Hydraulic optimization control method and system for flow balance between cascade pumping stations

Abstract

This application relates to the field of pump station regulation technology, providing a hydraulic optimization control method and system for flow balance among cascade pump stations. The method includes: constructing a water distribution correlation topology through water balance analysis; loading real-time interval water distribution demand to locate the M water distribution flows of M pump stations; simulating the pressure wave propagation process of water distribution demand changes and outputting transient terminal flow rates; calculating initial PID weight allocation based on the deviation between the target flow rate of the pump station and the water distribution flow rate; compensating the initial PID weights based on the deviation between the target terminal flow rate and the transient terminal flow rate, and executing independent PID control and pressure surge regulation coordinated control. This application solves the technical problem that in the process of flow scheduling among cascade pump stations, due to changes in water source and fluctuations in pump station flow demand, it is impossible to accurately regulate the flow balance among pump stations, leading to water resource waste and low efficiency of cascade pump stations. It achieves the technical effect of improving the stability and flow regulation efficiency of cascade pump stations and ensuring optimal allocation of water resources.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of pump station regulation, in particular to a hydraulic optimization control method and system for flow balance between cascade pump stations. BACKGROUND

[0002] In the process of water resource regulation of cascade pump station groups, flow balance is a key factor to ensure the stable operation of reservoirs and pump station systems. However, the traditional pump station flow control method relies on static models and independent regulation strategies, which is difficult to cope with real-time water demand fluctuations and complex hydraulic coupling effects. Especially in the dynamic flow allocation scene, the existing technology often causes flow deviation accumulation and supply-demand matching lag due to the lack of global topology correlation analysis; and the propagation of hydraulic transient pressure waves easily causes severe flow fluctuations at the end, threatening the safety of the pipe network. In addition, the independent PID control strategy of multiple pump stations often triggers chain shock due to local pressure mutation, which aggravates the system instability and energy loss. SUMMARY

[0003] The application provides a hydraulic optimization control method and system for flow balance between cascade pump stations, aiming to solve the technical problem that in the process of flow regulation between cascade pump stations, due to changes in water sources and fluctuations in pump station flow demand, the flow balance between pump stations cannot be accurately adjusted, resulting in waste of water resources and low efficiency of cascade pump stations, to achieve the technical effect of dynamically adjusting pump station flow and accurately responding to real-time water demand changes through water balance analysis and construction of water distribution correlation influence topology, improving the stability and flow regulation efficiency of cascade pump stations, and ensuring the optimal allocation of water resources.

[0004] The first aspect of the application provides a hydraulic optimization control method for flow balance between cascade pump stations, which comprises: performing water distribution node topology mapping through water balance analysis to construct a water distribution correlation influence topology; loading real-time interval water distribution demand to the water distribution correlation influence topology to locate M water distribution correlation flow of M water distribution correlation pump station nodes; in the water distribution correlation influence topology, simulating the pressure wave propagation process after the change of water distribution demand according to the real-time interval water distribution demand, and outputting transient end flow; calculating the output initial PID control weight distribution according to the flow deviation of M pump station target flow of M water distribution correlation pump station nodes and M water distribution correlation flow; compensating the initial PID control weight distribution according to the deviation characteristics of target end flow and transient end flow to output target PID control weight distribution; in the independent PID control process of M water distribution correlation pump station nodes using the target PID control weight distribution, performing pressure regulation and collaborative control according to pressure mutation.

[0005] In another aspect of the present disclosure, a hydraulic optimization control system for flow balancing between cascade pumping stations is provided, which comprises: a topology mapping module: performing water balance analysis to map a water distribution node topology, and constructing a water distribution correlation influence topology; a real-time demand loading module: loading real-time interval water distribution demands to the water distribution correlation influence topology, and locating M water distribution correlation flows of M water distribution correlation pumping station nodes; a water distribution simulation module: simulating a pressure wave propagation process after the water distribution demand changes in the water distribution correlation influence topology according to the real-time interval water distribution demands, and outputting transient end flows; a weight distribution module: calculating and outputting initial PID control weight distribution according to flow deviations between M pumping station target flows of the M water distribution correlation pumping station nodes and the M water distribution correlation flows; a weight compensation module: compensating the initial PID control weight distribution according to deviation characteristics of target end flows and the transient end flows, and outputting target PID control weight distribution; and a cooperative control module: performing pressure regulating cooperative control according to pressure mutations in an independent PID control process of the M water distribution correlation pumping station nodes using the target PID control weight distribution.

[0006] The one or more technical solutions provided in the present disclosure have at least the following technical effects or advantages:

[0007] The above-mentioned hydraulic optimization control method for flow balancing between cascade pumping stations constructs a water distribution node topology through water balance analysis, loads real-time water distribution demand data into the topology, and locates multiple pumping station nodes and corresponding flows. Subsequently, according to the data, a pressure wave propagation process caused by water distribution demand changes is simulated, and transient end flows are calculated and output. Then, according to deviations between pumping station target flows and actual flows, weight distribution of PID control is preliminarily calculated. Then, according to changes in end flows, the weight of PID control is adjusted to ensure more accurate control effect. Finally, using the adjusted PID control weight, independent PID control of each pumping station is performed, and in the case of large pressure changes, pressure regulating cooperative control is performed to ensure stable operation of the system.

[0008] The above description is only a summary of the technical solutions of the present disclosure. In order to more clearly understand the technical means of the present disclosure, the technical solutions can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present disclosure more obvious and easy to understand, the following specific embodiments of the present disclosure are described. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description only constitute some of the embodiments of the present application, and all other drawings obtained by those of ordinary skill in the art without creative effort based on these drawings also belong to the protection scope of the present application.

[0010] Figure 1 A flowchart of a hydraulic optimization control method for flow balance between cascade pump stations in an embodiment.

[0011] Figure 2 A system architecture diagram of a hydraulic optimization control system for flow balance between cascade pump stations in an embodiment.

[0012] Legend: Topology mapping module 11, real-time demand loading module 12, water distribution simulation module 13, weight distribution module 14, weight compensation module 15, and collaborative control module 16. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a hydraulic optimization control method and system for flow balance between cascade pump stations, which solve the technical problem that in the flow scheduling process between cascade pump stations, due to water source changes and pump station flow demand fluctuations, the flow balance between pump stations cannot be accurately adjusted, resulting in waste of water resources and low efficiency of cascade pump stations, and achieve the technical effect of constructing a topology affected by water balance analysis and water distribution association, dynamically adjusting pump station flow and accurately responding to real-time water demand changes, improving the stability and flow regulation efficiency of cascade pump stations, and ensuring the optimal allocation of water resources.

[0014] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0015] It should be noted that the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those clearly listed steps or units, but can include other steps or modules not clearly listed or inherent to the process, method, product or device.

[0016] Embodiment one, as shown in the present application provides a hydraulic optimization control method for flow balance between cascade pump stations, which comprises: Figure 1

[0017] ​The water distribution node topology mapping is performed through water balance analysis, and a water distribution correlation influence topology is constructed.

[0018] In the embodiments of the application, the water distribution node topology mapping is performed through water balance analysis, and a network structure is formed. In this process, the water balance analysis helps to identify the water flow relationship and mutual influence between the nodes, so as to construct a water distribution correlation influence topology. The topology map shows the flow path of water flow between different nodes and how the change of the flow of each node affects other nodes. Through this mapping, the hydraulic correlation between the nodes can be clearly understood, and the subsequent hydraulic optimization control is provided with basic data support.

[0019] Further, the application provides a method for performing water distribution node topology mapping through water balance analysis and constructing a water distribution correlation influence topology, which comprises the following steps:

[0020] The water distribution equipment parameter is obtained through interaction; the Huffman tree data structure is used to map the hierarchical correlation of the water distribution equipment parameter to a water distribution equipment hierarchical topology; through water balance analysis, a plurality of water distribution correlation influence functions of a plurality of water distribution equipment nodes in the water distribution equipment hierarchical topology are constructed; and the plurality of water distribution correlation influence functions are loaded to the water distribution equipment hierarchical topology to obtain the water distribution correlation influence topology.

[0021] Preferably, first, the relevant parameter information of each device (such as a pump station, a reservoir, a pipeline, etc.) in the water distribution process is collected through interaction with the device management platform. These device parameters include the flow, pressure, pump station power and other key data of each device. Subsequently, each device in the water distribution equipment is regarded as an independent node. Assuming that there are n device nodes, these nodes will form a forest R = {R n}, and each device node is a separate tree (representing a water supply unit). Then, according to the construction rule of Huffman tree, each tree in the forest is converted into a binary tree. Specifically, a parent node (representing an upstream device) is selected as a root node, the water supply unit thereof is taken as the root node of the left subtree, the right subtree is kept empty, the direct connection between the parent water supply node and other device units is disconnected, and the other device nodes are connected to the right subtree of the parent node. The remaining device nodes are sequentially changed into the right subtree of the previous unit, and a new subtree structure R' = {R 11 ,R 21 ,…,R n1Next, the root nodes of these subtrees are recursively linked to the root node of the previous subtree. That is, the root node of each subtree becomes the right subtree of the root node of the previous subtree. In this way, in the binary tree structure, the left subtree of each parent node is still the direct water supply object, while the right subtree represents the indirect water supply object or the lower-level node. Through the above steps, all equipment nodes are linked sequentially and transformed into a complete binary tree structure, forming a hierarchical topology of water transmission equipment. This topology tree clearly represents the water supply path, dependencies, and impact on upstream / downstream nodes between equipment. Then, through water balance analysis, based on the relationships between the nodes in the hierarchical topology of water transmission equipment, multiple water distribution correlation influence functions are constructed. These functions represent the impact of each equipment node on the water flow of downstream nodes, reflecting how equipment parameters (such as flow rate and pressure) transmit influence to each other over time. For example, the flow rate change of a pumping station may affect the flow rate and pressure of multiple downstream nodes. These effects are represented in functional form. Finally, these multiple water distribution correlation influence functions are loaded into the water conveyance equipment hierarchical topology to form a complete water distribution correlation influence topology. This water distribution correlation influence topology not only includes the hierarchical structure between equipment, but also clarifies how each node adjusts according to the status of other nodes (such as changes in flow and pressure), thereby achieving more accurate flow balance and optimized control.

[0022] Table 1: Example of Hierarchical Topology for Water Conveyment Equipment

[0023] Device Upstream node Flow Downstream node Water source A 10 Pump station B Water source A 8 Pump station C Pump station C Pump station B 6 Water plant D Water plant D Pump station C 4 Water consuming node E Water consuming node E Water plant D 2

[0024] As shown in Table 1, this example table of hierarchical topology for water conveyance equipment illustrates the structure of a hierarchical topology, listing five key devices (water source A, pump station B, pump station C, water treatment plant D, and water user node E), as well as their upstream and downstream node relationships. The flow requirements of each device are also listed in the table. This table clearly illustrates the entire process of water flow from water source A, through pump station B, pump station C, and water treatment plant D, finally reaching water user node E. This hierarchical topology helps analyze the role of each device in the entire water conveyance network and effectively optimizes flow scheduling and control.

[0025] Furthermore, this application provides a method for constructing multiple water distribution correlation influence functions for multiple water conveyance equipment nodes in the hierarchical topology of the water conveyance equipment through water balance analysis, the method comprising:

[0026] Based on the downstream single-level node decomposition of the water conveyance equipment hierarchical topology, multiple sets of downstream nodes of multiple water conveyance equipment nodes are obtained; multiple sample input flow rates and multiple sample water diversion volumes of the first water conveyance equipment node in the water distribution scenario are interactively obtained; based on the multiple timestamps of the multiple sample input flow rates and multiple sample water diversion volumes, multiple sets of downstream output flow rates of the first set of downstream nodes are mapped and called; a water balance transfer function is defined for the multiple sample input flow rates, multiple water diversion volumes, and multiple sets of downstream output flow rates, and the first water distribution correlation influence function is output; and so on, multiple water distribution correlation influence functions of the multiple water conveyance equipment nodes are constructed.

[0027] Preferably, firstly, the hierarchical topology of the water conveyance equipment is analyzed. Based on the characteristics of downstream single-level nodes, the topology is decomposed to determine the multiple downstream nodes connected to each water conveyance equipment node. Each downstream node represents equipment or water usage points at different levels, and each downstream node plays a different role in flow distribution. For multiple water conveyance equipment nodes, one is randomly selected as the first water conveyance equipment node, and multiple sets of sample input flow and sample water distribution data are collected in the water distribution scenario, and marked with corresponding timestamps. Subsequently, the timestamps of the collected sample input flow and sample water distribution, along with the first group of downstream nodes corresponding to the first water conveyance equipment node, are mapped to the water distribution log. By matching downstream nodes and timestamps, multiple sets of downstream output flow are retrieved. Then, the multiple sample input flow, water distribution, and downstream output flow are combined, and a water balance transfer function is defined through water balance analysis. Specifically, a linear expression between the input flow, water distribution, and downstream output flow is established as the water balance transfer function, which is as follows: ;in, This represents the output flow of the i-th downstream node; This represents the input flow to the upstream node, which is the flow of water flowing into the i-th downstream node; The water diversion volume refers to the flow rate from the i-th downstream node through the water diversion point. The coefficients to be determined are then input into the water balance transfer function using multiple sample input flow rates, multiple water diversion volumes, and multiple sets of downstream output flow rates. The final coefficients are then fitted using the least squares method. After these three undetermined coefficients, the current water balance transfer function is used as the first water allocation correlation influence function. This first water allocation correlation influence function represents the degree of influence of this node on the flow of downstream nodes and how it responds to flow changes. Then, following the same method, the same calculations and analyses are performed on other water conveyance equipment nodes. Each node constructs a corresponding water balance transfer function based on water transfer with downstream nodes and changes in water allocation demand, ultimately generating a water allocation correlation influence function for each node. Through these functions, a comprehensive understanding of the water flow influence between nodes can be obtained, thus providing necessary data support for overall hydraulic optimization control. Finally, through this process, water allocation correlation influence functions for multiple water conveyance equipment nodes are constructed, enabling precise optimization of flow scheduling for the entire water conveyance network.

[0028] The real-time interval water distribution demand is loaded into the water distribution correlation influence topology, and the M water distribution correlation flow rates of M water distribution correlation pump station nodes are located.

[0029] In one embodiment, real-time water demand data is loaded into the water distribution correlation topology. This real-time water demand data typically includes water demand information for each node within a certain time interval. This demand data is mapped to the corresponding nodes in the topology, ensuring that the water demand of each node is reflected in real time. Subsequently, the locations of M water distribution-related pump station nodes in the topology are determined. These nodes represent key pump stations or water sources. For each node, based on the water demand information and the water distribution correlation influence function in the topology, the water distribution-related flow rate for each pump station node is calculated; this is the target flow rate for these nodes. These flow rate values ​​will guide the scheduling of pump stations and water allocation, ensuring that each pump station adjusts its flow rate appropriately according to actual demand.

[0030] Furthermore, this application provides a method for loading real-time interval water distribution demand into the water distribution correlation influence topology and locating M water distribution correlation flow rates of M water distribution correlation pump station nodes, the method comprising:

[0031] The water distribution demand node for the real-time interval water distribution demand is located in the water distribution correlation influence topology; the water distribution demand node is used as the real-time parent node, and a breadth-first traversal is performed on the downstream nodes along the water conveyance path in the water distribution correlation influence topology to generate a downstream hierarchical topology network; the real-time input flow of the water distribution demand node is extracted, and the real-time input flow and the real-time interval water distribution demand are used as inputs, and the water distribution correlation influence function is called layer by layer along the downstream hierarchical topology to calculate the downstream output flow of a single node, so as to obtain the M water distribution correlation flow of the M water distribution correlation pump station nodes.

[0032] Preferably, in the water distribution correlation topology, firstly, based on the real-time water distribution demand of the interval, the corresponding water distribution demand nodes are determined. These nodes represent the locations of water demand that need to be met, such as certain key pumping stations or water distribution points. Each water distribution demand node is calibrated according to the actual water demand information. Subsequently, the selected water distribution demand nodes are used as parent nodes, and a breadth-first traversal is performed along the water conveyance path to downstream nodes in the water distribution correlation topology. This process, through layer-by-layer search, filters all relevant downstream nodes in hierarchical order, forming a downstream hierarchical topology network. In this network, each node represents a different water conveyance device or water flow path and is interconnected with the parent node. Next, the real-time input flow information of the water distribution demand nodes is extracted. This flow data usually comes from flow sensors. Then, these input flows are combined with the real-time interval water distribution demand data as input information. The input flow represents the water supply of the current node, while the water distribution demand represents the amount of water that the node expects to obtain. In the downstream hierarchical topology network, the water distribution correlation influence function is called layer by layer for calculation. The water distribution correlation influence function calculates the flow of each downstream node based on the input flow and water distribution demand of each node, thereby obtaining the flow values ​​of M water distribution correlation pump station nodes in the topology. The flow value of each pump station node reflects its operating status under specific water distribution demand. These flow values ​​will be used to control the operation of the pump station to ensure that each node can make precise flow adjustments according to actual needs, thereby achieving the goal of water balance and optimized control.

[0033] In the water distribution correlation influence topology, the pressure wave propagation process after the change of water distribution demand is simulated based on the real-time interval water distribution demand, and the transient terminal flow is output.

[0034] In one embodiment, in the topology of water allocation correlation, changes in water allocation demand are simulated based on real-time interval water allocation demand. These demand changes cause fluctuations in water flow, leading to the propagation of pressure waves. In this process, the real-time interval water allocation demand is first considered as a hydraulic disturbance source and loaded into the downstream hierarchical topology to simulate the impact of water allocation demand changes on pressure waves. Subsequently, the real-time input flow and real-time interval water allocation demand are used as disturbance factors, and pressure wave propagation simulation is performed in the downstream hierarchical topology. Through this process, pressure waves propagate in the topology, affecting the flow changes of each downstream node. Finally, the transient terminal flow is output based on the simulation results. This transient terminal flow refers to the instantaneous flow change caused by changes in water allocation demand and pressure wave propagation. This flow value reflects the actual situation after dynamic adjustment of water flow and is a comprehensive result of the flow changes of each node during pressure wave propagation.

[0035] Furthermore, this application provides a method for simulating the pressure wave propagation process after changes in water distribution demand based on the real-time interval water distribution demand in the aforementioned water distribution correlation influence topology, and outputting transient terminal flow rates. The method includes:

[0036] The real-time interval water distribution demand is used as a hydraulic disturbance source. Based on the water distribution demand nodes, the real-time interval water distribution demand is loaded into the downstream hierarchical topology network to perform dynamic simulation of transient pressure wave propagation. The real-time input flow and real-time interval water distribution demand are used as disturbance factors to simulate the propagation of transient pressure waves caused by water distribution disturbance in the downstream hierarchical topology network, and the transient terminal flow is output.

[0037] Optionally, firstly, the real-time interval water allocation demand is used as a hydraulic disturbance source, referring to changes in flow and pressure caused by variations in water demand. Based on these real-time demand changes, the system uses simulation software to simulate how these demand changes affect water flow. Then, according to the water allocation demand nodes, the real-time interval water allocation demand is loaded into the downstream hierarchical topology network. Initial states are set for each node in the downstream hierarchical topology network, including the flow rate, pressure, and hydraulic relationships between nodes. These initial conditions are used to initiate the simulation of transient pressure wave propagation. Afterward, the downstream hierarchical topology network loaded with the real-time interval water allocation demand is loaded into the hydraulic simulation software to perform a dynamic simulation of transient pressure wave propagation, thereby constructing a virtual water flow transmission scenario. Then, the real-time input flow rate and the real-time water allocation demand of the interval are input as disturbance factors into this water flow transmission scenario. The real-time input flow rate reflects the current state of the water flow, while the real-time interval water allocation demand reflects the actual demand of downstream nodes. These factors work together to simulate the propagation of transient pressure waves caused by water allocation disturbances in the hydraulic simulation software. During the simulation, pressure fluctuations occur in the water flow according to changes in water allocation demand. These fluctuations propagate downstream along the water transmission path, affecting the water flow state at each node. Through simulation, changes in water flow can be dynamically captured, and the instantaneous fluctuations in water volume and pressure can be understood, thus obtaining the transient terminal flow rate. This transient terminal flow rate has peak flow rate, steady-state recovery time, and transient fluctuation amplitude indicators, representing the flow change at the terminal node after the propagation of the instantaneous pressure wave triggered by changes in water allocation demand. In summary, through the above process, the system can simulate the propagation of pressure waves caused by changes in water allocation demand in real time, ensuring precise control and optimized regulation of the flow, especially improving the system's response capability to water flow fluctuations in the case of flow changes at the terminal nodes.

[0038] Furthermore, this application provides that the transient terminal flow rate has a peak flow rate, a steady-state recovery time, and an identifier for the transient fluctuation amplitude.

[0039] Optionally, transient terminal flow refers to the change in terminal flow during the propagation of pressure waves caused by changes in water demand. This flow change has three characteristics: peak flow, steady-state recovery time, and transient fluctuation amplitude. Peak flow refers to the maximum value of the terminal flow during pressure wave propagation, which typically occurs in the initial stage of the pressure wave and reflects the maximum instantaneous impact on the flow. Steady-state recovery time refers to the time required from the onset of flow fluctuations caused by the pressure wave to the eventual recovery to a stable flow rate. This time indicates the recovery capability after responding to hydraulic disturbances and reflects the speed and efficiency of flow regulation. Transient fluctuation amplitude refers to the magnitude of flow fluctuations during pressure wave propagation, representing the maximum change in flow within a short period. This amplitude indicates the sensitivity to changes in water demand and reflects the magnitude of the flow fluctuation. These characteristics together describe the dynamic change process of transient terminal flow, helping to understand how the system responds to changes in water demand and the propagation of pressure waves, ensuring precise flow control and system stability.

[0040] Based on the flow deviation between the target flow of the M pumping stations and the flow of the M water distribution associated pumping station nodes, the initial PID control weight allocation is calculated and output.

[0041] In one embodiment, the flow difference of each pumping station is first calculated based on the deviation between the target flow and the actual flow of the M water-distribution associated pumping station nodes. Specifically, the target flow is a preset ideal flow value, while the actual flow is the actual flow monitored by the pumping station during operation. By calculating these flow deviations, it can be determined whether the flow of each pumping station meets expectations and identify any existing deviations. Subsequently, based on these flow deviations, an initial PID control weight allocation is calculated using a PID (Proportional-Integral-Derivative) control algorithm. The purpose of PID control is to adjust the operating parameters of the pumping station according to the magnitude and rate of change of the deviation to minimize the flow deviation. In this process, the proportional part (P) adjusts the control intensity according to the current flow deviation, the integral part (I) accumulates past deviations to eliminate long-term deviations, and the derivative part (D) predicts the trend of deviation changes and makes a prediction. Finally, the initial PID control weight allocation is output to further adjust the operating state of the pumping station, thereby controlling the flow near the target value.

[0042] Furthermore, this application provides a method for calculating and outputting initial PID control weight allocation based on the flow deviation between the target flow of the M pumping stations and the flow of the M water distribution associated pumping station nodes. The method includes:

[0043] Extract the M node-level features and M downstream node association numbers of the M water distribution associated pumping station nodes from the water conveyance equipment hierarchical topology; calculate M water distribution priority factors based on the M node-level features and M downstream node association numbers; calculate the M node flow deviations between the target flow of the M pumping stations and the flow of the M water distribution associated flow; define the initial PID control weight allocation based on the M water distribution priority factors and M node flow deviations.

[0044] Optionally, firstly, M node-level features of the M water-distribution associated pumping station nodes are extracted from the water conveyance equipment hierarchical topology. These node-level features reflect the relative position and hierarchy of each node in the topology. For example, an upstream pumping station node may be at the first or second level, while a downstream pumping station node may belong to a lower level (such as the fourth level). This hierarchical information helps the system determine the priority and role of each node in flow distribution and control. Simultaneously, the number of associations between each pumping station node and downstream nodes is extracted, i.e., how many downstream nodes each node is directly associated with. Subsequently, based on the extracted node-level features and the number of downstream node associations, the node-level features and the number of downstream node associations of each node are fused using a weighted method to calculate the water distribution priority factor for each pumping station node. This factor reflects the importance of the node in flow distribution. It is important to note that before weighting, the data involved in the weighting needs to be standardized (e.g., using max-min normalization) to ensure that the data are on the same scale. Subsequently, the deviation between the target flow rate and the actual flow rate for each pumping station is calculated. This deviation represents the difference between the actual flow rate and the expected target flow rate. The flow deviation is a crucial basis for adjusting control parameters and directly affects subsequent control weight calculations. Then, the water allocation priority factor and node flow deviation are input into the initial weight allocation function, which is as follows: ;in, Assign initial control weights to the i-th pump station node; Let be the flow deviation of the i-th pump station node; Let be the water allocation priority factor for the i-th pumping station node; M is the total number of pumping station nodes. Let be the flow deviation of the j-th pump station node; Let be the water allocation priority factor for the j-th pump station node; The safety factor is used to adjust the calculation results to ensure the safety of system operation and can be set according to the actual system characteristics or operational requirements. Through the calculation of the initial weight allocation function, an initial PID control weight allocation can be obtained, including the initial PID control weight for each pump station. These initial PID control weights ensure that the flow regulation between pump stations meets the expected target and guarantees system stability.

[0045] Furthermore, this application provides a weight allocation constraint with a preset weight amplitude limit when defining the initial PID control weight allocation based on M water diversion priority factors and M node flow deviations.

[0046] Optionally, when defining the initial PID control weight allocation, in addition to considering the M water priority factors and M node flow deviations, it is also necessary to set preset weight amplitude limits to ensure that the calculated control weights are within a reasonable range. Specifically, preset weight amplitude limits refer to constraining the upper and lower limits of the control weights for each pumping station during the weight allocation process. These constraints prevent the weight values ​​from being too large or too small, thus ensuring that the pumping station does not over-adjust or under-adjust when adjusting the flow rate. In actual operation, the system calculates the initial control weights based on the water priority factors and flow deviations of each pumping station. However, if the calculation result exceeds the preset maximum or minimum amplitude limit, it will be adjusted. For example, if the initial control weight calculation result of a pumping station exceeds the preset maximum value, it will be forcibly set to that maximum value; conversely, if it is lower than the minimum value, the weight will be limited to that minimum value. In this way, preset weight amplitude limits ensure the rationality of the control weights and the stability of the system, avoiding adverse effects on the pumping stations caused by inappropriate control strategies.

[0047] Based on the deviation characteristics between the target terminal flow and the transient terminal flow, the initial PID control weight allocation is compensated, and the target PID control weight allocation is output.

[0048] In one embodiment, the initial PID control weight allocation is adjusted to compensate for the deviation between the target terminal flow and the transient terminal flow. The target terminal flow is a preset ideal flow, while the transient terminal flow is a flow fluctuation caused by the propagation of pressure waves during actual operation. Specifically, firstly, flow sensors deployed at the terminal regulating reservoir collect terminal flow data in real time. Then, based on the difference between the target terminal flow and the real-time terminal flow, the deviation of the terminal flow is calculated. This calculation is then combined with the transient terminal flow to calculate the deviation characteristics for subsequent compensation. Finally, the deviation characteristics, peak flow, transient fluctuation amplitude, and steady-state recovery time are combined to compensate for the initial PID control weights, thereby outputting the target PID control weight allocation. This ensures accurate and stable flow regulation at the pumping station and its ability to adapt to real-time changes in water flow demand.

[0049] Furthermore, this application provides a method for compensating for the initial PID control weight allocation based on the deviation characteristics between the target terminal flow rate and the transient terminal flow rate, and outputting a target PID control weight allocation. The method includes:

[0050] Interactive flow sensors deployed at the end-of-pipe reservoir collect real-time end-of-pipe flow; the end-of-pipe flow deviation is calculated based on the target end-of-pipe flow and the real-time end-of-pipe flow, and the ratio of the end-of-pipe flow deviation to the transient end-of-pipe flow is used as the deviation feature; deviation scenario matching is performed according to the deviation feature, peak flow, and transient fluctuation amplitude to call the scenario compensation coefficient; the benchmark compensation amplitude is adjusted according to the steady-state recovery time, and the scenario compensation amplitude is output; complementary weight compensation is calculated for the initial PID control weight allocation based on the scenario compensation coefficient and the scenario compensation amplitude, and the target PID control weight allocation is output.

[0051] Optionally, flow sensors are deployed at the end-of-pipe reservoir to monitor and collect end-of-pipe flow data in real time. These sensors continuously measure and record flow changes at the end, providing real-time data for subsequent analysis. Then, by comparing the target end-of-pipe flow with the actual collected real-time end-of-pipe flow, the deviation between them is calculated. The calculated deviation reflects the gap between the current flow and the target flow. The ratio between the end-of-pipe flow deviation and the transient end-of-pipe flow (i.e., the flow that fluctuates instantaneously during flow changes) is used as the deviation characteristic. This ratio indicates the magnitude and rate of flow change, serving as the basis for subsequent compensation adjustments. Afterward, based on the calculated deviation characteristic and the peak flow (i.e., the maximum value reached during flow changes) and transient fluctuation amplitude (i.e., the amplitude of flow fluctuations) marked on the transient end-of-pipe flow, scenario matching is performed. That is, the deviation characteristic, peak flow, and transient fluctuation amplitude are synchronized to the scenario matching table to obtain the scenario compensation coefficient corresponding to the current scenario. Then, based on the steady-state recovery time (i.e., the time required for pressure fluctuations to recover to a stable flow rate), the baseline compensation range is adjusted. This involves multiplying the steady-state recovery time by the baseline compensation range. This adjusted baseline compensation range is used as the scenario compensation range to ensure that over-adjustment or under-adjustment does not occur during the recovery process. Finally, based on the selected scenario compensation coefficient and compensation range, complementary compensation calculations are performed on the initial PID control weights. This involves multiplying the scenario compensation coefficient, compensation range, and initial PID control weight allocation, and then adding the result to the initial PID control weight allocation to obtain the final target PID control weight allocation. This target weight allocation guides the pumping station on how to precisely adjust the flow rate in subsequent operations, ensuring that the entire system remains stable and efficient in response to changes in water demand.

[0052] Table 2: Example Table of Scene Matching

[0053] Deviation feature interval Peak flow interval Transient fluctuation amplitude interval Scenario compensation coefficient 0.0-0.1 0-50 0-10 0.1 0.1-0.3 50-100 10-20 0.3 0.3-0.5 100-150 20-30 0.5 0.5-0.7 150-200 30-40 0.7 0.7-1.0 200+ 40+ 1

[0054] As shown in Table 2, the scenario matching example table is used to select appropriate scenario compensation coefficients based on different characteristics of real-time traffic data (deviation characteristics, peak traffic and transient fluctuation amplitude), helping the system to make precise adjustments under different traffic fluctuations and demand changes.

[0055] During the independent PID control of the M water distribution pump station nodes using the target PID control weight allocation, pressure regulation coordinated control is performed based on pressure surges.

[0056] In one embodiment, when using target PID control weight allocation, PID control for each pumping station is executed independently based on the target PID control weight for each water distribution associated pumping station node. Each pumping station adjusts according to the deviation between its actual flow and the target flow. The PID control algorithm dynamically calculates the proportional, integral, and derivative terms to optimize flow control and ensure that the flow gradually approaches the target value. However, during the entire control process, if a pressure surge occurs (e.g., due to sudden demand changes or system failures), such pressure fluctuations may affect the normal operation of the pumping stations. To address this situation, pressure regulation coordination control is triggered. The role of pressure regulation coordination control is to quickly balance the system pressure by coordinating the adjustments between different pumping stations when pressure changes drastically, preventing equipment damage or flow imbalance caused by excessively high or low pressure. Specifically, when a pressure surge occurs, the weights of the PID control are adjusted according to the magnitude and distribution of the pressure change, enabling coordinated pressure regulation among multiple pumping stations. This means that although each pumping station performs independent PID control, they will coordinate and adjust in the event of sudden pressure changes to achieve stable operation of the entire system. This coordinated control ensures that the cascade pumping stations can still effectively maintain water flow balance and system stability in unstable water flow environments.

[0057] In summary, the embodiments of this application have at least the following technical effects:

[0058] This application embodiment first performs topology mapping of water distribution nodes through water balance analysis to construct a water distribution correlation influence topology. Then, real-time interval water distribution demand is loaded into the water distribution correlation influence topology to locate the M water distribution correlation flow rates of the M water distribution correlation pumping station nodes. Next, in the water distribution correlation influence topology, the pressure wave propagation process after the change in water distribution demand is simulated based on the real-time interval water distribution demand, and the transient terminal flow rate is output. Further, based on the flow deviation between the M pumping station target flow rates and the M water distribution correlation flow rates of the M water distribution correlation pumping station nodes, an initial PID control weight allocation is calculated and output. Then, based on the deviation characteristics between the target terminal flow rate and the transient terminal flow rate, the initial PID control weight allocation is compensated, and a target PID control weight allocation is output. Finally, during the independent PID control process of the M water distribution correlation pumping station nodes using the target PID control weight allocation, pressure regulation coordinated control is performed based on pressure mutations. These technologies collectively address the technical problems of water waste and low efficiency of cascade pumping stations due to the inability to accurately adjust the flow balance between pumping stations during flow scheduling, caused by changes in water sources and fluctuations in pumping station flow demand. They achieve the technical effect of dynamically adjusting pumping station flow and accurately responding to real-time changes in water demand through water balance analysis and the construction of water distribution correlation topology, thereby improving the stability and flow regulation efficiency of cascade pumping stations and ensuring optimal allocation of water resources.

[0059] Example 2, based on the same inventive concept as the hydraulic optimization control method for flow balance between cascade pumping stations in the foregoing examples, such as... Figure 2 As shown, this application provides a hydraulic optimization control system for flow balance between cascade pumping stations. The system includes: a topology mapping module 11: performing topology mapping of water distribution nodes through water balance analysis to construct a water distribution correlation influence topology; a real-time demand loading module 12: loading real-time interval water distribution demand into the water distribution correlation influence topology to locate the M water distribution correlation flow rates of the M water distribution correlation pumping station nodes; a water distribution simulation module 13: simulating the pressure wave propagation process after the change in water distribution demand in the water distribution correlation influence topology based on the real-time interval water distribution demand, and outputting the transient terminal flow rate; a weight allocation module 14: calculating and outputting the initial PID control weight allocation based on the flow deviation between the M pumping station target flow rates and the M water distribution correlation flow rates of the M water distribution correlation pumping station nodes; a weight compensation module 15: compensating the initial PID control weight allocation based on the deviation characteristics between the target terminal flow rate and the transient terminal flow rate, and outputting the target PID control weight allocation; and a collaborative control module 16: performing pressure regulation collaborative control based on pressure sudden changes during the independent PID control process of the M water distribution correlation pumping station nodes using the target PID control weight allocation.

[0060] Furthermore, the topology mapping module 11 is also used to perform the following method:

[0061] The parameters of the entire water conveyance process equipment are obtained interactively; the parameters of the entire water conveyance process equipment are hierarchically mapped to the water conveyance equipment hierarchical topology using a Huffman tree data structure; multiple water distribution correlation influence functions are constructed for multiple water conveyance equipment nodes in the water conveyance equipment hierarchical topology through water balance analysis; the multiple water distribution correlation influence functions are loaded into the water conveyance equipment hierarchical topology to obtain the water distribution correlation influence topology.

[0062] Furthermore, the topology mapping module 11 is also used to perform the following method:

[0063] Based on the downstream single-level node decomposition of the water conveyance equipment hierarchical topology, multiple sets of downstream nodes of multiple water conveyance equipment nodes are obtained; multiple sample input flow rates and multiple sample water diversion volumes of the first water conveyance equipment node in the water distribution scenario are interactively obtained; based on the multiple timestamps of the multiple sample input flow rates and multiple sample water diversion volumes, multiple sets of downstream output flow rates of the first set of downstream nodes are mapped and called; a water balance transfer function is defined for the multiple sample input flow rates, multiple water diversion volumes, and multiple sets of downstream output flow rates, and the first water distribution correlation influence function is output; and so on, multiple water distribution correlation influence functions of the multiple water conveyance equipment nodes are constructed.

[0064] Furthermore, the real-time demand loading module 12 is also used to perform the following method:

[0065] The water distribution demand node for the real-time interval water distribution demand is located in the water distribution correlation influence topology; the water distribution demand node is used as the real-time parent node, and a breadth-first traversal is performed on the downstream nodes along the water conveyance path in the water distribution correlation influence topology to generate a downstream hierarchical topology network; the real-time input flow of the water distribution demand node is extracted, and the real-time input flow and the real-time interval water distribution demand are used as inputs, and the water distribution correlation influence function is called layer by layer along the downstream hierarchical topology to calculate the downstream output flow of a single node, so as to obtain the M water distribution correlation flow of the M water distribution correlation pump station nodes.

[0066] Furthermore, the water separation simulation module 13 is also used to perform the following method:

[0067] The real-time interval water distribution demand is used as a hydraulic disturbance source. Based on the water distribution demand nodes, the real-time interval water distribution demand is loaded into the downstream hierarchical topology network to perform dynamic simulation of transient pressure wave propagation. The real-time input flow and real-time interval water distribution demand are used as disturbance factors to simulate the propagation of transient pressure waves caused by water distribution disturbance in the downstream hierarchical topology network, and the transient terminal flow is output.

[0068] Furthermore, the water separation simulation module 13 is also used to perform the following method:

[0069] The transient terminal flow rate is identified by its peak flow rate, steady-state recovery time, and transient fluctuation amplitude.

[0070] Furthermore, the weight allocation module 14 is also used to perform the following method:

[0071] Extract the M node-level features and M downstream node association numbers of the M water distribution associated pumping station nodes from the water conveyance equipment hierarchical topology; calculate M water distribution priority factors based on the M node-level features and M downstream node association numbers; calculate the M node flow deviations between the target flow of the M pumping stations and the flow of the M water distribution associated flow; define the initial PID control weight allocation based on the M water distribution priority factors and M node flow deviations.

[0072] Furthermore, the weight allocation module 14 is also used to perform the following method:

[0073] When defining the initial PID control weight allocation based on M water priority factors and M node flow deviations, a preset weight amplitude limit is used to constrain the weight allocation.

[0074] Furthermore, the weight compensation module 15 is also used to perform the following method:

[0075] Interactive flow sensors deployed at the end-of-pipe reservoir collect real-time end-of-pipe flow; the end-of-pipe flow deviation is calculated based on the target end-of-pipe flow and the real-time end-of-pipe flow, and the ratio of the end-of-pipe flow deviation to the transient end-of-pipe flow is used as the deviation feature; deviation scenario matching is performed according to the deviation feature, peak flow, and transient fluctuation amplitude to call the scenario compensation coefficient; the benchmark compensation amplitude is adjusted according to the steady-state recovery time, and the scenario compensation amplitude is output; complementary weight compensation is calculated for the initial PID control weight allocation based on the scenario compensation coefficient and the scenario compensation amplitude, and the target PID control weight allocation is output.

[0076] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0077] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0078] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A hydraulic optimization control method for flow balancing between step pump stations, characterized by, The method comprises: water balance analysis is performed to map the water diversion node topology, and a water diversion correlation influence topology is constructed; real-time interval water diversion demand is loaded to the water diversion correlation influence topology, and M water diversion correlation flows of M water diversion correlation pump station nodes are located; in the water diversion correlation influence topology, a pressure wave propagation process after a change in water diversion demand is simulated according to the real-time interval water diversion demand, and a transient end flow is output; an initial PID control weight distribution is calculated according to a flow deviation between M pump station target flows of the M water diversion correlation pump station nodes and the M water diversion correlation flows; a target PID control weight distribution is output by compensating the initial PID control weight distribution according to a deviation feature of the target end flow and the transient end flow; in the process of performing independent PID control of the M water diversion correlation pump station nodes by using the target PID control weight distribution, pressure mutation is used to perform pressure regulation collaborative control; wherein the transient end flow has a peak flow, a steady-state recovery time and a transient fluctuation amplitude identifier; wherein the target PID control weight distribution is output by compensating the initial PID control weight distribution according to a deviation feature of the target end flow and the transient end flow, comprising: deploying a flow sensor at an end regulation and storage reservoir to collect real-time end flow; calculating an end flow deviation based on the target end flow and the real-time end flow, and taking a ratio of the end flow deviation to the transient end flow as the deviation feature; performing deviation scene matching according to the deviation feature, the peak flow and the transient fluctuation amplitude to call a scene compensation coefficient; adjusting a reference compensation amplitude according to the steady-state recovery time to output a scene compensation amplitude; complementarily compensating the initial PID control weight distribution according to the scene compensation coefficient and the scene compensation amplitude to output the target PID control weight distribution.

2. The hydraulic optimization control method for flow balancing between step-pump stations according to claim 1, characterized by, Water balance analysis is performed to map the water diversion node topology, and a water diversion correlation influence topology is constructed, the method comprising: interactively obtaining water conveyance whole-process equipment parameters; using Huffman tree data structure to hierarchically correlate and map the water conveyance whole-process equipment parameters to a water conveyance equipment hierarchical topology; constructing, through water balance analysis, a plurality of water diversion correlation influence functions of a plurality of water conveyance equipment nodes in the water conveyance equipment hierarchical topology; loading the plurality of water diversion correlation influence functions to the water conveyance equipment hierarchical topology to obtain the water diversion correlation influence topology.

3. The hydraulic optimization control method for flow balancing between step-pump stations according to claim 2, characterized by, Water balance analysis is performed to construct a plurality of water diversion correlation influence functions of a plurality of water conveyance equipment nodes in the water conveyance equipment hierarchical topology, the method comprising: decomposing the water conveyance equipment hierarchical topology based on a downstream single-level node to obtain a plurality of groups of downstream nodes of the plurality of water conveyance equipment nodes; interactively obtaining a plurality of sample input flows and a plurality of sample water diversion quantities of a first water conveyance equipment node in a water diversion scene; mapping and calling a plurality of groups of downstream output flows of a first group of downstream nodes according to a plurality of time stamps of the plurality of sample input flows and the plurality of sample water diversion quantities; The water balance transfer function definition is performed on the plurality of sample input flows, the plurality of water diversion flows and the plurality of groups of downstream output flows, and a first water diversion correlation influence function is output; By analogy, a plurality of water diversion correlation influence functions of the plurality of water conveyance equipment nodes are constructed.

4. The hydraulic optimization control method for flow balancing between step-pump stations according to claim 1, characterized by, The real-time interval water diversion demand is loaded to the water diversion correlation influence topology, and M water diversion correlation flows of M water diversion correlation pump station nodes are located, and the method comprises the following steps: Locating a water diversion demand node of the real-time interval water diversion demand in the water diversion correlation influence topology; Taking the water diversion demand node as a real-time parent node, breadth-first traversal is performed on downstream nodes along a water conveyance path in the water diversion correlation influence topology, and a downstream hierarchical topology network is screened and generated; Real-time input flow of the water diversion demand node is extracted, and the real-time input flow and the real-time interval water diversion demand are taken as inputs, and downstream output flow calculation of a single node is performed by layer-by-layer calling of a water diversion correlation influence function along the downstream hierarchical topology, so that M water diversion correlation flows of the M water diversion correlation pump station nodes are obtained.

5. The hydraulic optimization control method for flow balancing between step-pump stations according to claim 4, characterized by, In the water diversion correlation influence topology, a pressure wave propagation process after a change of the water diversion demand is simulated according to the real-time interval water diversion demand, and a transient end flow is output, and the method comprises the following steps: Taking the real-time interval water diversion demand as a hydraulic disturbance source, the real-time interval water diversion demand is loaded to the downstream hierarchical topology network according to the water diversion demand node to perform dynamic simulation of transient pressure wave propagation; The real-time input flow and the real-time interval water diversion demand are taken as disturbance factors, transient pressure wave propagation caused by water diversion disturbance is simulated in the downstream hierarchical topology network, and the transient end flow is output.

6. The hydraulic optimization control method for step-pool station-to-station flow balancing of claim 2, wherein, According to flow deviations of M pump station target flows of the M water diversion correlation pump station nodes and M water diversion correlation flows, an initial PID control weight distribution is calculated and output, and the method comprises the following steps: M node hierarchical features and M downstream node correlation quantities of the M water diversion correlation pump station nodes are extracted from the water conveyance equipment hierarchical topology; M water diversion priority factors are calculated according to the M node hierarchical features and the M downstream node correlation quantities; M node flow deviations of the M pump station target flows and the M water diversion correlation flows are calculated; The initial PID control weight distribution is defined based on the M water diversion priority factors and the M node flow deviations.

7. The hydraulic optimization control method for step-pool station-to-station flow balancing of claim 6, wherein, When the initial PID control weight distribution is defined based on the M water diversion priority factors and the M node flow deviations, a preset weight amplitude limit is used for weight distribution constraint.

8. A hydraulic optimization control system for flow balancing between step-pumping stations, characterized by, The system is used for performing the hydraulic optimization control method for flow balance between cascade pump stations according to any one of claims 1-7, and the system comprises: A topology mapping module: water diversion node topology mapping is performed through water balance analysis, and a water diversion correlation influence topology is constructed; A real-time demand loading module: real-time interval water diversion demand is loaded to the water diversion correlation influence topology, and M water diversion correlation flows of M water diversion correlation pump station nodes are located; A water diversion simulation module: in the water diversion correlation influence topology, a pressure wave propagation process after a change of the water diversion demand is simulated according to the real-time interval water diversion demand, and a transient end flow is output. The weight distribution module calculates an initial PID control weight distribution according to the flow deviation between the M pump station target flows of the M pump station nodes associated with the M water divisions and the M flows associated with the M water divisions; The weight compensation module compensates the initial PID control weight distribution according to the deviation characteristics of the target end flow and the transient end flow, and outputs a target PID control weight distribution; The cooperative control module executes pressure regulating cooperative control according to the pressure mutation in the independent PID control process of the M pump station nodes associated with the M water divisions by using the target PID control weight distribution.

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