe segments along the confluence path.

[0093] Alternatively, if the target area has a simple pipeline network structure and no shared bottleneck pipe sections, an extreme value extraction scheme can be adopted. This involves further traversing all pipe sections along the confluence path and directly extracting the pipe section with the smallest capacity margin as a substitute value for the path-weighted bottleneck margin.

[0094] Optionally, the path-weighted bottleneck margin is normalized to obtain the hydrological topological influence coefficient that reflects the sensitivity of the drainage zone.

[0095] Accordingly, the maximum and minimum values ​​of the path-weighted bottleneck margin for all partitions in the entire region are extracted. The difference between the maximum value and the target partition's margin value is divided by the difference between the maximum and minimum values ​​to calculate the initial topology sensitivity r. _topo_iThis allows the partition with the most strained network capacity (lowest margin) to obtain the highest topology sensitivity.

[0096] r _topo_i =(u _max -u _w_i ) / (u _max -u _min );

[0097] Where, r _topo_i For the initial topological sensitivity, u _w_i Add a bottleneck margin to the path of the target partition, u _max u is the maximum value of the weighted bottleneck margin of the partitioned paths across the entire region. _min It is the minimum value.

[0098] When u _w_i The smaller the value, the more congested the pipe network. _topo_i The larger the value, the more sensitive the zone is to the area's drainage system. This initial topological sensitivity is jointly calculated with the pre-configured ecological sensitivity to generate the final coefficient.

[0099] One example, h _i =1+c _1 ×r _topo_i +c _2 ×r _eco_i ;

[0100] In the formula, h _i The hydrological topological influence coefficient, r _topo_i The initial topology sensitivity of the target partition is represented by r. _eco_i c represents the normalized sensitivity of the distance from the partition to the nearest ecologically sensitive area. _1 c represents the topology weight parameter. _2 Let c represent the ecological weight parameter, and the constraint condition be c. _1 +c _2 =1.

[0101] Another example, r _eco_i To determine the ecological proximity sensitivity of a partition, inverse distance normalization can be used for calculation, i.e.:

[0102] Extract the maximum distance (dist) from all partitions of the entire area to the nearest ecologically sensitive area. _max with minimum value dist _min Press r _eco_i =(dist _max -dist _i ) / (dist _max -dist _min ) Calculate the partition r that is closer to the ecologically sensitive area. _eco_i The larger h is _iThe higher the level, the smaller the total allocation and the stricter the control and constraints.

[0103] The above-mentioned ecologically sensitive areas include water source protection areas and buffer zones of nature reserves.

[0104] When h _i The larger the value, the more sensitive the zone is to the overall water system of the area, and the more stringent the constraints and controls will be in subsequent allocations.

[0105] Step 202 involves determining the differentiated allocation priority matrix for different control indicators across various update partitions based on existing update type data. This includes:

[0106] Furthermore, based on the baseline data of the current water system in the area and the expected development characteristics of various renewal zones, the expected change in water impact per unit area before and after the renewal of various renewal zones is calculated.

[0107] Based on the existing stock update type attributes of each zone, the objective hydrological impact differences before and after the implementation of the update and renovation project are calculated.

[0108] Accordingly, the expected runoff coefficient after the zoning update and the current background runoff coefficient before the update are obtained, and the difference between the two is calculated to obtain the amount of new runoff impact.

[0109] For demolition and reconstruction zones, the difference is positive because it involves hardening of the underlying surface;

[0110] For the zones undergoing improvement and rectification, this difference approaches 0;

[0111] For reserved protected partitions, this difference is always 0.

[0112] Furthermore, the ratio of the expected change in water per unit area to that of different stock renewal types was extracted;

[0113] The expected changes in water impact per unit area of ​​the demolition and reconstruction zone, the renovation and upgrading zone, and the preservation and protection zone are compared horizontally, and the multiple relationships between various values ​​are calculated.

[0114] In some cases, a minimum positive real number compensation term can be preset in the calculation of the comparison benchmark to prevent program execution abnormalities caused by division by zero.

[0115] Furthermore, using the ratio relationship as the allocation benchmark, a differentiated allocation priority matrix is ​​constructed, so that the update type partition with a larger expected increase in water impact receives a higher total allocation weight.

[0116] The matrix assignment operation is completed based on the extracted ratio relationship.

[0117] For example, the baseline weight for demolition and reconstruction can be set at 1.5, for improvement and upgrading at 1.0, and for preservation and protection at 0.3. This ensures that development types with significant hydrological impacts receive a corresponding share of the water system capacity budget. Other values ​​can also be used for these baseline weights.

[0118] Furthermore, the allocation weights of various update partitions in the differentiated allocation priority matrix are extracted;

[0119] Before entering the collaborative calculation of the dual-control mechanism, the allocation weight value bound to the type is retrieved and extracted from the differentiated allocation priority matrix based on the stock update type of the partition identifier.

[0120] Furthermore, the correlation adjustment coefficient is calculated by normalizing and back-calculating using the allocation weights and the average proportion of the physical area of ​​each partition.

[0121] For example, v _k =(B _k ×A _k ) / (B _avg ×A _avg );

[0122] Among them, v _k B is the correlation adjustment coefficient. _k Assign weights to partitions of a predefined type, A _k B represents the average physical area of ​​this type of partition. _avg The weights are assigned to the entire area using a weighted average method. _avg This represents the average physical area of ​​the entire region.

[0123] Furthermore, the pre-configured regional baseline strength control values ​​are calibrated by multiplying the correlation adjustment coefficients to generate initial values ​​for the strength control upper limit of each partition, so that partition types that receive higher allocation weights are automatically assigned appropriately relaxed initial strength control upper limits.

[0124] Multiply the calculated correlation adjustment coefficient by the area benchmark intensity control value set by the urban regulations, and output the initial value of the intensity control upper limit for that area.

[0125] For example, for demolition and reconstruction zones, the correlation adjustment coefficient is usually greater than 1, the initial intensity control upper limit is moderately relaxed, and the total amount control becomes the main constraint boundary of the zone.

[0126] For protected zones, the correlation adjustment coefficient is usually less than 1, and the initial intensity standard is moderately tightened to prevent the regional ecological environment from degrading.

[0127] The specific value of the correlation adjustment coefficient depends on the allocation weight and area distribution of various update partitions within the area, and can be directly calculated based on the actual situation of the area.

[0128] Step 203: Using the hydrological topological influence coefficient and the differentiated allocation priority matrix, the total quotas in the list of total carrying capacity limits for area updates are broken down to each sub-district to form total control thresholds.

[0129] Furthermore, after completing the weight calculation and coefficient inversion, spatial decomposition is performed. Based on the set total quota, the allocation ratio coefficient is determined according to the physical area of ​​the zones, the differentiated allocation weights, and the hydrological topological influence coefficient. The total quota is then allocated according to the allocation ratio coefficient to obtain the total control threshold for each zone.

[0130] In other words, T _i =(A _i ×(1 / h _i )×B _k ) / Σ(A _x ×(1 / h _x )×B _x )×T _total ;

[0131] Among them, T _i A is the total control threshold for the predetermined partition. _i h represents the physical area of ​​the predetermined partition. _i B is the hydrological topological influence coefficient. _k To assign weights, A _x with h _x With B _x For each partition within a region, Σ represents the corresponding attribute variable for iterating through all partitions within the region. Σ indicates summing the products of all partitions within the region. _total Update the total limit value in the list of maximum carrying capacity for the area.

[0132] In some embodiments, based on the existing update type data and infiltration-related constraints in the rigid constraint indicators of the regional water system, the intensity control upper limit for each zone is set. The consistency between the intensity control upper limit and the total control threshold is checked, and the verified water management dual control threshold is output. A two-stage iterative verification mechanism is adopted, specifically including:

[0133] Step 301: Combine the upper limit of intensity control with the corresponding physical area of ​​the partition to convert it into the expected value of the total intensity conversion.

[0134] Furthermore, the intensity control upper limit for each corresponding zone needs to be converted into the corresponding total value.

[0135] For total runoff control indicators, the upper limit of intensity control is expressed as a comprehensive runoff coefficient. This coefficient is multiplied by the average annual rainfall and the area of ​​the zone, and a unit conversion constant is introduced to complete the conversion calculation, i.e.:

[0136] T _I_T_runoff =ψ _i ×a1×P _annual ×A _i ;

[0137] Above, T _I_T_runoff That is, the expected total intensity conversion value corresponding to the runoff index, ψ _i That is, the comprehensive runoff coefficient of the corresponding zone, where 'a' is the conversion constant for rainfall units, and 'P' is the total runoff coefficient of the zone. _annual That is, the average annual rainfall, A _i That is, the physical area of ​​the corresponding partition.

[0138] The pollution load control index corresponds to the amount of pollution generated per unit area, and the conversion process is as follows:

[0139] T _I_T_pollution =I _p ×A _i ;

[0140] Among them, T _I_T_pollution That is, the expected total value of the intensity conversion corresponding to the pollution index, I _p That is, the upper limit of pollution load control per unit area, A _i That is, the physical area of ​​the corresponding partition.

[0141] Step 302: Compare the total control threshold with the expected value of the total intensity conversion;

[0142] After acquiring the dataset, the numerical value determination logic is executed. When the total control threshold is greater than or equal to the expected value of the total intensity conversion, it indicates that the quota allocated to the partition based on the spatial topology is sufficient to support its intensity control requirements, and the dual control indicators are in a physically self-consistent state.

[0143] Conversely, when the total emission control threshold is less than the expected value of the total emission conversion from intensity, it indicates that the allocated quota is insufficient. Even if the zone meets the intensity control upper limit requirement, its total emissions will still exceed the quota limit, resulting in a logical conflict.

[0144] Step 303: When the total control threshold is less than the expected value of the total intensity conversion, the first stage adjustment is triggered, that is, the intensity control upper limit of the corresponding partition is tightened locally to the same level as the two.

[0145] Upon detecting a logical conflict, the system initiates a local variable adjustment procedure.

[0146] In some cases, the total intensity control threshold for the zone is kept constant. The required critical intensity target value is calculated using a reverse division operation, tightening the intensity control upper limit downwards to a level equal in quantity to the total intensity control threshold. Dividing this critical intensity target value by the zone's baseline intensity control value yields the updated adjustment coefficient, as shown in the following formula:

[0147] I _adj =T _i / (f _j ×A _i );

[0148] In the formula, I _adj T represents the adjusted critical intensity target value for the corresponding partition. _i f is the total control threshold for this partition. _j A is the constant conversion factor corresponding to the control index. _i This represents the physical area of ​​the corresponding partition.

[0149] In some cases, i is the partition number subscript and j is the control indicator type subscript.

[0150] Step 304: When the tightened intensity control upper limit is lower than the pre-configured reasonable limit, the second stage adjustment is triggered, that is, the allocation weight of the corresponding partition in the differentiated allocation priority matrix is ​​globally increased, and the total control threshold is recalculated.

[0151] Among them, setting the upper and lower limits of the adjustment coefficient constitutes the reasonable boundaries of the pre-configuration.

[0152] When the updated adjustment coefficient is lower than the lower limit of the reasonable boundary, it indicates that changing the intensity target alone has led to extreme control requirements in the zoning that are impossible to achieve in engineering. At this time, it is necessary to intervene in the global weight allocation system.

[0153] Accordingly, the minimum total quota required for the partition under extreme conditions is extracted, and its allocation weight in the differentiated allocation priority matrix is ​​adjusted upward through algebraic back-calculation until the total control threshold obtained by recalculating based on the new weight is greater than or equal to the minimum total quota.

[0154] As the weighting of partitions increases, the denominator of the total weighting of the entire region increases accordingly, and the total control threshold allocated to the remaining partitions will decrease synchronously, thereby completing the system-level redistribution of spatial capacity.

[0155] In some cases, a regional carrying capacity early warning mechanism is also introduced. After the Phase 1 adjustment is implemented, the number of zones with adjustment coefficients below the lower limit is counted. When the ratio of this number of zones to the total number of zones in the entire region is ≥0.5, the system generates an interrupt command indicating insufficient carrying capacity and stops triggering Phase 2 adjustment.

[0156] The above information is used to identify imbalances between water system capacity and overall urban renewal intensity, and to instruct planners to reduce the overall renewal scale or change the red line standards.

[0157] Step 305: Repeatedly execute the Phase 1 adjustment and Phase 2 adjustment until the total control threshold of all zones is not less than the expected value of the total intensity conversion. Output the water management dual control threshold that has passed the verification.

[0158] Since the global redistribution in Phase 2 may cause new conflicts in other previously self-consistent partitions, the system has constructed a cyclic monitoring callback mechanism. After each Phase 2 adjustment, the comparison logic is re-executed on the current state variables of all partitions in the entire region.

[0159] Among them, the weight adjustment operation in the differentiated allocation priority matrix is ​​monotonic, and the maximum weight that each partition can obtain is strictly constrained by the ratio of its physical area to the total area of ​​the partition.

[0160] When no conflict markers are detected during the full domain traversal and comparison, the loop terminates, generating a self-consistent water management dual control threshold.

[0161] After obtaining the conflict-free dual control threshold, it must be applied to extrapolate the built-up area.

[0162] On the one hand, the implementation process of the linear dimensionality reduction mapping mechanism and the stock degree-of-freedom constraint law describing the multidimensional heterogeneous water constraint is as follows:

[0163] Step 401: Establish the mapping relationship between various water management dual control thresholds and the built-up area with building density, green space ratio and hardened proportion as coordinate axes;

[0164] In urban renewal planning, parameters used for water systems may include runoff coefficient, permeability, and pollution load, while parameters used at the planning level mainly include building density and green space ratio. This step is used to establish the physical connection between the two, converting various heterogeneous hydrological indicators into mathematical expressions that directly relate to the underlying physical state, thereby avoiding data barriers between water management and spatial planning.

[0165] Furthermore, the total runoff control threshold is transformed into a first-type linear constraint by utilizing the runoff discharge characteristics of different underlying surfaces;

[0166] Empirical runoff coefficients were extracted from four typical underlying surfaces: roofs, paved surfaces, green spaces, and permeable pavements. The comprehensive runoff coefficient of each zone was represented as a weighted sum of the empirical runoff coefficients of each underlying surface and their corresponding area proportions.

[0167] Or rather, ψ _i =ρ _i ×ψ _roof +p _i×ψ _hard +g _i ×ψ _green +s _i ×ψ _perm ;

[0168] Where, ψ _i ρ is the comprehensive runoff coefficient for the zone. _i Let ψ be the building density variable. _roof p is the roof runoff coefficient. _i Let ψ be the hardening proportional variable. _hard For the runoff coefficient of hardened ground, g _i Let ψ be the green space ratio variable. _green S is the green space runoff coefficient. _i ψ is the variable representing the proportion of permeable pavement. _perm The runoff coefficient for permeable pavement.

[0169] Based on the calculated comprehensive runoff coefficient, constraints on total runoff control are further established, generating the first type of linear constraints.

[0170] Furthermore, the pollution load control threshold is transformed into a second type of linear constraint using the event-averaged concentration method;

[0171] For example, the event-averaged concentration method can be used to estimate the mass of non-point source pollutants carried by runoff. The concentration of pollutants carried by runoff varies across different underlying surfaces. By combining the corresponding runoff coefficient and area, the predicted pollution load can be calculated. The specific formula can be written as:

[0172] W _i =P _annual ×a×A _i ×(ρ _i ×ψ _roof ×C _roof +p _i ×ψ _hard ×C _hard +g _i ×ψ _green ×C _green +s _i ×ψ _perm ×C _perm );

[0173] Among them, W _i As the annual average non-point source pollution load, P _annual A is the average annual rainfall, where 'a' is the unit conversion constant for rainfall. _i For the physical area of ​​the partition, C _roof C _hard C _green C _permThe average pollutant concentrations correspond to events on rooftops, hardened surfaces, green spaces, and permeable pavers, respectively.

[0174] In the formula, the predicted pollution load value is required to be less than or equal to the allocated pollution load control threshold, thus forming the second type of linear constraint.

[0175] Furthermore, by combining the pre-configured water consumption quota per unit building area, the total water consumption control threshold is transformed into an upper bound constraint for the corresponding floor area ratio.

[0176] The average daily water consumption of each zone is directly related to the building function and floor area ratio. Based on the planned building functions, a pre-configured average daily water consumption quota per unit building area is obtained.

[0177] In this case, F _i ≤T _D_i / (d _k ×A _i );

[0178] Among them, F _i T represents the zoning floor area ratio. _D_i To control the total water consumption threshold, d _k A is the daily water consumption quota per unit building area corresponding to the intended land use function. _i This refers to the physical area of ​​the zone. The red line for water resource use is directly transformed into a rigid limit on the total amount of building development, forming an upper bound constraint.

[0179] Furthermore, a preset infiltration rate parameter is introduced to transform the zonal infiltration intensity constraint into a third type of linear constraint.

[0180] The design infiltration rate parameters of green spaces and permeable pavements were extracted, and the average daily comprehensive infiltration rate of each zone was calculated, i.e.:

[0181] R _inf_i =g _i ×f _green +s _i ×f _perm ;

[0182] Among them, R _inf_i For the estimated overall infiltration rate of the zone, g _i f is the green space ratio variable. _green For the design infiltration rate of green space, s _i f is the variable representing the proportion of permeable pavement. _perm Let be the design infiltration rate for permeable pavement. The formula requires that the estimated comprehensive infiltration rate be greater than or equal to the converted lower limit of infiltration intensity, thus constituting a third type of linear constraint.

[0183] Based on this, a unified set of linear inequalities is constructed by merging them, specifically including an auxiliary algebraic dimensionality reduction mechanism, which can be done through the following steps:

[0184] Step 401a: The first type of linear constraint, the second type of linear constraint, and the third type of linear constraint are all characterized as multivariate linear inequalities composed of building density variable, green space ratio variable, hardening ratio variable, and permeable pavement ratio variable.

[0185] In other words, through independent transformations, all environmental indicator requirements are uniformly expressed as a set of multivariate linear inequalities containing four independent variables. This set of inequalities constitutes the constraints in a high-dimensional space.

[0186] Step 401b introduces the total land use balance constraint, expresses the permeable pavement ratio variable as the algebraic remainder of the other three types of variables, and substitutes it into the multivariate linear inequality to perform elimination;

[0187] In actual site space allocation, the sum of the proportions of various underlying surfaces is constrained by the law of area conservation. By establishing land use balance equations, the algebraic coupling relationships between the variables are identified.

[0188] Accordingly, s _i =1-ρ _i -g _i -p _i ;

[0189] In the formula, s _i ρ is the variable representing the proportion of permeable pavement. _i For building density variable, g _i Let p be the green space ratio variable. _i This represents the hardening ratio variable.

[0190] Substitute the algebraic remainder equation into the first, second, and third linear constraints, and apply the algebraic distributive law to perform a merging operation of like terms to eliminate redundant variables, i.e., the permeable pavement ratio.

[0191] Step 401c: Output a set of linear inequalities after eliminating the permeable pavement ratio variable and reducing the dimension to the three-dimensional core planning variable space;

[0192] After the elimination operation, the high-dimensional model that originally relied on four types of morphological variables was reduced to a three-dimensional orthogonal coordinate system containing only building density, green space ratio, and hardened area ratio. This reduced the matrix dimension of subsequent operations research optimization calculations.

[0193] Furthermore, the first type of linear constraints, the second type of linear constraints, the upper bound constraints, and the third type of linear constraints are merged to construct a unified system of linear inequalities;

[0194] By combining the various inequalities after dimensionality reduction, a unified constraint system is formed, covering multiple technical objectives such as flood control, water quality protection, water resource allocation, and groundwater conservation.

[0195] Step 402: Transform the mapping relationship into a system of linear inequalities in the planning parameter space as a prerequisite constraint.

[0196] Based on the merger results, all water boundary standards have been equivalently translated into morphological parameter control requirements that urban planning managers can directly interpret and implement.

[0197] The determined set of linear inequalities constitutes the prerequisite constraints that guide the subsequent generation of physical space.

[0198] Step 403: Combine the set of statutory planning parameters for the area with the set of linear inequalities, and use the geometric envelope rule to define a convex polyhedron in the planning parameter space to form a water-constrained feasible development boundary;

[0199] Optionally, by combining the statutory planning parameter set of the area with a set of linear inequalities, a semi-space intersection algorithm is used to define a convex polyhedron in the planning parameter space, forming a water-constrained feasible development boundary.

[0200] In the planning parameter space, legal rules such as the upper and lower limits of plot ratio and the lower limit of green space ratio are introduced into the pre-constraint condition matrix for simultaneous solution. Each inequality equation corresponds to a hyperplane in the three-dimensional coordinate system.

[0201] Furthermore, the half-space intersection algorithm in computational geometry can be used to cut the envelope by all hyperplanes, defining a closed convex polyhedral region, in which all coordinate combinations represent compliant development schemes.

[0202] Based on this, differentiated degrees of freedom for decision variables are set according to the stock update type data of each partition, specifically:

[0203] Step 403a: For demolition and reconstruction partitions, allocate three-dimensional free variables to assist in constructing a convex polyhedron with a complete shape;

[0204] Specifically, when defining the outer contour of a convex polyhedron, differentiated feasible domain boundary control needs to be applied based on the existing update category of the partition.

[0205] For zones undergoing demolition and reconstruction, since existing surface attachments will be removed, their building density, green space ratio, and paved area ratio are not constrained by historical conditions and are considered free decision variables.

[0206] Therefore, the generated convex polyhedron maintains a complete three-dimensional spatial shape.

[0207] Step 403b: For the improvement and upgrading zones, set the restricted variable range based on the current building density and the allowable adjustment range, and compress the convex polyhedron into a restricted feasible region;

[0208] For zones undergoing renovation and upgrading, since local micro-modifications are required while preserving most of the main structure, the building density variable cannot float freely across the entire range.

[0209] Accordingly, the existing building density parameters of the zone are extracted, and the building density variable is strictly constrained within a predetermined narrow range of values ​​by combining the allowable adjustment range set in the engineering renovation plan.

[0210] By adding this boundary constraint, the three-dimensional convex polyhedron is compressed in the direction of the building density coordinate axis, degenerating into a restricted feasible region sheet.

[0211] Step 403c: For the protected partitions, extract the fixed variables of the unchangeable building foundation and reduce the convex polyhedron to a two-dimensional feasible region.

[0212] For protected zones, due to strict regulations such as the historical preservation list, the building foundation form cannot be changed. The system directly locks the building density variable of this zone to a fixed constant. The convex polyhedron is directly dimension-reduced and projected onto a two-dimensional plane composed of the green space ratio and the hardened area ratio, forming a degraded two-dimensional feasible domain.

[0213] In some embodiments, the constructed envelope may face the problem of having no solution to the mathematical equations when water resources are extremely scarce or the impact of the existing water in the conservation zone exceeds the standard.

[0214] In light of this, a compensation mechanism specifically designed for abnormal situations is provided, including:

[0215] Step 501: Perform a feasibility test on the system of linear inequalities;

[0216] For example, after transforming various water resources red line standards into pre-constraint conditions in a multi-dimensional space, before performing the optimization objective solution, it is verified whether there exists a solution set that satisfies all conditions in the multi-dimensional constraint space.

[0217] That is, the system of linear inequalities, land use balance constraints, and the set of statutory planning parameters for the area are input into the linear programming solver. The two-stage method of linear programming is adopted, and an initial basic feasible solution is constructed by introducing artificial variables and the first stage of solution is performed.

[0218] If the optimal value of the objective function in the first stage is greater than 0, then the system of linear inequalities is determined to be contradictory, and the common region enclosed by them is an infeasible solution domain, that is, the feasible domain is determined to be an empty set.

[0219] In other words, this step identifies unsolvable situations caused by extreme water shortages or deterioration of the current state of zoning in advance.

[0220] Step 502: When it is determined that the feasible development boundary of water constraints for the preservation and protection zone or the remediation and improvement zone is an empty set, calculate the total amount of current water discharge exceeding the standard for that zone.

[0221] For demolition and reconstruction partitions, which have a three-dimensional free variable space and an empty feasible region, it usually indicates that the initial weight allocation of the entire area is unreasonable and can be corrected through a callback weight allocation mechanism.

[0222] However, the building parameters of the protected and remediation zones have low freedom, and their feasible development boundaries for water constraints are empty sets, indicating that the current water discharge indicators of these zones have exceeded the newly allocated stringent threshold standards while maintaining the existing building foundation.

[0223] To quantify the degree of breakthrough, the expected total intensity conversion value calculated based on the current physical parameters of the zone is extracted, and the difference ΔT between this value and the total control threshold allocated to the zone is calculated. _i_j =T _I_T_i_j -T _i_j ;

[0224] Where, ΔT _i_j T represents the total amount of water discharge exceeding the standard corresponding to the predetermined target. _I_T_i_j T represents the expected total intensity conversion value calculated based on the current status parameters of the zoning area. _i_j This is the total control threshold actually allocated to this partition.

[0225] Since the zone is in a state of exceeding the standard, the calculated total amount of current water discharge exceeding the standard must be a positive real number, which reflects that the current discharge of the zone exceeds the environmental carrying capacity.

[0226] Alternatively, environmental carrying capacity can be expressed as volume or mass.

[0227] Step 503: Convert the current total amount of water discharge exceeding the standard into historical debt amount;

[0228] In some embodiments, the total amount of water discharge exceeding the standard is calculated and defined in business logic as the historical debt amount, which represents the equivalent of the negative environmental impact left over from historical urban construction and restricted by the current requirements for the protection of cultural relics or landscape that cannot be demolished, and cannot be directly absorbed by engineering means within the original zone.

[0229] Further extracting this value and encapsulating it as a compensation constraint to be transferred will help to seek cross-regional solutions without lowering the overall ecological and environmental protection standards.

[0230] Step 504: Implement a proportional deduction of historical arrears from the total allocation share of surrounding demolition and reconstruction zones, triggering the operation of re-decomposing the water management dual control threshold.

[0231] The surrounding area refers to demolition and reconstruction zones that are located within the same drainage catchment area or have adjacent drainage topologies.

[0232] Furthermore, based on the management approach of preserving and protecting zones without compromise in urban stock renewal, and assigning more responsibility to demolition and reconstruction zones, the spatial perspective of indicator transfer is implemented.

[0233] In some embodiments, the system retrieves demolition and reconstruction zones that are located in the same catchment area or are physically adjacent to the over-limit zone in the area topology network. Based on the physical area ratio of each retrieved demolition and reconstruction zone, the historical arrears amount is divided into multiple sub-amounts, which are deducted from the original total control thresholds of these demolition and reconstruction zones. This is equivalent to tightening the water discharge limit for newly built areas, forcing them to configure larger-scale green infrastructure in subsequent construction, and giving up the water environment capacity space required to maintain the status quo for the preserved protection zones.

[0234] After the proportional deduction is completed, the total control threshold for some demolition and reconstruction zones may be forcibly compressed externally, potentially causing new conflicts between intensity and total quantity indicators. Therefore, based on the updated allocation base, the system triggers a return to execute a two-stage consistency check and threshold redistribution calculation process until the entire area achieves physical consistency again after the introduction of the historical debt transfer mechanism.

[0235] In other embodiments, if there are no demolition and reconstruction zones within the target area that can be used to share historical debts, a scheme that introduces a penalty function may be adopted.

[0236] That is, the feasible development boundary of water constraints in protected zones is allowed to exceed the rigid constraints within a set upper limit to generate virtual solutions, but the ecological restoration funding penalty function corresponding to the excess indicators is calculated simultaneously, and the mandatory engineering quantity indicators are output to maintain the overall conservation of water environment carrying capacity.

[0237] In some embodiments, when the envelope is non-empty and clearly defined, a better planning index is directly locked within the boundary. The specific execution mechanism is as follows:

[0238] Step 601: Set the objective function for maximizing the floor area ratio, guided by the goal of achieving the peak development capacity of the zone;

[0239] Or, to put it another way, it is guided by maximizing the development capacity of each zone.

[0240] Since the development capacity parameters of the partition constrain the physical carrying capacity potential of the project, this step transforms the constraint into an operations research expression, that is, setting an objective function to find the maximum value under predetermined boundary conditions.

[0241] For example, the building height limit parameters and benchmark floor height parameters are extracted from the statutory planning parameter set of the area, and the ratio between the two is calculated to obtain the maximum number of building floors allowed in the area. Based on the building density variable and the maximum number of building floors constant, a relational expression is constructed to characterize the floor area ratio, and this relational expression is input into the system as the standard objective function of linear programming.

[0242] An example, F _max =ρ _i ×n _max ;

[0243] In the formula, F _max ρ is the maximum volume ratio defined by the objective function. _i Let n be the building density used as a decision variable. _max This is the maximum number of building floors constant calculated based on the statutory planning parameter set for the area.

[0244] Step 602: After locking the minimum green space ratio requirement within each water-constrained feasible development boundary, extract the remaining shared available area subject to land use balance constraints.

[0245] Before initiating the specific matrix solution process, dimensionality reduction processing is performed on the boundary dimensions of each decision variable. The minimum green space ratio value, mandated for the predetermined land use, is extracted from the set of statutory planning parameters for the area. The green space ratio variable within the water-constrained feasible development boundary is temporarily assigned and locked to the minimum green space ratio value.

[0246] Furthermore, based on the principle of land use balance, the remaining space of the zoning area, building footprint, and green space is calculated to obtain the remaining space share available for paved surfaces and permeable paving. The calculation formula is as follows:

[0247] A _remain =1-ρ _i -g _min ;

[0248] Among them, A _remain ρ represents the remaining shared available area subject to land use balance constraints, 1 represents the normalized total area baseline value of the zoning, and ρ is the area of ​​the remaining shared available area. _i For building density variable, g _min This is the pre-configured minimum green space ratio value.

[0249] Step 603: Remove the independent restrictions on the zoning hardening ratio and the permeable pavement ratio, and set both as joint optimization variables for dynamic complementary allocation within the remaining shared available area;

[0250] Optionally, the hardening ratio or permeable pavement ratio can be set as a static prior value.

[0251] If the hardening ratio is forcibly set to a large fixed constant, when the building density is at a low level, the comprehensive runoff coefficient of the zone will still exceed the hydrological control red line due to the existence of a large area of ​​impermeable hardened surface, causing the linear programming solver to return an error code with no solution.

[0252] Optionally, the hardening ratio and the permeable pavement ratio can be decoupled from independent constants and redefined as interdependent dynamic free variables. The sum of the two can be constrained to equal the remaining shared usable area to prevent program failure caused by the independent solidification of such dependent variables.

[0253] Step 604: By performing complementary allocation synchronous optimization, ensure that the solution of the maximum feasible floor area ratio does not cause false empty set errors due to the pre-set rigidity of the underlying surface;

[0254] Under the joint optimization variable setting mechanism, when the algorithm solver attempts to increase the building density variable and compress the remaining shared available area, the complementary allocation logic of the area quota is executed, that is, the limited remaining area quota is preferentially allocated to the permeable pavement ratio variable with a lower runoff discharge coefficient, while the hardening ratio variable is reduced simultaneously.

[0255] Step 605: Within the water-constrained feasible development boundary, perform a linear programming solution for the objective function of maximizing the plot ratio, in conjunction with the statutory planning parameter set for the area.

[0256] Based on the established objective function and the joint setting of variables, the standard linear programming solution module is invoked to perform optimization calculations on the feasible region space enclosed by the multidimensional hyperplane. That is, using a shape method or interior point method algorithm engine, with the objective function of maximizing the volume ratio as the optimization direction, all geometric vertices and surfaces contained in the water-constrained feasible development boundary are traversed.

[0257] Under the condition of ensuring global convergence, the linear programming solution module can calculate the optimal floating-point values ​​of each decision variable when the objective function reaches its maximum value.

[0258] In some implementations, when the input data contains a large-scale urban renewal area layer with tens of thousands of partitioned grids, a heuristic pruning algorithm can be introduced to perform boundary removal processing on the linear programming solution module.

[0259] Step 606: Extract the maximum feasible floor area ratio obtained from the solution and the optimal underlying surface ratio of the synchronous mapping, and assemble them together to form an updated core index set.

[0260] Accordingly, after the linear programming solution module outputs the optimal solution matrix, data feature extraction and structured assembly operations are performed. The value returned by the objective function at the optimization endpoint is extracted as the maximum feasible floor area ratio. At the same time, the specific distribution ratios of building density, green space ratio, hardening ratio, and permeable pavement ratio under the corresponding state at the optimization endpoint are extracted as the optimal underlying surface mix.

[0261] By merging and encapsulating spatial morphology control indicators, a set of updated core indicators specific to the predetermined zone is generated, providing engineering development baseline parameters that are accurate to the zone level and filtered by the hydrological environment.

[0262] On the other hand, it provides an offline verification and feedback closed-loop mechanism to ensure the long-term iteration of the system parameter model, specifically including:

[0263] Step 701: Based on the final area renewal plan, conduct full-cycle dynamic monitoring of the existing built environment and water management compliance assessment.

[0264] After urban renewal projects transition from the planning stage to the physical construction and long-term operation and maintenance stage, the long-term data collection module for the built environment is activated.

[0265] Among them, the dynamic monitoring of the entire life cycle of stock updates includes collecting real-time hydrological flow field data using IoT sensing devices deployed at key nodes of the urban water supply system in the area, such as pipe network node liquid level parameters, outlet flow parameters, and surface runoff pollutant concentration parameters.

[0266] Alternatively, high-resolution satellite remote sensing image layers of the target area can be acquired periodically to extract the long-term evolution characteristics of the physical components of the actual underlying surface.

[0267] Alternatively, the operational indicators collected based on real physical space can be numerically compared with the theoretical control thresholds set in the final area update plan to generate water management compliance assessment results.

[0268] Step 702: Generate a dynamic update package for control thresholds based on the actual operational deviations obtained from the assessment;

[0269] When the water management compliance assessment results indicate that the actual operational indicators of a predetermined zone or predetermined catchment area fail to meet the theoretical design expectations, the system triggers the deviation quantification calculation module to perform difference calculation. The calculation formula can be described as follows:

[0270] E _dev =V _actual -V _simulated ;

[0271] In the formula, E _dev V represents the actual operational deviation. _actual V is the actual runoff discharge calculated based on data collected from front-end sensor devices._simulated The theoretical simulated discharge volume is derived from the hydrodynamic model under the same meteorological boundary conditions for the final area renewal plan.

[0272] If the actual operational deviation of the calculation output is >0, it indicates that the actual negative hydrological impact after the construction of the zone exceeds the previously set control red line limit.

[0273] The system automatically extracts the deviation value, the corresponding spatial geographic projection coordinates, and the category attributes of the failed underlying surface facilities, and then structures and merges the data series.

[0274] The data set generated by this encapsulation is the dynamic update package for control thresholds, which is used to convert physical environment monitoring data into parsable parameter correction control instructions.

[0275] Step 703: The dynamic update package of the control threshold is fed back to the basic data acquisition end, which is used to drive the iterative optimization of the rigid constraint indicators of the water system in the area in the next planning calculation cycle, forming a closed loop of the whole process.

[0276] Extract the parameter correction control instructions carried in the dynamic update package of the control threshold, and transmit them in reverse to the data acquisition end.

[0277] As an example, when starting the next urban stock renewal planning calculation cycle, such as when the statutory planning parameter set of the area is revised every 5 years, the underlying input data is covered and iteratively calibrated based on the received update package.

[0278] For a predetermined physical block that has a negative deviation in the previous period, the calculation directly lowers the baseline value of the remaining available environmental capacity assessment of the block in the current status data of the regional water system, or correspondingly increases its sensitivity weight parameter in the topology influence coefficient calculation formula.

[0279] In some implementations, a pre-built machine learning regression network can be introduced to perform time series mining on the historically accumulated control threshold dynamic update package; based on the mined and extracted surface hydrological performance decay function, the parameter degradation magnitude of the target zone at a predetermined time node can be predicted in advance.

[0280] In this embodiment, a physical closed loop of the computer technology system is constructed through automated data acquisition by physical sensing devices, multi-objective optimization calculations by computer hardware, and physical flood control and hydraulic scheduling for urban water facilities.

[0281] Furthermore, regarding the physical contours of the Earth's surface, the system receives remote sensing images transmitted back by flight equipment equipped with multispectral sensors;

[0282] The image analysis algorithm is used to automatically extract the geometric coordinates of the actual physical boundary between the hardened road surface and the green space.

[0283] For concealed underground drainage facilities, it receives pipeline three-dimensional coordinates and pipe diameter detection data transmitted back from a building non-destructive testing and data processing terminal based on equipment such as ground penetrating radar;

[0284] A directed acyclic graph data structure that reflects the actual underground spatial connections is automatically constructed in the memory.

[0285] Correspondingly, Doppler current meters and ultrasonic level gauges deployed in inspection wells of the urban water supply system collect physical simulation electrical signals of the current base flow in the pipe section in real time, and input them into the system via analog-to-digital conversion interface as boundary conditions for dynamic hydrological calculations.

[0286] Furthermore, multi-source heterogeneous physical data is loaded into the computer's memory;

[0287] The computer is equipped with a central processing unit and random access memory, and performs floating-point matrix operations by calling the instruction set;

[0288] The central processing unit performs semi-space intersection matrix operations frequently in the multi-dimensional vector space, transforming the extracted environmental constraint redline into a hyperplane equation in the parameter space, and defining a closed convex polyhedron of the feasible development boundary of the water constraint in the memory stack.

[0289] Furthermore, the central processing unit runs a multi-objective optimization algorithm engine to automatically find the optimal solution in the vertex matrix of the convex polyhedron and output the maximum feasible volume ratio and the underlying surface ratio parameters corresponding to the objective function.

[0290] Optionally, when the target update area covers more than one million geographic grid units, or when high-precision real-time flow field simulation calculations are required, a distributed cloud server cluster can be deployed to carry out matrix calculation tasks.

[0291] After the final area update plan is output, the generated data is directly converted into digital instructions to guide the construction of physical entities and control the operation of water facilities.

[0292] On the one hand, the generated updated core indicator set can be packaged into a spatial entity boundary control file in standard building information model format; and then distributed to the project management terminal to limit the hardened area boundary of engineering machinery operations and the minimum physical construction area of ​​green infrastructure in the subsequent physical construction stage.

[0293] On the other hand, the runoff prediction time series data calculated based on the optimal underlying surface ratio will be transmitted to the flood forecasting and hydraulic dispatching system of the urban water management department;

[0294] This time series data was used as a threshold input for the start-up and shutdown strategy of downstream physical pumping stations, automatically triggering the early pumping action of physical pumping units under extreme rainfall weather conditions.

[0295] According to one aspect of this application, the constant 'a' simultaneously includes a dimensional transformation of rainfall and a dimensional transformation of the event's average concentration, such that W _i The output dimensions are unified; the specific value of 'a' can be determined based on the actual units of rainfall and concentration used.

[0296] According to another aspect of this application, an apparatus for generating urban stock renewal parameters based on pre-emptive water constraints is provided, comprising:

[0297] The data parsing module is used to parse the baseline data of the current status of the water system in the target area and the topological data of the urban water system in the area, which are objectively collected by physical detection equipment, and to simultaneously read the statutory planning parameter set of the area and the existing update type data of each sub-district.

[0298] The red line conversion module is used to extract rigid constraint indicators of the water system in the area based on the baseline data of the current status of the water system in the area, and convert them into a list of the upper limit of the total carrying capacity of the area for renewal.

[0299] The threshold decomposition module is used to combine the urban water system topology data and existing update type data of the area, and execute the underlying data processing logic to decompose the list of the total update carrying capacity of the area into differentiated water management dual control thresholds for each zone.

[0300] The constraint mapping module is used to introduce the statutory planning parameter set of the area and map the water management dual control threshold into the pre-constraint conditions in the planning parameter space.

[0301] The boundary construction module is used to construct the water-constrained feasible development boundaries of each partition in the planning parameter space by utilizing the pre-constraint conditions.

[0302] The parameter solving module is used to run the optimization solver within the water-constrained feasible development boundary to generate the updated core index set for each partition, and summarize them to form the initial update scheme for the area.

[0303] The verification and control output module is used to perform water system coupling simulation verification on the initial area update scheme, output the final area update scheme that passes the verification, and encapsulate the final area update scheme into a digital control boundary model for guiding physical space construction and hydraulic flood control scheduling.

[0304] According to another aspect of this application, the memory can be used to store software programs and modules of application software, such as program instructions / data storage devices required for execution as in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the method shown in this application.

[0305] The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0306] In some instances, the memory may further include memory remotely located relative to the processor, which can be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0307] In this application, the event-average concentration, quota conversion and algebraic elimination are used to reduce the dimension of water indicators with different physical dimensions such as runoff and pollution to a unified set of linear inequalities in the planning parameter space. This weakens the dimensional barriers between indicators and makes environmental constraints have a basis for comparison at the same scale.

[0308] Furthermore, differentiated variable degrees of freedom and a historical debt compensation mechanism are introduced to construct a convex polyhedron envelope in a multidimensional parameter space and perform linear programming optimization. The process of determining planning parameters is transformed into locking extreme values ​​within the multidimensional mathematical boundary, so that the output development capacity satisfies the ecological red line while maximizing the global utilization efficiency of the physical space of the zone.

[0309] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0310] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining city location based on water level, characterized in that, include: Acquire baseline data on the current status of the water system in the target area, topological data of the urban water system in the area, the set of statutory planning parameters for the area, and the stock update type data for each sub-area; Based on the baseline data of the current status of the water system in the area, rigid constraint indicators of the water system in the area are extracted and transformed into a list of the upper limit of the total carrying capacity of the area for renewal. By combining the urban water system topology data and existing stock update type data of the area, the list of the upper limit of the total update capacity of the area is decomposed into the water management dual control thresholds of each zone; By introducing a set of statutory planning parameters for the area, the water management dual control thresholds are mapped as pre-constraint conditions within the planning parameter space; Based on this, water-constrained feasible development boundaries for each zone are constructed in the planning parameter space in a forward manner. Within the water-constrained feasible development boundary, generate updated core indicator sets for each zone, and summarize them to form an initial plan for the area's renewal; Perform a water system coupling simulation verification on it, and output the final area update scheme that passes the verification.

2. The method according to claim 1, characterized in that, Combining the area's urban water system topology data and existing water supply type data, the area's total water supply capacity limit list is decomposed into differentiated water management dual control thresholds for each zone, including: The hydrological topological influence coefficient of each zone was calculated based on the urban water system topology data of the area. By combining existing update type data, a differentiated allocation priority matrix for different control indicators is determined for each type of update partition; By using the hydrological topological influence coefficient and the differentiated allocation priority matrix, the total quotas in the list of total carrying capacity limits for area renewal are broken down to each zone to form total control thresholds; Based on the existing update type data, set the intensity control upper limit for each zone, perform consistency verification between the intensity control upper limit and the total control threshold, and output the water management dual control threshold that has passed the verification.

3. The method according to claim 2, characterized in that, Based on existing update type data, a differentiated allocation priority matrix for different control indicators was determined for each update partition, specifically including: Calculate the expected change in water volume per unit area before and after the update for various updated zones; Extract the ratio of the expected change in water per unit area between different stock renewal types; Using the ratio relationship as the allocation benchmark, a differentiated allocation priority matrix is ​​constructed.

4. The method according to claim 1, characterized in that, By introducing a set of statutory planning parameters for the area, water management thresholds are mapped to pre-constraint conditions within the planning parameter space. Based on this, feasible development boundaries for water constraints in each zone are constructed in the planning parameter space, specifically including: Establish a mapping relationship between various water management and control thresholds and the built-up area; The mapping relationship is transformed into a system of linear inequalities in the planning parameter space, which serves as a pre-constraint condition. By combining the statutory planning parameter set and the set of linear inequalities of the joint area, and using the geometric envelope rule to define the convex polyhedron in the planning parameter space, a water-constrained feasible development boundary is formed.

5. The method according to claim 1, characterized in that, Within the water-constrained feasible development boundary, an updated set of core metrics is generated for each partition, specifically including: The objective function for maximizing the floor area ratio is set with the goal of achieving the peak development capacity of each zone as the guiding principle. Within the water-constrained feasible development boundary, a linear programming solution is performed to maximize the plot ratio objective function, taking into account the statutory planning parameter set for the area. The maximum feasible floor area ratio obtained from the solution and the optimal underlying surface ratio of the synchronous mapping are extracted and assembled together to form an updated core index set.

6. The method according to claim 1, characterized in that, After the final area update scheme that has passed the output verification is output, it also includes offline dynamic feedback across cycles, specifically: Based on the final area renewal plan, conduct full-cycle dynamic monitoring of the existing built environment and water management compliance assessment. Dynamically updated control threshold packages are generated based on actual operational deviations obtained from assessments. The dynamic update package of the control threshold is fed back to the basic data acquisition end, which is used to drive the iterative optimization of the rigid constraint indicators of the water system in the area in the next planning calculation cycle.

7. The method according to claim 2, characterized in that, The intensity control upper limit for each partition is set based on the existing update type data. Specifically, this includes using a differentiated allocation priority matrix to perform reverse linkage settings, namely: Extract the allocation weights of various update partitions in the differential allocation priority matrix; By normalizing and back-calculating using the allocated weights and the average proportion of the physical area of ​​each partition, the correlation adjustment coefficient is calculated. By using the correlation adjustment coefficient to product and calibrate the pre-configured area benchmark intensity control values, the initial value of the intensity control upper limit for each area is generated, so that the area type that receives a higher allocation weight is automatically assigned a moderately relaxed initial intensity control upper limit.

8. The method according to claim 2, characterized in that, The hydrological topological influence coefficients of each zone are calculated based on the topological data of the urban water supply system in the area, specifically including: The topological data of the urban water system in the area is abstracted into a directed acyclic graph of the pipe network; The capacity margin of each pipe segment is obtained by calculating the percentage difference between the design flow rate and the current base flow rate in the directed acyclic diagram of the pipeline network. Extract the confluence path from each zone to the area's drainage outlet; Calculate the path-weighted bottleneck margin using the capacity margin and length of each pipe segment along the confluence path; The path-weighted bottleneck margin is normalized to obtain the hydrological topological influence coefficient that reflects the sensitivity of drainage zones.

9. The method according to claim 4, characterized in that, After determining the water-constrained feasible development boundary, the process also includes determining and compensating for the empty set of the envelope surface, specifically: Perform a feasibility test on the system of linear inequalities; When the water constraint feasible development boundary of a protected zone or a remediation and upgrading zone is determined to be an empty set, the total amount of water discharge exceeding the standard in the current status of the zone is calculated. The current total amount of water discharge exceeding standards will be converted into historical backlog; The allocation of the total share of demolition and reconstruction zones in the surrounding areas will be reduced proportionally based on the amount of historical arrears, triggering the operation of re-decomposing the water management dual control threshold.

10. A water-based city planning system, characterized in that, include: At least one processor; And at least one memory storing program instructions that, when executed by at least one processor, cause at least one processor to perform the method of any one of claims 1 to 9.

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