A sewer network control node intelligent identification method, device, equipment and product

Abstract

The present application relates to the technical field of real-time control of urban drainage system, and discloses a kind of sewer network control node intelligent identification method, device, equipment and product, the present application is by obtaining the drainage process simulation data set of to be identified sewer network and using model simulation and flow direction correction, ensure that the flow direction of sewer network meets actual hydraulic characteristics.Further, each inspection well node in target sewer network is control node, and the slope value of the flow energy line of sewer network is calculated, which can accurately reflect the hydraulic characteristics of the upstream of the control node.Further, through the space capacity evaluation in the influence range of the inspection well node regulation and control and the dynamic optimization selection of the control node, the relevance of the control node selection can be ensured, and the calculation efficiency of the key control node identification and its applicability in complex drainage scenarios are improved.

Description

Technical Field

[0001] This invention relates to the field of real-time control technology for urban drainage systems, specifically to a method, device, equipment, and product for intelligent identification of control nodes in drainage networks. Background Technology

[0002] The traditional "drainage-oriented" urban drainage system operation concept cannot fully utilize the water storage capacity of the drainage network. How to dynamically activate the network's storage capacity through intelligent regulation has become an urgent problem to be solved in order to improve the resilience of urban drainage systems.

[0003] The selection of optimal control nodes in urban drainage systems involves a two-tiered optimization process: node selection and scheme evaluation. Scheme evaluation based on physical process simulation is extremely time-consuming and difficult to implement. A more intuitive approach is to use intelligent optimization algorithms such as genetic algorithms for the outer control node optimization, while the inner layer combines mechanistic models with real-time control methods such as rule-based control or model predictive control to optimize the dynamic control strategy of the control nodes, analyzing their operational objective functions under different precipitation conditions to determine the optimal control node. However, this approach can take several months for both the inner and outer optimization stages. Therefore, developing an intelligent identification method for key control nodes in drainage networks based on the optimal utilization of water storage space is of great significance.

[0004] Current research primarily focuses on developing optimal control node selection methods based on graph theory and static spatial evaluation. Graph theory-based methods define indicators such as degree, in-degree, and betweenness centrality to characterize network topology, selecting control nodes by ranking them according to their index values. Static spatial evaluation methods assess the drainage pipe range that each potential control node can influence, thus determining the relationship between the node and the controllable pipe space. Control nodes are then selected by ranking them according to the size of the controllable pipe space capacity of each node. However, existing methods struggle to consider the interrelationships between control nodes, leading to insufficient interoperability and consequently, low computational efficiency in control node identification. Summary of the Invention

[0005] In view of this, the present invention provides a method, device, equipment and product for intelligent identification of control nodes in drainage pipe networks, in order to solve the problem that existing intelligent identification methods for key control nodes in drainage pipe networks are difficult to consider the correlation between control nodes, resulting in insufficient linkage of control nodes and thus low computational efficiency of control node identification.

[0006] In a first aspect, the present invention provides an intelligent identification method for control nodes of drainage pipe networks, the method comprising:

[0007] Obtain a drainage process simulation dataset of the drainage network to be identified; based on the drainage process simulation dataset, use a preset drainage process simulation model to correct the flow direction of the drainage network to be identified, and obtain the target drainage network after flow direction correction; take each inspection well node in the target drainage network as a control node, and calculate the slope value of the flow energy line of the target drainage network; based on the ground elevation and slope value of each control node, calculate the pipeline space capacity of the upstream pipeline of each control node; take the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective, and use a multi-state dynamic programming method to make node decisions and determine the target control node based on the pipeline space capacity of each control node.

[0008] The intelligent identification method for control nodes in drainage pipe networks provided by this invention solves the potential error problem in traditional pipe network flow direction assessment by acquiring a simulated dataset of the drainage process of the drainage pipe network to be identified and using model simulation and flow direction correction, ensuring that the flow direction of the drainage pipe network conforms to the actual hydraulic characteristics. Furthermore, by taking each inspection well node in the target drainage pipe network as a control node and calculating the slope value of the flow energy line of the drainage pipe network, the hydraulic characteristics upstream of the control node can be accurately reflected. Further, by combining the ground elevation and slope value of each control node to calculate the pipe space capacity of the upstream pipeline of each control node, the adjustable upstream pipeline storage space of each control node can be accurately quantified, avoiding duplication or omission in capacity assessment. Finally, the selection of control nodes is transformed into a multi-stage optimal decision problem. A multi-state dynamic programming method ensures that the selected target control nodes are correlated, maximizing the utilization of the pipe network storage space, achieving rapid identification within minutes, and demonstrating strong applicability in complex drainage scenarios such as routine and fault scenarios, effectively improving the resilience of urban drainage systems. Therefore, by implementing this invention, through spatial capacity assessment within the influence range of the inspection well node and dynamic optimization selection of the control node, the relevance of the control node selection can be ensured, and the computational efficiency of key control node identification and its applicability in complex drainage scenarios can be improved.

[0009] In one optional implementation, based on a drainage process simulation dataset, a preset drainage process simulation model is used to correct the flow direction of the drainage network to be identified, resulting in a flow direction-corrected target drainage network, including:

[0010] Obtain multiple flow direction correction pipes of the drainage network to be identified; input the drainage process simulation dataset into the preset drainage process simulation model to obtain the drainage process simulation results; use the drainage process simulation results to correct the flow direction of the multiple flow direction correction pipes to obtain the target drainage network after flow direction correction.

[0011] The intelligent identification method for control nodes in drainage pipe networks provided by this invention avoids the redundant operation of indiscriminately verifying all pipes by filtering out pipes whose flow direction may have errors, thus improving the pertinence and efficiency of flow direction correction. Furthermore, by using a drainage process simulation dataset, the hydraulic state of the drainage pipe network in actual operation can be realistically reproduced. Moreover, by using the drainage process simulation results for flow direction correction, the method solves the error problems that may exist in traditional pipe network flow direction assessment, ensuring that the flow direction of the drainage pipe network conforms to the actual hydraulic characteristics.

[0012] In one optional implementation, acquiring multiple flow direction pipes of the drainage network to be identified includes:

[0013] Calculate the degree and in-degree of each manhole node in the drainage network to be identified; based on the degree and in-degree of each manhole node, determine multiple flow directions of pipelines to be corrected.

[0014] The intelligent identification method for control nodes in drainage pipe networks provided by this invention quantifies the connection relationship between manhole nodes and surrounding pipes by calculating the degree and in-degree of each manhole node in the drainage pipe network to be identified. Furthermore, based on the calculation results, it can accurately locate multiple pipes whose flow direction may be ambiguous or erroneous, ensuring that the initial selection of pipes to be corrected focuses on key pipes with abnormal topological characteristics, reducing unnecessary correction work, and further improving the efficiency and accuracy of flow direction correction.

[0015] In one optional implementation, using each inspection well node in the target drainage network as a control node, the slope value of the flow energy line of the target drainage network is calculated, including:

[0016] Using each manhole node in the target drainage network as a control node, the first maximum flow rate of the upstream pipeline and the second maximum flow rate of the downstream pipeline of each control node are calculated using the Manning formula. Based on a preset flow update formula, the first maximum flow rate and the second maximum flow rate of each control node are iteratively updated until the updated second maximum flow rate of each control node is greater than the updated first maximum flow rate, thus obtaining the target maximum flow rate of the upstream pipeline of each control node. Based on the target maximum flow rate, the slope of the flow energy line of the target drainage network is calculated using the Manning formula.

[0017] The intelligent identification method for control nodes in drainage pipe networks provided by this invention calculates the maximum flow rate of upstream and downstream pipes using the Manning formula, reflecting the impact of pipe hydraulic characteristics on flow rate. Furthermore, by considering the constraint relationship between upstream and downstream flow rates during the iterative update process, it ensures that the flow rate value conforms to the water flow distribution logic in the actual pipe network structure, avoiding distortion of the energy line slope due to unreasonable flow rate calculations and improving the accuracy of flow rate data. Moreover, by calculating the slope of the flow energy line using the target maximum flow rate value after iterative updates, it accurately characterizes the sloping water surface line formed by the head difference upstream of the control node, providing a reliable hydraulic slope basis for subsequent pipe space capacity calculations and ensuring consistency between capacity assessment and actual water flow conditions.

[0018] In one optional implementation, the pipeline space capacity of the upstream pipeline of each control node is calculated based on the ground elevation and slope values ​​of each control node, including:

[0019] The starting elevation of the flow energy line is calculated based on the ground elevation of each control node; based on the starting elevation and slope value, the pipe space capacity of each upstream pipe in the flow energy line is calculated and superimposed until the preset superposition conditions are met, thus obtaining the pipe space capacity of the upstream pipe of each control node.

[0020] The intelligent identification method for control nodes of drainage pipe networks provided by this invention calculates the upstream pipeline space capacity by using the ground elevation and slope values ​​of the control node and superimposes them to meet preset conditions. This method accurately quantifies the controllable upstream pipeline space capacity of each control node, ensuring the completeness and accuracy of capacity calculation, clarifying the calculation boundary of pipeline space capacity, avoiding duplication or omission in capacity assessment, and providing a scientific quantitative basis for the optimal selection of control nodes.

[0021] In one optional implementation, the total pipeline space capacity of the upstream pipelines of multiple control nodes is used as the optimization objective. Based on the pipeline space capacity of each control node, a multi-state dynamic programming method is used to make node decisions and determine the target control node, including:

[0022] Taking the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective, a multi-state dynamic programming recursive equation is constructed based on the pipeline space capacity of each control node. The multi-state dynamic programming recursive equation is solved using the multi-state dynamic programming method to obtain the target control node.

[0023] The intelligent identification method for control nodes in drainage pipe networks provided by this invention transforms the selection of control nodes into a multi-stage optimal decision problem by constructing a multi-state dynamic programming recursive equation. This is then efficiently solved using dynamic programming, ensuring the selected target control nodes are correlated and maximizing the utilization of the pipe network's storage space, achieving rapid identification within minutes. Simultaneously, it considers the correlation between different control nodes and the state changes of the controlled pipelines, effectively improving the utilization of pipe network space under both normal and fault scenarios.

[0024] Secondly, the present invention provides an intelligent identification device for control nodes of drainage pipe networks, the device comprising:

[0025] The system comprises the following modules: an acquisition module for acquiring a simulated drainage process dataset of the drainage network to be identified; a correction module for correcting the flow direction of the drainage network to be identified based on the simulated drainage process dataset using a preset simulation model; a first calculation module for calculating the slope value of the flow energy line of the target drainage network, with each manhole node in the target drainage network as a control node; a second calculation module for calculating the pipe space capacity of the upstream pipe of each control node based on the ground elevation and slope value of each control node; and a decision determination module for determining the target control node by using the total pipe space capacity of the upstream pipes of multiple control nodes as the optimization objective and employing a multi-state dynamic programming method based on the pipe space capacity of each control node.

[0026] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the intelligent identification method for drainage network control nodes described in the first aspect or any corresponding embodiment.

[0027] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the intelligent identification method for drainage network control nodes described in the first aspect or any corresponding embodiment thereof.

[0028] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the intelligent identification method for drainage network control nodes described in the first aspect or any corresponding embodiment. Attached Figure Description

[0029] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating the intelligent identification method for drainage network control nodes according to an embodiment of the present invention.

[0031] Figure 2 This is a flowchart illustrating another intelligent identification method for drainage network control nodes according to an embodiment of the present invention.

[0032] Figure 3 This is a flowchart illustrating another intelligent identification method for drainage network control nodes according to an embodiment of the present invention;

[0033] Figure 4 This is a schematic diagram of the optimal control node and controlled pipeline distribution according to an embodiment of the present invention;

[0034] Figure 5 This is a flowchart illustrating the intelligent identification method for key control nodes of drainage pipe networks based on the optimal utilization of storage space according to an embodiment of the present invention.

[0035] Figure 6 This is a diagram showing the correspondence between the maximum available drainage network capacity under different numbers of control nodes according to embodiments of the present invention.

[0036] Figure 7 This is a schematic diagram comparing the potential for space utilization of drainage pipe networks in different scenarios using the intelligent identification method for key control nodes of drainage pipe networks based on the optimal utilization of storage space (DPOCLPS) according to embodiments of the present invention, the graph theory-based method (TBC), and the static space evaluation method (CENTAUR_LOC).

[0037] Figure 8 This is a structural block diagram of an intelligent identification device for drainage network control nodes according to an embodiment of the present invention;

[0038] Figure 9 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] This invention provides an intelligent identification method for control nodes in drainage networks. By assessing the spatial capacity within the influence range of inspection well nodes and dynamically optimizing the selection of control nodes, the method ensures the relevance of control node selection, improves the computational efficiency of key control node identification, and enhances its applicability in complex drainage scenarios.

[0041] According to an embodiment of the present invention, an embodiment of a method for intelligent identification of control nodes in a drainage network is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0042] This embodiment provides a method for intelligent identification of control nodes in drainage pipe networks, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 1 This is a flowchart of a method for intelligent identification of drainage network control nodes according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0043] Step S101: Obtain the drainage process simulation dataset of the drainage network to be identified.

[0044] The drainage process simulation dataset can include relevant data such as rainfall data, drainage network data, flow or water level monitoring data, and control facility operation data.

[0045] Furthermore, rainfall data is used to reflect information on different precipitation conditions and is an important input factor affecting the operation status of drainage pipe networks. Drainage pipe network data can include the topology of the pipe network, pipe parameters (such as pipe diameter, length, roughness, etc.), and manhole node information (such as elevation, location, etc.).

[0046] Furthermore, flow or water level monitoring data can be obtained through monitoring equipment, and may include real-time flow and water level change data within the pipeline network.

[0047] Furthermore, the control facility operation data can include the operating parameters and status information of control facilities such as pump stations and gates, which can affect the water flow in the drainage network.

[0048] Step S102: Based on the drainage process simulation dataset, the flow direction of the drainage network to be identified is corrected using a preset drainage process simulation model to obtain the target drainage network after flow direction correction.

[0049] The preset drainage process simulation model is a mathematical model established and calibrated by integrating rainfall data, pipeline parameters, monitoring records and facility operation information, and using hydrological and hydraulic principles. It is used to simulate the operation of the drainage pipeline network under different conditions and output simulation results of elements such as real-time flow, water level changes and overflow within the pipeline network.

[0050] Specifically, by acquiring a simulation dataset of the drainage process of the drainage network to be identified and using a model for simulation, simulation results of real-time flow, water level changes, and overflow within the drainage network to be identified can be output.

[0051] Furthermore, flow direction correction was performed based on simulation results, which solved the error problem that may exist in the traditional pipeline flow direction assessment and ensured that the flow direction of the drainage pipeline network conforms to the actual hydraulic characteristics.

[0052] Step S103: Using each inspection well node in the target drainage network as a control node, calculate the slope value of the flow energy line of the target drainage network.

[0053] Among them, the flow energy line represents an inclined water surface line driven by the head difference that forms upstream from the control node when the control node is not completely closed.

[0054] Specifically, to avoid water flow accumulation at the control node in the event of facility failure, the control facility is usually not completely shut down. In this case, an inclined water surface line driven by the head difference will form upstream from the control node, i.e., the flow energy line. At this time, assuming the water flow is in a steady state, the slope value of the flow energy line can be calculated using the Manning formula, as shown in the following relationship (1):

[0055] (1)

[0056] In the formula: Indicates the maximum flow rate of the pipeline; Represents the roughness coefficient; Represents the cross-sectional area; Indicates the hydraulic radius; Indicates the slope.

[0057] Furthermore, by calculating the slope value of the flow energy line of the drainage network, the hydraulic characteristics upstream of the control node can be accurately reflected.

[0058] Step S104: Calculate the pipeline space capacity of the upstream pipeline of each control node based on the ground elevation and slope values ​​of each control node.

[0059] Among them, the ground elevation of the control node represents the altitude of the ground where the inspection well node is located.

[0060] Furthermore, the pipeline space capacity of the upstream pipeline of each control node represents the total pipeline space below the flow energy line, starting from that control node and extending along the upstream pipeline direction.

[0061] Specifically, by combining the ground elevation and slope values ​​of each control node to calculate the pipeline space capacity of the upstream pipeline of each control node, the adjustable upstream pipeline storage space of each control node can be accurately quantified, avoiding duplication or omission in capacity assessment.

[0062] Step S105: Taking the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective, and based on the pipeline space capacity of each control node, a multi-state dynamic programming method is used to make node decisions and determine the target control node.

[0063] The total pipeline space capacity represents the sum of the pipeline space capacities corresponding to the upstream pipelines of multiple control nodes in the drainage network.

[0064] Furthermore, the multi-state dynamic programming method represents a solution method that treats the selection of the optimal control node as a multi-stage optimal decision problem.

[0065] Furthermore, the target control node refers to the key control node selected from the manhole nodes of the drainage network through a multi-state dynamic programming method, which can maximize the total storage capacity of the upstream pipeline.

[0066] Specifically, each selected control node is taken as a decision-making stage, and the controlled pipeline number and corresponding controlled space capacity are taken as the system state. Taking the correlation between control nodes as the core, and comprehensively considering complex drainage scenarios such as different precipitation conditions, normal and fault conditions, decision-making is carried out through multi-state dynamic programming method, and the key control node that can maximize the total storage space capacity of the upstream pipeline is determined, namely the target control node.

[0067] Furthermore, the selection of control nodes is transformed into a multi-stage optimal decision problem. The multi-state dynamic programming method ensures that the selected target control nodes are related, which can maximize the utilization of the pipeline network storage space, achieve rapid identification within minutes, and has strong applicability in complex drainage scenarios such as normal and faults, effectively improving the resilience of urban drainage systems.

[0068] The intelligent identification method for control nodes of drainage pipe networks provided in this embodiment solves the error problem that may exist in the traditional pipe network flow direction assessment by acquiring the drainage process simulation dataset of the drainage pipe network to be identified and using model simulation and flow direction correction, ensuring that the flow direction of the drainage pipe network conforms to the actual hydraulic characteristics. Furthermore, by taking each inspection well node in the target drainage pipe network as a control node and calculating the slope value of the flow energy line of the drainage pipe network, the hydraulic characteristics upstream of the control node can be accurately reflected. Furthermore, by combining the ground elevation and slope value of each control node to calculate the pipe space capacity of the upstream pipe of each control node, the adjustable upstream pipe storage space of each control node can be accurately quantified, avoiding duplication or omission in capacity assessment. Finally, the selection of control nodes is transformed into a multi-stage optimal decision problem. A multi-state dynamic programming method ensures that the selected target control nodes are correlated, maximizing the utilization of the pipe network storage space, achieving rapid identification within minutes, and demonstrating strong applicability in complex drainage scenarios such as routine and fault scenarios, effectively improving the resilience of urban drainage systems. Therefore, by implementing this invention, through spatial capacity assessment within the influence range of the inspection well node and dynamic optimization selection of the control node, the relevance of the control node selection can be ensured, and the computational efficiency of key control node identification and its applicability in complex drainage scenarios can be improved.

[0069] This embodiment provides a method for intelligent identification of control nodes in drainage pipe networks, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 2 This is a flowchart of a method for intelligent identification of drainage network control nodes according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0070] Step S201: Obtain the drainage process simulation dataset of the drainage network to be identified. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0071] Step S202: Based on the drainage process simulation dataset, the flow direction of the drainage network to be identified is corrected using a preset drainage process simulation model to obtain the target drainage network after flow direction correction.

[0072] Specifically, step S202 includes:

[0073] Step S2021: Obtain multiple flow direction pipes of the drainage network to be identified and corrected.

[0074] Among them, the flow direction to be corrected pipeline refers to the connecting pipeline between manhole nodes in the drainage network that are selected by calculating the degree and ingress degree of the manhole node.

[0075] In some optional implementations, step S2021 above includes:

[0076] Step a1: Calculate the degree and in-degree of each manhole node in the drainage network to be identified.

[0077] Step a2: Based on the degree and in-degree of each inspection well node, determine multiple flow direction pipelines to be corrected.

[0078] Specifically, an adjacency matrix representing the topological relationship between inspection well nodes and drainage pipes is constructed. The following relation (2) is shown:

[0079] (2)

[0080] in, Indicates the inspection well node and The connection between them is shown in the following relation (3):

[0081] (3)

[0082] In the formula: Represents a collection of drainage pipes; Indicates no connection. Indicates a connection.

[0083] Furthermore, based on the adjacency matrix It can calculate the degree of each inspection well node in the drainage network to be identified. and in-degree The following relationships (4) and (5) are shown:

[0084] (4)

[0085] (5)

[0086] In the formula: Indicates the indicator function, when the edge When the endpoint is a node It is 1 if it is true, otherwise it is 0.

[0087] Furthermore, under normal drainage logic, there is a specific matching relationship between degree and in-degree. When the two are equal, the pipe flow direction may need to be corrected. Therefore, the degree is selected. with in degree Connect the pipes of equal inspection well nodes and use them as the flow direction to the pipe to be corrected.

[0088] Step S2022: Input the drainage process simulation dataset into the preset drainage process simulation model to obtain the drainage process simulation results.

[0089] Specifically, according to the description in step S102 above, the obtained drainage process simulation dataset is input into the preset drainage process simulation model, and the drainage process simulation results containing elements such as real-time flow, water level changes and overflow in the pipe network can be output.

[0090] Step S2023: The flow direction of multiple pipes to be corrected is corrected using the simulation results of the drainage process, so as to obtain the target drainage network after flow direction correction.

[0091] Specifically, based on the obtained drainage process simulation results, the hydraulic head at the upstream and downstream nodes of the pipeline to be corrected during the simulation period can be statistically analyzed. The hydraulic head reflects the total mechanical energy per unit weight of fluid.

[0092] Furthermore, by taking the point with higher hydraulic head as the upstream node, the flow direction of the pipeline is adjusted accordingly, and the flow direction correction of all pipelines with initial flow direction to be corrected is completed, finally obtaining the target drainage network with flow direction conforming to the actual hydraulic characteristics.

[0093] Step S203: Using each inspection well node in the target drainage network as a control node, calculate the slope value of the flow energy line of the target drainage network.

[0094] Specifically, step S203 includes:

[0095] Step S2031: Using each manhole node in the target drainage network as a control node, calculate the first maximum flow rate of the upstream pipeline and the second maximum flow rate of the downstream pipeline for each control node using the Manning formula.

[0096] Specifically, the initial slope of the drainage network can be obtained from the drainage network data.

[0097] Furthermore, by taking each manhole node in the target drainage network as a control node and substituting the initial slope of the drainage network into the Manning formula shown in the above relationship (1), the first maximum flow rate of the upstream pipeline and the second maximum flow rate of the downstream pipeline of each control node can be calculated.

[0098] Step S2032: Based on the preset flow update formula, iteratively update the first maximum flow value and the second maximum flow value of each control node until the updated second maximum flow value of each control node is greater than the updated first maximum flow value, thereby obtaining the target maximum flow value of the upstream pipeline of each control node.

[0099] Specifically, for each control node, the maximum flow rate of its upstream pipeline, i.e., the first maximum flow rate value, can be iteratively updated based on the maximum flow rates of its upstream and downstream pipelines, i.e., the first maximum flow rate value and the second maximum flow rate value, as shown in the following relationship (6):

[0100] (6)

[0101] In the formula: This indicates the maximum flow rate after the upstream pipeline of the control node is updated; This represents the maximum flow rate of the upstream pipeline of the control node calculated according to the above relationship (1), i.e., the first maximum flow rate value; The maximum flow rate downstream of the control node after the update can be obtained by iteratively updating the second maximum flow rate using the above relationship (6).

[0102] Furthermore, the above update process is repeated continuously, ensuring that the maximum flow rate of the upstream pipeline is always less than or equal to the maximum flow rate of the downstream pipeline. That is, when the updated second maximum flow rate value of each control node is greater than the updated first maximum flow rate value, the iterative update stops, and the updated target maximum flow rate value of the upstream pipeline of each control node is obtained.

[0103] Furthermore, for the three special cases of pipeline connection with control facilities, pipeline merging, and pipeline bifurcation, the maximum flow rate of the upstream pipeline of the control node can be updated using the following relationships (7) to (9):

[0104] (7)

[0105] (8)

[0106] (9)

[0107] In the formula: This represents the sum of the maximum flow rates of the upstream pipelines of the merged node.

[0108] Furthermore, the updated target maximum flow value of the upstream pipeline for each control node can be obtained through iterative updates.

[0109] Step S2033: Based on the target maximum flow rate, calculate the slope of the flow energy line of the target drainage network using the Manning formula.

[0110] Specifically, by substituting the updated target maximum flow rate value of the upstream pipeline of the updated control node into the Manning formula shown in the above relationship (1), the slope of the flow energy line of the target drainage network can be calculated. The specific value.

[0111] Step S204: Based on the ground elevation and slope values ​​of each control node, calculate the pipeline space capacity of the upstream pipeline for each control node. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0112] Step S205: Using the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective, and based on the pipeline space capacity of each control node, a multi-state dynamic programming method is used to make node decisions and determine the target control node. For details, please refer to [link to relevant documentation]. Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0113] The intelligent identification method for control nodes in drainage pipe networks provided in this embodiment quantifies the connection relationship between manhole nodes and surrounding pipes by calculating the degree and in-degree of each manhole node in the drainage pipe network to be identified. Furthermore, based on the calculation results, it can accurately locate multiple pipes with potentially ambiguous or erroneous flow directions, ensuring that the initial selection of pipes to be corrected focuses on key pipes with abnormal topological characteristics, reducing unnecessary correction work and further improving the efficiency and accuracy of flow direction correction. Furthermore, by using a drainage process simulation dataset, the hydraulic state of the drainage pipe network in actual operation can be realistically reproduced. Furthermore, by using the drainage process simulation results for flow direction correction, it solves the error problems that may exist in traditional pipe network flow direction assessment, ensuring that the flow direction of the drainage pipe network conforms to the actual hydraulic characteristics. Furthermore, by calculating the maximum flow rate of upstream and downstream pipes using the Manning formula, it can reflect the impact of pipe hydraulic characteristics on flow rate. Furthermore, by considering the constraint relationship of upstream and downstream flow rates of control nodes during the iterative update process, it can ensure that the flow rate value conforms to the water flow distribution logic in the actual pipe network structure, avoiding subsequent energy line slope distortion due to unreasonable flow rate calculation, and improving the accuracy of flow rate data. Furthermore, by calculating the slope of the flow energy line using the updated target maximum flow value, the characteristics of the inclined water surface line formed by the head difference upstream of the control node can be accurately characterized. This provides a reliable hydraulic slope basis for the subsequent calculation of pipeline space capacity, ensuring that the capacity assessment is consistent with the actual flow state.

[0114] This embodiment provides a method for intelligent identification of control nodes in drainage pipe networks, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 3 This is a flowchart of a method for intelligent identification of drainage network control nodes according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0115] Step S301: Obtain the drainage process simulation dataset of the drainage network to be identified. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0116] Step S302: Based on the drainage process simulation dataset, the flow direction of the drainage network to be identified is corrected using a preset drainage process simulation model to obtain the target drainage network after flow direction correction. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0117] Step S303: Using each inspection well node in the target drainage network as a control node, calculate the slope value of the flow energy line of the target drainage network. For details, please refer to [link to relevant documentation]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0118] Step S304: Calculate the pipeline space capacity of the upstream pipeline of each control node based on the ground elevation and slope values ​​of each control node.

[0119] Specifically, step S304 includes:

[0120] Step S3041: Calculate the starting elevation of the flowing energy line based on the ground elevation of each control node.

[0121] Specifically, the ground elevation of each control node minus the safety superelevation (set to 0.1m) can be used as the starting elevation of the flowing energy line.

[0122] Step S3042: Based on the initial elevation and slope value, calculate the pipe space capacity of each upstream pipe in the flow energy line and superimpose them until the preset superposition conditions are met, so as to obtain the pipe space capacity of the upstream pipe of each control node.

[0123] Specifically, based on the calculated initial elevation and the slope value of the kinetic energy line calculated in step S203 above, the elevation of the kinetic energy line at the upstream inspection well node can be calculated.

[0124] Furthermore, the pipe space capacity below the flow energy line can be calculated, which is the controllable capacity of the current pipe segment, as shown in the following equation (10):

[0125] (10)

[0126] In the formula: This indicates the volume of space below the flow energy line of pipe l; This indicates the cross-sectional area of ​​the water passage.

[0127] Furthermore, the controllable capacity of the upstream pipeline is continuously added until the stopping condition is reached, and the pipeline space capacity of the upstream pipeline at each control node is obtained.

[0128] The stopping conditions may include:

[0129] ①If the elevation of the flow energy line is less than the bottom elevation of the manhole node, then the current controllable capacity of the pipeline is calculated and the calculation is stopped;

[0130] ②If the elevation of the flow energy line is greater than the bottom elevation of the manhole node, but less than the ground elevation of the manhole, then the current controllable capacity of the pipeline is calculated by superposition and the search is performed upstream;

[0131] ③ If the elevation of the flow energy line is greater than the ground elevation of the inspection well, then the ground elevation of the current inspection well node is taken as the elevation of the flow energy line, and the search continues upstream.

[0132] Furthermore, the stopping condition may also include:

[0133] ① There is no upstream inspection well node, meaning the current inspection well node is the starting node;

[0134] ②The upstream nodes are control nodes such as pumping stations and gates.

[0135] Step S305: Taking the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective, and based on the pipeline space capacity of each control node, a multi-state dynamic programming method is used to make node decisions and determine the target control node.

[0136] Specifically, step S305 includes:

[0137] Step S3051: Taking the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective, a multi-state dynamic programming recursive equation is constructed based on the pipeline space capacity of each control node.

[0138] Specifically, the selection of the optimal control node, i.e. the target control node, can be regarded as a multi-stage optimal decision problem. Each selected control node is a stage of decision-making, and the controlled pipeline number and the corresponding controlled space capacity can be regarded as the state of the system.

[0139] Furthermore, based on this, taking the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective, and combining the pipeline space capacity of each control node, a corresponding multi-state dynamic programming recursive equation can be constructed, as shown in the following relationship (11):

[0140] (11)

[0142] In the formula: Represents decision variables, i.e., stages New control node; Indicates to stage The set of all controlled pipeline numbers; Indicates to stage The set of controlled space capacities corresponding to all controlled pipelines; Indicates to stage Total space capacity of all controlled pipes; The objective function represents the subproblem of dynamic programming, referring to the objective function derived from the stage. The increase in controllable pipeline capacity resulting from the addition of a new control node is shown in the following equation (12):

[0143] (12)

[0145] In the formula: Representation phase pipeline The increase in controllable pipeline capacity; Representation phase The addition of a control node affects the total number of pipelines; The scenario in which the objective function is evaluated can include normal scenarios or fault scenarios.

[0146] Furthermore, It can be determined by the following relations (13) to (17):

[0147] (13)

[0149] (14)

[0150] (15)

[0151] (16)

[0152] (17)

[0153] In the formula: Representation phase Pipelines within the influence range of the newly added control node The number.

[0154] Furthermore, if the conditions are met (That is, the newly added controlled pipeline is not in the current set of controlled pipelines), then the pipeline The increase in controllable space capacity is the stage. Adding control nodes to pipelines Controllable space capacity;

[0155] Furthermore, if the conditions are met simultaneously and (That is, the newly added controlled pipeline is within the current set of controlled pipelines, and the newly added control node controls the pipeline.) The controllable space capacity is less than the number of pipes in the current controlled pipe set. (Controllable space capacity), then the pipeline The increase in controllable space capacity is 0;

[0156] Furthermore, if the conditions are met simultaneously and (That is, the newly added controlled pipeline is within the current set of controlled pipelines, and the newly added control node controls the pipeline.) The controllable space capacity is greater than the number of pipes in the current controlled pipe set. (Controllable space capacity), then the pipeline The increase in controllable space capacity is due to the addition of new control nodes to the pipeline. The controllable space capacity and the pipelines in the current controlled pipeline set Interpolation in controllable space capacity.

[0157] Step S3052: Solve the recursive equation of the multi-state dynamic programming using the multi-state dynamic programming method to obtain the target control node.

[0158] Specifically, determining the initial state can start from the base state where no control node is selected (or the first control node is selected), and set the initial state. (An empty set or a set containing a small number of initial pipe numbers) (Corresponding to the initial capacity set) (Initial total capacity), etc.

[0159] Furthermore, starting from the initial stage, decision calculations are performed sequentially for each stage according to the recursive equation of multi-state dynamic programming. Specifically, for each possible newly added control node... Calculate its corresponding and and continuously update the system status ( , ) and total capacity .

[0160] Furthermore, after traversing all possible control node selection stages (or reaching preset termination conditions, such as all nodes have been considered, or specific capacity requirements are met), the results obtained after each stage are compared. The value is selected by choosing the control node selection sequence that maximizes the total pipeline space capacity. The control node (or node combination) determined in the final stage is the target control node, which is the optimal control node that can maximize the total pipeline space capacity.

[0161] In some alternative implementations, the optimal control nodes and controlled pipelines are distributed as follows: Figure 4 As shown.

[0162] The intelligent identification method for drainage network control nodes provided in this embodiment calculates the upstream pipeline space capacity by using the ground elevation and slope values ​​of the control nodes and superimposing them to meet preset conditions. This accurately quantifies the controllable upstream pipeline space capacity of each control node, ensuring the completeness and accuracy of capacity calculation, clarifying the calculation boundary of pipeline space capacity, avoiding duplication or omission in capacity assessment, and providing a scientific quantitative basis for the optimal selection of control nodes. Furthermore, by constructing a multi-state dynamic programming recursive equation, the selection of control nodes is transformed into a multi-stage optimal decision problem, which is then efficiently solved using dynamic programming methods. This ensures that the selected target control nodes are correlated, maximizing the utilization of the pipeline network's storage space and achieving rapid identification within minutes. Simultaneously, considering the correlation between different control nodes and the state changes of the controlled pipelines, it effectively improves the utilization of pipeline network space under both normal and fault scenarios.

[0163] In one example, a method for intelligent identification of key control nodes in drainage networks based on the optimal utilization of storage space is provided. This method ensures the linkage of control node selection, improves the computational efficiency of key control node identification, and enhances its applicability in complex drainage scenarios. Figure 5 As shown, the specific steps include:

[0164] Step 1: Establish a simulation model of the drainage process and correct the flow direction of the drainage network.

[0165] ① First, the basic data for establishing a drainage process simulation model should include rainfall data, drainage network data, flow or water level monitoring data, and control facility operation data. The model construction process includes integrating rainfall data, network parameters, monitoring records, and facility operation information, establishing a mathematical model based on hydrological and hydraulic principles, and calibrating and verifying it. The input is the aforementioned basic data, and the output is the simulation results of real-time flow, water level changes, and overflow within the network.

[0166] ② Next, using the above relationships (2) to (5), calculate the degree and in-degree index of each manhole node, find the connecting pipe of the manhole node with equal degree and in-degree, and use it as the flow direction to be corrected pipe.

[0167] ③ Finally, run the drainage process model constructed above, and count the hydraulic head of the upstream and downstream nodes of the pipeline to be corrected during the simulation period. The point with the higher hydraulic head is taken as the upstream node, and the pipeline flow direction is corrected accordingly.

[0168] Step 2: Treat each inspection well node as a potential control node and calculate its flow energy line. To avoid water flow accumulation at the control node under facility failure scenarios, the control facility is usually not completely shut down. In this case, an inclined water surface line driven by the head difference will form upstream from the control node, which is called the flow energy line. Assuming the water flow is in a steady state, the head of the upstream node of the control node can be calculated using the Manning formula, and the specific calculation expression is shown in the above relation (1).

[0169] ① First, calculate the maximum flow rate of the upstream pipeline for each inspection well node based on the above equation.

[0170] ② Secondly, for each inspection well node, based on the maximum flow rate of its upstream and downstream pipelines, the maximum flow rate of its upstream pipeline is iteratively updated, as shown in the above relationship (6).

[0171] Furthermore, for three special cases—pipeline connection with control facilities, pipeline merging, and pipeline bifurcation—the maximum flow rate of the upstream pipeline of the manhole node is updated using the following formulas (7) to (9).

[0172] ③ Finally, substitute the updated maximum flow rate of the pipeline into the above relationship (1) to calculate the slope of the flow energy line. .

[0173] Step 3: Calculate the pipeline space capacity within the controllable range upstream of each manhole node. The starting elevation of the flow energy line is determined by subtracting the safety freeboard (set to 0.1m) from the manhole ground elevation. Combined with the flow energy line slope J obtained in Step 2, the elevation of the flow energy line at the upstream manhole node is calculated. The pipeline space capacity below the flow energy line is then calculated, which is the controllable capacity of the current pipe section. The specific calculation formula is shown in the above relationship (10).

[0174] Furthermore, the controllable capacity of the upstream pipeline is continuously added until a stopping condition is reached. The criteria for determining whether to add or stop calculating the controllable capacity of the pipeline are as follows:

[0175] ①If the elevation of the flow energy line is less than the bottom elevation of the manhole node, then the current controllable capacity of the pipeline is calculated and the calculation is stopped;

[0176] ②If the elevation of the flow energy line is greater than the bottom elevation of the manhole node, but less than the ground elevation of the manhole, then the current controllable capacity of the pipeline is calculated by superposition and the search is performed upstream;

[0177] ③ If the elevation of the flow energy line is greater than the ground elevation of the inspection well, then the ground elevation of the current inspection well node is taken as the elevation of the flow energy line, and the search continues upstream.

[0178] In addition, the stopping conditions also include the following:

[0179] ① There is no upstream inspection well node, meaning the current inspection well node is the starting node;

[0180] ②The upstream nodes are control nodes such as pumping stations and gates.

[0181] Step 4: Select the optimal control node based on the multi-state dynamic programming method. The selection of the optimal control node is considered a multi-stage optimal decision problem, with each selected control node representing a stage of the decision process. The controlled pipeline number and the corresponding controlled space capacity can be considered as the state of the system. Therefore, this multi-stage optimal decision problem can be solved using the multi-state dynamic programming method. The recursive equation for the multi-state dynamic programming of the control node selection problem is constructed as shown in the above relation (11).

[0182] This example provides an intelligent identification method for key control nodes in drainage networks based on the optimal utilization of storage space. By assessing the spatial capacity within the influence range of manhole nodes and dynamically optimizing the selection of control nodes, it can ensure the relevance of the selected control nodes, improve the computational efficiency of key control node identification, and enhance its applicability in complex drainage scenarios.

[0183] In some alternative embodiments, such as Figure 6 As shown, the correspondence between the maximum available drainage network capacity under different numbers of control nodes is provided.

[0184] Furthermore, Figure 6 Different curves correspond to different analysis cases. Based on the analysis results, assuming no overflow occurs, the recommended number of control nodes for the rainwater storage tank area in location A is 7, and the maximum available pipe network space is 1384m². 3 The recommended number of control nodes for the combined sewer system storage area in Area A is 7, and the maximum available pipeline space is 2974m². 3 The recommended number of control nodes for location B is 3, with a maximum available pipeline space of 4781m. 3 .

[0185] Furthermore, such as Figure 7 The example shows a comparison of the potential for space utilization in drainage networks under different scenarios between the intelligent identification method for key control nodes of drainage networks based on the optimal utilization of storage space (DPOCLPS), the graph theory-based method (TBC), and the static space evaluation method (CENTAUR_LOC).

[0186] in, Figure 7 The horizontal axis represents different control scenarios. Furthermore, a normal scenario refers to a scenario where all equipment operates without failure, while a fault scenario corresponds to a failure of the control facility at any given point.

[0187] Furthermore, according to Figure 7As can be seen, the intelligent identification method for key control nodes of drainage pipe networks based on the optimal utilization of storage space provided in this example can effectively improve the utilization of pipe network space in both normal and fault scenarios compared with existing methods.

[0188] This embodiment also provides an intelligent identification device for drainage network control nodes, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementations, or a combination of software and hardware, are also possible and contemplated.

[0189] This embodiment provides an intelligent identification device for drainage pipe network control nodes, such as... Figure 8 As shown, the device includes:

[0190] The acquisition module 801 is used to acquire a simulation dataset of the drainage process of the drainage network to be identified.

[0191] The correction module 802 is used to correct the flow direction of the drainage network to be identified based on the drainage process simulation dataset and a preset drainage process simulation model, so as to obtain the target drainage network after flow direction correction.

[0192] The first calculation module 803 is used to calculate the slope value of the flow energy line of the target pipeline to be corrected, with each inspection well node in the target drainage network as the control node.

[0193] The second calculation module 804 is used to calculate the pipeline space capacity of the upstream pipeline of each control node based on the ground elevation and slope value of each control node.

[0194] The decision determination module 805 is used to make node decisions and determine the target control node based on the pipeline space capacity of each control node, with the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective and using a multi-state dynamic programming method.

[0195] In some alternative implementations, the correction module 802 includes:

[0196] The acquisition submodule is used to acquire multiple flow directions of the drainage network to be identified and corrected.

[0197] The input submodule is used to input the drainage process simulation dataset into the preset drainage process simulation model to obtain the drainage process simulation results.

[0198] The correction submodule is used to correct the flow direction of multiple pipes to be corrected using the simulation results of the drainage process, so as to obtain the target drainage network after flow direction correction.

[0199] In some optional implementations, the acquisition submodule includes:

[0200] The calculation unit is used to calculate the degree and in-degree of each manhole node in the drainage network to be identified.

[0201] The determination unit is used to determine multiple flow direction pipelines to be corrected based on the degree and in-degree of each inspection well node.

[0202] In some alternative implementations, the first computing module 803 includes:

[0203] The first calculation submodule is used to calculate the first maximum flow rate of the upstream pipeline and the second maximum flow rate of the downstream pipeline for each control node, using the Manning formula, with each inspection well node in the target drainage network as the control node.

[0204] The iterative update submodule is used to iteratively update the first maximum flow value and the second maximum flow value of each control node based on a preset flow update formula, until the updated second maximum flow value of each control node is greater than the updated first maximum flow value, thereby obtaining the target maximum flow value of the upstream pipeline of each control node.

[0205] The second calculation submodule is used to calculate the slope value of the flow energy line of the target drainage network based on the target maximum flow value using the Manning formula.

[0206] In some alternative implementations, the second computing module 804 includes:

[0207] The third calculation submodule is used to calculate the starting elevation of the flowing energy line based on the ground elevation of each control node.

[0208] The superposition calculation submodule is used to calculate and superimpose the pipe space capacity of each upstream pipe in the flow energy line based on the initial elevation and slope value until the preset superposition conditions are met, so as to obtain the pipe space capacity of the upstream pipe of each control node.

[0209] In some alternative implementations, the decision determination module 805 includes:

[0210] A submodule is constructed to build a multi-state dynamic programming recursive equation based on the pipeline space capacity of each control node, with the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective.

[0211] The solver submodule is used to solve the recursive equations of multi-state dynamic programming using the multi-state dynamic programming method to obtain the target control nodes.

[0212] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0213] In this embodiment, the intelligent identification device for drainage network control nodes is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0214] This invention also provides a computer device having the above-described features. Figure 8 The intelligent identification device for the control node of the drainage pipe network is shown.

[0215] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 9 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 9 Take a processor 10 as an example.

[0216] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0217] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0218] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0219] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0220] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0221] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0222] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0223] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for intelligent identification of control nodes in a drainage pipe network, characterized in that, The method includes: Obtain a simulated dataset of the drainage process of the drainage network to be identified; Based on the drainage process simulation dataset, the flow direction of the drainage network to be identified is corrected using a preset drainage process simulation model to obtain the target drainage network after flow direction correction. Using each inspection well node in the target drainage network as a control node, calculate the slope value of the flow energy line of the target drainage network; Based on the ground elevation and slope value of each control node, calculate the pipeline space capacity of the upstream pipeline of each control node; Taking the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective, and based on the pipeline space capacity of each control node, a multi-state dynamic programming method is used to make node decisions and determine the target control node. The optimization objective is to optimize the total pipeline space capacity of the upstream pipelines of multiple control nodes. Based on the pipeline space capacity of each control node, a multi-state dynamic programming method is used to make node decisions and determine the target control node, including: Taking the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective, a multi-state dynamic programming recursive equation is constructed based on the pipeline space capacity of each control node. The target control node is obtained by solving the recursive equation of the multi-state dynamic programming using the multi-state dynamic programming method.

2. The method according to claim 1, characterized in that, Based on the drainage process simulation dataset, the flow direction of the drainage network to be identified is corrected using a preset drainage process simulation model to obtain the target drainage network after flow direction correction, including: Obtain multiple flow directions of the drainage network to be identified; Input the drainage process simulation dataset into the preset drainage process simulation model to obtain the drainage process simulation results; The flow direction of the multiple pipes to be corrected is corrected using the simulation results of the drainage process, resulting in the target drainage network after flow direction correction.

3. The method according to claim 2, characterized in that, Obtaining multiple flow direction pipes of the drainage network to be identified, including: Calculate the degree and in-degree of each manhole node in the drainage network to be identified; Based on the degree and in-degree of each inspection well node, the multiple flow directions of the pipelines to be corrected are determined.

4. The method according to claim 1, characterized in that, Using each inspection well node in the target drainage network as a control node, the slope value of the flow energy line of the target drainage network is calculated, including: Using each manhole node in the target drainage network as a control node, the first maximum flow rate of the upstream pipeline and the second maximum flow rate of the downstream pipeline of each control node are calculated using the Manning formula. Based on a preset flow update formula, the first maximum flow value and the second maximum flow value of each control node are iteratively updated until the updated second maximum flow value of each control node is greater than the updated first maximum flow value, thereby obtaining the target maximum flow value of the upstream pipeline of each control node. Based on the target maximum flow rate, the slope value of the flow energy line of the target drainage network is calculated using the Manning formula.

5. The method according to claim 1, characterized in that, Based on the ground elevation and slope value of each control node, the pipeline space capacity of the upstream pipeline of each control node is calculated, including: The initial elevation of the flowing energy line is calculated based on the ground elevation of each control node; Based on the initial elevation and the slope value, the pipe space capacity of each upstream pipe in the flow energy line is calculated and superimposed until a preset superposition condition is met, thereby obtaining the pipe space capacity of the upstream pipe of each control node.

6. A smart identification device for control nodes of a drainage pipe network, characterized in that, The device includes: The acquisition module is used to acquire a simulated dataset of the drainage process of the drainage network to be identified. The correction module is used to correct the flow direction of the drainage network to be identified based on the drainage process simulation dataset and a preset drainage process simulation model, so as to obtain the target drainage network after flow direction correction. The first calculation module is used to calculate the slope value of the flow energy line of the target drainage network, taking each inspection well node in the target drainage network as a control node. The second calculation module is used to calculate the pipeline space capacity of the upstream pipeline of each control node based on the ground elevation and the slope value of each control node. The decision determination module is used to make node decisions and determine the target control node based on the pipeline space capacity of each control node, using the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective and employing a multi-state dynamic programming method. The decision determination module includes: A submodule is constructed to build a multi-state dynamic programming recursive equation based on the pipeline space capacity of each control node, with the total pipeline space capacity of the upstream pipelines of multiple control nodes as the optimization objective. The solution submodule is used to solve the multi-state dynamic programming recursive equation using the multi-state dynamic programming method to obtain the target control node.

7. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the intelligent identification method for drainage network control nodes as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the intelligent identification method for drainage network control nodes as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, It includes computer instructions, which are used to cause a computer to execute the intelligent identification method for drainage network control nodes as described in any one of claims 1 to 5.

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