Water supply scheduling planning method for resident accumulation area, medium and equipment

Abstract

The invention discloses a water supply scheduling planning method, medium and equipment for a resident accumulation district, and the method comprises the steps: collecting basic water supply data of an adjacent water source, and carrying out the preprocessing to generate basic water supply data; adopting a constraint shortest path algorithm to generate an alternative scheduling route meeting pipe pressure bearing limitation and construction period constraint, and calculating a comprehensive cost weight of the alternative scheduling route; extracting water supply abrupt change characteristics through time domain analysis, and establishing a response strategy library containing typical water supply abnormal scenes; constructing an optimization decision model of multi-water-source collaborative scheduling and emergency switching; establishing a feature mapping relation between the reference water supply working condition and the emergency water supply mode; and finally, constructing a multi-dimensional water supply scheduling knowledge base including water source characteristics, pipe network topology, scheduling strategies and emergency responses. According to the invention, the response speed of the water supply system to emergencies is improved, the dynamic optimization scheduling of the water supply system is realized, the stability of multi-water-source collaborative scheduling is enhanced, and the emergency response efficiency and the system reliability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water supply system optimization scheduling, in particular to a water supply scheduling planning method for a residential area, a medium and equipment. BACKGROUND

[0002] Residential area water supply scheduling is an important part of urban infrastructure management, and its core is to coordinate the dynamic balance between water source supply and user demand. The current water supply system mainly relies on fixed pipe network topology and preset scheduling rules for operation, and in actual application, it faces many challenges. Water quality fluctuations, seasonal water consumption changes, and pipe network aging make it difficult for traditional scheduling methods based on static models to adapt to complex and changing operating environments. In particular, in the face of sudden events such as water source pollution or pipe network rupture, manual intervention is often required for emergency plan formulation, and the response efficiency is greatly limited; in terms of path optimization, conventional methods focus more on the optimal solution of a single objective, and lack comprehensive consideration of multiple factors such as construction feasibility, operating energy consumption, and system reliability; in addition, there is no effective dynamic decision-making mechanism for the coordinated scheduling of different water sources, which may cause pressure fluctuations or water quality mixing problems when switching water supply paths. With the expansion of urban scale and the diversification of water demand, how to establish a more intelligent and flexible water supply scheduling system has become a technical problem that needs to be solved in the water supply industry. SUMMARY

[0003] Therefore, the present application aims to provide a water supply scheduling planning method for a residential area, a medium and equipment, which realizes intelligent coordinated scheduling of the water supply system through multi-source data fusion and dynamic optimization decision-making, and solves the problems of response lag and insufficient risk resistance in water supply scheduling.

[0004] In order to achieve the above technical purposes, in a first aspect, the present application provides a water supply scheduling planning method for a residential area, comprising: Collecting basic water supply data of adjacent water sources, the basic water supply data including water quality parameters, available water quantity, pipe network topology information, and construction access conditions; Pretreating the basic water supply data to obtain optimization scheduling input data, the optimization scheduling input data including water quality compliance evaluation results, dynamic water supply capacity curves, and a set of construction feasible paths; Based on the set of construction feasible paths, generating candidate scheduling routes using a constrained shortest path algorithm, the candidate scheduling routes needing to meet pipe material pressure limitation and construction duration constraints, and calculating a comprehensive cost weight of each candidate scheduling route, the comprehensive cost weight including water transmission energy consumption, construction cost, and emergency redundancy; Perform time-domain analysis on the dynamic water supply capacity curve to extract water supply mutation characteristics and establish a response strategy library that includes typical water supply anomaly scenarios, including at least one of sudden pollution, equipment failure, and demand peak. Based on water quality assessment results, alternative dispatching routes, water supply mutation characteristics, and response strategy database, an optimization decision-making model for multi-water source coordinated dispatching and emergency switching is constructed; Based on the optimization decision model, a characteristic mapping relationship between the baseline water supply condition and the emergency water supply mode is established. The characteristic mapping relationship includes the pressure fluctuation transfer function, the water quality mixing prediction matrix, and the construction interference compensation coefficient. The alternative scheduling routes, water supply mutation characteristics, response strategy library, optimization decision model and feature mapping relationship are structured and stored to build a multidimensional water supply scheduling knowledge base including water source characteristics, pipe network topology, scheduling strategy and emergency response.

[0005] In some embodiments, basic water supply data is preprocessed to obtain optimized scheduling input data, which includes water quality compliance assessment results, dynamic water supply capacity curves, and a set of feasible construction paths, including: Conduct a multi-index weighted assessment of source water quality parameters to generate a water quality compliance assessment result. The multi-index weighted assessment includes at least one of heavy metal content, turbidity, and residual chlorine concentration, and establish a compliance matrix between multiple indicators and preset water use standards; Based on the available water volume and water pipe network topology information, a hydraulic transient model is used to calculate the dynamic water supply capacity curve under different working conditions. The dynamic water supply capacity curve includes at least one of the maximum water supply volume, the pressure fluctuation range, and the critical switching time. Based on the construction access conditions, a three-dimensional geographic constraint model is constructed. The three-dimensional geographic constraint model integrates road excavation restrictions, existing pipeline distribution and geological survey data. The improved Dijkstra algorithm is used to search for K feasible construction paths in the constraint space to form a set of feasible construction paths. The water quality assessment results, dynamic water supply capacity curves and feasible construction paths are aligned in time and space to generate standardized optimization scheduling input data.

[0006] In some embodiments, based on a set of feasible construction paths, a constrained shortest path algorithm is used to generate alternative scheduling routes. The alternative scheduling routes must meet the pipe pressure limit and construction period constraints, including: Establish a multi-dimensional path evaluation model, which includes at least one of the hydraulic performance index, the construction feasibility index, and the economic index, and construct a correlation matrix between the hydraulic performance index, the construction feasibility index, the economic index and the engineering constraints; Based on the set of feasible construction paths, the hydraulic characteristic parameters of each path are calculated using a pressure-flow coupling algorithm. The hydraulic characteristic parameters include at least one of the steady-state water supply, transient pressure fluctuation value, and water hammer risk level. Based on the pressure limit of the pipe, a pressure safety assessment model is established. The pressure safety assessment model integrates the pipe strength coefficient, operating pressure peak and safety margin threshold to generate a set of pressure compliance paths; Combined with construction period constraints, a spatiotemporal conflict detection model is constructed. The spatiotemporal conflict detection model integrates construction machinery scheduling plans, traffic control periods, and resident impact assessments to output a set of feasible paths within the construction period. A multi-objective optimization algorithm is used to collaboratively solve the pressure-bearing compliance path set and the construction period feasible path set to generate a Pareto optimal alternative scheduling route set, which includes multiple alternative scheduling routes.

[0007] In some embodiments, a comprehensive cost weight of each alternative dispatching route is calculated, where the comprehensive cost weight includes water transmission energy consumption, construction and modification costs, and emergency redundancy, including: For each alternative dispatch route, do the following: The hydrophobic energy consumption value of the alternative scheduling route is calculated and expressed by formula (1). Formula (1) is as follows: ; In formula (1), For the The water transmission energy consumption value of the alternative dispatching route is: For the The friction coefficient of the pipe section, For the The length of the pipe segment, For the The design flow rate of the pipe section, is the water density, is the standard acceleration due to gravity, For the The height difference between the alternative dispatch routes; The construction and modification cost of the alternative scheduling route is calculated using formula (2), which is as follows: ; In formula (2), For the The construction and modification cost of the alternative dispatching route, For the The pipe cost of the alternative dispatch route, For the The construction cost of an alternative dispatch route, For the Road repair costs for alternative dispatch routes; The emergency redundancy of the alternative dispatching route is calculated using formula (3), which is as follows: ; In formula (3), For the The emergency redundancy of the alternative dispatch routes, is the first weight coefficient, is the number of standby pumping stations, is the second weight coefficient, To quickly switch the number of valves, is the third weight coefficient, For emergency water reserves; The comprehensive cost weight of the alternative dispatching route is calculated and expressed by formula (4), which is as follows: ; In formula (4), is the weight coefficient of hydrophobic energy consumption, is the weight coefficient of construction and renovation cost, is the inverse weight coefficient of emergency redundancy, ,and ; Repeat the above steps until the comprehensive cost weights of all alternative scheduling routes are calculated, and the comprehensive cost weights and the alternative scheduling paths are mapped and stored; And, sort the alternative scheduling paths from high to low according to the comprehensive cost weight.

[0008] In some embodiments, a time domain analysis is performed on the dynamic water supply capacity curve to extract water supply mutation characteristics and establish a response strategy library containing typical water supply abnormality scenarios, including: Perform spectrum analysis and waveform decomposition on the dynamic water supply capacity curve to identify at least one feature of a sudden change point in water supply pressure, a sudden drop point in flow rate, and an abnormal point in water quality parameters; Based on the identification results, water supply anomaly levels are divided into level one, level two, and level three. Level one is configured as a main water supply pipeline failure, level two is configured as a local pipeline network leak, and level three is configured as a sudden increase in water demand. Establish a database of typical abnormal water supply scenarios, including main pump station shutdown scenarios, pipeline burst scenarios, and peak water consumption scenarios; Develop response strategies for each typical water supply anomaly scenario, including: If the main pump station is shut down, start the backup pump station and adjust the valve opening of the pipeline network; In the event of a pipe burst, close the nearest valve and activate the emergency water supply truck; For peak water consumption scenarios, adjust the water release from the reservoir and start the booster pump group; The water supply anomaly characteristics, anomaly level classification, typical water supply anomaly scenario library and response strategies are associated and stored to form a response strategy library.

[0009] In some embodiments, based on water quality compliance assessment results, alternative scheduling routes, water supply mutation characteristics, and a response strategy library, an optimization decision model for multi-water source coordinated scheduling and emergency switching is constructed, including: A hydraulic-water quality coupling calculation model is established and expressed by formula (5). Formula (5) is as follows: ; In formula (5), is the water head of the pressure measuring pipe section, is the gravitational acceleration component, is the flow rate at the pipe inlet, is the outlet flow rate of the pipe section, is the source term, is the water concentration, is the diffusion coefficient, is the spatial second-order derivative of water concentration, is the flow rate of the pipe section, is the cross-sectional area of ​​the pipe, is the water concentration gradient, is the water quality reaction rate constant; Solve the hydraulic-water quality coupling calculation model to obtain multiple solution results, each of which corresponds to a scheduling decision under a working condition; The finite state machine principle is used in combination with the water supply mutation characteristics to screen the scheduling decisions, and the selected scheduling decisions are recorded as screening decisions. The screening conditions are defined, including: when When the emergency dispatch plan is activated, when When , switch to the hybrid regulation scheme; when When , switch to the boost scheduling plan; Multiple screening decisions are generated into a multi-level buffer control strategy, which is expressed by formula (6): ; In formula (6), is the valve opening adjustment amount, is the proportional control coefficient, is the head deviation, is the integral control coefficient, is the historical head deviation integral variable, is the differential control coefficient; That is, an optimized decision model is generated.

[0010] In some embodiments, based on the optimization decision model, a characteristic mapping relationship between the baseline water supply condition and the emergency water supply mode is established. The characteristic mapping relationship includes a pressure fluctuation transfer function, a water quality mixing prediction matrix, and a construction interference compensation coefficient, including: Conduct hydraulic modeling of a benchmark water supply condition and extract characteristic parameters of pressure fluctuations, wherein the characteristic parameters of pressure fluctuations include at least one of a main frequency amplitude, an attenuation rate, and a propagation speed; The pressure fluctuation transfer function is established and expressed by formula (7), which is as follows: ; In formula (7), is the transfer function of pressure fluctuation, is the Laplace complex variable, is the base of natural logarithm, is the pressure wave propagation delay, is the system time constant, is the Laplace transform of the input pressure disturbance, is the Laplace transform of the output pressure response; Construct the water quality mixed prediction matrix, which is expressed by formula (8). Formula (8) is as follows: ; In formula (8), For the Pipe section Mixed transfer functions of various water quality parameters; The construction interference compensation coefficient is constructed and expressed by formula (9), which is as follows: ; In formula (9), is the construction interference compensation coefficient, is the fourth weight coefficient, for Class construction interference index; The pressure fluctuation transfer function, water quality mixing prediction matrix and construction disturbance compensation coefficient are integrated to obtain the mapping characteristic relationship, which is expressed by formula (10). Formula (10) is as follows: ; In formula (10), is the pressure domain, For water quality domain, is the flow domain, is the adjusted flow rate, is the base flow rate.

[0011] In some embodiments, alternative scheduling routes, water supply mutation characteristics, response strategy library, optimization decision model and feature mapping relationship are structured and stored to build a multi-dimensional water supply scheduling knowledge base including water source characteristics, pipe network topology, scheduling strategy and emergency response, including: Perform topological coding on the alternative dispatching routes to generate a route feature description set including the path node sequence, link characteristic parameters and switching constraints; The water supply mutation characteristics are classified and stored according to the abnormal type, and a mutation characteristic database including pressure mutation pattern, flow mutation pattern and water quality mutation pattern is established. Each mutation pattern is associated with the corresponding time-frequency characteristic parameters and trigger thresholds; Organize the response strategy library hierarchically to form a hierarchical response strategy tree; The optimization decision model is decomposed into three sub-libraries: model parameter set, constraint condition set and solution algorithm set. The model parameter set includes hydraulic calculation parameters, water quality simulation parameters and economic evaluation parameters. Multi-dimensional indexing is performed on the feature mapping relationship to form a pressure-water quality-flow collaborative mapping relationship network, supporting multi-dimensional retrieval by working condition type, abnormality level and construction stage; An update mechanism for the multidimensional water supply scheduling knowledge base is established based on spatiotemporal characteristics, including scheduled updates, event-triggered updates, and incremental updates. Scheduled updates are configured to refresh basic data at a preset cycle. Event-triggered updates are configured to initiate a comprehensive update when major pipeline network modifications or water source changes occur. Incremental updates are configured to record the execution effects of scheduling decisions in real time and perform local optimization.

[0012] In a second aspect, the present invention further provides a computer-readable storage medium storing computer program instructions, which implement the method described in the first aspect when executed by a processor.

[0013] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method described in the first aspect.

[0014] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: The present invention provides a water supply scheduling planning method, medium, and equipment for residential areas. The method includes: collecting water quality parameters, available water volume, water pipe network topology information, and construction access conditions of adjacent water sources; pre-processing to generate water quality compliance assessment results, dynamic water supply capacity curves, and a set of feasible construction paths; using a constrained shortest path algorithm to generate alternative scheduling routes that meet pipe pressure constraints and construction period constraints, and calculating comprehensive cost weights including water transmission energy consumption, construction and modification costs, and emergency redundancy; extracting water supply mutation characteristics through time domain analysis, establishing a response strategy library for typical water supply anomaly scenarios including sudden pollution, equipment failure, and demand peaks; constructing an optimized decision model for multi-water source coordinated scheduling and emergency switching; establishing a feature mapping relationship between baseline water supply conditions and emergency water supply modes; and finally constructing a multidimensional water supply scheduling knowledge base including water source characteristics, pipe network topology, scheduling strategies, and emergency response. The present invention significantly improves the response speed of the water supply system to emergencies, effectively reduces the risk of pipe network pressure fluctuations, realizes dynamic optimization scheduling of the water supply system, enhances the stability of multi-water source coordinated scheduling, and improves emergency response efficiency and system reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 is a method step diagram of steps S101 to S107 of the planning method described in the specific implementation manner; Figure 2 is a method step diagram of steps S201 to S204 of the planning method described in the specific implementation manner; Figure 3 Schematic diagram of the structure of the electronic device described in the specific implementation.

[0017] The reference numerals in the above drawings are described as follows: 1. Electronic equipment; 11. Memory; 12. Processor. DETAILED DESCRIPTION

[0018] The present invention will be described in further detail below with reference to the accompanying drawings and examples. It is particularly noted that the following examples are intended only to illustrate the present invention and are not intended to limit the scope of the present invention. Similarly, the following examples are only some embodiments of the present invention and are not intended to be exhaustive. All other embodiments obtained by those of ordinary skill in the art without creative effort are intended to fall within the scope of protection of the present invention.

[0019] See also Figure 1 In a first aspect, this embodiment provides a water supply scheduling planning method for a residential area, comprising: S101. Collect basic water supply data from nearby water sources, including water quality parameters, available water volume, water pipe network topology information, and construction access conditions; S102: Preprocess the basic water supply data to obtain optimized scheduling input data, where the optimized scheduling input data includes water quality compliance assessment results, a dynamic water supply capacity curve, and a set of feasible construction paths; S103. Based on the set of feasible construction paths, a constrained shortest path algorithm is used to generate alternative scheduling routes. The alternative scheduling routes must meet the pipe pressure limit and construction period constraints. The comprehensive cost weight of each alternative scheduling route is calculated. The comprehensive cost weight includes water transmission energy consumption, construction and modification costs, and emergency redundancy. S104. Perform time-domain analysis on the dynamic water supply capacity curve to extract water supply mutation characteristics and establish a response strategy library containing typical water supply abnormality scenarios, where the typical water supply abnormality scenarios include at least one of sudden pollution, equipment failure, and demand peak. S105. Build an optimization decision model for multi-water source coordinated scheduling and emergency switching based on water quality assessment results, alternative scheduling routes, water supply mutation characteristics, and response strategy library; S106. Based on the optimization decision model, establish a characteristic mapping relationship between the baseline water supply condition and the emergency water supply mode, wherein the characteristic mapping relationship includes a pressure fluctuation transfer function, a water quality mixing prediction matrix, and a construction interference compensation coefficient; S107. Alternative scheduling routes, water supply mutation characteristics, response strategy library, optimization decision model and feature mapping relationship are structured and stored to build a multi-dimensional water supply scheduling knowledge base including water source characteristics, pipe network topology, scheduling strategy and emergency response.

[0020] In step S101, the basic water supply data of the neighboring water sources constitutes the original input for the water supply scheduling plan. The water quality parameters of the water sources are obtained through online monitoring equipment, including key indicators such as turbidity, residual chlorine, and pH value. The monitoring frequency of these indicators can be dynamically adjusted according to seasonal changes. For example, the sampling rate can be increased during the flood season to cope with the risk of water quality fluctuations. The available water volume is collected in real time by the flow meter at the water source to represent the current total amount of schedulable water resources. Furthermore, meteorological forecast data can be introduced to assist in predicting the water volume change trend in the next 72 hours. The water supply network topology information comes from the GIS system database, which records the pipeline connection relationship and spatial distribution, and specially marks old pipe sections and key nodes to give priority to key areas. The construction access conditions are determined through engineering surveys, including factors such as pipeline interface specifications and construction space restrictions. The interface compatibility assessment uses three-dimensional scanning technology to achieve millimeter-level precision matching.

[0021] During the preprocessing process of step S102, the water quality compliance assessment result is obtained by comparing water quality parameters with national standard limits. Preferably, fuzzy logic is introduced to handle boundary value conditions, and the review and detection process is initiated when a certain indicator reaches a critical value. The dynamic water supply capacity curve is generated by fitting historical water consumption data. Preferably, time series decomposition technology is used to separate trend items, periodic items, and random items to more accurately reflect the changing patterns of water supply demand in different time periods. The set of feasible construction paths is screened based on the pipe network topology and construction conditions. Preferably, augmented reality technology is introduced to perform virtual pipe laying simulation and visually verify the feasibility of the path. Furthermore, the preprocessing process uses adaptive data cleaning and feature extraction technology to implement clustering-based intelligent repair of abnormal data to ensure the integrity and availability of input data.

[0022] In step S103, a constrained shortest path algorithm is run based on a set of feasible construction paths. Pipe pressure limits are determined based on the pipe material and service life, and the residual strength coefficient of the pipe wall can be dynamically updated based on acoustic testing data. Construction period constraints are derived from project schedule requirements, and critical path analysis is used to identify bottleneck processes. In calculating the comprehensive cost weight, water transmission energy consumption is optionally estimated using a pipeline hydraulic model, with model parameters synchronized with actual operating conditions using an online calibration mechanism. Construction and modification costs are calculated based on the project budget quota and dynamically adjusted based on the material price index. Emergency redundancy is assessed based on the coverage of alternative paths. This step achieves a multi-dimensional decision-making balance between project feasibility and economic efficiency.

[0023] In the time-domain analysis of step S104, water supply mutation characteristics can be identified using an improved sliding window algorithm. The window size is adaptively adjusted based on the signal frequency band characteristics, effectively capturing abnormal patterns at different time scales. Optionally, a hybrid intelligent approach is used to construct a response strategy library. Sudden pollution scenarios correspond to water source switching strategies, and an integrated water quality diffusion model is used to predict the arrival time of the pollution front. Equipment failure scenarios utilize a fault tree-based intelligent diagnosis solution, automatically generating disposal recommendations including spare parts inventory information. Demand peak scenarios trigger hierarchical pressure scheduling measures, with the pressure adjustment range set based on user sensitivity zones. Furthermore, the judgment thresholds for typical water supply anomaly scenarios utilize transfer learning technology, extracting empirical values ​​from historical data in similar areas for local calibration.

[0024] In step S105, preferably, an improved multi-objective planning method is used to construct an optimization decision model, in which the water quality compliance assessment results are converted into constraint weights through the hierarchical analysis method; a reliability assessment module is introduced into the alternative scheduling route to calculate the Monte Carlo simulation of the path failure probability; the water supply mutation characteristics trigger strategy switching through an event-driven engine; the response strategy library adopts a hybrid mechanism of case reasoning and rule reasoning, and the new strategy is automatically archived after being verified through online trial operation.

[0025] In the characteristic mapping relationship established in step S106, preferably, the pressure fluctuation transfer function is constructed using the system identification method, and the dynamic characteristics of the pipeline network are obtained through pulse response tests; the water quality mixing prediction matrix introduces computational fluid dynamics simulation to simulate the material diffusion process under different mixing ratios; the construction interference compensation coefficient establishes a dynamic evaluation model based on the Bayesian network, and absorbs construction monitoring data in real time to update the prediction accuracy.

[0026] In step S107, the multidimensional water supply scheduling knowledge base is stored in a spatiotemporal graph database. This database includes water source characteristics, including water quality fingerprints and ecological flow thresholds; network topology maintenance, pipeline health scores, and remaining life predictions; scheduling strategy storage, including performance degradation curves and applicable condition metadata; and emergency response management plan failure mode libraries and drill records. Furthermore, the multidimensional water supply scheduling knowledge base utilizes reinforcement learning mechanisms to enable autonomous strategy evolution. When deviations from strategy execution exceed tolerance limits are detected, the knowledge reconstruction process is automatically triggered.

[0027] This embodiment establishes a multi-dimensional data base encompassing water quality, water quantity, pipe network, and construction conditions, and generates optimized scheduling input data after intelligent preprocessing. It then uses constrained optimization and feature analysis to develop alternative solutions that balance engineering feasibility and economic efficiency. Finally, it constructs a knowledge base with self-learning capabilities to achieve continuous strategy evolution. By building a dynamic mirror image of the water supply system through intelligent perception and digital twin technology, it injects adaptive learning capabilities into traditional optimization methods to form an intelligent decision-making system with metabolic update characteristics. For example, when a rainstorm warning is triggered, the system automatically activates the flood control plan, assesses the impact range of the emergency project through the construction interference compensation coefficient, and adjusts the water storage strategy in advance based on the dynamic water supply capacity curve. Ultimately, it generates a composite scheduling instruction that includes a temporary pressurization plan and a backup water source switching sequence. This solution enables the water supply system to transition from passive response to active defense, significantly improving the water supply system's response speed to abnormal scenarios such as sudden pollution and equipment failure. It effectively controls the risk of pipeline pressure fluctuations through feature mapping relationships such as pressure fluctuation transfer function and water quality mixing prediction matrix, and relies on the reinforcement learning mechanism of the multi-dimensional water supply scheduling knowledge base to achieve dynamic optimization of the scheduling strategy, enhancing the stability of multi-source coordinated supply and the reliability of emergency switching, and providing intelligent protection for water supply safety in residential areas.

[0028] See also Figure 2 In some embodiments, the basic water supply data is preprocessed to obtain optimized scheduling input data, which includes water quality compliance assessment results, dynamic water supply capacity curves, and a set of feasible construction paths, including: S201. Perform a multi-index weighted assessment of water source water quality parameters to generate a water quality compliance assessment result. The multi-index weighted assessment includes at least one of heavy metal content, turbidity, and residual chlorine concentration, and establish a compliance matrix between the multiple indicators and the preset water use standards. S202. Based on the available water volume and the water pipe network topology information, a hydraulic transient model is used to calculate a dynamic water supply capacity curve under different working conditions, where the dynamic water supply capacity curve includes at least one of the maximum water supply volume, the pressure fluctuation range, and the critical switching time; S203. Based on the construction access conditions, a three-dimensional geographic constraint model is constructed. The three-dimensional geographic constraint model integrates road excavation restrictions, existing pipeline distribution, and geological survey data. An improved Dijkstra algorithm is used to search for K feasible construction paths within the constraint space to form a set of feasible construction paths. S204: Perform spatiotemporal alignment processing on the water quality compliance assessment results, the dynamic water supply capacity curve, and the set of feasible construction paths to generate standardized optimization scheduling input data.

[0029] In step S201, the water quality compliance assessment result is a decision-making basis generated through a systematic evaluation of water source water quality parameters, wherein key indicators such as heavy metal content, turbidity, and residual chlorine concentration are obtained in real time through online monitoring equipment, and the compliance matrix is ​​constructed by comparing the measured values ​​of each indicator with the national drinking water hygiene standard limit values ​​in multiple dimensions. Preferably, the multi-indicator weighted assessment uses a hierarchical analysis method to determine the weight of each parameter, and automatically triggers a review mechanism when a certain indicator is at a critical threshold to ensure the reliability of the assessment results. Furthermore, the setting of the critical threshold introduces a dynamic adjustment mechanism, which automatically tightens the allowable fluctuation range of the heavy metal content during the flood season or the peak period of industrial emissions, and makes the assessment standard more in line with actual environmental changes by establishing a seasonal correction coefficient. This adaptive assessment system can effectively identify potential water quality risk inflection points and provide more accurate early warning signals for subsequent scheduling decisions.

[0030] In step S202, the dynamic water supply capacity curve is monitoring data that reflects the changes in the water supply parameters of the pipeline network over time, characterizing the water delivery performance of the pipeline network system, and can be collected in real time through pressure sensors, flow meters and water quality probes. The maximum water supply is determined based on the pipe diameter and pump station capacity calculation, the pressure fluctuation range is captured by transient analysis to capture the influence of the water hammer effect, and the critical switching time is calculated based on the propagation speed of the pipeline network pressure wave. Preferably, the model parameters are calibrated through historical operating data to ensure that the curve reflects the dynamic characteristics of the actual system. Furthermore, the boundary condition setting of the hydraulic model introduces a machine learning prediction module, which predicts the load change trend of the pipeline network in the next 24 hours by analyzing external factors such as meteorological data and user water use patterns, so that the dynamic water supply capacity curve has a short-term prediction function. This forward-looking modeling method can significantly improve the system's response capability to peak water use periods and provide data support for preventive scheduling.

[0031] In step S203, the three-dimensional geographic constraint model is a digital engineering environment that integrates spatial constraints. Road excavation restrictions can be set based on municipal planning data, existing pipeline distribution comes from the underground pipeline network database, and geological survey data includes soil bearing capacity and groundwater level information. The improved Dijkstra algorithm runs in the constraint space and, by introducing obstacle penalty factors and construction cost weights, selects K feasible paths that meet engineering requirements from the topological network. Preferably, the feasibility of the path is verified through virtual construction simulation to ensure the practical operability of the solution. Furthermore, a construction risk quantification assessment module can be introduced during the algorithm optimization process to automatically increase the safety redundancy factor in areas with dense underground pipeline intersections. At the same time, the path optimization strategy is dynamically adjusted based on the operating radius of construction machinery to ensure the economy of the path while minimizing the risk of construction accidents.

[0032] In step S204, the spatiotemporal alignment process is a key step in realizing multi-source information fusion through a unified data benchmark, and the spatial distribution of water quality assessment, the temporal variation characteristics of water supply capacity and the spatial constraints of the construction path are collaboratively calibrated. Preferably, the standardized optimization scheduling input data is stored in a spatiotemporal coding format to ensure that the subsequent optimization decision model can accurately analyze the spatiotemporal correlation of each parameter. Furthermore, the alignment process can use multi-resolution matching technology to establish a hierarchical mapping relationship for the spatiotemporal accuracy differences of different data sources, such as dynamically coupling high-frequency collected water quality data with daily updated construction progress data through a sliding time window. This step effectively solves the scale inconsistency problem during heterogeneous data fusion and provides a more complete data view for global optimization.

[0033] This embodiment introduces methods such as adaptive assessment, predictive modeling, risk quantification search, and multi-resolution alignment to enable the water supply scheduling system to have environmental perception and forward-looking decision-making capabilities. For example, when the system detects that a certain area is about to enter a peak water consumption period, it can initiate preventive scheduling 12 hours in advance: adjusting the dynamic water supply capacity curve based on the predictive model, screening high-quality water sources based on real-time water quality assessment results, and quickly completing the pipe network configuration switch through a risk-optimized construction path, ultimately forming a comprehensive solution that takes into account water quality safety, water supply stability, and construction efficiency. This embodiment achieves a paradigm shift from passive response to active optimization by integrating environmental intelligence with engineering decision-making technology.

[0034] In some embodiments, based on a set of feasible construction paths, a constrained shortest path algorithm is used to generate alternative scheduling routes. The alternative scheduling routes must meet the pipe pressure limit and construction period constraints, including: Establish a multi-dimensional path evaluation model, which includes at least one of the hydraulic performance index, the construction feasibility index, and the economic index, and construct a correlation matrix between the hydraulic performance index, the construction feasibility index, the economic index and the engineering constraints; Based on the set of feasible construction paths, the hydraulic characteristic parameters of each path are calculated using a pressure-flow coupling algorithm. The hydraulic characteristic parameters include at least one of the steady-state water supply, transient pressure fluctuation value, and water hammer risk level. Based on the pressure limit of the pipe, a pressure safety assessment model is established. The pressure safety assessment model integrates the pipe strength coefficient, operating pressure peak and safety margin threshold to generate a set of pressure compliance paths; Combined with construction schedule constraints, a spatiotemporal conflict detection model is constructed. The spatiotemporal conflict detection model integrates construction machinery scheduling plans, traffic control periods, and resident impact assessments to output a set of feasible paths within the construction schedule. A multi-objective optimization algorithm is used to collaboratively solve the pressure-bearing compliance path set and the construction period feasible path set to generate a Pareto optimal alternative scheduling route set, which includes multiple alternative scheduling routes.

[0035] In the present embodiment, the hydraulic performance index characterizes the water delivery efficiency of the pipe network, which can be calculated by parameters such as pressure loss rate and flow uniformity; the construction feasibility index reflects the difficulty of project implementation, including geological condition score and construction interference degree; and the economic index is used to measure the construction cost, including pipe material cost and construction cycle conversion cost. Preferably, the geological condition score introduces the ground penetrating radar detection data to update the soil stability parameters in real time, and the construction interference degree is dynamically adjusted by monitoring the crowd density around the construction site through the Internet of Things equipment.

[0036] The pressure-flow coupling algorithm is a numerical method for simulating water flow state by solving the pipe network hydraulic balance equation, wherein the steady-state water supply reflects the water delivery capacity of the pipeline under constant flow conditions, which can be calculated by the node flow balance equation; the transient pressure fluctuation value captures the water pressure variation amplitude caused by valve opening and closing and other operations, which can be solved by the method of characteristics; and the water hammer risk level can be classified and evaluated according to parameters such as pressure wave propagation speed and valve closing time. Further, the algorithm introduces a pipeline aging coefficient to dynamically adjust the friction coefficient according to the service life of the pipe material and corrosion detection data, so that the hydraulic simulation is closer to the actual attenuation state.

[0037] The pressure safety evaluation model is an analysis framework for verifying the pressure-bearing capacity of the pipeline, the pipe material strength coefficient is determined according to the pipe material and service life, the working condition pressure peak value is taken from the maximum value of the hydraulic simulation result, and the safety margin threshold is preferably set to 1.5 times the design pressure according to the engineering specification. Further, the model establishes a pipe segment health record, integrates annual pressure test records and leakage repair data, and automatically increases the safety coefficient requirement for pipe segments with hidden damage. Optionally, by introducing an online monitoring system based on acoustic emission technology, real-time capture of pipe micro-crack propagation signals is realized, and the pressure-bearing limit value is dynamically adjusted to ensure that the pipe network operates within the material elastic deformation range while maximizing the utilization of the pipe material.

[0038] The space-time conflict detection model is a scheduling tool for coordinating construction plans and external constraints, the construction machinery scheduling plan considers the equipment transfer time and parallel operation restriction, the traffic control period integrates municipal traffic management data, and the resident influence evaluation quantifies the influence degree of construction noise and road closure on the community. Optionally, the model accesses real-time data streams of urban traffic, automatically re-plans the construction period in the event of sudden traffic control; at the same time, a community complaint prediction module is introduced, which analyzes historical complaint data and resident characteristics to predict construction schemes that may cause strong opposition; and a construction impact heat map is established to visually display the differences in social acceptance of different path schemes, providing visual support for decision-making.

[0039] The multi-objective optimization algorithm utilizes evolutionary algorithm frameworks such as NSGA-II, taking pressure safety and construction feasibility as mutually constrained optimization objectives. A Pareto frontier solution set is generated through non-dominated sorting and congestion calculation. The algorithm's population initialization phase incorporates an expert experience database, prioritizing gene combinations that conform to engineering practices. An emergency plan completeness score is incorporated into the fitness function to enhance solution robustness. Each alternative scheduling route includes not only traditional technical parameters but also an implementation risk radar chart and emergency response flowchart, providing decision makers with a comprehensive basis for selection.

[0040] This embodiment establishes a multi-dimensional evaluation system that integrates real-time monitoring data to comprehensively quantify path attributes. It verifies technical feasibility by introducing a dynamic model of pipeline health status, then coordinates internal and external constraints during the construction process to ultimately generate an optimal solution set that balances engineering specifications with social benefits. For example, in the renovation of a pipeline network through a commercial district, the system may recommend a nighttime construction plan. While this plan increases labor costs, it significantly reduces the impact on businesses. At the same time, it uses reinforced pipes to compensate for the pressure risks brought about by the complex underground environment, resulting in a technically feasible, economically reasonable, and socially acceptable optimization solution. This intelligent decision-making method that integrates IoT monitoring and social perception enables path optimization to dynamically adapt to complex environments.

[0041] In some embodiments, a comprehensive cost weight of each alternative dispatching route is calculated, where the comprehensive cost weight includes water transmission energy consumption, construction and modification costs, and emergency redundancy, including: For each alternative dispatch route, do the following: The hydrophobic energy consumption value of the alternative scheduling route is calculated and expressed by formula (1). Formula (1) is as follows: ; In formula (1), For the The water transmission energy consumption value of the alternative dispatching route is: For the The friction coefficient of the pipe section, For the The length of the pipe segment, For the The design flow rate of the pipe section, is the water density, is the standard acceleration due to gravity, For the The height difference between the alternative dispatch routes; The construction and modification cost of the alternative scheduling route is calculated using formula (2), which is as follows: ; In formula (2), For the The construction and modification cost of the alternative dispatching route, For the The pipe cost of the alternative dispatch route, For the The construction cost of an alternative dispatch route, For the Road repair costs for alternative dispatch routes; The emergency redundancy of the alternative dispatching route is calculated using formula (3), which is as follows: ; In formula (3), For the The emergency redundancy of the alternative dispatch routes, is the first weight coefficient, is the number of standby pumping stations, is the second weight coefficient, To quickly switch the number of valves, is the third weight coefficient, For emergency water reserves; The comprehensive cost weight of the alternative dispatching route is calculated and expressed by formula (4), which is as follows: ; In formula (4), is the weight coefficient of hydrophobic energy consumption, is the weight coefficient of construction and renovation cost, is the inverse weight coefficient of emergency redundancy, ,and ; Repeat the above steps until the comprehensive cost weights of all alternative scheduling routes are calculated, and the comprehensive cost weights and the alternative scheduling paths are mapped and stored; And, sort the alternative scheduling paths from high to low according to the comprehensive cost weight.

[0042] In this embodiment, the water delivery energy consumption value represents the energy loss during the operation of the pipe network and is calculated using formula (1), where Friction coefficient of the pipe section It can be determined by looking up the table based on the pipe type and pipe wall roughness. Design flow of the pipe section According to the water demand prediction model, the height difference It can be extracted through a digital elevation model. Preferably, a seasonal correction factor can be introduced into the formula to dynamically adjust the flow parameters based on historical water usage data, so that the energy consumption calculation is more in line with actual working conditions.

[0043] The construction and renovation costs are used to quantify the economic investment in project implementation. Pipe material cost of an alternative dispatch route Based on current market prices and usage, Construction cost of an alternative dispatch route Considering the labor rate and machine hourly rate, Road repair costs for alternative dispatch routes The cost of construction and renovation can be determined based on the road surface type and the area of ​​damage. Furthermore, regional economic coefficients can be introduced into the construction and renovation cost calculation to weight the additional construction costs in special areas (such as commercial areas and cultural heritage protection areas), thereby improving the accuracy of construction and renovation cost estimates.

[0044] Emergency redundancy reflects the system's reserve capacity to deal with emergencies, including the number of standby pump stations Determine the number of valves to be quickly switched based on the pipe network topology Calculated according to the configuration standard of each kilometer of pipe section, the emergency water reserve Refer to the peak water demand configuration of the region. The first weight coefficient , the second weight coefficient , the third weight coefficient Through the analytic hierarchy process, priority is given to ensuring the redundancy of critical facilities. Optionally, the system establishes an emergency equipment health scoring mechanism to automatically reduce the redundancy contribution value of spare equipment that has exceeded its service life.

[0045] The comprehensive cost weight is normalized by weighted summation to achieve multi-objective normalization, among which the hydrophobic energy consumption value weight coefficient , construction and renovation cost weight coefficient , emergency redundancy inverse weight coefficient It can be determined by expert scoring method and set The system establishes a dynamic weight adjustment mechanism to automatically increase the emergency redundancy weight during the flood season and appropriately increase the economic weight during the off-season, making the optimal solution more environmentally adaptable.

[0046] This embodiment establishes a quantitative cost assessment system to transform multiple objectives, such as energy consumption, economy, and safety, into comparable comprehensive indicators. For example, in the renovation of pipeline networks in older urban areas, the system might recommend the use of more expensive corrosion-resistant pipes. Despite the larger initial investment, this option ultimately receives the optimal overall weight due to its superior emergency redundancy and lower long-term energy consumption. This multi-dimensional cost-benefit analysis effectively avoids the problem of one-sidedly pursuing the extreme value of a single indicator in comparison.

[0047] In some embodiments, a time domain analysis is performed on the dynamic water supply capacity curve to extract water supply mutation characteristics and establish a response strategy library containing typical water supply abnormality scenarios, including: Perform spectrum analysis and waveform decomposition on the dynamic water supply capacity curve to identify at least one feature of a sudden change point in water supply pressure, a sudden drop point in flow rate, and an abnormal point in water quality parameters; Based on the identification results, water supply anomaly levels are divided into level one, level two, and level three. Level one is configured as a main water supply pipeline failure, level two is configured as a local pipeline network leak, and level three is configured as a sudden increase in water demand. Establish a database of typical abnormal water supply scenarios, including main pump station shutdown scenarios, pipeline burst scenarios, and peak water consumption scenarios; Develop response strategies for each typical water supply anomaly scenario, including: If the main pump station is shut down, start the backup pump station and adjust the valve opening of the pipeline network; In the event of a pipe burst, close the nearest valve and activate the emergency water supply truck; For peak water consumption scenarios, adjust the water release from the reservoir and start the booster pump group; The water supply anomaly characteristics, anomaly level classification, typical water supply anomaly scenario library and response strategies are associated and stored to form a response strategy library.

[0048] In this embodiment, sudden changes in water supply pressure are identified using a derivative rate of change detection algorithm, sudden drops in flow rate are captured using a sliding window variance analysis method, and abnormal water quality parameters trigger alarms based on set thresholds. Preferably, the system incorporates wavelet transform technology to separate noise signals and improve the accuracy of feature recognition.

[0049] Water supply anomalies are categorized based on the scope of the fault and the difficulty of repair. Level 1 anomalies correspond to systemic failures such as mainline breaks, identified by the magnitude of the pressure drop and the number of affected users. Level 2 anomalies target localized pipeline leaks, identified by pressure gradient changes and acoustic detection signals. Level 3 anomalies reflect surges in regional water demand, identified by analyzing the rate of sudden changes in flow and user water usage patterns. Furthermore, the system has established a dynamic adjustment mechanism for anomaly levels, automatically escalating to a level 1 response if a Level 2 anomaly continues to deteriorate.

[0050] The typical water supply anomaly scenario library is a pre-established collection of failure modes. Preferably, main pump station shutdown scenarios are confirmed through power status monitoring and pump unit vibration data, pipeline rupture scenarios are identified by combining pressure wave propagation characteristics and acoustic emission signals, and peak water consumption scenarios are predicted based on historical water consumption curves and weather data. Optionally, the scenario library incorporates case-based reasoning technology to automatically generate temporary scenario classifications when new anomaly patterns are detected, and to form standard scenarios after sufficient cases have been accumulated.

[0051] The response strategy considers the matching relationship between fault types and treatment resources, the standby pump station starting strategy preferentially selects the pump station with the closest topological distance and the matching capacity, and the valve opening adjustment is based on the hydraulic model to calculate the optimal configuration; the emergency water supply vehicle dispatching can introduce GIS path planning and comprehensively consider road conditions and water use priority; the fuzzy PID algorithm can be used for pressurized pump group control to realize smooth transition of flow and pressure. Preferably, the strategy library establishes an effect feedback mechanism to dynamically optimize the priority of the strategy according to the historical treatment success rate.

[0052] The embodiment realizes rapid diagnosis and accurate treatment of water supply system faults by establishing an intelligent water supply anomaly identification and response mechanism. Time domain analysis of the dynamic water supply capacity curve can accurately capture various abnormal characteristics, combined with multi-level abnormal grade division and typical scene matching, to ensure the comprehensiveness and accuracy of fault determination. The establishment of the response strategy library enables the system to automatically trigger the optimal treatment scheme for different scenarios such as main pump station shutdown and pipeline burst, such as intelligent dispatching of standby pump stations and precise control of valve opening. The embodiment significantly improves the response efficiency and treatment accuracy of the water supply system in response to emergencies, ensuring water supply safety while minimizing the impact of faults. In particular, the introduction of dynamic adjustment of abnormal grades and the strategy effect feedback mechanism enables the system to continuously optimize, effectively addressing the problems of lagging response and insufficient decision-making basis in traditional manual treatment.

[0053] In some embodiments, an optimization decision model for multi-source collaborative dispatching and emergency switching is constructed based on water quality compliance evaluation results, alternative dispatching routes, water supply mutation characteristics, and a response strategy library, including: A hydraulic-water quality coupled calculation model is established, represented by formula (5) as follows: ; In formula (5), is the piezometric tube section head, is the gravity acceleration component, is the pipe section inlet flow, is the pipe section outlet flow, is the source term, is the water quality concentration, is the diffusion coefficient, is the spatial second-order derivative of water quality concentration, is the pipe flow, is the pipe cross-sectional area, is the water quality concentration gradient, is the water quality reaction rate constant; The hydraulic-water quality coupled calculation model is solved to obtain multiple solution results, each solution result corresponding to a dispatching decision under a working condition; The scheduling decisions are screened by using the principle of finite state machine combined with the characteristics of water supply mutation to obtain screened scheduling decisions, which are denoted as screening decisions, and screening conditions are defined, including: When , the emergency scheduling scheme is switched to, is a water head pressure change rate, is a pressure change rate threshold, the pressure change rate threshold is determined based on pressure fluctuation data statistics of historical pipe burst / leakage events, and when , it indicates that the system may have a sudden pipe burst or a large-scale leakage, and the emergency scheduling scheme (such as valve closing and fault area isolation) needs to be switched to urgently; When , the mixed regulation scheme is switched to, is a water quality concentration safety standard, is a water quality deviation threshold, the water quality deviation threshold is set according to the safety limit value proportion of the water quality standard, and when , a pollution event (such as chemical pollution or turbidity anomaly) is prompted, and the mixed regulation scheme (such as dilution and addition of reagent) needs to be started; When , the pressure boosting scheduling scheme is switched to, is a pipe segment flow rate change rate, is a flow rate change rate threshold, the flow rate change rate threshold is determined by analyzing normal water demand peak-valley fluctuations, and when , it indicates that demand surges or local blockages, and the pressure boosting scheduling scheme (such as starting a pump and adjusting a valve) needs to be executed to stabilize the flow; The multiple screening decisions generate a multi-level buffer control strategy, which is represented by formula (6): ; In formula (6), is a valve opening degree adjustment amount, is a proportional control coefficient, is a water head deviation, is an integral control coefficient, is a historical water head deviation integral variable, is a differential control coefficient; that is, an optimized decision model is generated.

[0054] In this embodiment, a hydraulic-water quality coupling calculation model is used to simultaneously simulate the hydraulic characteristics and water quality transmission process of the pipe network, in which the water head of the pressure measuring pipe segment is monitored in real time by a pressure sensor, the water quality concentration is collected by an online water quality monitor, the diffusion coefficient is determined according to a pipe material characteristic table, and the water quality reaction rate constant Obtained through laboratory measurements. Optimally, the model introduces adaptive grid technology to automatically encrypt computational nodes in water-sensitive areas to improve simulation accuracy.

[0055] Finite state machine principle is used to construct the conversion logic of scheduling decision, preferably, the pressure change rate threshold The water quality deviation threshold is determined by analyzing the 95th percentile value of pressure fluctuations in historical pipe burst events. Take 20% of the water quality standard limit, the flow rate change threshold The system is set based on the flow fluctuation characteristics during peak water usage periods. Furthermore, the system establishes a dynamic threshold adjustment mechanism to automatically increase sensitivity during special periods (such as major event security periods).

[0056] The multi-level buffer control strategy uses the PID algorithm to achieve gradual adjustment of pipe network parameters and proportional control coefficients. According to the impedance characteristics of the pipe section, the integral control coefficient Used to eliminate steady-state error, differential control coefficient Suppress system overshoot. Preferably, the control parameters are dynamically adjusted using fuzzy logic and automatically re-adjusted when changes in the pipe network topology are detected.

[0057] This embodiment achieves intelligent scheduling through a three-stage process: coupled modeling, state determination, and strategy generation. For example, when the system simultaneously detects a sudden pressure drop and excessive water quality, the coupled model accurately predicts the contamination's spread. The finite state machine immediately triggers an emergency isolation plan, while the buffer control strategy smoothly adjusts valve openings, rapidly isolating the contamination while preventing water hammer. This embodiment combines physical models with intelligent control, effectively resolving the response lag caused by the water quality-hydraulic separation in traditional scheduling and achieving a balance between safety and stability.

[0058] In some embodiments, based on the optimization decision model, a characteristic mapping relationship between the baseline water supply condition and the emergency water supply mode is established. The characteristic mapping relationship includes a pressure fluctuation transfer function, a water quality mixing prediction matrix, and a construction interference compensation coefficient, including: Conduct hydraulic modeling of a benchmark water supply condition and extract characteristic parameters of pressure fluctuations, wherein the characteristic parameters of pressure fluctuations include at least one of a main frequency amplitude, an attenuation rate, and a propagation speed; The pressure fluctuation transfer function is established and expressed by formula (7), which is as follows: ; In formula (7), is the pressure fluctuation transfer function, is the Laplace complex variable, is the base of natural logarithm, is the pressure wave propagation delay, is the system time constant, is the Laplace transform of the input pressure disturbance, is the Laplace transform of the output pressure response; Construct the water quality mixed prediction matrix, which is expressed by formula (8). Formula (8) is as follows: ; In formula (8), For the Pipe section Mixed transfer functions of various water quality parameters; The construction interference compensation coefficient is constructed and expressed by formula (9), which is as follows: ; In formula (9), is the construction interference compensation coefficient, is the fourth weight coefficient, for Construction interference index, When it corresponds to the mechanical construction interference index, The corresponding traffic construction interference index is The corresponding environmental interference type construction interference index; The pressure fluctuation transfer function, water quality mixing prediction matrix and construction disturbance compensation coefficient are integrated to obtain the mapping characteristic relationship, which is expressed by formula (10). Formula (10) is as follows: ; In formula (10), is the pressure domain, For water quality domain, is the flow domain, is the adjusted flow rate, is the base flow rate, Typical range under normal operating conditions; The feature map library supports the following functions: Real-time output pressure compensation , is the real-time input pressure disturbance; Predicting water quality mixing concentration , is the input pollutant concentration; Generate flow regulation instructions , The planned base flow.

[0059] In this embodiment, the baseline water supply condition represents the hydraulic and water quality characteristics of the pipe network under normal operation, established through statistical analysis of long-term monitoring data. The amplitude of the main frequency reflects the energy distribution characteristics of the pressure fluctuation, the decay rate characterizes the damping characteristics of the pipe network, and the propagation velocity depends on the elastic modulus of the pipe material and the fluid density. Preferably, the system uses wavelet packet decomposition technology to extract characteristic parameters of pressure fluctuations in different frequency bands to improve the resolution of feature recognition.

[0060] The pressure fluctuation transfer function is used to describe the propagation law of pressure disturbance in the pipe network. The system time constant It can be measured by step response test to characterize the overall inertia of the pipe network; pressure wave propagation delay The pressure fluctuation transfer function is determined based on the length of the pipe section and the wave velocity. Furthermore, the pipeline aging correction factor can be introduced into the pressure fluctuation transfer function to dynamically adjust the function parameters according to the pipe age and corrosion degree, thereby improving the adaptability of the model.

[0061] Mixing transfer function in water quality mixing prediction matrix Calibration can be performed using tracer experiments, taking into account the impact of factors such as flow rate, pipe diameter, and number of elbows on mixing performance. Preferably, the system establishes a deep learning-based mixing performance prediction model that automatically generates a temporary transfer function when a new pollutant is detected and gradually refines it into the matrix.

[0062] The construction interference compensation coefficient comprehensively evaluates the impact of various construction activities on the water supply system. Preferably, the mechanical construction interference index is monitored by vibration sensors, the traffic index is calculated based on vehicle load data, and the environmental interference index takes into account factors such as sudden temperature changes. The analytic hierarchy process can be used to determine the relative importance of different types of interference. Furthermore, the system establishes a construction record database and enters planned construction information in advance to optimize the prediction of compensation coefficients.

[0063] This embodiment significantly improves the water supply system's ability to respond to sudden disturbances by constructing a multi-dimensional feature mapping relationship between the baseline water supply condition and the emergency water supply mode. The pressure fluctuation transfer function accurately describes the propagation law of pressure disturbances in the pipeline network, the water quality mixing prediction matrix achieves accurate prediction of pollutant diffusion, and the construction interference compensation coefficient effectively quantifies the impact of external construction on the system. This embodiment combines steady-state characteristics with dynamic interference to achieve coordinated optimization and control of pressure, water quality, and flow, effectively solving the problem of insufficient adaptability of traditional scheduling models under emergency conditions and ensuring the stable operation of the water supply system in complex environments.

[0064] In some embodiments, the alternative scheduling routes, water supply mutation features, response strategy library, optimization decision model and feature mapping relationship are stored in a structured manner, and a multi-dimensional water supply scheduling knowledge base containing water source characteristics, pipe network topology, scheduling strategy and emergency response is constructed, including: The alternative scheduling routes are topologically coded to generate a route feature description set containing path node sequence, pipe segment characteristic parameters and switching constraint conditions; The water supply mutation features are stored according to the type of abnormality, and a mutation feature database containing pressure mutation mode, flow mutation mode and water quality mutation mode is established, each mutation mode being associated with corresponding time-frequency feature parameters and trigger threshold; The response strategy library is hierarchically organized to form a hierarchical response strategy tree; The optimization decision model is divided into a model parameter set, a constraint condition set and a solution algorithm set, wherein the model parameter set contains hydraulic calculation parameters, water quality simulation parameters and economic evaluation parameters; The feature mapping relationship is constructed with multi-dimensional indexing to form a pressure-water quality-flow collaborative mapping relationship network, supporting multi-dimensional retrieval according to working condition type, abnormality level and construction phase; Based on the spatiotemporal features, an update mechanism for the multi-dimensional water supply scheduling knowledge base is established, including timed updating, event-triggered updating and incremental updating, the timed updating being configured to refresh the basic data at a preset period, the event-triggered updating being configured to start a comprehensive update when a major pipe network modification or water source change occurs, and the incremental updating being configured to record the scheduling decision execution effect in real time and perform local optimization.

[0065] In this embodiment, the topological coding of the alternative scheduling routes is implemented using the adjacency list structure in graph theory, the path node sequence records the complete delivery path from the water source to the user, the pipe segment characteristic parameters include key attributes such as pipe diameter, material and age, and the switching constraint conditions define operation restrictions such as minimum switching interval time. Preferably, the system uses spatial indexing technology to speed up route retrieval, supporting fast filtering of feasible scheduling schemes according to geographic regions.

[0066] The classification storage of water supply mutation features is based on historical event statistical analysis, the pressure mutation mode contains feature waveforms of scenes such as pipe explosion and pump station failure, the flow mutation mode distinguishes between user-side demand mutation and pipe network-side leakage, and the water quality mutation mode covers types such as pollutant invasion and abnormal residual chlorine. The time-frequency feature parameters are extracted through short-time Fourier transform, and the trigger threshold is set according to the severity level of the event. Further, the system establishes a mutation mode evolution map to record the transformation rules between different types of abnormalities.

[0067] The hierarchical response strategy tree is organized using a decision tree structure. The top layer branches out by event type, the middle layer divides response timeliness, and the bottom layer details specific operational instructions. Hydraulic calculation parameters include fluid dynamics parameters such as pipe friction coefficient, water quality simulation parameters involve reaction kinetic parameters such as pollutant decay rate, and economic evaluation parameters include operational indicators such as energy consumption cost. The model parameter set preferably incorporates a parameter sensitivity analysis module to automatically identify key parameters and increase their update frequency.

[0068] The pressure-water quality-flow collaborative mapping network is stored in a graph database, with nodes representing operating condition characteristics and edge weights reflecting the strength of feature associations. The system supports semantically based hybrid searches, such as querying for the control strategy corresponding to "abnormal water quality during construction." Scheduled updates are dynamically adjusted based on the rate of data change. Major pipeline network renovations trigger full database verification. Incremental updates utilize online learning algorithms to optimize strategies. For example, if the performance of a scheduling decision deviates from expectations after repeated execution, the system automatically adjusts relevant parameters and updates the knowledge base.

[0069] This embodiment achieves systematic management and intelligent application of scheduling elements by constructing a structured, multidimensional water supply scheduling knowledge base. This multidimensional water supply scheduling knowledge base transforms discrete scheduling experience into computable and optimizable digital assets, preserving the logical integrity of expert decision-making while also possessing the structured features required for machine learning. In particular, an update mechanism driven by spatiotemporal features ensures the continuous evolution of the knowledge base, providing core support for the transition of water supply systems from empirical scheduling to intelligent scheduling.

[0070] In a second aspect, this embodiment further provides a computer-readable storage medium storing computer program instructions, which implement the method described in the first aspect when executed by a processor.

[0071] The computer program involved in this embodiment can be stored in a computer device readable storage medium, which includes but is not limited to a disk, a magnetic tape, a magnetic card, a floppy disk, a flash memory, an optical disc, an optical card, a read-only memory (ROM), a random access memory (RAM), an erasable programmable ROM (EPROM) and an electrically erasable programmable ROM (EEPROM), etc., and also includes other biological, physical or chemical structures that can achieve similar or equivalent functions to the storage media listed above, such as DNA, RNA, proteins and other units with information storage capabilities. In a specific embodiment, the storage medium involved can be one of the above-mentioned media types or a combination of the above-mentioned media types. In different embodiments, the computer program involved in the embodiment can be stored in a single medium in a centralized manner or in a distributed manner in multiple media. The memory containing the computer device readable storage medium can be a non-volatile memory or a random access memory. These computer device readable storage media can be built into the device or can be connected to the device involved in the embodiment as an external device or part of an external device. In some embodiments, a memory having a computer-readable storage medium is deployed locally; in other embodiments, a solution of deploying the memory away from the processor may also be adopted, such as a network-attached memory accessed via an RF circuit or an external port and a communication network, wherein the communication network may be the Internet, one or more intranets, a local area network (LAN), a wide area network (WLAN), a storage area network (SAN), etc., or a suitable combination thereof, as long as the computer device can access the memory. In addition, the computer program involved in the embodiment can be stored in plaintext / ciphertext form, or can be designed as training data, which can be integrated and reorganized through model training and implicitly stored in the parameter state of a deep neural network or other machine learning model.

[0072] See also Figure 3 In a third aspect, this embodiment further provides an electronic device 1, comprising a memory 11 and a processor 12, wherein the memory 11 is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor 12 to implement the method described in the first aspect.

[0073] The processor described in this embodiment can be implemented by hardware, firmware, software or a combination thereof, and can use a circuit, a single or multiple application-specific integrated circuits (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, or at least one of a microprocessor. It also includes other physical, biological or chemical structures that can achieve similar or equivalent functions to the processors listed above, such as biological neurons, quantum computing units, DNA computing units, etc., so that the processor can execute some or all of the steps in the computer program or method involved in each embodiment of the present application, or any combination of the steps mentioned therein.

[0074] Unlike existing technologies, this technical solution offers the following benefits: By constructing a multidimensional water supply scheduling knowledge base and intelligent decision-making system, it effectively addresses the technical challenges of delayed response and insufficient control precision in traditional water supply systems when responding to sudden anomalies. A multidimensional data base is established based on water source characteristics, pipe network topology, and construction conditions, and a constrained optimization algorithm is used to generate alternative scheduling routes that balance engineering feasibility and economic efficiency. Time-frequency analysis is used to extract water supply mutation characteristics and establish a hierarchical response mechanism to accurately identify and rapidly address typical water supply anomaly scenarios, ranging from pipe bursts and leaks to water pollution. Furthermore, through feature mapping relationships such as the pressure fluctuation transfer function and the water quality mixing prediction matrix, coordinated optimization of pressure, water quality, and flow is achieved. This technical solution deeply integrates physical models with intelligent algorithms. A hydraulic-water quality coupling calculation model is used to accurately predict system states. Construction disturbance compensation coefficients are used to quantify external environmental impacts. A self-learning knowledge base is constructed to continuously optimize strategies, enabling the water supply system to transition from passive response to active defense. This significantly improves the stability of multi-source coordinated supply and the reliability of emergency switching, providing intelligent protection for water supply security in residential areas.

[0075] The above descriptions are only some embodiments of the present invention and do not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A water supply scheduling planning method for a residential area, characterized in that: include: Collect basic water supply data from nearby water sources, including water quality parameters, available water volume, water pipe network topology information, and construction access conditions; Preprocess the basic water supply data to obtain the optimized scheduling input data, which includes the water quality assessment results, dynamic water supply capacity curves, and a set of feasible construction paths; Based on the set of feasible construction paths, a constrained shortest path algorithm is used to generate alternative scheduling routes. The alternative scheduling routes must meet the pipe pressure limit and construction period constraints. The comprehensive cost weight of each alternative scheduling route is calculated. The comprehensive cost weight includes water transmission energy consumption, construction and modification costs, and emergency redundancy. Perform time-domain analysis on the dynamic water supply capacity curve to extract water supply mutation characteristics and establish a response strategy library that includes typical water supply anomaly scenarios, including at least one of sudden pollution, equipment failure, and demand peak. Based on water quality assessment results, alternative dispatching routes, water supply mutation characteristics, and response strategy database, an optimization decision-making model for multi-water source coordinated dispatching and emergency switching is constructed; Based on the optimization decision model, a characteristic mapping relationship between the baseline water supply condition and the emergency water supply mode is established. The characteristic mapping relationship includes the pressure fluctuation transfer function, the water quality mixing prediction matrix, and the construction interference compensation coefficient. The alternative scheduling routes, water supply mutation characteristics, response strategy library, optimization decision model and feature mapping relationship are structured and stored to build a multidimensional water supply scheduling knowledge base including water source characteristics, pipe network topology, scheduling strategy and emergency response.

2. The water supply scheduling planning method for residential areas according to claim 1 is characterized in that: Preprocess the basic water supply data to obtain the optimized scheduling input data. The optimized scheduling input data includes water quality assessment results, dynamic water supply capacity curves, and a set of feasible construction paths, including: Performing a multi-index weighted assessment on the water quality parameters of the water source to generate a water quality compliance assessment result, wherein the multi-index weighted assessment includes at least one of heavy metal content, turbidity, and residual chlorine concentration, and establishing a compliance matrix of the multiple indicators with the preset water use standards; Based on the available water volume and the water pipe network topology information, a hydraulic transient model is used to calculate a dynamic water supply capacity curve under different working conditions, wherein the dynamic water supply capacity curve includes at least one of a maximum water supply volume, a pressure fluctuation range, and a critical switching time; Based on the construction access conditions, a three-dimensional geographic constraint model is constructed. The three-dimensional geographic constraint model integrates road excavation restrictions, existing pipeline distribution, and geological survey data, and uses an improved Dijkstra algorithm to search for K feasible construction paths within the constraint space to form a set of feasible construction paths. The water quality compliance assessment results, dynamic water supply capacity curve and feasible construction path set are subjected to spatiotemporal alignment processing to generate standardized optimization scheduling input data.

3. The water supply scheduling planning method for residential areas according to claim 1 is characterized in that: Based on the set of feasible construction paths, a constrained shortest path algorithm is used to generate alternative scheduling routes. The alternative scheduling routes must meet the pipe pressure limit and construction period constraints, including: Establishing a multidimensional path evaluation model, the multidimensional path evaluation model including at least one of a hydraulic performance index, a construction feasibility index, and an economic index, and constructing a correlation matrix between the hydraulic performance index, the construction feasibility index, the economic index, and the engineering constraint conditions; Based on the set of feasible construction paths, a pressure-flow coupling algorithm is used to calculate hydraulic characteristic parameters of each path, where the hydraulic characteristic parameters include at least one of a steady-state water supply, a transient pressure fluctuation value, and a water hammer risk level; Based on the pressure limit of the pipe, a pressure safety assessment model is established. The pressure safety assessment model integrates the pipe strength coefficient, the operating pressure peak and the safety margin threshold to generate a set of pressure compliance paths; Combined with construction period constraints, a spatiotemporal conflict detection model is constructed. The spatiotemporal conflict detection model integrates construction machinery scheduling plans, traffic control periods, and resident impact assessments to output a set of feasible paths within the construction period. A multi-objective optimization algorithm is used to collaboratively solve the pressure-bearing compliance path set and the construction period feasible path set to generate a Pareto optimal alternative scheduling route set, which includes multiple alternative scheduling routes.

4. The water supply scheduling planning method for residential areas according to claim 3 is characterized in that: Calculate the comprehensive cost weight of each alternative dispatch route. The comprehensive cost weight includes water transmission energy consumption, construction and modification costs, and emergency redundancy, including: For each alternative dispatch route, do the following: The hydrophobic energy consumption value of the alternative scheduling route is calculated and expressed by formula (1), which is as follows: ； In formula (1), For the The water transmission energy consumption value of the alternative dispatching route is: For the The friction coefficient of the pipe section, For the The length of the pipe segment, For the The design flow rate of the pipe section, is the water density, is the standard acceleration due to gravity, For the The height difference between the alternative dispatch routes; The construction and modification cost of the alternative scheduling route is calculated using formula (2), which is as follows: ； In formula (2), For the The construction and modification cost of the alternative dispatching route, For the The pipe cost of the alternative dispatch route, For the The construction cost of an alternative dispatch route, For the Road repair costs for alternative dispatch routes; The emergency redundancy of the alternative dispatching route is calculated and expressed by formula (3), which is as follows: ； In formula (3), For the The emergency redundancy of the alternative dispatch routes, is the first weight coefficient, is the number of standby pumping stations, is the second weight coefficient, To quickly switch the number of valves, is the third weight coefficient, For emergency water reserves; The comprehensive cost weight of the alternative dispatch route is calculated and expressed by formula (4), which is as follows: ； In formula (4), is the weight coefficient of hydrophobic energy consumption, is the weight coefficient of construction and renovation cost, is the inverse weight coefficient of emergency redundancy, ,and ; Repeat the above steps until the comprehensive cost weights of all the alternative scheduling routes are calculated, and the comprehensive cost weights and the alternative scheduling paths are mapped and stored; And, sorting the alternative scheduling paths from high to low according to the comprehensive cost weight.

5. The water supply scheduling planning method for residential areas according to claim 1 is characterized in that: Perform time-domain analysis on the dynamic water supply capacity curve to extract water supply mutation characteristics and establish a response strategy library containing typical water supply anomaly scenarios, including: Performing spectrum analysis and waveform decomposition on the dynamic water supply capacity curve to identify at least one feature of a water supply pressure mutation point, a flow rate drop point, and an abnormal water quality parameter point; Based on the identification results, the water supply anomaly levels are divided into level one, level two, and level three. The level one anomaly is configured as a main water supply pipeline failure, the level two anomaly is configured as a local pipe network leak, and the level three anomaly is configured as a sudden increase in water demand. Establish a library of typical water supply abnormality scenarios, including main pump station shutdown scenarios, pipeline burst scenarios, and peak water consumption scenarios; Develop response strategies for each typical water supply anomaly scenario, including: If the main pump station is shut down, start the backup pump station and adjust the valve opening of the pipeline network; In the event of a pipe burst, close the nearest valve and activate the emergency water supply truck; For peak water consumption scenarios, adjust the water release from the reservoir and start the booster pump group; The water supply abnormality characteristics, abnormality level classification, typical water supply abnormality scenario library and response strategy are associated and stored to form a response strategy library.

6. The water supply scheduling planning method for residential areas according to claim 1 is characterized in that: Based on the water quality assessment results, alternative scheduling routes, water supply mutation characteristics, and response strategy library, an optimization decision-making model for multi-water source coordinated scheduling and emergency switching is constructed, including: A hydraulic-water quality coupling calculation model is established and expressed by formula (5), which is as follows: ； In formula (5), is the water head of the pressure measuring pipe section, is the gravitational acceleration component, is the flow rate at the pipe inlet, is the outlet flow rate of the pipe section, is the source term, is the water concentration, is the diffusion coefficient, is the spatial second-order derivative of water concentration, is the flow rate of the pipe section, is the cross-sectional area of ​​the pipe, is the water concentration gradient, is the water quality reaction rate constant; Solving the hydraulic-water quality coupling calculation model to obtain multiple solution results, each of which corresponds to a scheduling decision under a working condition; The scheduling decisions are screened by using the finite state machine principle combined with the water supply mutation characteristics to obtain the screened scheduling decisions, which are recorded as screening decisions. The screening conditions are defined, including: when When the emergency dispatch plan is activated, when When , switch to the hybrid regulation scheme; when When , switch to the boost scheduling plan; The multiple screening decisions are used to generate a multi-level buffer control strategy, which is expressed by formula (6): ； In formula (6), is the valve opening adjustment amount, is the proportional control coefficient, is the head deviation, is the integral control coefficient, is the historical head deviation integral variable, is the differential control coefficient; That is, the optimization decision model is generated.

7. The water supply scheduling planning method for residential areas according to claim 1 is characterized in that: Based on the optimization decision model, a characteristic mapping relationship between the baseline water supply condition and the emergency water supply mode is established. The characteristic mapping relationship includes the pressure fluctuation transfer function, the water quality mixing prediction matrix, and the construction interference compensation coefficient, including: Performing hydraulic modeling on a benchmark water supply condition to extract pressure fluctuation characteristic parameters, wherein the pressure fluctuation characteristic parameters include at least one of a main frequency amplitude, an attenuation rate, and a propagation speed; The pressure fluctuation transfer function is established and expressed by formula (7), which is as follows: ； In formula (7), is the transfer function of pressure fluctuation, is the Laplace complex variable, is the base of natural logarithm, is the pressure wave propagation delay, is the system time constant, is the Laplace transform of the input pressure disturbance, is the Laplace transform of the output pressure response; The water quality mixing prediction matrix is ​​constructed and expressed by formula (8), which is as follows: ； In formula (8), For the Pipe section Mixed transfer functions of various water quality parameters; The construction interference compensation coefficient is constructed and expressed by formula (9), which is as follows: ； In formula (9), is the construction interference compensation coefficient, is the fourth weight coefficient, for Construction interference index; The pressure fluctuation transfer function, water quality mixing prediction matrix and construction interference compensation coefficient are integrated to obtain the mapping characteristic relationship, which is expressed by formula (10). The formula (10) is as follows: ； In formula (10), is the pressure domain, For water quality domain, is the flow domain, is the adjusted flow rate, is the base flow rate.

8. The water supply scheduling planning method for residential areas according to claim 1 is characterized in that: The alternative scheduling routes, water supply mutation characteristics, response strategy library, optimization decision model and feature mapping relationship are structured and stored to build a multi-dimensional water supply scheduling knowledge base that includes water source characteristics, pipe network topology, scheduling strategy and emergency response, including: Topologically encoding the alternative scheduling route to generate a route feature description set including a path node sequence, link characteristic parameters, and switching constraint conditions; The water supply mutation characteristics are classified and stored according to the abnormal type, and a mutation characteristic database including pressure mutation pattern, flow mutation pattern and water quality mutation pattern is established, and each mutation pattern is associated with corresponding time-frequency characteristic parameters and trigger thresholds; The response strategy library is hierarchically organized to form a hierarchical response strategy tree; Decomposing the optimization decision model into three sub-libraries: a model parameter set, a constraint condition set, and a solution algorithm set, wherein the model parameter set includes hydraulic calculation parameters, water quality simulation parameters, and economic evaluation parameters; A multi-dimensional index is constructed for the characteristic mapping relationship to form a pressure-water quality-flow collaborative mapping relationship network, supporting multi-dimensional retrieval by working condition type, abnormality level and construction stage; An update mechanism for the multidimensional water supply scheduling knowledge base is established based on spatiotemporal characteristics, including scheduled updates, event-triggered updates, and incremental updates. The scheduled updates are configured to refresh basic data at a preset period, the event-triggered updates are configured to initiate a comprehensive update when a major pipeline network renovation or water source change occurs, and the incremental updates are configured to record the execution effects of scheduling decisions in real time and perform local optimization.

9. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 8 when executed by a processor.

10. An electronic device comprising a memory and a processor, characterized in that: The memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1 to 8.

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