Self-adaptive calculation method for working point of multi-pump combined operation of pump station based on graph theory algorithm

Abstract

The invention discloses a pump station multi-pump combined operation working point self-adaptive calculation method based on a graph theory algorithm, and belongs to the technical field of pump station operation optimization. The method comprises the steps that firstly, a pump station directed topological graph is constructed, and pump station physical elements are converted into edges and nodes of the graph; then identifying parallel pipelines through breadth-first traversal and combining the parallel pipelines into an equivalent pump, and identifying series pipelines through depth-first traversal and combining resistance parameters; and after the topological graph is iteratively simplified to a single equivalent pump, an equivalent pump performance curve and a required lift curve are combined to solve a total working point, and finally working condition parameters of each single pump are obtained through reverse backtracking decomposition. The limitation that a traditional method depends on a fixed structure is broken through, different pump station configurations can be adapted only by adjusting input parameters, the calculation efficiency and precision are greatly improved, and intelligent operation and maintenance and optimized operation of the pump station are effectively supported.

Description

Technical Field

[0001] This invention relates to the field of pump station operation optimization technology, specifically to an adaptive calculation method for the operating point of multiple pumps operating in a pump station based on graph theory algorithms. Background Technology

[0002] The operating point of a pumping station is a core hydraulic parameter during its operation, and its accurate solution is crucial for project safety and energy efficiency optimization. The operating point refers to the intersection of the pump's head-flow performance curve and the required head curve, determining the actual operating state of the pump. Currently, the technology for solving the operating point of a pumping station faces systemic bottlenecks, severely restricting the refined operation and management of large-scale water transfer projects. These bottlenecks are mainly reflected in: First, traditional calculation methods suffer from severe specialization and rigidity. Existing technologies typically require writing dedicated programs for the fixed structure of a specific pumping station, including preset pump numbers, piping layouts, and valve configurations. This highly customized solution cannot adapt to dynamic adjustments during operation. When actual operating conditions require adding or removing pumps, opening or closing valves, or changing piping connections, the original program immediately becomes invalid because it cannot recognize the new topology. This rigid architecture necessitates reprogramming for every pumping station structural adjustment, resulting not only in wasted time and economic costs but also making it difficult to implement real-time optimization strategies.

[0003] Secondly, the efficiency of manually simplifying complex pipeline topologies urgently needs improvement. Faced with the intricate series and parallel pipelines in pumping stations, existing methods rely on engineers manually identifying and simplifying the topological relationships. This process is not only time-consuming and labor-intensive but also prone to human error. Especially in scenarios requiring rapid response to changes in operating conditions, such as dynamically adjusting pump unit combinations based on water demand, the inefficiency of manual simplification becomes particularly prominent, becoming a bottleneck restricting pumping stations from achieving their "forecasting, early warning, rehearsal, and contingency planning" functions.

[0004] Furthermore, existing graph theory research exhibits a significant directional deviation in the field of pump station operation analysis. Although graph theory algorithms have been applied in pipeline network analysis, they primarily focus on static analysis scenarios such as pipe burst detection and hydraulic balance, with insufficient attention paid to adaptive calculation problems in multi-pump joint operation conditions. In particular, regarding dynamic operating point solutions, there is a lack of general algorithms capable of simultaneously handling topology changes and hydraulic parameter coupling, resulting in a lack of effective joint operation analysis tools for large-scale projects.

[0005] Therefore, there is an urgent need to solve the problems of poor adaptability and low efficiency of existing technologies by finding a new solution for the working point of multi-pump joint operation in pumping stations. Summary of the Invention

[0006] The purpose of this invention is to provide an adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithms, so as to solve the problems of poor adaptability and low efficiency in the existing technology.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithms includes the following steps: S1: Construct a directed topology graph of the pumping station, transforming the physical components of the pumping station into edges and nodes of the graph; S2: Identify parallel pipelines and merge parallel pumps through breadth-first traversal; S3: Identify series pipelines and merge resistance parameters through depth-first traversal; S4: Iterate through S2 and S3 to simplify the topology to a single equivalent pump; S5: Solve for the operating point of multiple pumps operating together in the pumping station, and backtrack the operating conditions of a single pump.

[0008] Furthermore, the process of transforming the physical components of the pumping station into edges and nodes of a graph includes: A water pump is abstracted as a directed edge with a performance curve; The pipe segment is abstracted as a directed edge with resistance parameters; Valve status is achieved by dynamically adjusting edge attributes; Nodes represent connection points and record elevation, location, and the direction and quantity of associated pipe segments.

[0009] Furthermore, the breadth-first traversal described in S2 includes: implementing it using a queue data structure, the traversal process starting from the starting vertex, visiting adjacent vertices in order from near to far, and identifying edge groups with the same starting and ending points as parallel pipelines.

[0010] Furthermore, the merging of parallel pumps described in S2 includes: incorporating the resistance loss of the pipe section before the parallel point into the pump's internal loss, adjusting the original pump performance curve to generate an equivalent pump performance curve, merging the parallel pump performance curves according to the rule of adding flow rates under the same head, and deleting the original parallel edge and adding an edge containing the equivalent pump attribute.

[0011] Furthermore, the depth-first traversal described in S3 includes: implementing it using a stack data structure, storing the traversal path through recursion or an explicit stack, and identifying the serial pipelines corresponding to nodes with both in-degree and out-degree of 1.

[0012] Furthermore, the merging resistance parameters described in S3 include: merging the resistance parameters of series pipe segments, deleting intermediate nodes and associated edges, and adding edges containing equivalent resistance attributes.

[0013] Furthermore, the solution for the multi-pump joint operation working point of the pumping station described in S5 includes: combining the equivalent pump performance curve and the required head curve of the water conveying device, and obtaining the total flow and total head by finding the intersection point through numerical methods. The required head curve is calculated from the static head and water conveying resistance parameters.

[0014] Furthermore, the reverse backtracking single-pump operating condition described in S5 includes: The total flow is decomposed into each parallel branch by using the inverse operation of superimposing the flow rates under the same head in parallel pipelines; The process is repeated layer by layer, starting with the simplified equivalent pump and gradually restoring it to the initial topology, outputting the operating conditions of each pump.

[0015] Another object of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithms.

[0016] Another object of the present invention is to provide an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the aforementioned adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithms.

[0017] In summary, the present invention has at least one of the following beneficial technical effects: It is highly adaptable and can adapt to different pump station configurations simply by adjusting the input parameters, completely solving the problem of rigid programs in traditional methods. It has high computational efficiency, and the topology is dynamically simplified based on breadth-first and depth-first traversal algorithms, avoiding manual enumeration of working conditions and greatly improving the computation speed. The results are accurate and reliable. By backtracking and decomposing the equivalent pump parameters, the key operating parameters such as flow rate and head of each pump can be accurately restored, with an error rate significantly lower than that of traditional methods. The project has significant practical value, providing a real-time hydraulic model for the pump station's digital twin platform. This directly supports intelligent operation and maintenance functions such as forecasting, early warning, pre-drills, and contingency plans, helping the pump station operate safely and efficiently. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a water pump conveying device; Figure 2 This is a schematic diagram of the water pump's operating points. Figure 3 This is a topology diagram of the pump station inlet and outlet water system of the present invention; Figure 4 This is a schematic diagram of the breadth-first traversal algorithm of the present invention; Figures 5 to 9 This is a schematic diagram illustrating the execution of step S2 in an embodiment of the present invention; Figure 10 This is a schematic diagram of the depth-first traversal algorithm of the present invention; Figures 11 to 14 This is a schematic diagram illustrating the execution of step S3 in an embodiment of the present invention; Figures 15 to 17This is a schematic diagram illustrating the execution of step S4 in an embodiment of the present invention; Figure 18 This is a schematic diagram illustrating the solution process for the working point of multi-pump joint operation in the pumping station according to the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0020] This embodiment provides an adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithms, including the following steps: S1: Construct a directed topology graph of the pumping station, transforming the physical components of the pumping station into edges and nodes of the graph; S2: Identify parallel pipelines and merge parallel pumps through breadth-first traversal; S3: Identify series pipelines and merge resistance parameters through depth-first traversal; S4: Iterate through S2 and S3 to simplify the topology to a single equivalent pump; S5: Solve for the operating point of multiple pumps operating together in the pumping station, and backtrack the operating conditions of a single pump.

[0021] The following is a detailed description with reference to specific embodiments.

[0022] S1: Construct a directed topology graph of the pumping station, transforming the physical components of the pumping station into edges and nodes of the graph.

[0023] The core of this invention lies in abstracting the complex physical pumping station system into a mathematical graph theory model, thereby enabling efficient processing using computer algorithms. Constructing a directed topological graph of the pumping station is the fundamental step of the entire method. It transforms physical components such as pumps, pipe sections, and valves into edges and nodes of the graph, achieving a digital representation of the system structure. This process not only provides data structure support for subsequent graph traversal and simplification but also lays the framework foundation for adaptive computation.

[0024] To understand the application of topology diagrams, it is first necessary to grasp the concept of the operating point of a water pump conveying device. A water pump conveying device is like... Figure 1 As shown in the figure, d is the liquid level in the outlet pool, s is the liquid level in the inlet pool, and H is... stThe static head is the net height difference that the pump needs to lift the liquid. Sections 1-1 and 2-2 are commonly used reference surfaces in fluid mechanics. Section 1-1 refers to the liquid surface in the inlet pool (water intake point or source point), and section 2-2 refers to the liquid surface in the outlet pool (water delivery point or end point). The circular element represents the pump, which is the energy source of the entire system and is responsible for doing work on the liquid, increasing the liquid's energy (manifested as increasing the liquid's pressure / head).

[0025] The required head of the water conveyance system From Jingyangcheng and resistance loss of pipeline system Composition. For non-circular pipes, the resistance loss of the piping system. Including friction loss and local resistance loss ,Right now (1) In the formula: and Let represent the friction loss coefficients and local resistance loss coefficients, respectively; L, d, and A represent the pipe length, diameter, and cross-sectional area, respectively; i is the pipe segment number; and Q is the flow rate through the pipe. Let: , , Then we have: (2) In the formula: , and These are respectively called the friction resistance parameter, local resistance parameter, and resistance parameter of the pipeline.

[0026] Therefore, the equation for the required head curve of the water conveyance system can be written as follows: (3) The performance curve equation of a water pump can be written as follows: (4) In the formula: H is the pump head, and Q is the pump flow rate. are the coefficients of the polynomial.

[0027] The stable operating point of the pump is defined by the pump's Q~H curve and the required head curve of the system (i.e., Q~H curve). The intersection of the Q~H curve and the required head curve of the device (i.e., Q~H curve). Curves) are plotted in the same coordinate system (e.g. Figure 2 As shown in the figure, the intersection point A of these two curves is the operating point of the water pump in this device. After point A is determined, its corresponding flow rate is... ,power ,efficiency and cavitation margin This information can be found in the corresponding performance curves.

[0028] The directed topology of the pumping station is constructed starting from the inlet pool and ending at the outlet pool, simulating the direction of water flow to form a directed graph structure. Among them: A water pump is abstracted as a directed edge with a performance curve. Each edge represents a water pump, and its properties include the direction of the head-flow performance curve from the inlet side to the outlet side.

[0029] Pipe segments are abstracted as directed edges with resistance parameters. Each edge represents a segment of the pipe, and its properties include resistance parameters (such as friction resistance). and local resistance parameters Total resistance ), with the direction along the water flow direction.

[0030] Valve status is achieved by dynamically adjusting edge attributes. The opening and closing of the valve affects the resistance parameters of the pipe segment, so the valve status is encoded as the dynamic attributes of the edge (such as changes in the resistance coefficient).

[0031] Nodes represent connection points. Each node records elevation, location information, and the direction and number of associated pipe segments (such as in-degree and out-degree). Nodes are vertices in the topology graph, used to connect edges and form the network structure.

[0032] This abstraction process allows the pumping station system to be converted into a computer-processable graph model, facilitating subsequent dynamic simplification using graph theory algorithms such as breadth-first search and depth-first search. The construction of the topology graph ensures the mathematical consistency of the system structure and provides a unified input format for adaptive computation.

[0033] Taking the Liyuzhou Pumping Station as an example, this paper explains the process of constructing the directed topology graph of the pumping station. The inlet and outlet water pipeline system of the Liyuzhou Pumping Station is a complex network, containing multiple pumps, pipe segments, and valves. When constructing the topology graph, the physical system is first generalized into a graph model composed of two types of elements: edges (pipe segments) and vertices (nodes). The characteristics of pipe segments and nodes include three aspects: structural properties, topological properties, and hydraulic properties.

[0034] Construction properties: determined through system design, including pipe section length, pipe diameter, roughness, and node elevation and location.

[0035] Topological properties: expressed using mathematical graph theory, including the direction, start point, and end point of the pipe segment, as well as the pipe segments associated with the node and their directions, and the degree of the node (i.e., the number of pipe segments associated with the node).

[0036] Hydraulic properties: Analysis and calculation using hydraulic theory, including flow rate, velocity, head, hydraulic loss, pressure drop of pipe sections, as well as flow rate (flow rate from or out of the system at nodes), energy, and free head (the height of the node head above the ground elevation).

[0037] In the Liyuzhou Pumping Station, the pipeline loss between vertices is represented by the symbol "". The simplified topology diagram of the pump station's inlet and outlet water system is shown below. Figure 3 As shown in the diagram, this figure clearly illustrates the connections between the inlet pool, outlet pool, pump edges, and pipe segments. For example, the pump is abstracted as an edge pointing from the inlet pool to the outlet pool, pipe segments contain resistance parameters, nodes represent connection points (such as tees and elbows), and record elevation and associated pipe segment information. This topology diagram not only simplifies complex pipe networks but also provides an input structure for subsequent graph traversal algorithms (such as breadth-first search and depth-first search).

[0038] S2: Identify parallel pipelines and merge parallel pumps through breadth-first traversal.

[0039] Based on the topology graph constructed in Step 1, a breadth-first search (BFS) is performed to identify all edge groups (i.e., parallel pipelines) with the same start and end points. BFS merges parallel pumps, aiming to simplify complex multi-pump parallel systems into equivalent single-pump systems. This method, based on graph theory algorithms, achieves adaptive solution for pump station operating points through systematic topology analysis and mathematical calculations. This step is performed based on the directed topology graph of the pump station constructed in Step 1 and mainly includes key technical aspects such as the application of the breadth-first search algorithm and the identification and merging of parallel pumps.

[0040] Breadth-first search (BFS) is implemented using a queue data structure. The traversal starts from the initial vertex and visits adjacent vertices in ascending order, identifying edge pairs with the same start and end point as parallel paths. The basic idea of ​​BFS is to maximize the coverage of all possible nodes and visit them. It is typically implemented using a queue data structure, by adding nodes to the queue sequentially according to the traversal order. Starting from a vertex v in the graph, after visiting v, visit each of v's unvisited adjacent vertices in turn. Then, starting from each of these adjacent vertices, visit their adjacent vertices in turn, ensuring that the adjacent vertices of the first visited vertex are visited before the adjacent vertices of the last visited vertex, until all the adjacent vertices of the visited vertices in the graph have been visited. If there are still unvisited vertices in the graph at this point, a new unvisited vertex is selected as the new starting point, and the above process is repeated until all vertices in the graph have been visited. The breadth-first search traversal process of the graph starts from v and proceeds from nearest to farthest. In a directed graph, the breadth-first traversal of a vertex also needs to consider the vertex corresponding to its out-degree, and count the vertex corresponding to that out-degree as an adjacent vertex. The steps of the breadth-first search algorithm are as follows: Figure 4 As shown.

[0041] The resistance loss of the pipe section before the parallel connection point is included in the pump's internal loss. The original pump performance curve is adjusted to generate an equivalent pump performance curve. The parallel pump performance curves are merged according to the rule of adding the flow rates under the same head. After deleting the original parallel edges, new edges containing the equivalent pump attribute are added. The system with multiple pumps in parallel within the pumping station is simplified to a simple system with only one pump. The simplification process of the diagram is as follows: The resistance loss of the pipe section before the parallel connection point is included in the pump's internal loss to generate an equivalent pump performance curve. First, the pipe loss where the pump is located is included in the pump's internal loss, as shown in the topology diagram (…). Figure 3 ) becomes like Figure 5 As shown, for example, the performance of the original pump #0 is given by equation (4). Then, after taking internal losses into account, the performance of the equivalent pump #0 is... The same applies to other pumps.

[0042] By performing a breadth-first search, parallel edges are found (edges with the same starting and ending points are considered parallel edges). These parallel edges are then merged into parallel pumps (parallel processing) to obtain a new equivalent pump. The performance curve of this equivalent pump is obtained by adding the flow rates of the two parallel pumps at the same head. For example, pump #0 and pump #1 are combined into a new pump #0 (equivalent pump). This involves deleting the original two parallel edges AB, one containing information about pump #0 and the other containing information about pump #1, and adding a new edge AB containing information about the equivalent pump after the parallel connection. Pump #2 and pump #3 are combined into a new pump #1 (equivalent pump). Pump #4 and pump #5 are combined into a new pump #2 (equivalent pump). Pump #6 and pump #7 are combined into a new pump #3 (equivalent pump). For example... Figures 6 to 9As shown.

[0043] This step simplifies the complex multi-pump parallel system into an equivalent single-pump system, significantly reducing the complexity of subsequent calculations. Furthermore, this method provides reliable technical support for the optimized operation of pumping stations and digital twin systems, possessing significant engineering application value.

[0044] S3: Identify series pipelines and merge resistance parameters through depth-first traversal.

[0045] Depth-first search (DFS) is implemented using a stack data structure. It stores the traversal path recursively or explicitly using a stack, identifying the connecting pipes corresponding to nodes with both in-degree and out-degree of 1. The basic idea of ​​DFS is to continuously move forward and then backtrack. If an unexplored branch is encountered during the backtracking process, it is entered to continue exploring deeper until all vertices have been visited. It is typically implemented using a stack data structure, storing the traversal path recursively or explicitly using a stack. Assuming the initial state is that all vertices in the graph are unvisited, starting from a vertex v, the vertex is first visited and marked as "visited." Then, the DFS traverses the graph sequentially from each unvisited adjacent vertex until all vertices connected to v by a path have been visited. However, there may still be other branches that have not been visited. If there are still unvisited vertices, backtracking is required, selecting another unvisited vertex as the starting point, and repeating the above process until all vertices in the graph have been visited. Depth-first search is a recursive process. In a directed graph, depth-first traversal of a vertex involves considering the vertices with out-degrees, and counting those vertices as adjacent vertices. The steps of the depth-first traversal algorithm are as follows: Figure 10 As shown.

[0046] By using depth-first traversal, find the connected edges (if the in-degree of a vertex is equal to its out-degree and both are 1, then the in-edge and out-edge of that vertex are connected edges), merge the resistance parameters of the connected pipe segments, delete intermediate nodes and associated edges, and add edges containing equivalent resistance attributes.

[0047] The resistance parameters of the series-connected pipe segments are merged sequentially (series processing), that is, the series-connected edges are deleted and new edges are added. The new edges contain the new equivalent pump and new pipeline resistance loss information. Edges AB and BF are deleted sequentially, and then a new edge AF is added, as follows: Figure 11 ; Delete edge AC and edge CF, and then add a new edge AF, such as Figure 12 ; Delete edges AD and DF, and then add a new edge AF, such as Figure 13 ; Delete edges AE and EF, and then add a new edge AF, such as Figure 14 .

[0048] Through this series of operations, the originally complex pump station topology was effectively simplified, laying the foundation for subsequent operating point calculations. The systematic and complete nature of the depth-first traversal in this process ensured that all series pipelines were correctly identified and processed.

[0049] S4: Iterate through S2 and S3 to simplify the topology to a single equivalent pump.

[0050] Repeat steps two through three (if there are still series pipes, proceed to step three; otherwise, proceed only to step two), including the pipe loss of #0 in its internal loss calculation. Figure 15 After successively including the pipeline losses of #1, #2, and #3 into the pump's internal losses, as follows: Figure 16 The final simplified topology is as follows: Figure 17 As shown, this figure has only 2 vertices and 1 edge, and the edge contains new equivalent pump information (i.e., information on the overall performance curve of the combined operation).

[0051] This iterative process not only demonstrates the algorithm's adaptive capabilities but also achieves gradual simplification of complex systems through graph theory methods, ensuring a balance between computational accuracy and practicality.

[0052] S5: Solve for the operating point of multiple pumps operating together in the pumping station, and backtrack the operating conditions of a single pump.

[0053] The purpose of solving the operating point of a multi-pump joint operation in a pumping station is to determine the stable operating state of the pumping station under specific operating conditions, including total flow rate, total head, and operating parameters of each individual pump. This process is based on the topology simplified by graph theory algorithms in the previous steps, transforming the complex multi-pump system into an equivalent single-pump model, thereby simplifying calculations and improving efficiency.

[0054] After the iterative simplification in step four, the pump station's inlet and outlet pipeline system is simplified to a topology containing only two nodes (i.e., the inlet and outlet pools) and one equivalent edge (which contains information about the equivalent pump, i.e., the overall performance curve of the combined operation). Assume the simplified equation for the overall performance curve of the combined operation is: (5) In the formula: For the equivalent pump head, The flow rate of the equivalent pump, The coefficients are those of the polynomial. This curve integrates the performance of all pumps and pipeline resistance losses, reflecting the overall hydraulic characteristics of the pumping station.

[0055] Meanwhile, the head requirement curve of the water conveying device describes the energy required to transport liquid from the inlet pool to the outlet pool, and its equation is: (6) In the formula: To meet the required head of the water conveyance system; The static head of the water conveying device is the difference between the water level at the end of the water conveying process and the water level in the inlet pool. This refers to the water flow rate. These are parameters related to water conveyance resistance. The calculation depends on the piping configuration: for series piping, the equivalent resistance parameter is the sum of the resistance parameters of each pipe segment. For parallel pipelines, the equivalent resistance parameter is given by the parallel formula. Confirmed. This curve reflects the external characteristics of the water conveyance system and is unrelated to the performance of the pumping station.

[0056] The combined equivalent pump performance curve (i.e., the combined overall performance curve) and the required head curve of the water conveying device are intersected by numerical methods to obtain the total flow rate and total head. The required head curve is calculated from the static head and water conveying resistance parameters. Specifically, the operating point of the multi-pump combined operation of the pumping station can be obtained by solving equations (5) and (6) simultaneously. In summary, the solution process for the operating point of multiple pumps operating in conjunction at a pumping station is as follows: Figure 18 As shown.

[0057] After determining the operating points of the multiple pumps operating together at the pumping station, the operating point of each individual pump needs to be determined through a "backtracking" mechanism. This process proceeds in reverse order of the simplified topology diagram: Based on the total flow rate, and following the flow distribution rules for parallel pipelines (the inverse operation of flow rate superposition under equal head), the total flow rate is decomposed into each parallel branch. For example, for a parallel pump group, the flow distribution of each pump depends on its performance curve and resistance characteristics. The operating point of each pump is determined, and by combining the performance curves of the original pumps and solving the head-flow relationship, the specific operating point of each pump is determined. Layer-by-layer reconstruction is performed, starting with the simplified equivalent pump and gradually restoring to the initial topology, outputting the operating conditions (flow rate, head, power, and efficiency, etc.) of each pump. This process ensures the accuracy of individual pump parameters, providing data support for optimized operation of the pumping station.

[0058] The backtracking algorithm utilizes the merging path and parameters recorded during topology simplification to efficiently reconstruct the operating conditions through reverse calculation. It has a low error rate and is suitable for complex systems.

[0059] In summary, this method first constructs a directed topology graph of the pump station, transforming the physical components of the pump station into edges and nodes of the graph. Then, it identifies parallel pipelines and merges them into equivalent pumps using breadth-first traversal, and identifies series pipelines and merges their resistance parameters using depth-first traversal. After iteratively simplifying the topology graph to a single equivalent pump, it solves for the total operating point by combining the performance curves of the equivalent pump with the required head curve. Finally, it backtracks to decompose and obtain the operating parameters of each individual pump. This invention overcomes the limitations of traditional methods that rely on fixed structures, adapting to different pump station configurations simply by adjusting input parameters, significantly improving computational efficiency and accuracy, and effectively supporting intelligent operation and maintenance and optimized operation of pump stations.

[0060] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithms.

[0061] This embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to execute the adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithms.

[0062] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Contents not described in detail in this specification are prior art known to those skilled in the art. It is hereby indicated that the above description is intended to help those skilled in the art understand this invention, but does not limit the scope of protection of this invention. Any equivalent substitutions, modifications, improvements, or simplifications of the above descriptions that do not depart from the essential content of this invention fall within the scope of protection of this invention.

Claims

1. An adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithms, characterized in that, Includes the following steps: S1: Construct a directed topology graph of the pumping station, transforming the physical components of the pumping station into edges and nodes of the graph; S2: Identify parallel pipelines and merge parallel pumps through breadth-first traversal; S3: Identify series pipelines and merge resistance parameters through depth-first traversal; S4: Iterate through S2 and S3 to simplify the topology to a single equivalent pump; S5: Solve for the operating point of multiple pumps operating together in the pumping station, and backtrack the operating conditions of a single pump.

2. The adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithm according to claim 1, characterized in that, The process of transforming the physical components of the pumping station into edges and nodes of a graph includes: A water pump is abstracted as a directed edge with a performance curve; The pipe segment is abstracted as a directed edge with resistance parameters; Valve status is achieved by dynamically adjusting edge attributes; Nodes represent connection points and record elevation, location, and the direction and quantity of associated pipe segments.

3. The adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithm according to claim 1, characterized in that, The breadth-first traversal described in S2 includes: using a queue data structure, the traversal process starts from the starting vertex, visits adjacent vertices in order from near to far, and identifies edge groups with the same starting and ending points as parallel pipelines.

4. The adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithm according to claim 1, characterized in that, The merging of parallel pumps described in S2 includes: incorporating the resistance loss of the pipe section before the parallel point into the pump's internal loss, adjusting the original pump performance curve to generate an equivalent pump performance curve, merging the parallel pump performance curves according to the rule of adding the flow rate under the same head, and deleting the original parallel edge and adding an edge containing the equivalent pump attribute.

5. The adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithm according to claim 1, characterized in that, The depth-first traversal described in S3 includes: using a stack data structure to implement the traversal path, and identifying the serial pipelines corresponding to nodes with both in-degree and out-degree of 1 through recursion or explicit stack.

6. The adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithm according to claim 1, characterized in that, The merging resistance parameters described in S3 include: merging the resistance parameters of series pipe segments, deleting intermediate nodes and associated edges, and adding edges with equivalent resistance attributes.

7. The adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithm according to claim 1, characterized in that, The solution for the working point of multi-pump joint operation in S5 includes: combining the performance curve of the equivalent pump with the head required by the water conveying device, and obtaining the total flow and total head by finding the intersection point through numerical methods. The head required curve is calculated from the static head and water conveying resistance parameters.

8. The adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithm according to claim 1, characterized in that, The reverse backtracking single-pump operating condition described in S5 includes: The total flow is decomposed into each parallel branch by using the inverse operation of superimposing the flow rates under the same head in parallel pipelines; The process is repeated layer by layer, starting with the simplified equivalent pump and gradually restoring it to the initial topology, outputting the operating conditions of each pump.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithms as described in any one of claims 1-8.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory is used to store computer programs, and the processor runs the computer programs to enable the electronic device to perform the adaptive calculation method for the operating point of multiple pumps operating in a pumping station based on graph theory algorithms as described in any one of claims 1-8.

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