A flow regulating valve opening degree prediction scheduling method and device based on a water supply pipe network model and electronic equipment

Abstract

The application provides a flow regulating valve opening degree prediction scheduling method and device based on a water supply pipe network model, and electronic equipment, and relates to the technical field of intelligent scheduling of water supply pipe networks, which comprises the following steps: obtaining the resistance coefficient of the flow regulating valve at different opening degrees, forming a sample data set, and establishing an opening degree-resistance coefficient mapping relationship model; according to a preset target flow and target valve downstream pressure, and in combination with the valve upstream pressure determined by the hydraulic model, the target resistance coefficient is calculated and input into the mapping model to inversely solve and predict the opening degree; the predicted opening degree is assigned to the hydraulic model for simulation verification, if the preset condition is not met, the model is corrected and recalculated until the optimal opening degree is obtained; the optimal opening degree is output for scheduling, and the mapping model is updated online by using real-time operation data. The application realizes data-driven valve characteristic self-learning and closed-loop prediction scheduling, has high opening degree prediction precision and strong adaptability, and greatly improves the automation level and operation stability of pipe network scheduling.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling technology for water supply networks, and in particular to a method, device, and electronic equipment for predicting and scheduling the opening degree of flow regulating valves based on a water supply network model. Background Technology

[0002] Currently, urban water supply network scheduling commonly employs hydraulic models such as EPANET for steady-state or transient calculations, combined with SCADA systems to monitor pressure and flow in real time. The opening of flow control valves is then adjusted manually or semi-automatically to achieve pressure balance, flow distribution, and reduce the risk of leakage and pipe bursts. As a core regulating device in the network, the opening of the flow control valve directly determines the pressure before and after the valve, the flow rate in the pipe section, and the overall hydraulic state.

[0003] The existing flow control valve scheduling technology has the following defects and shortcomings: (1) Relying on the Kv-opening curve or resistance coefficient K-opening curve provided by the manufacturer, but these curves deviate greatly from the actual characteristics of the valves on site due to scaling, wear and aging.

[0004] (2) When the flow rate and head loss are unknown, the hydraulic model of the pipeline network can only calculate the pressure and flow rate from the opening or resistance coefficient. It cannot directly calculate the valve opening from the target flow rate and the pressure after the valve. It requires repeated manual calculations, which is inefficient and has a large error.

[0005] (3) The valve hydraulic model is a static model and cannot be dynamically updated as the operating data accumulates, resulting in a continuous decrease in scheduling accuracy over time.

[0006] (4) The lack of a closed-loop mechanism of "prediction-simulation-execution-feedback" makes it difficult to cope with time-varying operating conditions, valve jamming, and pressure fluctuations. In addition, conventional models are difficult to accurately match the nonlinear characteristics of flow control valves, and are prone to inaccuracies in low-flow and high-flow scenarios.

[0007] Therefore, a method, device, and electronic equipment for predicting and scheduling the opening of flow control valves based on a water supply network model are proposed. Summary of the Invention

[0008] This specification provides a method, device, and electronic equipment for predicting and scheduling the opening degree of flow regulating valves based on a water supply network model. It realizes data-driven valve characteristic self-learning and closed-loop predictive scheduling, with high opening degree prediction accuracy and strong adaptability, which greatly improves the automation level and operational stability of network scheduling.

[0009] This specification provides a method for predicting and scheduling the opening of flow control valves based on a water supply network model, including: Obtain the resistance coefficient of the flow control valve at multiple different opening degrees to form a sample dataset; establish a mapping relationship model between the opening degree and the resistance coefficient based on the sample data set; calculate the required target resistance coefficient according to the preset target flow rate and target post-valve pressure, in combination with the pre-valve pressure determined by the hydraulic model of the water supply pipe network; input the target resistance coefficient into the mapping relationship model, and inversely calculate to obtain the predicted opening degree; assign the predicted opening degree to the hydraulic model of the water supply pipe network for hydraulic simulation verification, and if the verification result does not meet the preset condition, correct the mapping relationship model and perform inverse calculation again until the optimal opening degree is obtained; output the optimal opening degree to execute scheduling, and update the mapping relationship model by using the real-time operation data collected after scheduling.

[0010] Optionally, the acquiring the resistance coefficients of the flow regulating valve at a plurality of different opening degrees includes: when historical operation data exists, collect the valve opening degree, flow rate, pre-valve pressure and post-valve pressure, convert the pressure into water head, and inversely calculate to obtain the resistance coefficient K1 according to the local head loss formula; when there is no historical operation data, convert to obtain the resistance coefficient K2 based on the relationship curve between the flow coefficient Kv and the opening degree: wherein, is the gravitational acceleration, and D is the valve diameter.

[0011] Optionally, before the acquiring the resistance coefficients of the flow regulating valve at a plurality of different opening degrees to form a sample data set, the method includes: eliminating data with a flow rate less than a preset flow threshold, a water head difference less than or equal to zero, and an opening degree less than 1% or greater than 99%; eliminating jump data caused by start-stop impact, water hammer and sensor failure; eliminating abnormal data groups in which the dispersion degree of the resistance coefficient exceeds 30% at the same opening degree.

[0012] Optionally, the establishing a mapping relationship model between the opening degree and the resistance coefficient based on the sample data set includes: when o≤20%, ; when 20%<o≤80%, ; when o>80%, K=K min ; wherein, , , , , K min are all constant parameters obtained by fitting through a nonlinear least square method.

[0013] Optionally, the step of calculating the required target resistance coefficient based on the preset target flow rate and target downstream valve pressure, combined with the upstream valve pressure determined by the hydraulic model of the water supply network, includes: in, The target drag coefficient, For the flow area, Due to the difference in water head, For target traffic, For the water head before the valve, The target valve head.

[0014] Optionally, when historical operating data exists, the process of collecting valve opening, flow rate, upstream pressure, and downstream pressure, converting the pressure into head, and then back-calculating the resistance coefficient K1 using the local head loss formula includes: in, For traffic, This refers to the water head after the valve.

[0015] Optionally, updating the mapping relationship model using real-time runtime data collected after scheduling includes: The mapping relationship model is refitted using the accumulated full amount of operational data at a set periodic interval; An alarm mechanism is set up so that when the deviation of the resistance coefficient exceeds a preset threshold at the same opening degree, a valve maintenance prompt is issued.

[0016] This specification provides a flow control valve opening prediction and scheduling device based on a water supply network model, comprising: The acquisition module is used to acquire the resistance coefficient of the flow control valve at multiple different opening degrees, forming a sample dataset. A module is established to build a mapping model between aperture and drag coefficient based on the sample dataset; The calculation module is used to calculate the required target resistance coefficient based on the preset target flow rate and target downstream pressure, combined with the upstream pressure determined by the hydraulic model of the water supply network. The inverse calculation module is used to input the target drag coefficient into the mapping relationship model and inversely calculate the predicted opening. The verification module is used to assign the predicted opening degree to the hydraulic model of the water supply network for hydraulic simulation verification. If the verification result does not meet the preset conditions, the mapping relationship model is corrected and the calculation is reversed again until the optimal opening degree is obtained. an update module, configured to output the optimal opening to perform scheduling, and update the mapping relationship model using real-time operation data collected after scheduling.

[0017] Optionally, the acquisition module comprises: When historical operation data exists, the valve opening, flow rate, pressure before the valve and pressure after the valve are collected, the pressures are converted into water heads, and the resistance coefficient K1 is obtained by inverse calculation according to the local head loss formula; When there is no historical operation data, the resistance coefficient K2 is obtained by conversion based on the relationship curve between the flow coefficient Kv and the opening: wherein is the gravitational acceleration, and D is the valve diameter.

[0018] Optionally, before the acquisition module, the method comprises: eliminating data with a flow rate less than a preset flow threshold, a water head difference less than or equal to zero, and an opening less than 1% or greater than 99%; eliminating jump data caused by start-stop impact, water hammer and sensor failure; eliminating abnormal data groups in which the dispersion of the resistance coefficient exceeds 30% at the same opening.

[0019] Optionally, the establishing module comprises: When o≤20%, ; When 20%<o≤80%, ; When o>80%, K=K min ; wherein , , , , K min are all constant parameters obtained by fitting through a nonlinear least squares method.

[0020] Optionally, the calculation module comprises: wherein is the target resistance coefficient, is the flow area, is the required water head difference, is the target flow rate, is the water head before the valve, is the target water head after the valve.

[0021] Optionally, when historical operating data exists, the process of collecting valve opening, flow rate, upstream pressure, and downstream pressure, converting the pressure into head, and then back-calculating the resistance coefficient K1 using the local head loss formula includes: in, For traffic, This refers to the water head after the valve.

[0022] Optionally, the update module includes: The mapping relationship model is refitted using the accumulated full amount of operational data at a set periodic interval; An alarm mechanism is set up so that when the deviation of the resistance coefficient exceeds a preset threshold at the same opening degree, a valve maintenance prompt is issued.

[0023] This specification also provides an electronic device, wherein the electronic device includes: A processor; and a memory storing computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0024] This specification also provides a computer-readable storage medium that stores one or more programs that, when executed by a processor, implement any of the methods described above.

[0025] This invention employs a purely data-driven approach, establishing an opening-resistance coefficient model through back-calculation or conversion. This model is adaptable to all types of flow control valves, eliminating characteristic deviations caused by valve aging, scaling, etc. A piecewise power-exponential hybrid model is used to accurately match the nonlinear characteristics of the flow control valve across its full opening range, keeping the opening prediction error within a controllable range. It can directly calculate the required valve opening based on the target flow rate and target downstream valve pressure, combined with a hydraulic model, achieving fully automatic scheduling and eliminating repeated trial calculations. Real-time operational data is used to periodically refit the model, adapting to changes in valve characteristics and maintaining high accuracy over a long period. A prediction-simulation-execution-feedback closed-loop mechanism is constructed, effectively addressing time-varying operating conditions and ensuring scheduling safety through simulation verification and deviation alarms. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A schematic diagram illustrating the principle of a flow control valve opening prediction and scheduling method based on a water supply network model, provided for embodiments of this specification. Figure 2 A schematic diagram of a flow control valve opening prediction and scheduling device based on a water supply network model is provided for the embodiments of this specification. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification; Figure 4 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification. Detailed Implementation

[0028] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0029] The following is in conjunction with the appendix Figures 1-4 Exemplary embodiments of the invention will be described more fully here. However, exemplary embodiments can be implemented in many forms and should not be construed as limiting the invention to the embodiments set forth herein. Rather, these exemplary embodiments are provided to make the invention more comprehensive and complete, and to facilitate a full communication of the inventive concept to those skilled in the art. The same reference numerals in the figures denote the same or similar elements, components, or parts, and therefore repeated descriptions of them are omitted.

[0030] Subject to the technical concept of this invention, the features, structures, characteristics or other details described in a particular embodiment may be combined in one or more other embodiments in a suitable manner.

[0031] In the description of specific embodiments, the features, structures, characteristics, or other details described in this invention are intended to enable those skilled in the art to fully understand the embodiments. However, it is not excluded that those skilled in the art can practice the technical solutions of this invention without one or more of the specific features, structures, characteristics, or other details.

[0032] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0033] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0034] The terms “and / or” or “and / or” include all combinations of any one or more of the listed items.

[0035] Figure 1 This specification provides a schematic diagram illustrating the principle of a flow control valve opening prediction and scheduling method based on a water supply network model, which may include: Optionally, before S110, the following steps are included: Data with flow rates less than the preset flow rate threshold, head differences less than or equal to zero, and opening degrees less than 1% or greater than 99% will be excluded. Eliminate jump data caused by start-stop shocks, water hammer, and sensor malfunctions; Remove outlier data sets where the drag coefficient dispersion exceeds 30% at the same opening degree.

[0036] In the specific implementation of this specification, before forming the sample dataset, the raw data obtained from collection or conversion needs to be cleaned to remove outliers in order to ensure the quality of model fitting. Specific removal rules include: (1) Remove data with flow rate less than the preset flow rate threshold, head difference ΔH≤0, opening degree o<1% or o>99%; the preset flow rate threshold can be set according to the instrument measurement accuracy, for example, set to 0.001m³ / s, to exclude data with significant measurement error at extremely low flow rates.

[0037] (2) Remove pressure and flow rate fluctuation data caused by start-stop shocks, water hammer, or sensor malfunctions. Such data can be identified through rate of change detection or time-series anomaly detection algorithms.

[0038] (3) Perform statistical analysis on the drag coefficient K value under the same opening degree. If the dispersion (such as the coefficient of variation) exceeds 30%, mark the entire set of data corresponding to the opening degree as abnormal and remove it to avoid local disturbances affecting the global model.

[0039] S110: Obtain the resistance coefficient of the flow control valve at multiple different opening degrees to form a sample dataset; In the specific implementation method described in this specification, a hydraulic model of the target pipeline network is established, specifying the installation location, pipe diameter D, upstream and downstream nodes, and boundary conditions of the flow control valve. The resistance coefficient K of the flow control valve at multiple different opening degrees o is obtained, forming a sample dataset.

[0040] Optionally, S110 includes: When historical operating data exists, valve opening, flow rate, inlet pressure and outlet pressure are collected. After converting the pressure into head, the resistance coefficient K1 is calculated by back-calculating the local head loss formula. When historical operating data is unavailable, the drag coefficient K2 is calculated based on the relationship curve between the flow coefficient Kv and the opening degree. in, Let D be the acceleration due to gravity and D be the valve diameter.

[0041] In the specific implementation of this specification, for pipeline networks that are in initial operation and have no historical data, the resistance coefficient K can be obtained by using the Kv-opening regulating valve characteristic curve provided by the manufacturer and the conversion formula of the flow coefficient Kv.

[0042] The drag coefficient K is determined according to the local head loss formula: Flow coefficient Kv Kv: Flow rate of clean water per hour (m³ / h) when pressure difference ΔP = 1 bar. Calculate the drag coefficient K from Kv Substituting the constant g = 9.81, simplify the practical engineering formula: Optionally, when historical operating data exists, the process of collecting valve opening, flow rate, upstream pressure, and downstream pressure, converting the pressure into head, and then back-calculating the resistance coefficient K1 using the local head loss formula includes: in, For traffic, This refers to the water head after the valve.

[0043] In the specific implementation of this specification, for valid samples, K1 is calculated inversely using the local head loss formula: Among them, head difference Flow area: S120: Based on the aforementioned sample dataset, establish a mapping relationship model between opening degree and drag coefficient; In the specific embodiments of this specification, after cleaning the sample data, a mapping relationship model between the opening o and the resistance coefficient K is established, and a segmented power-exponential hybrid model is preferably adopted to accurately match the nonlinear characteristics of different opening intervals.

[0044] Optionally, the S120 includes: When o ≤ 20%, ; When 20% < o ≤ 80%, ; When o > 80%, K = K min ; Wherein, , , , , K min are all constant parameters obtained by fitting with the nonlinear least squares method.

[0045] S130: Calculate the required target resistance coefficient according to the preset target flow rate and target post-valve pressure, combined with the pre-valve pressure determined by the hydraulic model of the water supply pipe network; In the specific embodiments of this specification, when a scheduling demand arises, the preset target flow rate Q set and the target post-valve pressure P 2set are determined, the established hydraulic model of the water supply pipe network is used for forward simulation to obtain the pre-valve pressure P1 under the current working condition, which is then converted into the pre-valve water head H1; at the same time, the target post-valve pressure is converted into the target post-valve water head H 2set . The required water head difference ΔH need = H1 - H 2set is calculated, and then the required target resistance coefficient is obtained .

[0046] Optionally, the S130 includes: Wherein, is the target resistance coefficient, is the flow area, is the required water head difference, is the target flow rate, is the pre-valve water head, is the target post-valve water head.

[0047] S140: Input the target resistance coefficient into the mapping relationship model to inversely obtain the predicted opening; In the specific embodiments of this specification, K needSubstituting the above opening-drag coefficient mapping model, the opening o can be predicted using the bisection method or Newton's iteration method. opt The results are constrained to an opening range of 1% to 95%.

[0048] S150: Assign the predicted opening degree to the hydraulic model of the water supply network for hydraulic simulation verification. If the verification result does not meet the preset conditions, correct the mapping relationship model and recalculate until the optimal opening degree is obtained. In the specific implementation of this specification, the predicted opening degree is assigned to the flow control valve (such as TCV or PCV in EPANET) in the hydraulic model, and a hydraulic simulation is run to verify the errors between the calculated flow rate and the downstream head and the target values. The preset condition is that the flow rate error and the downstream pressure error do not exceed ±1%. If this condition is not met, the current operating point (opening degree and the verified resistance coefficient) is added to the sample dataset, the mapping relationship model is locally corrected or refitted globally, and then the calculation is repeated until the required accuracy is met to obtain the optimal opening degree. opt .

[0049] S160: Output the optimal opening degree to perform scheduling, and update the mapping relationship model using the real-time running data collected after scheduling.

[0050] In the specific implementation of this specification, the optimal opening command is sent to the field PLC or valve actuator to complete the scheduling. Simultaneously, the system continuously collects operational data and incrementally stores it in the database. At set intervals (e.g., every 7 or 30 days), the system refits the opening-resistance coefficient model using the accumulated full operational data, achieving online dynamic updates of the mapping relationship model and maintaining scheduling accuracy.

[0051] Optionally, S160 includes: The mapping relationship model is refitted using the accumulated full amount of operational data at a set periodic interval; An alarm mechanism is set up so that when the deviation of the resistance coefficient exceeds a preset threshold at the same opening degree, a valve maintenance prompt is issued.

[0052] In the specific implementation of this specification, after outputting the optimal opening degree and executing the scheduling, this method continuously updates the mapping relationship model using the operational data (opening degree, flow rate, and pressure before and after the valve) collected during and after the scheduling process. The update mechanism includes: collecting all valid operational data accumulated within a set period (e.g., every 7 days or 30 days), re-performing data cleaning, K-inverse calculation, and nonlinear fitting to generate updated opening degree-resistance coefficient mapping model parameters, and replacing the old model to achieve online self-updating. Simultaneously, the system establishes an alarm mechanism to continuously monitor the changing trend of the resistance coefficient K at each opening degree; when the deviation between the K value at the same opening degree and the model prediction value exceeds a preset threshold (e.g., 20%), a valve maintenance prompt is automatically issued, reminding maintenance personnel to check for scaling, jamming, or abnormal wear on the valve, thereby ensuring the long-term stable operation of the scheduling system.

[0053] In the specific implementation of this specification, for scenarios involving newly constructed pipelines or newly installed flow control valves without any historical operating data, the initial K sample set is obtained by converting the manufacturer's Kv curve during initial system operation, quickly establishing an initial mapping model. As the system operates, real-time collected data continuously accumulates. When the number of effective samples meets the fitting requirements, it automatically switches to a K model based on measured data and updates it periodically, gradually approximating the valve's true characteristics. The complete closed-loop scheduling chain is as follows: data acquisition → data cleaning → resistance coefficient back-calculation → model fitting → calculation of Kneed given target → back-calculation of predicted opening → hydraulic model simulation verification → if the ±1% accuracy is not met, the model is corrected and recalculated → outputting the optimal opening command to the actuator → feedback correction and periodic model updates. This closed-loop mechanism enables the scheduling system to have self-learning and adaptive capabilities, maintaining high accuracy over a long period.

[0054] This invention employs a purely data-driven approach, establishing an opening-resistance coefficient model through back-calculation or conversion. This model is adaptable to all types of flow control valves, eliminating characteristic deviations caused by valve aging, scaling, etc. A piecewise power-exponential hybrid model is used to accurately match the nonlinear characteristics of the flow control valve across its full opening range, keeping the opening prediction error within a controllable range. It can directly calculate the required valve opening based on the target flow rate and target downstream valve pressure, combined with a hydraulic model, achieving fully automatic scheduling and eliminating repeated trial calculations. Real-time operational data is used to periodically refit the model, adapting to changes in valve characteristics and maintaining high accuracy over a long period. A prediction-simulation-execution-feedback closed-loop mechanism is constructed, effectively addressing time-varying operating conditions and ensuring scheduling safety through simulation verification and deviation alarms.

[0055] Figure 2 This specification provides a schematic diagram of a flow control valve opening prediction and scheduling device based on a water supply network model, which may include: The acquisition module 10 is used to acquire the resistance coefficient of the flow control valve at multiple different opening degrees to form a sample dataset; The establishing module 20 is configured to establish a mapping relationship model between opening and resistance coefficient based on the sample dataset; The calculation module 30 is configured to calculate a required target resistance coefficient according to a preset target flow rate and target post-valve pressure, in combination with the pre-valve pressure determined by the hydraulic model of the water supply pipe network; The inverse solving module 40 is configured to input the target resistance coefficient into the mapping relationship model, and perform inverse solving to obtain a predicted opening; The verification module 50 is configured to assign the predicted opening to the hydraulic model of the water supply pipe network for hydraulic simulation verification, and if the verification result does not meet a preset condition, correct the mapping relationship model and perform inverse solving again until an optimal opening is obtained; The updating module 60 is configured to output the optimal opening to execute scheduling, and update the mapping relationship model by using real-time operation data collected after scheduling.

[0056] Optionally, the acquiring module 10 comprises: When historical operation data exists, collect the valve opening, flow rate, pre-valve pressure and post-valve pressure, convert the pressures into water heads, and perform inverse calculation according to the local head loss formula to obtain the resistance coefficient K1; When there is no historical operation data, convert to obtain the resistance coefficient K2 based on the relationship curve between the flow coefficient Kv and the opening: wherein, is the gravitational acceleration, and D is the valve diameter.

[0057] Optionally, before the acquiring module 10, the method comprises: eliminating data with a flow rate less than a preset flow threshold, a water head difference less than or equal to zero, or an opening less than 1% or greater than 99%; eliminating jump data caused by start-stop impact, water hammer and sensor failure; eliminating abnormal data groups in which the dispersion of the resistance coefficient exceeds 30% at the same opening.

[0058] Optionally, the establishing module 20 comprises: When o≤20%, ; When 20%<o≤80%, ; When o>80%, K=K min ; wherein, , , , , K min are all constant parameters obtained by fitting through a nonlinear least squares method.

[0059] Optionally, the computing module 30 includes: in, The target drag coefficient, For the flow area, Due to the difference in water head, For target traffic, For the water head before the valve, The target valve head.

[0060] Optionally, when historical operating data exists, the process of collecting valve opening, flow rate, upstream pressure, and downstream pressure, converting the pressure into head, and then back-calculating the resistance coefficient K1 using the local head loss formula includes: in, For traffic, This refers to the water head after the valve.

[0061] Optionally, the update module 60 includes: The mapping relationship model is refitted using the accumulated full amount of operational data at a set periodic interval; An alarm mechanism is set up so that when the deviation of the resistance coefficient exceeds a preset threshold at the same opening degree, a valve maintenance prompt is issued.

[0062] The functions of the apparatus in this embodiment have been described in the above method embodiments. Therefore, for any parts not detailed in this embodiment, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0063] Based on the same inventive concept, embodiments of this specification also provide an electronic device.

[0064] The following describes embodiments of the electronic device of the present invention, which can be considered as specific implementations of the methods and apparatus embodiments of the present invention described above. Details described in the embodiments of the electronic device of the present invention should be considered as supplements to the methods or apparatus embodiments described above; details not disclosed in the embodiments of the electronic device of the present invention can be implemented with reference to the methods or apparatus embodiments described above.

[0065] Figure 3 This is a schematic diagram of an electronic device provided as an embodiment of this specification. Refer to the following... Figure 3 The electronic device 300 according to this embodiment of the present invention will be described. Figure 3The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0066] like Figure 3 As shown, the electronic device 300 is presented in the form of a general-purpose computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including storage unit 320 and processing unit 310), a display unit 340, etc.

[0067] The storage unit stores program code that can be executed by the processing unit 310, causing the processing unit 310 to perform the steps described in the processing method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 310 can perform, for example... Figure 1 The steps are shown.

[0068] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache storage unit 3202, and may further include a read-only memory unit (ROM) 3203.

[0069] The storage unit 320 may also include a program / utility 3204 having a set (at least one) program module 3205, such program module 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0070] Bus 330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0071] Electronic device 300 can also communicate with one or more external devices 400 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable viewers to interact with electronic device 300, and / or with any device that enables electronic device 300 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 350. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 360. Network adapter 360 can communicate with other modules of electronic device 300 via bus 330. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0072] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described in this invention can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the method described above according to this invention. When the computer program is executed by a data processing device, it enables the computer-readable medium to implement the method described above, i.e.: as... Figure 1 The method shown.

[0073] Figure 4 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification.

[0074] accomplish Figure 1 The computer program of the method shown can be stored on one or more computer-readable media. A computer-readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0075] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0076] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the audience's computing device, partially on the audience's device, as a standalone software package, partially on the audience's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the audience's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0077] In summary, the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that in practice, general-purpose data processing devices such as microprocessors or digital signal processors (DSPs) can be used to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0078] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0079] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0080] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for predicting and scheduling the opening degree of flow regulating valves based on a water supply network model, characterized in that, include: Obtain the resistance coefficient of the flow control valve at multiple different opening degrees to form a sample dataset; Based on the aforementioned sample dataset, a mapping model between aperture and drag coefficient is established. Based on the preset target flow rate and target downstream pressure, and combined with the upstream pressure determined by the hydraulic model of the water supply network, the required target resistance coefficient is calculated. The target drag coefficient is input into the mapping model to obtain the predicted opening. The predicted opening degree is assigned to the hydraulic model of the water supply network for hydraulic simulation verification. If the verification result does not meet the preset conditions, the mapping relationship model is corrected and the calculation is repeated until the optimal opening degree is obtained. The optimal opening degree is output to perform scheduling, and the mapping relationship model is updated using the real-time running data collected after scheduling.

2. The method for predicting and scheduling the opening degree of flow regulating valves based on a water supply network model as described in claim 1, characterized in that, The process of obtaining the resistance coefficient of the flow control valve at multiple different opening degrees includes: When historical operating data exists, valve opening, flow rate, inlet pressure and outlet pressure are collected. After converting the pressure into head, the resistance coefficient K1 is calculated by back-calculating the local head loss formula. When historical operating data is unavailable, the drag coefficient K2 is calculated based on the relationship curve between the flow coefficient Kv and the opening degree. in, Let D be the acceleration due to gravity and D be the valve diameter.

3. The method for predicting and scheduling the opening degree of flow regulating valves based on a water supply network model as described in claim 2, characterized in that, Before obtaining the resistance coefficient of the flow control valve at multiple different opening degrees to form a sample dataset, the following steps are included: Data with flow rates less than the preset flow rate threshold, head differences less than or equal to zero, and opening degrees less than 1% or greater than 99% will be excluded. Eliminate jump data caused by start-stop shocks, water hammer, and sensor malfunctions; Remove outlier data sets where the drag coefficient dispersion exceeds 30% at the same opening degree.

4. The method for predicting and scheduling the opening degree of flow regulating valves based on a water supply network model as described in claim 2, characterized in that, The process of establishing a mapping model between aperture and drag coefficient based on the sample dataset includes: When o≤20% ; When 20% < o ≤ 80%, ; When o > 80%, K = K min ; in, , , , K min The constant parameters are obtained by fitting using the nonlinear least squares method.

5. The method for predicting and scheduling the opening degree of flow regulating valves based on a water supply network model as described in claim 1, characterized in that, The calculation of the required target resistance coefficient based on the preset target flow rate and target downstream pressure, combined with the upstream pressure determined by the water supply network hydraulic model, includes: in, The target drag coefficient, For the flow area, Due to the difference in water head, For target traffic, For the water head before the valve, The target valve head.

6. The method for predicting and scheduling the opening degree of flow regulating valves based on a water supply network model as described in claim 5, characterized in that, When historical operating data exists, valve opening, flow rate, upstream pressure, and downstream pressure are collected. After converting the pressure into head, the resistance coefficient K1 is calculated using the local head loss formula, including: in, For traffic, This refers to the water head after the valve.

7. The method for predicting and scheduling the opening degree of flow regulating valves based on a water supply network model as described in claim 1, characterized in that, The step of updating the mapping relationship model using real-time operational data collected after scheduling includes: The mapping relationship model is refitted using the accumulated full amount of operational data at a set periodic interval; An alarm mechanism is set up so that when the deviation of the resistance coefficient exceeds a preset threshold at the same opening degree, a valve maintenance prompt is issued.

8. A flow control valve opening prediction and scheduling device based on a water supply network model, characterized in that, include: The acquisition module is used to acquire the resistance coefficient of the flow control valve at multiple different opening degrees, forming a sample dataset. A module is established to build a mapping model between aperture and drag coefficient based on the sample dataset; The calculation module is used to calculate the required target resistance coefficient based on the preset target flow rate and target downstream pressure, combined with the upstream pressure determined by the hydraulic model of the water supply network. The inverse calculation module is used to input the target drag coefficient into the mapping relationship model and inversely calculate the predicted opening. The verification module is used to assign the predicted opening degree to the hydraulic model of the water supply network for hydraulic simulation verification. If the verification result does not meet the preset conditions, the mapping relationship model is corrected and the calculation is reversed again until the optimal opening degree is obtained. The update module is used to output the optimal opening degree to perform scheduling, and to update the mapping relationship model using the real-time running data collected after scheduling.

9. An electronic device, wherein, The electronic device includes: A processor; and a memory storing computer-executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein, The computer-readable storage medium stores one or more programs that, when executed by a processor, implement the method of any one of claims 1-7.

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