Real-time monitoring and intelligent control method for joint dispatch and joint drainage of water conservancy and water affairs

Abstract

The present application belongs to the technical field of water conservancy regulation, and particularly relates to a water conservancy joint regulation and joint drainage real-time monitoring and intelligent regulation method, which first acquires a user water demand state set to construct a user scheduling priority space, then combines a preset adjustable water demand scheduling network to carry out water demand joint control scheduling through a reinforcement scheduling model until the user water demand is met, wherein the adjustable water demand scheduling network is constructed according to a water source distribution state, a scheduling pipe network distribution state, a user water demand state and a water source water content, and with the aid of a topology algorithm and a hypergraph algorithm, efficient joint regulation and joint drainage real-time monitoring and intelligent regulation of water conservancy are realized, and the scientificity and rationality of water resource allocation are improved.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy and water management technology, and particularly relates to a method for real-time monitoring and intelligent control of water conservancy and water management joint operation and drainage. Background Technology

[0002] Traditional water conservancy and water management methods rely heavily on manual experience, lacking real-time monitoring and precise intelligent control of complex water supply networks and variable water demand. Currently, water conservancy and water management systems face numerous challenges, such as complex water supply network distribution with interwoven ordinary and transfer connections, making it difficult to fully grasp their operational status; variable supply relationships between water sources and users at different seasons and time periods, making it difficult to accurately grasp changes in water demand; and a lack of refined analysis of user water demand status, making it impossible to scientifically determine user scheduling priorities. This results in difficulties in meeting diverse user water demands and effectively responding to emergency water supply situations during demand-based joint control and scheduling. Furthermore, existing control methods fail to fully consider the complex connections and influencing factors between nodes when constructing the scheduling network, leading to low efficiency and reliability of water conservancy and water management joint control and drainage, hindering the optimal allocation and efficient utilization of water resources. Therefore, this invention provides a real-time monitoring and intelligent control method for water conservancy and water management joint control and drainage. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention proposes a method for real-time monitoring and intelligent control of water conservancy and water affairs. This method first acquires a set of user water demand statuses to construct a user scheduling priority space. Then, combined with a pre-set adjustable water demand scheduling network, it conducts joint water demand control scheduling through an enhanced scheduling model until user water demand is met. The adjustable water demand scheduling network is constructed based on the distribution status of water supply sources, the distribution status of scheduling pipelines, user water demand status, and water source moisture content. Utilizing topology and hypergraph algorithms, it achieves efficient real-time monitoring and intelligent control of water conservancy and water affairs, improving the scientific and rational allocation of water resources.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] Methods for real-time monitoring and intelligent control of water conservancy and water management joint operation and drainage include:

[0006] The user water demand status set is collected in real time through a preset sensor network to obtain the user scheduling priority space;

[0007] Based on the user scheduling priority set combined with the preset adjustable water demand scheduling network, water demand joint control scheduling is carried out through enhanced scheduling model until the user's water demand is met.

[0008] The adjustable water demand dispatching network is constructed based on the distribution status of water supply sources, the distribution status of dispatching pipelines, the water demand status of users, and the water content of water sources, using a combination of topology algorithms and hypergraph algorithms.

[0009] Specifically, the steps for constructing an adjustable water demand dispatch network include:

[0010] The distribution status of water demand users, the distribution of water supply network and the distribution of ordinary and transfer connections in the water supply network, the distribution status of water supply sources, the supply relationship between water supply sources and corresponding water demand users at different time periods, the water demand changes of water sources in different seasons, and the main supply area data corresponding to each water source are obtained to construct an adjustable water demand scheduling network status set.

[0011] Based on the distribution status of water demand users and their corresponding location information, a set of water demand nodes is constructed in the adjustable water demand scheduling network.

[0012] Based on the distribution status and location information of the water supply sources, a set of water supply nodes in the adjustable water demand dispatching network is constructed.

[0013] Based on the transfer connection locations in the water supply network, a set of primary scheduling nodes in the adjustable water demand scheduling network is constructed. At the same time, based on the ordinary connection locations and valve locations, a set of secondary pipeline path sub-nodes is constructed.

[0014] Specifically, the construction steps of an adjustable water demand dispatching network also include:

[0015] Based on the pipeline distances corresponding to the primary scheduling node set and the secondary pipeline path sub-node set, and the water supply path and control relationship corresponding to each water demand scheduling, an adaptive clustering algorithm combining membership function is used to perform membership clustering to obtain a subset of secondary membership nodes that meet the preset membership clustering conditions for each primary scheduling node.

[0016] Simultaneously, by establishing the membership degree between the primary scheduling nodes and the subset of secondary pipeline path nodes that meet the preset membership clustering conditions, the connection relationship between each primary scheduling node set and the corresponding secondary membership node subset is constructed.

[0017] Based on the number of secondary subordinate node subsets corresponding to each primary scheduling node set, the adjustable water source and user water supply volume, the frequency of successful handling of emergency water supply scheduling and the frequency of failures, and the water supply delay and demand satisfaction rate for each user, the importance level and trustworthiness of each water user corresponding to each primary scheduling node set are obtained through the evaluation algorithm and automatically marked.

[0018] The secondary pipeline path sub-node set is constructed from M secondary subordinate node subsets; the number of the secondary subordinate node subsets is the same as the primary scheduling node set.

[0019] Specifically, the construction steps of an adjustable water demand dispatching network also include:

[0020] Based on the distance between two adjacent sub-nodes in the secondary pipeline path sub-node set, the pipeline load rate, the flow rate per unit time, the roughness coefficient, and the maximum flow limit, a secondary connection relationship between two adjacent secondary pipeline path sub-nodes is constructed.

[0021] Based on the secondary connection relationship and the set of secondary pipeline path sub-nodes, a secondary pipeline path water supply subnet is constructed;

[0022] Based on the importance level and trustworthiness of each water user corresponding to each set of primary scheduling nodes, the scheduling delay between two adjacent primary scheduling nodes, the water supply load and probability of failure corresponding to the secondary connection relationship between the secondary subordinate node subsets, the shortest pipeline path distance and the degree of contribution of the control rate to the connection between two adjacent primary scheduling nodes and the secondary subordinate node subsets, a set of primary scheduling node connection relationships is constructed.

[0023] Based on the set of connection relationships between primary scheduling nodes and the set of primary scheduling nodes, the subnet of primary scheduling nodes is obtained;

[0024] Based on the connection relationship between the primary scheduling node and the corresponding secondary subordinate node subset, the primary scheduling node subnet and the secondary pipeline path water supply subnet are combined and mapped to obtain the water supply node subnet.

[0025] Specifically, the construction steps of an adjustable water demand dispatching network also include:

[0026] Based on the frequency and distance of centralized water supply scheduling of each primary scheduling node to the water demand node in the water supply node subnet, the supply satisfaction rate of each primary scheduling node and the pipeline connection relationship between each water demand node, a demand connection relationship between each water demand node and the primary scheduling node with a supply relationship is constructed.

[0027] Based on the water supply frequency and water supply satisfaction rate, the primary and secondary supply factors between each water demand node and the primary scheduling node with which there is a supply relationship are constructed, and marked on the connection relationship between the corresponding water demand node and the primary scheduling node with which there is a supply relationship.

[0028] Based on the supply relationship between the water supply source and the corresponding water demand user at different time periods, a water supply node set is constructed, and a water supply connection relationship is constructed between the primary scheduling nodes in the radiation area of ​​the water supply node and the water demand node.

[0029] Specifically, the construction steps of an adjustable water demand dispatching network also include:

[0030] Based on the water demand variation of each water supply node in different seasons, the water demand variation of the main supply area corresponding to the water supply node, the historical collaborative supply data of each main supply area, and the supply relationship between the water supply node and the corresponding water demand node in different time periods, the ratio of the time length for each water supply node to meet the main supply area under the collaborative state and the frequent water supply nodes corresponding to each water demand node are obtained.

[0031] Use this ratio to construct the self-sufficiency-coordinated adjustment factor function for each water supply node, and configure the corresponding self-sufficiency-coordinated adjustment factor function and frequent water supply nodes to the corresponding water demand nodes.

[0032] Based on the water supply node subnet, water demand node set, demand connection relationship, water supply node set and water supply connection relationship, an adjustable water demand scheduling network is obtained through topology algorithm and hypergraph algorithm;

[0033] Based on the historical emergency water supply scheduling frequency of each water demand node in the adjustable water demand scheduling network, the water supply node, water supply pipeline path and corresponding emergency coordinated scheduling frequency are used to construct a set of frequent scheduling paths for each water demand node, and the constructed set of frequent scheduling paths is configured on the corresponding water demand node.

[0034] Specifically, the steps for constructing a reinforced scheduling model include:

[0035] Real-time acquisition of the predicted water demand for each water demand node for 24 hours, the scheduling priority of each water demand node, the real-time operating status of the current adjustable water demand scheduling network, the water storage capacity of each water supply node, the self-sufficiency rate of the corresponding main supply area, and the coordinateable water supply, to construct the current input status of the enhanced scheduling model;

[0036] Based on the current input state of the enhanced scheduling model, the corresponding execution action trigger information is constructed, specifically including:

[0037] If the predicted water demand of all water demand nodes is in a normal state at the current moment and the water storage of the corresponding main water supply node meets the predicted water demand of the current node, then the self-sufficiency-cooperative adjustment factor function is used to determine that the current stage is self-sufficient water supply. Then, based on the predicted water demand, water supply is provided to each water demand node according to the frequent water supply nodes and corresponding water supply paths stored in each water demand node in the adjustable water demand scheduling network.

[0038] Specifically, the steps for constructing a reinforced scheduling model also include:

[0039] If at the current moment, at least one main water supply node corresponds to a set of water demand nodes whose predicted water demand is in an emergency state, but the water storage of the corresponding main water supply node meets the predicted water demand of the current node, then the self-sufficiency-cooperative adjustment factor function determines that the current state is in the self-sufficiency water supply stage. By using the frequent water supply nodes and their corresponding water supply paths stored in each water demand node in the adjustable water demand scheduling network, and the priority of each water demand node in the set of water demand nodes corresponding to the main water supply node in the emergency state, water supply is provided to each water demand node based on the predicted water demand.

[0040] If at the current moment, at least one main water supply node corresponds to a set of water demand nodes whose predicted water demand is in an emergency state, and at least one main water supply node's water storage capacity cannot meet the predicted water demand of the corresponding water demand nodes in the main supply area, then a collaborative judgment is made based on the water storage capacity of all water supply nodes and the water supply capacity of the corresponding water supply node set through the self-sufficiency-cooperative adjustment factor function.

[0041] If the water storage capacity of all water supply nodes at the current moment is determined, and the predicted water demand of all water demand nodes can be met through coordinated scheduling, then the scheduled water supply capacity and corresponding location distribution of the scheduled water supply nodes are obtained, the predicted water demand difference of each water demand node in the emergency water demand node set and the priority of the corresponding water demand node, and the operating status of each primary scheduling node and secondary pipeline path sub-node in the current adjustable water demand scheduling network are obtained to construct a coordinated scheduling set.

[0042] Specifically, the steps for constructing a reinforced scheduling model also include:

[0043] Based on the collaborative scheduling set, the optimal collaborative scheduling path set corresponding to the water demand node in emergency state is obtained by combining the path algorithm optimized by particle swarm optimization with the frequent scheduling path set, the importance level and trustworthiness of the first-level scheduling node set for each water demand user, and the preset scheduling constraint set. At the same time, the water storage volume is scheduled by combining the optimal collaborative scheduling path set corresponding to the water demand node in emergency state with the predicted water demand difference through the enhanced scheduling model.

[0044] If at least one primary scheduling node or secondary pipeline path sub-node is faulty on the optimal coordinated scheduling path between the water demand node and the corresponding water supply node, the current optimal coordinated scheduling path is optimized by combining the primary and secondary supply factors with the particle swarm optimization algorithm, so that the optimized path satisfies the preset scheduling constraint set.

[0045] If the water storage capacity of all water supply nodes at the current moment cannot meet the predicted water demand of all water demand nodes through coordinated scheduling, then the optimal coordinated scheduling path for each water demand node is generated by combining the path optimization algorithm of particle swarm optimization with a preset scheduling constraint set, according to the priority order of the corresponding water demand nodes in emergency state, until the schedulable water supply corresponding to the optimal coordinated scheduling path reaches the sum of the maximum schedulable water supply value of all water supply nodes at the current moment.

[0046] Specifically, the construction of the preset scheduling constraint set includes a first objective function, a first constraint function, a second constraint function, and a third constraint function;

[0047] The first constraint function is: the dispatchable water supply corresponding to each water supply node must be less than or equal to the difference between the water storage of the corresponding water supply node and the sum of the predicted water demand of all water demand nodes in the main water supply area corresponding to the current water supply node;

[0048] The second constraint function is: the water supply satisfaction rate of the main water supply node set corresponding to each water supply node is greater than the preset water supply satisfaction rate threshold.

[0049] The first objective function is constructed by taking the maximum value of the importance level, trust level and supply satisfaction rate of the first-level scheduling node and the corresponding water demand node on the optimal coordinated scheduling path, as well as the minimum value of the scheduling delay of each pair of adjacent first-level scheduling nodes, the path length of the scheduling network and the total energy consumption corresponding to each optimal coordinated scheduling path.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] This invention addresses the shortcomings of existing technologies by comprehensively collecting complex multi-dimensional data on water demand distribution, water supply networks, and water sources. It constructs an adjustable water demand scheduling network state set and then uses topology and hypergraph algorithms to build the scheduling network, accurately presenting the water conservancy and water affairs system architecture. This effectively overcomes the difficulty of traditional methods in comprehensively understanding the network and water source status. Through in-depth analysis of user water demand status, a scheduling priority space is generated, accurately allocating water resources according to user needs, avoiding resource waste and supply shortages. During the scheduling network construction process, factors such as pipeline distance, water supply path, and node importance are fully considered. Multiple algorithms are used to determine node relationships and connection methods, effectively optimizing water supply paths and improving water resource transmission efficiency. Simultaneously, the configuration of primary and secondary supply factors ensures that water demand nodes can quickly find backup water supply networks for replacement when the primary supply network fails. Furthermore, the introduction of primary and secondary supply factors and self-sufficiency-coordinated adjustment factor functions enables the water supply strategy to be flexibly adjusted according to different situations, enhancing the system's ability to cope with complex water demand scenarios, ensuring the rational and efficient allocation of water resources during both normal and emergency water supply, improving the efficiency and reliability of water conservancy and water affairs joint regulation and drainage, and providing strong support for the optimal allocation of water resources. Attached Figure Description

[0052] Figure 1 This is a flowchart of the real-time monitoring and intelligent control method for water conservancy and water affairs joint regulation and drainage in Embodiment 1 of the present invention;

[0053] Figure 2 This is a simplified node diagram of the adjustable water demand dispatching network according to Embodiment 1 of the present invention. Detailed Implementation

[0054] In large industrial parks or drought-prone scenarios, seasonal changes and fluctuating water demands from industrial production and residential life, coupled with seasonal reductions in water storage at some water sources and malfunctions in certain nodes of the water supply network, make traditional scheduling methods ineffective in addressing complex and changing water demand conditions and network failures. This hinders the rational allocation of water resources and can easily lead to water shortages or waste in certain areas. Therefore, please refer to [link / reference needed]. Figure 1 The present invention provides an embodiment of a method for real-time monitoring and intelligent control of water conservancy and water management joint operation, comprising the following steps:

[0055] S1, collects user water demand status sets in real time through a preset sensor network to obtain user scheduling priority space;

[0056] It should be further explained that the preset sensor network in this embodiment is specifically set by those skilled in the art according to the needs of data collection and the specific information of the target reservoir, pumping station, pipeline network and water supply point. It will not be described in detail here.

[0057] Furthermore, the specific steps for obtaining the user scheduling priority space in this embodiment include:

[0058] The system obtains the user's real-time water demand, 24-hour predicted water demand, user type label, geographical location, and 24-hour water demand difference. Furthermore, in this embodiment, the user type label T is: residential area, industrial area, agricultural area, and emergency facilities, including but not limited to hospitals and fire stations.

[0059] Based on user type tags, each type of user is assigned a corresponding basic priority weight factor through expert experience. For example, emergency facilities are relatively important occasions and have the highest priority for water needs, so they are assigned a high priority factor, such as 0.8. Industrial areas are medium, such as 0.6, and residential areas are low, such as 0.4.

[0060] The system obtains real-time water demand, 24-hour predicted water demand, and 24-hour water demand difference to obtain the water demand gap ratio. At the same time, it constructs the historical water supply stability factor for the corresponding user by the number of water outages in the past 7 days and the frequency of historical emergency water demand states.

[0061] Based on the real-time water demand, 24-hour predicted water demand, user type tags, geographical location, 24-hour water demand difference, basic priority weight factor, water demand gap ratio and historical water supply stability factor constructed above, the scheduling priority of the corresponding user is obtained by combining the sum optimized by the entropy weight method and the evaluation algorithm with the preset level range.

[0062] Based on the scheduling priority set of all users and the location coordinates of the corresponding users, the scheduling priority space of the corresponding users is constructed.

[0063] This process achieves refined and intelligent management of user water demand scheduling by constructing a dynamic priority allocation mechanism and a multi-dimensional evaluation system. Specifically, it presets basic priority weight factors based on user type tags according to expert experience, and quantifies the urgency by combining real-time water demand, predicted water demand, and the proportion of 24-hour water demand gap, effectively identifying sudden high water demand scenarios. Furthermore, it integrates historical water outage frequency and emergency event frequency to construct historical water supply stability factors, and dynamically optimizes the weights of each indicator through the entropy weight method, solving the subjective bias problem of traditional manual weighting, and making priority assessment have the dual advantages of objective data-driven and expert rule-guided approaches. The final generated scheduling priority space maps user geographic location and priority level to a multi-dimensional vector field in the topology network, which can guide the dynamic optimization of water supply paths in real time. For example, in the event of a sudden fire, the priority weight of emergency facilities will surge due to the proportion of water demand gap, and the system will automatically trigger cross-water source coordinated water transfer. At the same time, the hierarchical scheduling strategy for industrial areas and residential areas can reduce the fluctuation of pipeline load during non-emergency periods. Combined with historical stability factors, it avoids high-frequency fault paths, which can improve the overall water supply efficiency and reduce the peak load of the pipeline network. It systematically solves the problems of delayed emergency response and insufficient supply and demand matching accuracy in complex pipeline networks.

[0064] S2, based on the user scheduling priority set combined with the preset adjustable water demand scheduling network, performs water demand joint control scheduling through an enhanced scheduling model until the user's water demand is met;

[0065] The adjustable water demand dispatching network is constructed based on the distribution status of water supply sources, the distribution status of dispatching pipelines, the water demand status of users, and the water content of water sources, using a combination of topology algorithms and hypergraph algorithms.

[0066] Further, please refer to Figure 2 The construction steps of the adjustable water demand scheduling network in this embodiment include:

[0067] The distribution status of water demand users, the distribution of water supply network and the distribution of ordinary and transfer connections in the water supply network, the distribution status of water supply sources, the supply relationship between water supply sources and corresponding water demand users at different time periods, the water demand changes of water sources in different seasons, and the main supply area data corresponding to each water source are obtained to construct an adjustable water demand scheduling network status set.

[0068] Furthermore, in this embodiment, the transfer connection refers to the connection between transfer stations used for water transfer in the water supply network. Here, the connection between transfer stations is constructed by integrating the distribution status of the corresponding connecting pipelines between the transfer stations.

[0069] Furthermore, the ordinary connection in this embodiment is constructed based on the distribution, length and corresponding health status of each section of the water supply pipeline between transfer stations, and is for each section of pipeline.

[0070] Based on the distribution status of water demand users and their corresponding location information, a set of water demand nodes is constructed in the adjustable water demand scheduling network.

[0071] Furthermore, in this embodiment, the corresponding water-demand node is, for example... Figure 2 The corresponding U1, U2, and U3;

[0072] Based on the distribution status and location information of the water supply sources, a set of water supply nodes in the adjustable water demand dispatching network is constructed.

[0073] Furthermore, in this embodiment, the corresponding water supply nodes are S1, S2, and S3;

[0074] Based on the transfer connection locations in the water supply network, a set of primary scheduling nodes in the adjustable water demand scheduling network is constructed. At the same time, based on the ordinary connection locations and valve locations, a set of secondary pipeline path sub-nodes is constructed.

[0075] Furthermore, in this embodiment, the primary scheduling nodes are such as N1, N2, N3, and N4;

[0076] Furthermore, in this embodiment, the secondary pipeline path sub-nodes are such as n31, n41, n32, and n42;

[0077] For example, to better illustrate the adjustable water demand dispatching network in this embodiment, the following examples are provided:

[0078] Data collection on the distribution of water users in the eastern new district of a certain city:

[0079] Water demand node set: Map residential area A (coordinates X1, Y1), factory B (X2, Y2), and hospital C (X3, Y3) to water demand nodes U1, U2, and U3.

[0080] Water supply node set: Reservoir S1 and regenerated water supply node S2 are marked as water supply nodes S1 and S2.

[0081] Level 1 Dispatch Nodes: Pumping stations N1 and N2 at the junction of the main pipeline network are designated as Level 1 dispatch nodes.

[0082] Based on the pipeline distances corresponding to the primary scheduling node set and the secondary pipeline path sub-node set, and the water supply path and control relationship corresponding to each water demand scheduling, an adaptive clustering algorithm combining membership function is used to perform membership clustering to obtain a subset of secondary membership nodes that meet the preset membership clustering conditions for each primary scheduling node.

[0083] Simultaneously, by establishing the membership degree between the primary scheduling nodes and the subset of secondary pipeline path nodes that meet the preset membership clustering conditions, the connection relationship between each primary scheduling node set and the corresponding secondary membership node subset is constructed.

[0084] Furthermore, for a more vivid illustration of second-level membership node subsets, please refer to [link / reference needed]. Figure 2 In the dashed box, n31 and n32 are the second-level member nodes of N3, and n41 and n42 are the second-level member nodes of N4;

[0085] Based on the number of secondary subordinate node subsets corresponding to each primary scheduling node set, the adjustable water source and user water supply volume, the frequency of successful handling of emergency water supply scheduling and the frequency of failures, and the water supply delay and demand satisfaction rate for each user, the importance level and trustworthiness of each water user corresponding to each primary scheduling node set are obtained through the evaluation algorithm and automatically marked.

[0086] Furthermore, in this embodiment, the number of secondary subordinate nodes corresponding to each primary scheduling node set can intuitively reflect the importance of the corresponding primary scheduling node in the entire water transmission network. This makes it easy to find the optimal transfer control path during water transmission. At the same time, in the event of a fault, the corresponding secondary subordinate nodes and defects in the corresponding connected network can be directly identified through the primary scheduling node, reducing the difficulty of fault detection and the risk of faults in the water transmission network.

[0087] The secondary pipeline path sub-node set is constructed from M secondary subordinate node subsets; the number of the secondary subordinate node subsets is the same as the primary scheduling node set.

[0088] Based on the distance between two adjacent sub-nodes in the secondary pipeline path sub-node set, the pipeline load rate, the flow rate per unit time, the roughness coefficient, and the maximum flow limit, a secondary connection relationship between two adjacent secondary pipeline path sub-nodes is constructed.

[0089] Based on the secondary connection relationship and the set of secondary pipeline path sub-nodes, a secondary pipeline path water supply subnet is constructed;

[0090] Based on the importance level and trustworthiness of each water user corresponding to each set of primary scheduling nodes, the scheduling delay between two adjacent primary scheduling nodes, the water supply load and probability of failure corresponding to the secondary connection relationship between the secondary subordinate node subsets, the shortest pipeline path distance and the degree of contribution of the control rate to the connection between two adjacent primary scheduling nodes and the secondary subordinate node subsets, a set of primary scheduling node connection relationships is constructed.

[0091] Furthermore, in this embodiment, the contribution of the regulation rate is the contribution of the connection relationship between each primary scheduling node and the primary scheduling nodes connected to it to the connection relationship between the primary scheduling node and the primary scheduling nodes connected to it.

[0092] Based on the set of connection relationships between primary scheduling nodes and the set of primary scheduling nodes, the subnet of primary scheduling nodes is obtained;

[0093] Based on the connection relationship between the primary scheduling node and the corresponding secondary subordinate node subset, the primary scheduling node subnet and the secondary pipeline path water supply subnet are combined and mapped to obtain the water supply node subnet.

[0094] Based on the frequency and distance of centralized water supply scheduling of each primary scheduling node to the water demand node in the water supply node subnet, the supply satisfaction rate of each primary scheduling node and the pipeline connection relationship between each water demand node, a demand connection relationship between each water demand node and the primary scheduling node with a supply relationship is constructed.

[0095] Based on the water supply frequency and water supply satisfaction rate, the primary and secondary supply factors between each water demand node and the primary scheduling node with which there is a supply relationship are constructed, and marked on the connection relationship between the corresponding water demand node and the primary scheduling node with which there is a supply relationship.

[0096] Based on the supply relationship between the water supply source and the corresponding water demand user at different time periods, a water supply node set is constructed, and a water supply connection relationship is constructed between the primary scheduling nodes in the radiation area of ​​the water supply node and the water demand node.

[0097] It should be further explained that one implementation of the primary and secondary supply factors in this embodiment is as follows:

[0098] The system collects and organizes data such as the frequency and distance of centralized water supply scheduling from each primary scheduling node in the water supply subnet to the water demand nodes. It also combines this data with the supply satisfaction rate of the primary scheduling nodes and the pipeline connections between the water demand nodes to comprehensively assess the supply tightness between each primary scheduling node and the water demand nodes. Based on this, through quantitative calculations of water supply frequency and supply satisfaction rate, the system assigns differentiated weights to different primary scheduling nodes in the supply relationship. Primary scheduling nodes with both water supply frequency and supply satisfaction rate exceeding a preset threshold are assigned a high weight (e.g., 0.8) and are marked as primary supply factors. Conversely, primary scheduling nodes with lower water supply frequency and supply satisfaction rates below the preset threshold are marked as secondary supply factors.

[0099] Based on the water demand variation of each water supply node in different seasons, the water demand variation of the main supply area corresponding to the water supply node, the historical collaborative supply data of each main supply area, and the supply relationship between the water supply node and the corresponding water demand node in different time periods, the ratio of the time length for each water supply node to meet the main supply area under the collaborative state and the frequent water supply nodes corresponding to each water demand node are obtained.

[0100] Use this ratio to construct the self-sufficiency-coordinated adjustment factor function for each water supply node, and configure the corresponding self-sufficiency-coordinated adjustment factor function and frequent water supply nodes to the corresponding water demand nodes.

[0101] Based on the water supply node subnet, water demand node set, demand connection relationship, water supply node set and water supply connection relationship, an adjustable water demand scheduling network is obtained through topology algorithm and hypergraph algorithm;

[0102] Based on the historical emergency water supply scheduling frequency of each water demand node in the adjustable water demand scheduling network, the water supply node, water supply pipeline path and corresponding emergency coordinated scheduling frequency are used to construct a set of frequent scheduling paths for each water demand node, and the constructed set of frequent scheduling paths is configured on the corresponding water demand node.

[0103] Furthermore, the construction steps of the enhanced scheduling model in this embodiment include:

[0104] Real-time acquisition of the predicted water demand for each water demand node for 24 hours, the scheduling priority of each water demand node, the real-time operating status of the current adjustable water demand scheduling network, the water storage capacity of each water supply node, the self-sufficiency rate of the corresponding main supply area, and the coordinateable water supply, to construct the current input status of the enhanced scheduling model;

[0105] Based on the current input state of the enhanced scheduling model, the corresponding execution action trigger information is constructed, specifically including:

[0106] If the predicted water demand of all water demand nodes is in a normal state at the current moment and the water storage of the corresponding main water supply node meets the predicted water demand of the current node, then the self-sufficiency-cooperative adjustment factor function determines that the current stage is self-sufficiency water supply. Then, based on the predicted water demand, water supply is carried out for each water demand node by using the frequent water supply nodes and corresponding water supply paths stored in each water demand node in the adjustable water demand scheduling network.

[0107] Furthermore, in this embodiment, the exemplary implementation process corresponding to normal predicted water demand at all water-demand nodes and sufficient water storage at the main water supply node includes:

[0108] First, by using a hybrid model built with LSTM and Transformer, historical water consumption, meteorological and holiday data are integrated to accurately predict the water demand of each water demand node in the next 24 hours, and IoT sensors are used to collect the water storage of the main water supply node in real time, providing a data foundation for subsequent scheduling.

[0109] Second, based on the status detection results, the self-sufficiency judgment module uses the self-sufficiency-coordinated adjustment factor function to calculate and compare the available water volume of the main water supply node with the total water demand of the water demand node. When the self-sufficiency factor reaches or exceeds the preset self-sufficiency threshold, it is determined to enter the self-sufficiency water supply stage, thereby triggering the scheduling execution process.

[0110] Third, in the scheduling and execution phase, the system calls the pre-built water supply path knowledge graph, selects the optimal water supply path for each water demand node based on attributes such as historical usage frequency and water transmission efficiency, and uses a linear programming algorithm to dynamically allocate water volume with the goal of minimizing water transmission cost under multiple constraints.

[0111] Fourth, during the scheduling process, the dynamic monitoring module plays a continuous role, calculating the deviation rate between the actual and predicted water demand every 5 minutes. Once the deviation exceeds the threshold, a tiered alarm mechanism is immediately triggered, and emergency measures such as activating backup pump stations and cross-regional water transfer are taken to ensure the stable operation of the water supply system. The entire process is interconnected, realizing intelligent and refined management of water supply scheduling.

[0112] If at the current moment, at least one main water supply node corresponds to a set of water demand nodes whose predicted water demand is in an emergency state, but the water storage of the corresponding main water supply node meets the predicted water demand of the current node, then the self-sufficiency-cooperative adjustment factor function determines that the current state is in the self-sufficiency water supply stage. By using the frequent water supply nodes and their corresponding water supply paths stored in each water demand node in the adjustable water demand scheduling network, and the priority of each water demand node in the set of water demand nodes corresponding to the main water supply node in the emergency state, water supply is provided to each water demand node based on the predicted water demand.

[0113] Furthermore, in this embodiment, some water-demanding nodes are in emergency status, but the main water supply node still has sufficient water storage. The corresponding exemplary implementation process includes:

[0114] First, in the emergency identification phase, the system continuously analyzes the water demand change trends of each water demand node based on real-time water demand monitoring data and anomaly detection algorithms. When a significant surge in water demand is detected at a certain water demand node (such as Hospital C) that exceeds the normal fluctuation range, the system immediately marks the node as being in an emergency state. This process, through in-depth analysis of historical and current real-time data, can quickly and accurately identify sudden emergency situations, providing trigger signals for subsequent emergency responses. The preferred anomaly detection algorithm is the isolated forest algorithm or the dynamic threshold method.

[0115] Second, when a node is marked as being in an emergency state, the system immediately enters the self-sufficiency verification phase. In this phase, the system first identifies the main water supply node for the node in urgent need of water. Then, combining information such as the main water supply node's real-time water storage, allocated water supply, and safety reserve water volume, it comprehensively assesses whether its adjustable water supply can meet the sudden demand of the node in urgent need of water. By accurately calculating the difference between the main water supply node's water supply capacity and the urgently needed water volume, the system can determine whether the main water supply node has the capacity to meet emergency needs, thus providing crucial information for the formulation of subsequent dispatch strategies.

[0116] Third, based on the self-sufficiency capability verification results, if the main water supply node can meet emergency needs, the system will dynamically adjust priorities. To ensure that emergency needs are prioritized, the system will significantly upgrade the scheduling priority of nodes requiring urgent water supply (such as Hospital C) from the normal level to the highest level, giving them the highest priority in water supply scheduling. Simultaneously, to balance overall water supply resources, the system will reduce the water supply weight of non-emergency areas (such as agricultural area D) according to pre-set priority rules, appropriately reducing their water supply volume. This adjustment process, through the redistribution of priorities across different areas, achieves efficient utilization of limited water resources in emergency situations.

[0117] Fourth, after completing the dynamic priority adjustment, path optimization becomes a key step in ensuring the efficient delivery of emergency water supply. The system first calls the frequently used emergency paths pre-stored for nodes in urgent need of water (such as Hospital C). These paths are efficient paths derived from historical emergency water supply data and pipeline performance evaluation. However, the system does not blindly use these paths, but monitors the load of key nodes (such as valve J12) and pipelines on the paths in real time. If the system detects that the load of a frequently used path is too high, which may affect the water supply efficiency or cause pipeline failure, the system will automatically activate the path switching mechanism to select the second-best path from the alternative paths (e.g., "Reservoir S2 → Pump Station N4 → Valve J6 → C"), ensuring that emergency water supply can be delivered safely and quickly to the nodes in need of water.

[0118] Fifth, taking a sudden pipeline leak in an industrial zone as an example, after detecting a significant increase in water demand, the system quickly initiates the emergency response process. By verifying the self-sufficiency capacity, it confirms that the main water supply node can meet the emergency demand. Subsequently, it raises the scheduling priority of the industrial zone and adjusts the water supply to non-emergency areas. During the route optimization process, the system prioritizes high-frequency routes for water supply and continuously monitors the route load to ensure that the water supply system can operate stably and efficiently in the event of a sudden emergency, minimizing the impact of the emergency on the water supply order.

[0119] If at the current moment, at least one main water supply node corresponds to a set of water demand nodes whose predicted water demand is in an emergency state, and at least one main water supply node's water storage capacity cannot meet the predicted water demand of the corresponding water demand nodes in the main supply area, then a collaborative judgment is made based on the water storage capacity of all water supply nodes and the water supply capacity of the corresponding water supply node set through the self-sufficiency-cooperative adjustment factor function.

[0120] If the water storage capacity of all water supply nodes at the current moment is determined, and the predicted water demand of all water demand nodes can be met through coordinated scheduling, then the scheduled water supply capacity and corresponding location distribution of the scheduled water supply nodes are obtained, the predicted water demand difference of each water demand node in the set of water demand nodes in emergency state and the priority of the corresponding water demand node, and the operating status of each primary scheduling node and secondary pipeline path sub-node in the current adjustable water demand scheduling network are obtained to construct a coordinated scheduling set.

[0121] Based on the collaborative scheduling set, the optimal collaborative scheduling path set corresponding to the water demand node in emergency state is obtained by combining the path algorithm optimized by particle swarm optimization with the frequent scheduling path set, the importance level and trustworthiness of the first-level scheduling node set for each water demand user, and the preset scheduling constraint set. At the same time, the water storage volume is scheduled by combining the optimal collaborative scheduling path set corresponding to the water demand node in emergency state with the predicted water demand difference through the enhanced scheduling model.

[0122] The construction of the preset scheduling constraint set includes a first objective function, a first constraint function, a second constraint function, and a third constraint function;

[0123] The first constraint function is: the dispatchable water supply corresponding to each water supply node must be less than or equal to the difference between the water storage of the corresponding water supply node and the sum of the predicted water demand of all water demand nodes in the main water supply area corresponding to the current water supply node;

[0124] The second constraint function is: the water supply satisfaction rate of the main water supply node set corresponding to each water supply node is greater than the preset water supply satisfaction rate threshold.

[0125] The first objective function is constructed by taking the maximum value of the importance level, trust level and supply satisfaction rate of the first-level scheduling node and the corresponding water demand node on the optimal coordinated scheduling path, as well as the minimum value of the scheduling delay of each pair of adjacent first-level scheduling nodes, the path length of the scheduling network and the total energy consumption corresponding to each optimal coordinated scheduling path.

[0126] If at least one primary scheduling node or secondary pipeline path sub-node is faulty on the optimal coordinated scheduling path between the water demand node and the corresponding water supply node, the current optimal coordinated scheduling path is optimized by combining the primary and secondary supply factors with the particle swarm optimization algorithm, so that the optimized path satisfies the preset scheduling constraint set.

[0127] It should be further explained that in this embodiment, the various links in the water supply scheduling process are closely linked, forming a dynamic and responsive organic whole. The entire process starts from the determination of collaborative needs, gradually progresses to the construction of collaborative scheduling sets, path planning and conflict resolution, and finally completes dynamic balance and execution, ensuring the rational allocation of water resources when water supply is tight.

[0128] First, the collaborative demand determination stage involves the system continuously monitoring the water storage capacity of each main water supply node and the water demand of its main supply area. Based on the real-time collected data, the system compares and analyzes the available water storage capacity of the main water supply node with the predicted water demand of the main supply area. When it is found that the water storage capacity of a certain main water supply node (such as reservoir S4) is significantly lower than the water demand of the main supply area and cannot independently meet the water supply demand, the system triggers the "collaborative scheduling mode" according to the preset threshold rules. This determination lays the foundation for subsequent collaborative scheduling operations.

[0129] Second, in the collaborative scheduling set construction phase, after the system triggers the collaborative scheduling mode, it quickly conducts a comprehensive survey of all schedulable water supply nodes. Through real-time data collection and analysis, the system accurately calculates the geographical location information and current available water volume of each schedulable water supply node, and clarifies the water supply potential of different nodes. At the same time, for nodes with urgent water needs, the system combines their historical water consumption data, current water consumption status and future demand prediction models to accurately calculate the water demand gap, and determines the scheduling priority of each water demand node according to the pre-set priority rules (such as setting key units such as hospitals as special level and residential areas as level 1).

[0130] Third, after completing the construction of the collaborative scheduling set, path planning and conflict resolution become crucial steps. The system employs a particle swarm optimization algorithm, comprehensively considering factors such as the location of each water supply node, pipeline connections, and water delivery capacity to generate multiple candidate water supply paths to cope with complex water supply network environments. After path generation, the system monitors the operational status of each water supply node and pipeline in real time, automatically identifying and eliminating faulty nodes (such as the failure of pump station N6), avoiding the selection of invalid paths. Simultaneously, the system calculates and compares the load rates of the remaining paths, prioritizing the path with the lowest load rate as the final water supply path, ensuring efficient and stable water supply and reducing pipeline pressure and failure risks.

[0131] Fourth, during the dynamic balancing and execution phase, the system scientifically allocates the water supply for coordinated scheduling based on the previously determined priorities of each water demand node, prioritizing the water demand of high-priority nodes (such as Hospital C) and rationally allocating the remaining water to other water demand nodes (such as residential area A and industrial area B).

[0132] Fifth, during the water supply process, the system continuously monitors the pressure and flow data along the water supply path through real-time monitoring equipment and sensors. When it detects that a section of the pipeline is overloaded and may affect the safety and stability of the water supply, the system immediately activates the dynamic diversion mechanism to guide a portion of the water to the backup path, thereby achieving dynamic balance of the water supply and ensuring that the entire water supply system can operate stably and efficiently in the collaborative scheduling mode to meet the water demand of various regions.

[0133] If the water storage capacity of all water supply nodes at the current moment cannot meet the predicted water demand of all water demand nodes through coordinated scheduling, then the optimal coordinated scheduling path for each water demand node will be generated by combining the path optimization algorithm of particle swarm optimization with the preset scheduling constraint set, according to the priority order of the corresponding water demand nodes in emergency state, until the schedulable water supply corresponding to the optimal coordinated scheduling path reaches the sum of the maximum schedulable water supply value of all water supply nodes at the current moment.

[0134] Furthermore, when the coordinated scheduling in this embodiment still cannot meet all needs, the exemplary implementation process of allocating corresponding priorities includes:

[0135] First, when the water supply system faces extreme supply and demand imbalance, a rigorous resource allocation and path planning mechanism is needed to ensure critical needs. The entire process starts with resource limit determination, prioritizes and clarifies resource allocation strategies, and finally ensures stable water supply to high-priority users through forced path optimization. All links are closely connected to form a complete emergency response system.

[0136] The first step is the resource limit determination process. The system aggregates the available water volume of all dispatchable water source nodes in real time, including reservoirs, pumping stations, and reclaimed water facilities, and obtains accurate data through IoT sensors and data acquisition systems. At the same time, it obtains the total demand of all water-demanding nodes and compares the difference between the two. When it is found that the total dispatchable water supply is significantly lower than the total demand gap, and the demand cannot be met through conventional coordinated scheduling, the system determines that it has entered a resource limit state and triggers the subsequent emergency allocation process. This determination process relies on real-time data monitoring and accurate calculation, providing a key basis for subsequent decision-making.

[0137] Once the resource limit assessment confirms the entry into an emergency state, the system immediately activates a priority ranking mechanism. Based on pre-set user priority rules, the system categorizes water-demanding nodes into three levels: Level 1 (e.g., critical institutions like hospitals and fire stations), Level 2 (e.g., residential areas), and Level 3 and below (e.g., non-essential commercial water use, landscape irrigation). First, the water needs of Level 1 users are fully guaranteed to ensure that critical livelihoods and public safety are not affected. For the remaining allocable resources, the system allocates them proportionally according to priority: Level 1 users receive water according to a certain proportion of remaining resources, while Level 2 and below users have non-essential water supply suspended or only the minimum guaranteed amount maintained. This tiered allocation strategy achieves efficient utilization and fair distribution of water resources, minimizing the negative impacts in extreme situations.

[0138] After prioritization, path optimization becomes a crucial step in ensuring water supply to high-priority users. For top-priority users, the system uses a particle swarm optimization algorithm combined with pre-defined scheduling constraints (such as pipeline capacity and node load limits) to generate dedicated water supply paths, avoiding instability caused by sharing with other users. For first-level users, the system allocates shared paths but uses flow control technology to limit flow and ensure that water resources for top-priority users are not squeezed out. During path optimization, the system continuously monitors the real-time load and water delivery efficiency of each path, dynamically adjusting path planning to ensure the stability and reliability of water supply for high-priority users under resource constraints.

[0139] Through these three closely linked steps, the system achieves scientific resource allocation and efficient path planning when water resources are severely insufficient, prioritizing critical needs, minimizing the impact of supply-demand imbalance on social welfare, and enhancing the emergency response capability and overall resilience of the water supply system.

[0140] Based on the action trigger information, construct the corresponding action and reward function;

[0141] A reinforcement scheduling model is constructed based on reinforcement learning. The current input state, the trigger information of the action, the action to be executed, and the reward function are input into the reinforcement scheduling model. At the same time, a three-dimensional scheduling simulation space is constructed by combining a three-dimensional simulation model with an adjustable water demand scheduling network. The reinforcement scheduling model is subjected to cyclic simulation training until the corresponding reward function value meets the preset reward function threshold, and the trained reinforcement scheduling model is obtained.

[0142] The trained reinforcement scheduling model is integrated into the central control system corresponding to the adjustable water demand scheduling network, and self-learning training is performed based on the real-time acquired water demand scheduling data and the operating status data of the adjustable water demand scheduling network.

[0143] This process, through multi-dimensional data collection and algorithm application, constructs a precise and practical adjustable water demand scheduling network and an enhanced scheduling model, bringing significant benefits. In constructing the adjustable water demand scheduling network, comprehensive data on water demand user distribution, water supply network layout, and water source conditions are collected to build a state set, providing a solid foundation for the scheduling network. Based on the various node sets and their connections, considering factors such as pipeline distance, water supply path, water supply volume, and fault frequency, adaptive clustering algorithms based on membership functions and evaluation algorithms are used to scientifically determine the membership relationships, importance, and trustworthiness between nodes, optimizing water supply paths and improving water resource transmission efficiency. The introduction of primary and secondary supply factors and self-sufficiency-coordinated adjustment factor functions enables the water supply strategy to be flexibly adjusted according to different situations, achieving rational allocation of water resources.

[0144] In strengthening the construction of the scheduling model, real-time data on water demand and supply are acquired as input. Based on different water demand and emergency situations, action triggering information is constructed. Particle swarm optimization is used to optimize paths, combined with a pre-set scheduling constraint set, to schedule water storage, ensuring precise allocation of water resources during both normal and emergency water supply periods. A fault optimization mechanism for the optimal collaborative scheduling path, and a strategy for generating scheduling paths based on priority until water supply demand is met, further guarantee the stability and reliability of water supply. Simultaneously, the model is built through reinforcement learning and iteratively trained in a three-dimensional simulation space, then integrated into the central control system for self-learning training. This allows the model to continuously optimize, better adapting to complex and ever-changing water conservancy and water affairs scheduling scenarios, effectively avoiding water shortages or waste, improving water resource utilization efficiency, and achieving optimized allocation and efficient utilization of water resources.

[0145] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments under the guidance of the present invention without departing from the spirit and scope of the claims. All of these variations are within the protection scope of the present invention.

Claims

1. A method for real-time monitoring and intelligent control of water conservancy and water management joint regulation and discharge, characterized in that, include: The user water demand status set is collected in real time through a preset sensor network to obtain the user scheduling priority space; Based on the user scheduling priority set combined with the preset adjustable water demand scheduling network, water demand joint control scheduling is carried out through enhanced scheduling model until the user's water demand is met. The adjustable water demand dispatching network is constructed based on the distribution status of water supply sources, the distribution status of dispatching pipelines, the water demand status of users, and the water content of water sources, using a combination of topology algorithms and hypergraph algorithms. The construction steps of the adjustable water demand dispatching network include: The distribution status of water demand users, the distribution of water supply network and the distribution of ordinary and transfer connections in the water supply network, the distribution status of water supply sources, the supply relationship between water supply sources and corresponding water demand users at different time periods, the water demand changes of water sources in different seasons, and the main supply area data corresponding to each water source are obtained to construct an adjustable water demand scheduling network status set. Based on the distribution status of water demand users and their corresponding location information, a set of water demand nodes is constructed in the adjustable water demand scheduling network. Based on the distribution status and location information of the water supply sources, a set of water supply nodes in the adjustable water demand dispatching network is constructed. Based on the transfer connection locations in the water supply network, a set of primary scheduling nodes in the adjustable water demand scheduling network is constructed. At the same time, based on the ordinary connection locations and valve locations, a set of secondary network path sub-nodes is constructed. The construction steps of the adjustable water demand dispatching network also include: Based on the pipeline distances corresponding to the primary scheduling node set and the secondary pipeline path sub-node set, and the water supply path and control relationship corresponding to each water demand scheduling, an adaptive clustering algorithm combining membership function is used to perform membership clustering to obtain a subset of secondary membership nodes that meet the preset membership clustering conditions for each primary scheduling node. Simultaneously, by establishing the membership degree between the primary scheduling nodes and the subset of secondary pipeline path nodes that meet the preset membership clustering conditions, the connection relationship between each primary scheduling node set and the corresponding secondary membership node subset is constructed. Based on the number of secondary subordinate node subsets corresponding to each primary scheduling node set, the adjustable water source and user water supply volume, the frequency of successful handling of emergency water supply scheduling and the frequency of failures, and the water supply delay and demand satisfaction rate for each user, the importance level and trustworthiness of each water user corresponding to each primary scheduling node set are obtained through the evaluation algorithm and automatically marked. The secondary pipeline path sub-node set is constructed from M secondary subordinate node subsets; the number of the secondary subordinate node subsets is the same as the primary scheduling node set.

2. The method for real-time monitoring and intelligent control of water conservancy and water management joint operation as described in claim 1, characterized in that, The construction steps of the adjustable water demand dispatching network also include: Based on the distance between two adjacent sub-nodes in the secondary pipeline path sub-node set, the pipeline load rate, the flow rate per unit time, the roughness coefficient, and the maximum flow limit, a secondary connection relationship between two adjacent secondary pipeline path sub-nodes is constructed. Based on the secondary connection relationship and the set of secondary pipeline path sub-nodes, a secondary pipeline path water supply subnet is constructed; Based on the importance level and trustworthiness of each water user corresponding to each set of primary scheduling nodes, the scheduling delay between two adjacent primary scheduling nodes, the water supply load and probability of failure corresponding to the secondary connection relationship between the secondary subordinate node subsets, the shortest pipeline path distance and the degree of contribution of the control rate to the connection between two adjacent primary scheduling nodes and the secondary subordinate node subsets, a set of primary scheduling node connection relationships is constructed. Based on the set of connection relationships between primary scheduling nodes and the set of primary scheduling nodes, the subnet of primary scheduling nodes is obtained; Based on the connection relationship between the primary scheduling node and the corresponding secondary subordinate node subset, the primary scheduling node subnet and the secondary pipeline path water supply subnet are combined and mapped to obtain the water supply node subnet.

3. The method for real-time monitoring and intelligent control of water conservancy and water management joint operation as described in claim 2, characterized in that, The construction steps of the adjustable water demand dispatching network also include: Based on the frequency and distance of centralized water supply scheduling of each primary scheduling node to the water demand node in the water supply node subnet, the supply satisfaction rate of each primary scheduling node and the pipeline connection relationship between each water demand node, a demand connection relationship between each water demand node and the primary scheduling node with a supply relationship is constructed. Based on the water supply frequency and water supply satisfaction rate, the primary and secondary supply factors between each water demand node and the primary scheduling node with which there is a supply relationship are constructed, and marked on the connection relationship between the corresponding water demand node and the primary scheduling node with which there is a supply relationship. Based on the supply relationship between the water supply source and the corresponding water demand user at different time periods, a water supply node set is constructed, and a water supply connection relationship is constructed between the primary scheduling nodes in the radiation area of ​​the water supply node and the water demand node.

4. The method for real-time monitoring and intelligent control of water conservancy and water management joint operation as described in claim 3, characterized in that, The construction steps of the adjustable water demand dispatching network also include: Based on the water demand variation of each water supply node in different seasons, the water demand variation of the main supply area corresponding to the water supply node, the historical collaborative supply data of each main supply area, and the supply relationship between the water supply node and the corresponding water demand node in different time periods, the ratio of the time length for each water supply node to meet the main supply area under the collaborative state and the frequent water supply nodes corresponding to each water demand node are obtained. Use this ratio to construct the self-sufficiency-coordinated adjustment factor function for each water supply node, and configure the corresponding self-sufficiency-coordinated adjustment factor function and frequent water supply nodes to the corresponding water demand nodes. Based on the water supply node subnet, water demand node set, demand connection relationship, water supply node set and water supply connection relationship, an adjustable water demand scheduling network is obtained through topology algorithm and hypergraph algorithm; Based on the historical emergency water supply scheduling frequency of each water demand node in the adjustable water demand scheduling network, the water supply node, water supply pipeline path and corresponding emergency coordinated scheduling frequency are used to construct a set of frequent scheduling paths for each water demand node, and the constructed set of frequent scheduling paths is configured on the corresponding water demand node.

5. The method for real-time monitoring and intelligent control of water conservancy and water management joint regulation and discharge as described in claim 4, characterized in that, The construction steps of the enhanced scheduling model include: Real-time acquisition of the predicted water demand for each water demand node for 24 hours, the scheduling priority of each water demand node, the real-time operating status of the current adjustable water demand scheduling network, the water storage capacity of each water supply node, the self-sufficiency rate of the corresponding main supply area, and the coordinateable water supply, to construct the current input status of the enhanced scheduling model; Based on the current input state of the enhanced scheduling model, the corresponding execution action trigger information is constructed, specifically including: If the predicted water demand of all water demand nodes is in a normal state at the current moment and the water storage of the corresponding main water supply node meets the predicted water demand of the current node, then the self-sufficiency-cooperative adjustment factor function is used to determine that the current stage is self-sufficient water supply. Then, based on the predicted water demand, water supply is provided to each water demand node according to the frequent water supply nodes and corresponding water supply paths stored in each water demand node in the adjustable water demand scheduling network.

6. The method for real-time monitoring and intelligent control of water conservancy and water management joint operation as described in claim 5, characterized in that, The construction steps of the enhanced scheduling model also include: If at the current moment, at least one main water supply node corresponds to a set of water demand nodes whose predicted water demand is in an emergency state, but the water storage of the corresponding main water supply node meets the predicted water demand of the current node, then the self-sufficiency-cooperative adjustment factor function determines that the current state is in the self-sufficiency water supply stage. By using the frequent water supply nodes and their corresponding water supply paths stored in each water demand node in the adjustable water demand scheduling network, and the priority of each water demand node in the set of water demand nodes corresponding to the main water supply node in the emergency state, water supply is provided to each water demand node based on the predicted water demand. If at the current moment, at least one main water supply node corresponds to a set of water demand nodes whose predicted water demand is in an emergency state, and at least one main water supply node's water storage capacity cannot meet the predicted water demand of the corresponding water demand nodes in the main supply area, then a collaborative judgment is made based on the water storage capacity of all water supply nodes and the water supply capacity of the corresponding water supply node set through the self-sufficiency-cooperative adjustment factor function. If the water storage capacity of all water supply nodes at the current moment is determined, and the predicted water demand of all water demand nodes can be met through coordinated scheduling, then the scheduled water supply capacity and corresponding location distribution of the scheduled water supply nodes are obtained, the predicted water demand difference of each water demand node in the emergency water demand node set and the priority of the corresponding water demand node, and the operating status of each primary scheduling node and secondary pipeline path sub-node in the current adjustable water demand scheduling network are obtained to construct a coordinated scheduling set.

7. The method for real-time monitoring and intelligent control of water conservancy and water management joint regulation as described in claim 6, characterized in that, The construction steps of the enhanced scheduling model also include: Based on the collaborative scheduling set, the optimal collaborative scheduling path set corresponding to the water demand node in emergency state is obtained by combining the path algorithm optimized by particle swarm optimization with the frequent scheduling path set, the importance level and trustworthiness of the first-level scheduling node set for each water demand user, and the preset scheduling constraint set. At the same time, the water storage volume is scheduled by combining the optimal collaborative scheduling path set corresponding to the water demand node in emergency state with the predicted water demand difference through the enhanced scheduling model. If at least one primary scheduling node or secondary pipeline path sub-node is faulty on the optimal coordinated scheduling path between the water demand node and the corresponding water supply node, the current optimal coordinated scheduling path is optimized by combining the primary and secondary supply factors with the particle swarm optimization algorithm, so that the optimized path satisfies the preset scheduling constraint set. If the water storage capacity of all water supply nodes at the current moment cannot meet the predicted water demand of all water demand nodes through coordinated scheduling, then the optimal coordinated scheduling path for each water demand node is generated by combining the path optimization algorithm of particle swarm optimization with a preset scheduling constraint set, according to the priority order of the corresponding water demand nodes in emergency state, until the schedulable water supply corresponding to the optimal coordinated scheduling path reaches the sum of the maximum schedulable water supply value of all water supply nodes at the current moment.

8. The method for real-time monitoring and intelligent control of water conservancy and water management joint regulation and drainage as described in claim 7, characterized in that, The construction of the preset scheduling constraint set includes a first objective function, a first constraint function, a second constraint function, and a third constraint function; The first constraint function is: the dispatchable water supply corresponding to each water supply node must be less than or equal to the difference between the water storage of the corresponding water supply node and the sum of the predicted water demand of all water demand nodes in the main water supply area corresponding to the current water supply node; The second constraint function is: the water supply satisfaction rate of the main water supply node set corresponding to each water supply node is greater than the preset water supply satisfaction rate threshold. The first objective function is constructed by taking the maximum value of the importance level, trust level and supply satisfaction rate of the first-level scheduling node and the corresponding water demand node on the optimal coordinated scheduling path, as well as the minimum value of the scheduling delay of each pair of adjacent first-level scheduling nodes, the path length of the scheduling network and the total energy consumption corresponding to each optimal coordinated scheduling path.

Images