A water supply network pressure control method, device and medium

Abstract

The embodiment of the present application discloses a method, device and medium for regulating the pressure of a water supply network. It belongs to the field of water supply network regulation technology and solves the problem of low accuracy of pressure regulation of a water supply network. The acquired state data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data are preprocessed; a hydraulic model is constructed based on the preprocessed data, and the hydraulic model is corrected in real time to simulate the real-time flow state of water in the water supply network through the hydraulic model; a dynamic pressure regulation strategy is generated based on the objective function, the preset optimization algorithm and the hydraulic model; wherein the dynamic pressure regulation strategy is related to the valve opening and the pump station power; the dynamic pressure regulation strategy is sent to the on-site equipment to adjust the valve opening and the operating state of the pump station to achieve regulation of the pressure of the water supply network.

Description

Technical Field

[0001] The present application relates to the technical field of water supply network regulation, and in particular to a water supply network pressure regulation method, equipment and medium. Background Art

[0002] With the acceleration of urbanization and the improvement of residents' living standards, the water supply network, as an important part of urban infrastructure, its stable operation and water supply quality are crucial to residents' lives and economic development.

[0003] Pipeline network pressure is an important indicator that restricts the effective water supply of the tap water system. It is also a core factor affecting the health, maintenance and service life of the pipeline network. It is also an important cost item for water companies.

[0004] In the water supply network, the stability of water pressure is of great significance for ensuring water supply quality, preventing network damage and improving water supply efficiency. However, the existing water supply network pressure control usually relies on experience settings or simple pressure sensor feedback for valve adjustment, which has problems such as large pressure fluctuations, high energy consumption, and high leakage rate, resulting in low accuracy of water supply network pressure control. Summary of the invention

[0005] The embodiments of the present application provide a water supply network pressure control method, equipment and medium for solving the following technical problems: the existing water supply network pressure control usually relies on experience settings or simple pressure sensor feedback to adjust the valve, and there are problems such as large pressure fluctuations, high energy consumption, and high leakage rate, resulting in low accuracy of water supply network pressure control.

[0006] The present application embodiment adopts the following technical solutions:

[0007] The embodiment of the present application provides a water supply network pressure control method. The method includes preprocessing the acquired state data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data; constructing a hydraulic model based on the preprocessed data, and correcting the hydraulic model in real time, so as to simulate the real-time flow state of water in the water supply network through the hydraulic model; generating a dynamic pressure control strategy based on the objective function, the preset optimization algorithm and the hydraulic model; wherein the dynamic pressure control strategy is related to the valve opening and the pump station power; sending the dynamic pressure control strategy to the on-site equipment to adjust the valve opening and the pump station operation state, so as to realize the control of the water supply network pressure.

[0008] The embodiment of the present application improves data quality by preprocessing the data, removing noise and erroneous data. Secondly, by constructing a hydraulic model, it is possible to simulate the real-time flow state of water in the water supply network, providing a theoretical basis for pressure control and flow distribution. Real-time correction can ensure that the model is always consistent with the actual situation, improving the accuracy and reliability of the simulation. The dynamic pressure control strategy can be adjusted according to real-time data and prediction results to ensure that the water supply system always operates in the best state. By optimizing the valve opening and pump station power, energy saving and consumption reduction can be achieved, and the operating cost of the water supply system can be reduced. The embodiment of the present application combines a series of steps such as data preprocessing, hydraulic model construction and real-time correction, dynamic pressure control strategy generation, and field equipment control to achieve comprehensive monitoring and optimized management of the water supply system, improve the safety and reliability of the water supply system, and reduce operating costs.

[0009] In one implementation of the present application, the acquired status data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data are preprocessed, specifically including: based on sensors arranged at key nodes of the water supply network, obtaining the operating status data corresponding to the water supply network; wherein the operating status data includes at least one of the pressure value, flow value, flow velocity and temperature; performing time series analysis on the operating status data to determine the changing characteristics of the operating status data; constructing the topological structure of the water supply network based on the geographic information data corresponding to the water supply network, and allocating corresponding water supply network attribute data in the topological structure; predicting the water demand data based on historical water use data, and classifying the predicted water demand data based on water use type.

[0010] In one implementation of the present application, a topological structure of a water supply network is constructed based on geographic information data corresponding to the water supply network, and corresponding water supply network attribute data is allocated in the topological structure, specifically including: dividing the geographic information data corresponding to the water supply network based on preset geographic element types; wherein the preset geographic element types include at least one of pipelines, nodes, valves, and pumping stations; setting different marking symbols for the geographic information data corresponding to the divided water supply network to construct the topological structure of the water supply network; determining the water supply network data corresponding to each geographic element in the topological structure of the water supply network, and binding the water supply network data to the topological structure of the water supply network.

[0011] In one implementation of the present application, a hydraulic model is constructed based on preprocessed data, and the hydraulic model is corrected in real time, specifically including: based on a preset model construction software, the preprocessed data is used as input to construct a hydraulic model; the operation result of the hydraulic model is obtained to obtain a first detection result corresponding to the water supply network based on the operation result; based on a preset interval time, the status data of the water supply network is obtained to periodically update the hydraulic model; the operation result of the updated hydraulic model is obtained, the operation result is compared with the real-time data to determine the error index data, and the second detection result is determined based on the error index data; based on the first detection result, the second detection result and the preset weight, a reference error value is determined, and when the reference error value meets the preset error condition, it is determined that the correction is completed.

[0012] In one implementation of the present application, a dynamic pressure control strategy is generated based on an objective function, a preset optimization algorithm and a hydraulic model, specifically including: constructing an objective function based on minimizing total energy consumption, maximizing water supply stability, maximum limiting pressure and minimum limiting pressure; iterating based on a genetic algorithm and the objective function control parameters to adjust the control parameters according to the calculation results of the objective function, and predicting a new state through a hydraulic model; wherein the control parameters are related to the valve opening and the pump station power; determining the effect of the current control strategy based on the calculation results corresponding to the objective function, and comparing the current control strategy effect with the historical control strategy effect to determine the current optimal solution; when the preset number of iterations is reached or the convergence conditions are met, generating a dynamic pressure control strategy based on the current optimal solution.

[0013] In one implementation of the present application, a dynamic pressure control strategy is sent to on-site equipment to adjust the valve opening and the operating status of the pump station, specifically including: after adjusting the water supply network based on the dynamic pressure control strategy, when the water supply network pressure is not within the preset range, determining the difference between the water supply network pressure and the preset interval; comparing the difference and the dynamic pressure control strategy with the preset historical database respectively to determine the reference data with the highest similarity; based on the mapping relationship in the preset historical database, determining the secondary adjustment data associated with the reference data; and making secondary adjustments to the valve opening and the operating status of the pump station according to the secondary adjustment data.

[0014] In one implementation of the present application, after the dynamic pressure control strategy is sent to the on-site equipment to adjust the valve opening and the operating status of the pump station, the method also includes: obtaining historical work order data corresponding to the water supply network; determining the maintenance section corresponding to the water supply network based on the historical work order data, and determining the number of maintenance times and fault causes corresponding to different maintenance sections; based on the number of maintenance times and the fault causes, determining the risk level corresponding to each maintenance section, and associating the risk level with the hydraulic model.

[0015] In one implementation of the present application, after associating the risk level with the hydraulic model, the method also includes: marking the pipe sections with risk levels in the hydraulic model, and determining the corresponding causes of failure and maintenance times; after a preset time interval, inputting the risk level, cause of failure and maintenance times, and attribute data of the pipe sections with risk levels into a preset pipe section operating time prediction model to output the predicted operating time corresponding to the pipe sections with risk levels.

[0016] An embodiment of the present application provides a water supply network pressure control device, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: pre-process the acquired status data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data; construct a hydraulic model based on the pre-processed data, and perform real-time correction on the hydraulic model to simulate the real-time flow state of water in the water supply network through the hydraulic model; generate a dynamic pressure control strategy based on the objective function, a preset optimization algorithm and the hydraulic model; wherein the dynamic pressure control strategy is related to the valve opening and the pump station power; send the dynamic pressure control strategy to the on-site equipment to adjust the valve opening and the operating status of the pump station to achieve regulation of the water supply network pressure.

[0017] A non-volatile computer storage medium provided in an embodiment of the present application stores computer executable instructions, and the computer executable instructions are configured to: pre-process the acquired status data of a water supply network, the geographic information data corresponding to the water supply network, and the water demand data; construct a hydraulic model based on the pre-processed data, and perform real-time correction on the hydraulic model to simulate the real-time flow state of water in the water supply network through the hydraulic model; generate a dynamic pressure control strategy based on an objective function, a preset optimization algorithm, and a hydraulic model; wherein the dynamic pressure control strategy is related to the valve opening and the power of a pump station; and send the dynamic pressure control strategy to on-site equipment to adjust the valve opening and the operating state of the pump station to achieve control of the pressure of the water supply network.

[0018] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: The embodiments of the present application improve data quality by preprocessing the data, removing noise and erroneous data. Secondly, by constructing a hydraulic model, it is possible to simulate the real-time flow state of water in the water supply network, providing a theoretical basis for pressure regulation and flow distribution. Real-time correction can ensure that the model is always consistent with the actual situation, and improve the accuracy and reliability of the simulation. The dynamic pressure control strategy can be adjusted according to real-time data and prediction results to ensure that the water supply system always operates in the best state. By optimizing the valve opening and pump station power, energy saving and consumption reduction can be achieved, and the operating cost of the water supply system can be reduced. The embodiment of the present application combines a series of steps such as data preprocessing, hydraulic model construction and real-time correction, dynamic pressure control strategy generation, and field equipment control to achieve comprehensive monitoring and optimization management of the water supply system, improve the safety and reliability of the water supply system, and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0020] In the figure:

[0021] Figure 1 A flow chart of a water supply network pressure control method provided in an embodiment of the present application;

[0022] Figure 2 A schematic diagram of the structure of a water supply network pressure regulating device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The embodiments of the present application provide a water supply network pressure control method, equipment and medium.

[0024] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0025] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0026] Figure 1 A flow chart of a water supply network pressure control method provided in an embodiment of the present application, such as Figure 1 As shown, the water supply network pressure control method includes the following steps:

[0027] S101. Preprocess the acquired status data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data.

[0028] In one embodiment of the present application, based on sensors set at key nodes of the water supply network, the operating status data corresponding to the water supply network is obtained; wherein the operating status data includes at least one of a pressure value, a flow value, a flow rate, and a temperature. A time series analysis is performed on the operating status data to determine the change characteristics of the operating status data. A topological structure of the water supply network is constructed based on the geographic information data corresponding to the water supply network, and corresponding water supply network attribute data is allocated in the topological structure. Based on historical water use data, water demand data is predicted, and based on water use type, the predicted water demand data is classified.

[0029] Specifically, sensors are installed at key nodes of the water supply network (such as water pumping stations, large intersections, user access points, etc.) to obtain real-time or regular operating status data such as pressure, flow, flow rate, and temperature of the network. Time series analysis is performed on the collected operating status data to identify data trends, seasonal changes, cyclical changes, and other characteristics. Based on the geographic information data of the water supply network (such as GIS data), a topological structure diagram of the network is constructed to display the connection relationship, pipeline length, diameter and other attributes of the network. Corresponding attribute data is assigned to each pipeline or key node in the topological structure, such as pipeline material, service life, maintenance records, etc. According to the type of water use (such as residential water, industrial water, agricultural water, etc.), the predicted water demand data is classified to better understand and manage different types of water demand.

[0030] In one embodiment of the present application, the geographic information data corresponding to the water supply network is divided based on preset geographic element types; wherein the preset geographic element types include at least one of pipelines, nodes, valves, and pump stations. Different marking symbols are set for the divided geographic information data corresponding to the water supply network to construct the topological structure of the water supply network. The water supply network data corresponding to each geographic element in the topological structure of the water supply network is determined, and the water supply network data is bound to the topological structure of the water supply network.

[0031] Specifically, in a water supply network, geographic feature types usually refer to the various physical components and facilities that make up the network. These types include but are not limited to pipes, nodes, valves, and pumping stations. In a geographic information system (GIS), geographic information data of a water supply network usually includes spatial data (such as the direction, length, and location of the pipeline) and attribute data (such as the diameter, material, and burial depth of the pipeline). According to the preset geographic feature types, these data can be divided into different layers or data sets. In order to clearly display the topological structure of the water supply network on the map, different marking symbols need to be set for each geographic feature type. These symbols can be visual elements such as color, shape, and size to quickly distinguish different types of geographic features on the map.

[0032] Furthermore, using GIS software, the divided geographic information data is imported into the map, and the topological structure of the water supply network is constructed according to the set marking symbols. In the topological structure, different types of geographic elements will be displayed in different ways, such as pipelines represented by lines, nodes represented by dots or circles, and valves and pumping stations represented by specific icons. In the topological structure, each geographic element is associated with specific water supply network data. For example, a pipeline may contain attribute data such as its length, diameter, material, and burial depth; a node may contain information such as its location and type (such as water source point, intersection, etc.); a valve or pumping station may contain data such as its operating status and control parameters. In GIS, the binding of water supply network data and topological structure can be achieved by associating attribute data with spatial data. When a geographic element is selected on the map, its related attribute data can be immediately viewed and edited.

[0033] S102: construct a hydraulic model based on the preprocessed data, and perform real-time correction on the hydraulic model to simulate the real-time flow state of water in the water supply network through the hydraulic model.

[0034] In one embodiment of the present application, a hydraulic model is constructed based on a preset model construction software, using preprocessed data as input. The operation result of the hydraulic model is obtained to obtain a first detection result corresponding to the water supply network based on the operation result. Based on a preset interval duration, the status data of the water supply network is obtained to perform periodic data updates on the hydraulic model. The operation result of the updated hydraulic model is obtained, and the operation result is compared with the real-time data to determine the error index data, and the second detection result is determined based on the error index data. Based on the first detection result, the second detection result and the preset weight, a reference error value is determined, and when the reference error value meets the preset error condition, it is determined that the correction is completed.

[0035] Specifically, a hydraulic model is constructed using programming languages ​​such as Python or C++ in combination with hydraulic model libraries such as hydraulic analysis software. Before constructing the hydraulic model, the raw data needs to be preprocessed, such as cleaning, format conversion, missing value filling, etc., to ensure the quality and accuracy of the data. Using the preprocessed data, a hydraulic model is constructed in the software system, through which the dynamic changes of water flow, pressure, etc. in the water supply network can be simulated. The hydraulic model is run to obtain simulation results, such as pressure and flow at each node. Based on the operation results of the hydraulic model, a preliminary analysis of the state of the water supply network is performed to obtain the first detection result.

[0036] Furthermore, based on a preset interval (such as every hour, every day, etc.), the real-time status data of the water supply network is regularly obtained to update the hydraulic model. The operating results of the updated hydraulic model are compared with the real-time data, and error index data, such as mean square error, maximum error, etc., are calculated. Based on the error index data, the difference between the hydraulic model and the actual situation is analyzed to determine the second test result. Considering the first test result, the second test result and the preset weight, a reference error value is calculated. If the reference error value meets the preset error condition (such as less than a certain threshold), the correction is considered to be completed.

[0037] S103, generating a dynamic pressure control strategy based on the objective function, the preset optimization algorithm and the hydraulic model.

[0038] In one embodiment of the present application, an objective function is constructed based on minimizing total energy consumption, maximizing water supply stability, maximum limiting pressure, and minimum limiting pressure. Iteration is performed based on a genetic algorithm and the objective function control parameters to adjust the control parameters according to the calculation results of the objective function, and a new state is predicted through the hydraulic model; wherein the control parameters are related to the valve opening and the pump station power. The effect of the current control strategy is determined based on the calculation results corresponding to the objective function, and the effect of the current control strategy is compared with the effect of the historical control strategy to determine the current optimal solution. When the preset number of iterations is reached or the convergence conditions are met, a dynamic pressure control strategy is generated based on the current optimal solution. wherein the dynamic pressure control strategy is related to the valve opening and the pump station power.

[0039] Specifically, it is necessary to first construct objective functions that can quantitatively evaluate the performance of the water supply system under different control parameters. The objective functions include: minimizing total energy consumption, maximizing water supply stability, maximum limiting pressure, and minimum limiting pressure. Based on the genetic algorithm, a set of control parameter combinations are randomly generated as the initial population. Each parameter combination in the population is input into the hydraulic model to predict the new state of the water supply system, and the fitness value of each parameter combination is calculated according to the objective function. According to the fitness value, excellent individuals are selected as the parents of the next generation. The parent generation is crossover and mutation operations are performed to generate new individuals to increase the diversity and search ability of the population. The above steps are repeated to continuously iterate and update the population until the preset number of iterations or convergence conditions are met.

[0040] Furthermore, in the water supply system, the control parameters are usually directly related to the valve opening and the pump station power. The valve opening determines the distribution and flow direction of the water flow in the pipe network, while the pump station power determines the driving force and energy consumption of the water flow. Therefore, in the iterative process of the genetic algorithm, these parameters will be optimized as decision variables.

[0041] Furthermore, in each iteration, the effect of the current control strategy will be evaluated based on the calculation results of the objective function. At the same time, the effect of the current strategy will be compared with the effect of the historical strategy to determine the current optimal solution. Specifically, this is achieved by recording the optimal solution in each iteration and selecting the global optimal solution after the iteration. When the preset number of iterations is reached or the convergence condition is met, the genetic algorithm will stop iterating and output the current optimal solution. This optimal solution is the optimal control parameter combination derived from objective functions such as minimizing total energy consumption, maximizing water supply stability, maximum limiting pressure and minimum limiting pressure. Based on this optimal solution, a corresponding dynamic pressure control strategy can be generated to guide the actual operation of the water supply system.

[0042] S104: Send the dynamic pressure control strategy to the on-site equipment to adjust the valve opening and the operating status of the pump station to achieve the control of the water supply network pressure.

[0043] In one embodiment of the present application, after the water supply network is adjusted based on the dynamic pressure control strategy, when the water supply network pressure is not within the preset interval, the difference between the water supply network pressure and the preset interval is determined. The difference and the dynamic pressure control strategy are respectively compared with the preset historical database to determine the reference data with the highest similarity. Based on the mapping relationship in the preset historical database, the secondary adjustment data associated with the reference data is determined. The valve opening and the operating status of the pump station are adjusted secondary according to the secondary adjustment data.

[0044] Specifically, a preset pressure range is set according to actual needs (such as water consumption, water use time, etc.) to ensure the quality and efficiency of water supply. The system monitors the pressure of the water supply network in real time to determine whether the current pressure is within the preset range. The calculated difference is compared with the dynamic pressure control strategy and the preset historical database. The historical database may contain the adjustment data and corresponding pressure changes in various previous situations (such as different water consumption, different time periods, etc.). The reference data with the highest similarity to the current difference in the historical database is searched. Based on the mapping relationship in the preset historical database, the secondary adjustment data associated with the reference data can be determined. These secondary adjustment data may include the adjustment amount of the valve opening, the change of the operating status of the pump station, etc. According to the determined secondary adjustment data, the valve opening and the operating status of the pump station are adjusted secondary. For example, if the secondary adjustment data indicates that the valve opening needs to be increased to reduce the water supply resistance, the system will automatically adjust the opening of the relevant valve; if the operating status of the pump station needs to be adjusted (such as increasing or decreasing the number or speed of pumps), the system will also adjust accordingly.

[0045] In one embodiment of the present application, historical work order data corresponding to the water supply network is obtained. Based on the historical work order data, the maintenance section corresponding to the water supply network is determined, and the number of maintenance times and fault causes corresponding to different maintenance sections are determined. Based on the number of maintenance times and fault causes, the risk level corresponding to each maintenance section is determined, and the risk level is associated with the hydraulic model.

[0046] Specifically, the embodiment of the present application first collects historical work order data corresponding to the water supply network. These work orders usually record detailed information of maintenance activities, including maintenance time, location, maintenance personnel, maintenance content, fault causes, etc. These data are usually stored in a database or file system. Based on the historical work order data, different maintenance sections in the water supply network can be determined. The maintenance section can be a specific area, pipeline section or equipment unit in the pipe network. By analyzing the maintenance location information in the work order, the relevant maintenance activities can be classified into the corresponding maintenance section. For each maintenance section, its historical maintenance times and common fault causes are counted. The maintenance times reflect the fault frequency of the maintenance section, while the fault cause provides information about the fault type and possible causes. By counting and analyzing these data, the maintenance history and fault characteristics of each maintenance section can be understood. Based on the maintenance times and fault causes, a risk level can be determined for each maintenance section. The determination of the risk level can be based on certain evaluation criteria, taking into account factors such as the maintenance times, fault types, and fault consequences. For example, a maintenance section with a large number of maintenance times and serious fault consequences can be assessed as a high risk level. Finally, the determined risk level is associated with the hydraulic model of the water supply network. The hydraulic model is a mathematical model used to simulate and analyze the hydraulic characteristics of the water supply network, such as the flow state and pressure distribution. By associating the risk level with the hydraulic model, the physical characteristics and historical maintenance data of the network can be comprehensively considered to provide more comprehensive and accurate information support for the maintenance and management of the network.

[0047] In one embodiment of the present application, the pipe sections with risk levels in the hydraulic model are marked, and the corresponding fault causes and maintenance times are determined. After a preset time interval, the risk level, fault cause and maintenance times, and attribute data of the pipe section with risk level are input into the preset pipe section operation time prediction model to output the predicted operation time corresponding to the pipe section with risk level.

[0048] Specifically, in the hydraulic model, the pipe sections with risks need to be marked first. These risk levels are determined based on the number of repairs and causes of failures in the historical work order data. For example, the pipe sections can be divided into several risk levels, such as "low risk", "medium risk" and "high risk", and marked accordingly in the hydraulic model. For each pipe section with a risk level, its common failure causes and number of repairs need to be determined. This information can be extracted from the historical work order data and stored as part of the pipe section attributes. After determining the risk level, failure cause and number of repairs, it is necessary to re-evaluate the status of these pipe sections at a preset interval. This preset interval can be determined based on the actual situation of the pipeline network, for example, it can be set to be quarterly, semi-annually or annually. After the preset interval, the risk level, failure cause, number of repairs and attribute data of the pipe section with risk level are input into the preset pipe section operation time prediction model. This prediction model may be built based on machine learning or statistical methods to predict the future operation time of the pipe section based on historical data and current status. The prediction model will calculate based on the input data and output the predicted operation time corresponding to the pipe section with risk level. This predicted operating time can indicate how long a pipe segment is likely to continue to operate normally in the future, or when a pipe segment is likely to need repair or replacement.

[0049] Figure 2 This is a schematic diagram of the structure of a water supply network pressure control device provided in an embodiment of the present application. Figure 2 As shown, a water supply network pressure control device is characterized in that the device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: pre-process the acquired status data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data; construct a hydraulic model based on the pre-processed data, and perform real-time correction on the hydraulic model to simulate the real-time flow state of water in the water supply network through the hydraulic model; generate a dynamic pressure control strategy based on the objective function, the preset optimization algorithm and the hydraulic model; wherein the dynamic pressure control strategy is related to the valve opening and the pump station power; send the dynamic pressure control strategy to the on-site equipment to adjust the valve opening and the operating status of the pump station to achieve the regulation of the water supply network pressure.

[0050] The embodiment of the present application also provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to: pre-process the acquired status data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data; construct a hydraulic model based on the pre-processed data, and perform real-time correction on the hydraulic model to simulate the real-time flow state of water in the water supply network through the hydraulic model; generate a dynamic pressure control strategy based on the objective function, a preset optimization algorithm, and the hydraulic model; wherein the dynamic pressure control strategy is related to the valve opening and the pump station power; and send the dynamic pressure control strategy to the on-site equipment to adjust the valve opening and the operating state of the pump station to achieve control of the water supply network pressure.

[0051] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0052] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various changes and variations. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A water supply network pressure control method, characterized in that: The method comprises: Preprocessing the acquired status data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data; A hydraulic model is constructed based on the preprocessed data, and the hydraulic model is corrected in real time to simulate the real-time flow state of water in the water supply network through the hydraulic model; Based on the objective function, the preset optimization algorithm and the hydraulic model, a dynamic pressure control strategy is generated; wherein the dynamic pressure control strategy is related to the valve opening and the pump station power; Sending the dynamic pressure control strategy to the on-site equipment to adjust the valve opening and the operating status of the pump station to achieve the control of the water supply network pressure; The generating of the dynamic pressure control strategy based on the objective function, the preset optimization algorithm and the hydraulic model specifically includes: The objective function is constructed based on minimizing total energy consumption, maximizing water supply stability, maximum limiting pressure, and minimum limiting pressure; Iterating based on the genetic algorithm and the objective function control parameter to adjust the control parameter according to the calculation result of the objective function, and predicting a new state through the hydraulic model; wherein the control parameter is related to the valve opening and the pump station power; Determine the current control strategy effect according to the calculation result corresponding to the objective function, and compare the current control strategy effect with the historical control strategy effect to determine the current optimal solution; When a preset number of iterations is reached or a convergence condition is met, a dynamic pressure control strategy is generated based on the current optimal solution.

2. A water supply network pressure control method according to claim 1, characterized in that: The preprocessing of the acquired state data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data specifically includes: Based on sensors arranged at key nodes of the water supply network, the operating status data corresponding to the water supply network is obtained; wherein the operating status data includes at least one of a pressure value, a flow value, a flow velocity and a temperature; Performing time series analysis on the operating status data to determine the change characteristics of the operating status data; Constructing a topological structure of the water supply network based on the geographic information data corresponding to the water supply network, and allocating corresponding water supply network attribute data in the topological structure; Based on historical water use data, water demand data is predicted, and based on water use type, the predicted water demand data is classified.

3. A water supply network pressure control method according to claim 2, characterized in that: The step of constructing a topological structure of the water supply network based on the geographic information data corresponding to the water supply network and allocating corresponding water supply network attribute data in the topological structure specifically includes: Based on preset geographic element types, the geographic information data corresponding to the water supply network is divided; wherein the preset geographic element types include at least one of pipelines, nodes, valves and pump stations; Setting different marking symbols for the geographical information data corresponding to the divided water supply pipe network to construct a topological structure of the water supply pipe network; The water supply network data corresponding to each geographical element in the topological structure of the water supply network are determined, and the water supply network data are bound to the topological structure of the water supply network.

4. A water supply network pressure control method according to claim 1, characterized in that: The construction of a hydraulic model based on the preprocessed data and real-time correction of the hydraulic model specifically include: Building software based on a preset model, taking the preprocessed data as input, and building the hydraulic model; Obtaining an operation result of the hydraulic model to obtain a first detection result corresponding to the water supply network based on the operation result; Based on a preset interval duration, the status data of the water supply network is obtained to periodically update the data of the hydraulic model; Obtaining the updated operation result of the hydraulic model, comparing the operation result with the real-time data to determine error index data, and determining a second detection result based on the error index data; Based on the first detection result, the second detection result and the preset weight, a reference error value is determined, and when the reference error value meets the preset error condition, it is determined that the correction is completed.

5. A water supply network pressure control method according to claim 1, characterized in that: The dynamic pressure control strategy is sent to the field equipment to adjust the valve opening and the pump station operation state, specifically including: After the water supply network is adjusted based on the dynamic pressure control strategy, if the pressure of the water supply network is not within the preset interval, determining the difference between the pressure of the water supply network and the preset interval; Comparing the difference and the dynamic pressure control strategy with a preset historical database respectively to determine the reference data with the highest similarity; Determining secondary adjustment data associated with the reference data based on the mapping relationship in the preset historical database; The valve opening and the operating status of the pump station are secondarily adjusted according to the secondary adjustment data.

6. A water supply network pressure control method according to claim 1, characterized in that: After sending the dynamic pressure control strategy to the field equipment to adjust the valve opening and the pump station operation status, the method further includes: Obtaining historical work order data corresponding to the water supply network; Determine the maintenance section corresponding to the water supply network based on the historical work order data, and determine the maintenance times and fault causes corresponding to different maintenance sections; Based on the maintenance times and the fault causes, risk levels corresponding to the maintenance sections are determined, and the risk levels are associated with the hydraulic model.

7. A water supply network pressure control method according to claim 6, characterized in that: After associating the risk level with the hydraulic model, the method further comprises: Marking the pipe sections with risk levels in the hydraulic model, and determining the corresponding fault causes and maintenance times; After a preset time interval, the risk level, the fault cause and the number of maintenance times, and the attribute data of the pipe section with the risk level are input into the preset pipe section operation time prediction model to output the predicted operation time corresponding to the pipe section with the risk level.

8. A water supply network pressure control device, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Preprocessing the acquired status data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data; A hydraulic model is constructed based on the preprocessed data, and the hydraulic model is corrected in real time to simulate the real-time flow state of water in the water supply network through the hydraulic model; Based on the objective function, the preset optimization algorithm and the hydraulic model, a dynamic pressure control strategy is generated; wherein the dynamic pressure control strategy is related to the valve opening and the pump station power; Sending the dynamic pressure control strategy to the on-site equipment to adjust the valve opening and the operating status of the pump station to achieve the control of the water supply network pressure; The generating of the dynamic pressure control strategy based on the objective function, the preset optimization algorithm and the hydraulic model specifically includes: The objective function is constructed based on minimizing total energy consumption, maximizing water supply stability, maximum limiting pressure, and minimum limiting pressure; Iterating based on the genetic algorithm and the objective function control parameter to adjust the control parameter according to the calculation result of the objective function, and predicting a new state through the hydraulic model; wherein the control parameter is related to the valve opening and the pump station power; Determine the current control strategy effect according to the calculation result corresponding to the objective function, and compare the current control strategy effect with the historical control strategy effect to determine the current optimal solution; When a preset number of iterations is reached or a convergence condition is met, a dynamic pressure control strategy is generated based on the current optimal solution.

9. A non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to: Preprocessing the acquired status data of the water supply network, the geographic information data corresponding to the water supply network, and the water demand data; A hydraulic model is constructed based on the preprocessed data, and the hydraulic model is corrected in real time to simulate the real-time flow state of water in the water supply network through the hydraulic model; Based on the objective function, the preset optimization algorithm and the hydraulic model, a dynamic pressure control strategy is generated; wherein the dynamic pressure control strategy is related to the valve opening and the pump station power; Sending the dynamic pressure control strategy to the on-site equipment to adjust the valve opening and the operating status of the pump station to achieve the control of the water supply network pressure; The generating of the dynamic pressure control strategy based on the objective function, the preset optimization algorithm and the hydraulic model specifically includes: The objective function is constructed based on minimizing total energy consumption, maximizing water supply stability, maximum limiting pressure, and minimum limiting pressure; Iterating based on the genetic algorithm and the objective function control parameter to adjust the control parameter according to the calculation result of the objective function, and predicting a new state through the hydraulic model; wherein the control parameter is related to the valve opening and the pump station power; Determine the current control strategy effect according to the calculation result corresponding to the objective function, and compare the current control strategy effect with the historical control strategy effect to determine the current optimal solution; When a preset number of iterations is reached or a convergence condition is met, a dynamic pressure control strategy is generated based on the current optimal solution.

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