A mountainous area cascade water source intelligent dispatching method based on dynamic digital mapping

Abstract

The application discloses a mountainous area cascade water source intelligent scheduling method based on dynamic digital mapping and belongs to the technical field of water source intelligent scheduling. Sensing nodes are arranged at mountainous area cascade water sources to acquire water level parameters and water quality parameters; the parameters are input into a pre-constructed dynamic digital mapping structure to update water level mapping values and water quality mapping values of corresponding virtual nodes; the water level mapping values are compared with a storage safety threshold range, the water quality mapping values are compared with a pollution safety threshold, and storage abnormality marks or pollution abnormality marks are marked; a starting intervention node is determined from the virtual nodes marked as abnormal, and intervention influence node sets are determined downstream; scheduling instruction is generated according to the correlation of the water level mapping values and the water quality mapping values of the virtual nodes in the starting intervention node and the intervention influence node sets to control the opening and closing of the water release gate of the corresponding water source unit. The application realizes the collaborative scheduling of the mountainous area cascade water source under complex conditions through the storage and pollution balance deduction in the digital space.

Description

Technical Field

[0001] This invention discloses a method for intelligent scheduling of cascade water resources in mountainous areas based on dynamic digital mapping, belonging to the field of intelligent water resource scheduling technology. Background Technology

[0002] In the management of cascade water sources in mountainous areas, traditional scheduling methods typically rely on manual experience and fixed procedures. Mountainous terrain is complex, with water source units distributed in a cascade pattern. The discharge flow from upstream sources directly affects the water level changes and water quality of multiple downstream units. Current manual scheduling methods primarily rely on patrol personnel periodically observing the water level gauges and water quality of each unit. After communication via telephone or walkie-talkie, the dispatch center issues unified instructions to the gate operators to release water. This method suffers from problems such as delayed information acquisition, limited decision-making basis, and slow response time. Especially when there is a sudden rise in water level or water pollution incident at the upstream source, it is difficult to adjust the water release strategy of the entire cascade system in a timely manner, easily leading to excessively high water levels in downstream units, posing a flood risk, or water quality deterioration affecting water supply security. In addition, manual scheduling makes it difficult to comprehensively consider the mutual influence between multiple water source units. It often adopts a local optimization approach, adjusting only the single water source unit with problems, ignoring the hydraulic connection and water quality transfer effect between upstream and downstream, resulting in unsatisfactory scheduling effect, and may even transfer the problem to downstream water source units.

[0003] With the development of sensing and automation control technologies, some mountainous cascade water sources have begun to deploy automated monitoring and control systems. These systems collect data in real time using devices such as water level gauges and flow meters, and automatically control the opening and closing of gates based on preset logic rules. However, most existing automated scheduling systems adopt a single-point control mode, meaning each water source unit operates independently, automatically releasing water or closing gates based on local water level thresholds, lacking a coordinated consideration of the overall state of the cascade system. When multiple water source units experience anomalies simultaneously, the independent control logic of each unit may conflict. For example, upstream units may release large amounts of water to lower the water level, while downstream units are at high water levels and cannot receive incoming water, leading to water waste or increased flood control pressure. Furthermore, existing systems lack consideration for water quality parameters, typically focusing only on water level or flow rate indicators. They cannot effectively intervene through scheduling when water quality deteriorates, such as increasing the water flow to replace polluted water or reducing the water flow to extend the water's residence time and enhance its self-purification capacity. This scheduling method, which separates the storage state from the pollution state, makes it difficult to achieve the comprehensive goals of water resource protection and water environment improvement.

[0004] To address the aforementioned issues, a method is needed that can map the physical entities of cascaded water sources in mountainous areas to a digital space, and collaboratively extrapolate the storage and pollution states of each water source unit within this digital space, thereby generating scheduling instructions that balance water level and water quality safety. By constructing a dynamic digital mapping structure consistent with the physical entity topology, the water level and water quality mapping values ​​of each virtual node are updated in real time. Based on these mapping values, a balance extrapolation of storage and pollution is performed in the digital space to determine the starting node requiring intervention and its impact range, thereby generating scheduling instructions that coordinate upstream and downstream relationships. This effectively solves the problems of information silos, isolated decision-making, and delayed response in traditional scheduling methods, enabling intelligent scheduling of cascaded water sources in mountainous areas under complex conditions. Summary of the Invention

[0005] To achieve the above objectives, this application provides the following technical solution: A method for intelligent scheduling of cascade water resources in mountainous areas based on dynamic digital mapping, characterized by comprising: S1, deploy sensor nodes in the physical space of the cascade water source in the mountainous area to obtain the water level parameters and water quality parameters of each water source unit at the current moment; S2, input the water level parameters and the water quality parameters into a pre-constructed dynamic digital mapping structure. The dynamic digital mapping structure includes virtual nodes that are consistent with the topology of each water source unit in the mountain cascade water source and virtual channels connecting each virtual node. Each virtual node stores the dynamic mapping identifier code of the water source unit. S3, based on the water level parameter, update the water level mapping value of the corresponding virtual node in the dynamic digital mapping structure, and based on the water quality parameter, update the water quality mapping value of the corresponding virtual node; S4. Traverse all virtual nodes in the dynamic digital mapping structure, extract the water level mapping value and water quality mapping value of each virtual node, compare the water level mapping value of each virtual node with the preset storage safety threshold range, compare the water quality mapping value of each virtual node with the preset pollution safety threshold, and mark the storage abnormality mark or pollution abnormality mark on the virtual node according to the comparison result. S5, determine the starting intervention node that needs to perform scheduling operations from the virtual nodes marked with storage anomaly identifiers or pollution anomaly identifiers, and determine the set of intervention-affected nodes along the virtual channel downstream, starting from the starting intervention node; S6. Based on the correlation between the water level mapping value and water quality mapping value of each virtual node in the set of starting intervention nodes and intervention impact nodes, generate scheduling instructions for each water source unit.

[0006] Furthermore, the construction process of the dynamic digital mapping structure includes: Collect geographic information system data of cascade water sources in mountainous areas, and extract the latitude and longitude coordinates, elevation data and upstream and downstream connection relationships between each water source unit; The mapping coordinates of each water source unit in the digital space are determined based on the latitude and longitude coordinates and the elevation data, and a directed connection channel between virtual nodes is established based on the upstream and downstream connection relationship. A unique dynamic mapping identifier is generated for each virtual node, and the dynamic mapping identifier is associated with the mapping coordinates, initial water level mapping value, and initial water quality mapping value of the virtual node and stored in the mapping database. In the mapping database, a storage area for safety thresholds and a storage area for pollution safety thresholds are independently allocated for each virtual node. The storage area for safety thresholds is used to store the minimum ecological water level threshold and the maximum flood control limit water level threshold of the water source unit corresponding to the virtual node. The storage area for pollution safety thresholds is used to store the water quality safety threshold of the water source unit corresponding to the virtual node.

[0007] Further, S3 includes: Receive the data packet uploaded by the sensor node, and parse the data packet to obtain the device code of the sensor node that sent the data packet; The dynamic mapping identifier of the target virtual node that is bound to the device code is queried in the pre-established device-node lookup table. The device-node lookup table stores the one-to-one correspondence between the device codes of each sensing node in the physical space and the dynamic mapping identifiers of each virtual node in the digital space. Locate the corresponding target virtual node in the dynamic digital mapping structure based on the retrieved dynamic mapping identifier code; The water level parameter is converted into a first numerical stream that matches the data format of the water level mapping value of the target virtual node, and the first numerical stream is written into the water level mapping storage area of ​​the target virtual node to overwrite the original water level mapping value in the storage area. The water quality parameters are converted into a second numerical stream that matches the data format of the water quality mapping value of the target virtual node, and the second numerical stream is written into the water quality mapping storage area of ​​the target virtual node to overwrite the original water quality mapping value in the storage area. After updating the water level mapping value and water quality mapping value, a write success feedback signal is generated and sent to the sensing node; If no data packet is received from a certain sensor node within a preset time window, a retransmission request is sent to the gateway device corresponding to that sensor node. The retransmission request includes the device code of the sensor node and the timestamp information of the missing data.

[0008] Further, S4 includes: In the dynamic digital mapping structure, all virtual nodes are traversed in ascending order of hierarchical number. For each virtual node currently traversed, the minimum ecological water level threshold and the maximum flood control limit water level threshold are read from the storage safety threshold storage area of ​​the virtual node, and the water quality safety threshold is read from the pollution safety threshold storage area of ​​the virtual node. The minimum ecological water level threshold and the maximum flood control limit water level threshold read are used to form the storage safety threshold range. It is then determined whether the water level mapping value of the virtual node is lower than the minimum ecological water level threshold or higher than the maximum flood control limit water level threshold. If so, the virtual node is determined to be in a storage abnormal state. A storage abnormality identifier is written into the status identifier field of the virtual node. The storage abnormality identifier includes an abnormality type code and an abnormality degree code. The abnormality degree code is determined based on the difference between the water level mapping value and the corresponding threshold. Determine whether the water quality mapping value of the virtual node is higher than the water quality safety threshold. If so, determine that the virtual node is in an abnormal pollution state. Write an abnormal pollution identifier into the status identifier field of the virtual node. The abnormal pollution identifier includes a pollutant code and a pollution degree code. The pollutant code is determined according to the specific pollutant exceeding the standard in the water quality parameters. The pollution degree code is determined according to the ratio range of the water quality mapping value and the water quality safety threshold. When the same virtual node is simultaneously marked with both storage anomaly and pollution anomaly, a composite anomaly identifier is written into the status identifier field of the virtual node. The composite anomaly identifier is a concatenation of the storage anomaly and pollution anomaly identifiers.

[0009] Further, S5 includes: Obtain all abnormal virtual nodes marked with storage anomaly identifier or pollution anomaly identifier, assign a traversal flag bit to each abnormal virtual node and initialize the traversal flag bit to the untraversed state; Select one of the abnormal virtual nodes as the current search node, and recursively search upstream along the virtual channel in the dynamic digital mapping structure. When an upstream node is found, check whether the upstream node exists in the abnormal virtual nodes. If it exists and the level number of the upstream node is less than the level number of the current search node, mark the upstream node as the new current search node and continue searching upstream until no upstream node with a smaller level number that exists in the abnormal virtual nodes can be found. Record the node that can no longer be searched upstream as a candidate starting node, and update the traversal flag of all abnormal virtual nodes passed in this search process to the traversed state. Repeat until all abnormal virtual nodes are in the traversal flag state, and obtain at least one candidate starting node. When there is only one candidate starting node, the candidate starting node is determined as the starting intervention node. When there are multiple candidate starting nodes, the comprehensive value of the abnormal urgency of each candidate starting node is calculated, and the candidate starting node with the largest comprehensive value of the abnormal urgency is determined as the starting intervention node. The calculation process of the comprehensive value of the abnormal urgency is as follows: For each candidate starting node, obtain the first deviation difference between the water level mapping value of the candidate starting node and the minimum ecological water level threshold, and the second deviation difference between the water level mapping value of the candidate starting node and the maximum flood control limit water level threshold. Obtain the third deviation difference between the water quality mapping value of the candidate starting node and the water quality safety threshold. Take the maximum value among the first deviation difference, the second deviation difference, and the third deviation difference as the benchmark deviation value. According to the abnormal identification type marked on the candidate starting node, query the corresponding weight coefficient from the preset urgency weight table. Multiply the benchmark deviation value by the weight coefficient to obtain the comprehensive value of the abnormal urgency of the candidate starting node.

[0010] Furthermore, S5 also includes: In the dynamic digital mapping structure, the first downstream neighboring node of the starting intervention node is obtained, and both the starting intervention node and the first downstream neighboring node are added to the set of intervention-affected nodes. Starting from the first downstream adjacent node, continue traversing downstream. When traversing to a downstream node, determine whether the downstream node has a branch channel. If it has a branch channel, add all nodes on the branch channel to the set of nodes affected by the intervention. During the traversal, for each node added to the set of nodes affected by the intervention, the upstream node identifier and downstream node identifier list are recorded to form a tree-structured influence path diagram; When the downstream node encountered is the last node of the cascade water source, the search for downstream nodes stops, and the construction of the set of nodes affected by the intervention is completed. All nodes in the set of nodes affected by the intervention are sorted in ascending order of hierarchical number to generate a sequence of nodes affected by the intervention. Read the water level mapping value, water quality mapping value, minimum ecological water level threshold, maximum flood control limit water level threshold, and water quality safety threshold for each node in the intervention impact node sequence from the mapping database, and package the read data into an intervention node data table.

[0011] Further, S6 includes: The starting intervention node is taken as the first-level processing object. Based on the first comparison relationship between the water level mapping value of the starting intervention node and the highest flood control limit water level threshold, and the second comparison relationship between the water quality mapping value of the starting intervention node and the water quality safety threshold, the first water release demand level of the starting intervention node is determined. Following the sequence of intervention impact nodes, each subsequent intervention impact node is taken as the current treatment node. For each current treatment node, the estimated water release flow of the upstream node is obtained. The minimum water level maintenance requirement of the current treatment node is determined based on the third comparison relationship between the water level mapping value of the current treatment node and the minimum ecological water level threshold. The water quality improvement requirement of the current treatment node is determined based on the fourth comparison relationship between the water quality mapping value of the current treatment node and the water quality safety threshold. Based on the minimum water level maintenance requirement, the water quality improvement requirement, and the estimated water release flow of the upstream node, the upstream water inflow capacity that the current treatment node can accept is calculated. Convert the acceptable upstream water inflow capacity into the expected water discharge flow rate of the current treatment node; After traversal, the expected water release flow rate corresponding to each node in the sequence of nodes affected by the intervention is obtained; based on the node identifier of each node and the corresponding expected water release flow rate, a scheduling instruction for the water source unit corresponding to each node is generated. The scheduling instruction includes an instruction type field, a target node identifier field, a water release flow rate field, and an execution time window field.

[0012] This invention discloses an intelligent scheduling method for cascade water sources in mountainous areas based on dynamic digital mapping, belonging to the field of intelligent water source scheduling technology. The method involves deploying sensor nodes at cascade water sources in mountainous areas to acquire water level and water quality parameters; inputting these parameters into a pre-constructed dynamic digital mapping structure to update the water level and water quality mapping values ​​of the corresponding virtual nodes; comparing the water level mapping values ​​with a storage safety threshold range and the water quality mapping values ​​with a pollution safety threshold to mark storage anomalies or pollution anomalies; determining the initial intervention node from the marked anomaly virtual nodes and identifying a set of intervention-affected nodes downstream; and generating scheduling commands based on the correlation between the water level and water quality mapping values ​​of each virtual node in the initial intervention node and the intervention-affected node set to control the opening and closing of the corresponding water source unit's gate. This invention achieves coordinated scheduling of cascade water sources in mountainous areas under complex conditions through storage and pollution balance simulation in digital space. Attached Figure Description

[0013] Figure 1 A flowchart illustrating the workflow of a method for intelligent scheduling of cascade water sources in mountainous areas based on dynamic digital mapping, as claimed in an embodiment of the present invention. Figure 2The second workflow diagram is shown for a method for intelligent scheduling of cascade water sources in mountainous areas based on dynamic digital mapping, as claimed in an embodiment of the present invention. Figure 3 The third flowchart of a method for intelligent scheduling of cascade water sources in mountainous areas based on dynamic digital mapping, as claimed in an embodiment of the present invention, is shown. Detailed Implementation

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

[0015] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of those features. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications in the embodiments of this application, such as up, down, left, right, front, back, etc., are only used to explain the relative positional relationships and movements between components in a specific orientation as shown in the accompanying drawings. If the specific orientation changes, the directional indications will change accordingly. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0016] References to embodiments herein mean that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0017] According to a first embodiment of the present invention, the present invention claims protection for a method for intelligent scheduling of cascade water sources in mountainous areas based on dynamic digital mapping, referring to... Figure 1 ,include: S1, deploy sensor nodes in the physical space of the cascade water source in the mountainous area to obtain the water level parameters and water quality parameters of each water source unit at the current moment; S2, input the water level parameters and water quality parameters into the pre-constructed dynamic digital mapping structure. The dynamic digital mapping structure includes virtual nodes that are consistent with the topology of each water source unit in the mountain cascade water source and virtual channels that connect each virtual node. Each virtual node stores the dynamic mapping identifier code of the water source unit. S3, based on the water level parameters, update the water level mapping value of the corresponding virtual node in the dynamic digital mapping structure, and based on the water quality parameters, update the water quality mapping value of the corresponding virtual node; S4. Traverse all virtual nodes in the dynamic digital mapping structure, extract the water level mapping value and water quality mapping value of each virtual node, compare the water level mapping value of each virtual node with the preset storage safety threshold range, compare the water quality mapping value of each virtual node with the preset pollution safety threshold, and mark the storage abnormality mark or pollution abnormality mark on the virtual node according to the comparison result. S5. Determine the starting intervention node that needs to perform scheduling operations from the virtual nodes marked with storage anomaly or pollution anomaly, and determine the set of intervention-affected nodes along the virtual channel downstream, starting from the starting intervention node. S6. Based on the correlation between the water level mapping value and water quality mapping value of each virtual node in the set of starting intervention nodes and intervention impact nodes, generate scheduling instructions for each water source unit.

[0018] In this embodiment, sensor nodes are deployed at each water source unit of the mountain cascade water source. The sensor nodes include water level sensors and water quality sensors. The water level sensors are used to collect real-time water level data of the water source unit, and the water quality sensors are used to collect real-time water quality data of the water source unit. The sensor nodes collect data according to a preset sampling frequency and upload it through a wireless communication network. Receive data packets uploaded by sensor nodes, and parse the device code of the sensor node that sent the data packet, as well as the water level parameters and water quality parameters collected by the sensor node from each data packet; Based on the device code, the dynamic mapping identifier of the virtual node corresponding to the sensor node is looked up in the pre-established device-node lookup table. The device-node lookup table stores the one-to-one correspondence between the device codes of each sensor node in the physical space and the dynamic mapping identifiers of each virtual node in the digital space. Based on the queried dynamic mapping identifier, the target virtual node corresponding to the pre-constructed dynamic digital mapping structure is located. The dynamic digital mapping structure includes virtual nodes that are consistent with the topology of each water source unit in the mountain cascade water source and virtual channels connecting each virtual node. Each virtual node stores the dynamic mapping identifier, water level mapping value storage area and water quality mapping value storage area of ​​the water source unit. The parsed water level parameters are converted into a first data stream that matches the data format of the target virtual node's water level mapping value storage area. The first data stream is written into the target virtual node's water level mapping value storage area to update the virtual node's water level mapping value. The parsed water quality parameters are converted into a second data stream that matches the data format of the target virtual node's water quality mapping value storage area. The second data stream is written into the target virtual node's water quality mapping value storage area to update the virtual node's water quality mapping value. After updating the water level mapping value and water quality mapping value of all virtual nodes, all virtual nodes are traversed in the dynamic digital mapping structure. The water level mapping value and water quality mapping value of each virtual node are extracted. The water level mapping value of each virtual node is compared with the preset storage security threshold range, and the water quality mapping value of each virtual node is compared with the preset pollution security threshold. Based on the comparison results, a storage anomaly flag or a pollution anomaly flag is marked on the virtual node. From the virtual nodes marked with storage anomaly or pollution anomaly, determine the starting intervention node that needs to perform scheduling operations, and search downstream along the virtual channel starting from the starting intervention node. Record all downstream virtual nodes found as the set of intervention-affected nodes. Based on the water level mapping value, water quality mapping value, and preset threshold of each virtual node in the set of initial intervention nodes and intervention impact nodes, a scheduling instruction is generated for the water source unit corresponding to each virtual node. The scheduling instruction includes the unit identifier of the target water source unit and the target water discharge flow value. According to the dispatching instructions, the gate control system controls the opening and closing of the water release gate of the corresponding water source unit to regulate the inflow of the downstream water source unit.

[0019] Furthermore, referring to Figure 2 The construction process of the dynamic digital mapping structure includes: Collect geographic information system data of cascade water sources in mountainous areas, and extract the latitude and longitude coordinates, elevation data and upstream and downstream connection relationships between each water source unit; The mapping coordinates of each water source unit in the digital space are determined based on the latitude and longitude coordinates and the elevation data, and a directed connection channel between virtual nodes is established based on the upstream and downstream connection relationship. A unique dynamic mapping identifier is generated for each virtual node, and the dynamic mapping identifier is associated with the mapping coordinates, initial water level mapping value, and initial water quality mapping value of the virtual node and stored in the mapping database. In the mapping database, a storage area for safety thresholds and a storage area for pollution safety thresholds are independently allocated for each virtual node. The storage area for safety thresholds is used to store the minimum ecological water level threshold and the maximum flood control limit water level threshold of the water source unit corresponding to the virtual node. The storage area for pollution safety thresholds is used to store the water quality safety threshold of the water source unit corresponding to the virtual node.

[0020] In this embodiment, geographic information system (GIS) data is obtained from the management database of cascade water sources in mountainous areas. The GIS data includes boundary contour data, latitude and longitude coordinate data, altitude data, and natural river channel connection relationships and artificial channel connection relationships between water source units. The acquired geographic information system data is parsed, and the latitude and longitude coordinates of the geometric center point of each water source unit are extracted as the positioning coordinates of the water source unit. The elevation data of each water source unit is extracted as the vertical reference of the water source unit. The upstream water source unit identifier and the downstream water source unit identifier of each water source unit are extracted as the connection relationship data. Based on the location coordinates and elevation data of each water source unit, the mapping coordinates of the virtual node corresponding to each water source unit are determined in the digital space. The mapping coordinates are represented by three-dimensional coordinates, where the planar coordinates are obtained by projection transformation of the location coordinates and the vertical coordinates are obtained by scaling the elevation data. Based on the connection relationship data of each water source unit, a directed connection channel between virtual nodes is established in the digital space. The directed connection channel points from the upstream virtual node to the downstream virtual node, and a channel identifier is assigned to each directed connection channel. The channel identifier contains a combination of the upstream node identifier and the downstream node identifier. A unique dynamic mapping identifier is generated for each virtual node. The dynamic mapping identifier adopts a segmented encoding method. The first segment represents the watershed partition to which the water source unit belongs, the second segment represents the type code of the water source unit, and the third segment is the sequence number sorted according to the geographical location of the water source unit. Construct a mapping database, and create a record for each virtual node in the mapping database. Each record contains the dynamic mapping identifier field, mapping coordinate field, water level mapping initial value field, and water quality mapping initial value field for the virtual node. The water level mapping initial value field is initialized to the historical average water level data of the corresponding water source unit, and the water quality mapping initial value field is initialized to the historical average water quality data of the corresponding water source unit. In the mapping database, a separate storage area for safety thresholds and a storage area for pollution safety thresholds are allocated for each virtual node. The storage area for safety thresholds is used to store the minimum ecological water level threshold and the maximum flood control limit water level threshold of the water source unit corresponding to the virtual node. The minimum ecological water level threshold is determined according to the ecological base flow requirements of the river section where the water source unit is located, and the maximum flood control limit water level threshold is determined according to the flood control capacity and dike elevation of the water source unit. The storage area for pollution safety thresholds is used to store the water quality safety thresholds of the water source unit corresponding to the virtual node. The water quality safety thresholds are determined according to the water function zoning and water quality targets of the water source unit. The dynamic mapping identifier of each virtual node is associated with and stored in conjunction with the virtual node's mapping coordinates, initial water level mapping value, initial water quality mapping value, minimum ecological water level threshold, maximum flood control limit water level threshold, and water quality safety threshold, forming a complete dynamic digital mapping structure.

[0021] Further, S3 includes: Receive the data packet uploaded by the sensor node, and parse the data packet to obtain the device code of the sensor node that sent the data packet; The dynamic mapping identifier of the target virtual node that is bound to the device code is queried in the pre-established device-node lookup table. The device-node lookup table stores the one-to-one correspondence between the device codes of each sensing node in the physical space and the dynamic mapping identifiers of each virtual node in the digital space. Locate the corresponding target virtual node in the dynamic digital mapping structure based on the retrieved dynamic mapping identifier code; The water level parameter is converted into a first numerical stream that matches the data format of the water level mapping value of the target virtual node, and the first numerical stream is written into the water level mapping storage area of ​​the target virtual node to overwrite the original water level mapping value in the storage area. The water quality parameters are converted into a second numerical stream that matches the data format of the water quality mapping value of the target virtual node, and the second numerical stream is written into the water quality mapping storage area of ​​the target virtual node to overwrite the original water quality mapping value in the storage area. After updating the water level mapping value and water quality mapping value, a write success feedback signal is generated and sent to the sensing node; If no data packet is received from a certain sensor node within a preset time window, a retransmission request is sent to the gateway device corresponding to that sensor node. The retransmission request includes the device code of the sensor node and the timestamp information of the missing data.

[0022] In this embodiment, the communication port used by the sensor node to upload data is continuously monitored. When a data packet is detected to arrive, the complete data packet is read from the communication buffer. The data packet includes a packet header, a device code field, a timestamp field, a water level data field, a water quality data field, and a check field. The integrity of the read data packet is checked. The check value of the data packet is calculated based on the check field and compared with the check value carried in the data packet. If the comparison is inconsistent, the data packet is discarded and a retransmission request is returned to the sending node. If the comparison is consistent, the next step of parsing is performed. Parse the device code field from the verified data packet to obtain the device code of the sensor node that sent the data packet; The system queries the dynamic mapping identifier of the target virtual node that is bound to the device code in the pre-established device-node lookup table. The device-node lookup table is indexed by the device code and stores the one-to-one correspondence between the device code of each sensor node in the physical space and the dynamic mapping identifier of each virtual node in the digital space. If the corresponding dynamic mapping identifier is not found, the data packet is marked as unknown node data and stored in the abnormal data log. Based on the retrieved dynamic mapping identifier, the corresponding target virtual node is located in the virtual node index of the dynamic digital mapping structure. The virtual node index uses the dynamic mapping identifier as the key and stores the memory address of each virtual node. Extract the contents of the water level data field and water quality data field from the parsed data packet to obtain the original water level parameters and original water quality parameters; The original water level parameters are converted into a first numerical stream that matches the data format of the water level mapping value storage area of ​​the target virtual node. The conversion process includes: mapping the value of the original water level parameters proportionally to the integer range that the storage area can represent according to the preset data precision and range of the water level mapping value storage area, and splitting the mapped integer into multiple bytes according to the byte order of the storage area to form the first numerical stream. The original water quality parameters are converted into a second numerical stream that matches the data format of the water quality mapping value storage area of ​​the target virtual node. The conversion process includes: mapping the values ​​of the original water quality parameters proportionally to the integer range that the storage area can represent, according to the preset data precision and range of the water quality mapping value storage area, and splitting the mapped integers into multiple bytes according to the byte order of the storage area to form the second numerical stream. Write the first numerical stream into the water level mapping value storage area of ​​the target virtual node. When writing, the direct overwrite method is used, and each byte of the first numerical stream replaces the original byte at the corresponding position in the storage area. The second numerical stream is written to the water quality mapping value storage area of ​​the target virtual node. The writing method is a direct overwrite method, replacing the original byte at the corresponding position in the storage area with each byte of the second numerical stream. After updating the water level mapping value and water quality mapping value, a write success feedback signal is generated. The write success feedback signal contains the dynamic mapping identifier code of the target virtual node, the update timestamp, and the write status code. The feedback signal is then sent to the sensor node that sent the data through the communication port. Simultaneously, a timed scanning task is initiated. This task scans the most recent data reception time of all sensor nodes at preset time intervals. When a sensor node is found to have not uploaded any data packets within a preset time window, a retransmission request is sent to the gateway device corresponding to that sensor node. The retransmission request contains the device code of the sensor node and the timestamp information of the missing data. The timestamp information includes all expected data time points from the last successful reception to the current time.

[0023] Furthermore, referring to Figure 3 S4 includes: In the dynamic digital mapping structure, all virtual nodes are traversed in ascending order of hierarchical number. For each virtual node currently traversed, the minimum ecological water level threshold and the maximum flood control limit water level threshold are read from the storage safety threshold storage area of ​​the virtual node, and the water quality safety threshold is read from the pollution safety threshold storage area of ​​the virtual node. The minimum ecological water level threshold and the maximum flood control limit water level threshold read are used to form the storage safety threshold range. It is then determined whether the water level mapping value of the virtual node is lower than the minimum ecological water level threshold or higher than the maximum flood control limit water level threshold. If so, the virtual node is determined to be in a storage abnormal state. A storage abnormality identifier is written into the status identifier field of the virtual node. The storage abnormality identifier includes an abnormality type code and an abnormality degree code. The abnormality degree code is determined based on the difference between the water level mapping value and the corresponding threshold. Determine whether the water quality mapping value of the virtual node is higher than the water quality safety threshold. If so, determine that the virtual node is in an abnormal pollution state. Write the pollution anomaly identifier into the status identifier field of the virtual node. The pollution anomaly identifier includes the pollutant code and the pollution degree code. The pollutant code is determined according to the specific pollutant exceeding the standard in the water quality parameters. The pollution degree code is determined according to the ratio range of the water quality mapping value and the water quality safety threshold. When the same virtual node is simultaneously marked with both storage anomaly and pollution anomaly, a composite anomaly identifier is written into the status identifier field of the virtual node. The composite anomaly identifier combines the storage anomaly identifier and the pollution anomaly identifier for storage.

[0024] In this embodiment, a list of virtual nodes sorted by hierarchical number is maintained in the dynamic digital mapping structure. The hierarchical number is numbered according to the order of water source units from upstream to downstream, with the upstream water source unit having the smallest hierarchical number and the downstream water source unit having the largest hierarchical number. Start the anomaly detection task, and traverse each virtual node in the virtual node list in ascending order of hierarchical number. For the currently traversed virtual node, perform the following operations: The minimum ecological water level threshold and the maximum flood control limit water level threshold are read from the storage security threshold storage area of ​​the virtual node. The minimum ecological water level threshold and the maximum flood control limit water level threshold are stored in floating-point form, representing the minimum and maximum water levels allowed by the water source unit, respectively. Read the water quality safety threshold from the pollution safety threshold storage area of ​​the virtual node. The water quality safety threshold is stored in the form of a floating-point number, which represents the maximum pollutant concentration allowed for the water source unit. Read the current water level mapping value from the water level mapping value storage area of ​​the virtual node, and read the current water quality mapping value from the water quality mapping value storage area of ​​the virtual node; The read water level mapping value is compared with the minimum ecological water level threshold. If the water level mapping value is less than the minimum ecological water level threshold, the virtual node is determined to be in a low water level abnormal state, and a low water level abnormality identifier is generated. The low water level abnormality identifier includes an abnormality type code and an abnormality degree value. The abnormality type code is fixed as the first code indicating that the water level is too low. The abnormality degree value is calculated by dividing the difference between the minimum ecological water level threshold and the water level mapping value by the minimum ecological water level threshold and then multiplying it by a preset proportional coefficient. The abnormality degree value is then converted into an integer code. The read water level mapping value is compared with the highest flood control limit water level threshold. If the water level mapping value is greater than the highest flood control limit water level threshold, the virtual node is determined to be in a high water level anomaly state, and a high water level anomaly identifier is generated. The high water level anomaly identifier includes an anomaly type code and an anomaly degree value. The anomaly type code is fixed as a second code indicating that the water level is too high. The anomaly degree value is calculated by dividing the difference between the water level mapping value and the highest flood control limit water level threshold by the highest flood control limit water level threshold and then multiplying it by a preset proportional coefficient. The anomaly degree value is then converted into an integer code. The read water quality mapping value is compared with the water quality safety threshold. If the water quality mapping value is greater than the water quality safety threshold, the virtual node is determined to be in an abnormal water quality state, and a water quality abnormality identifier is generated. The water quality abnormality identifier includes an abnormality type code and an abnormality degree value. The abnormality type code is fixed as a third code indicating that the water quality exceeds the standard. The abnormality degree value is calculated by dividing the difference between the water quality mapping value and the water quality safety threshold by the water quality safety threshold and then multiplying it by a preset proportional coefficient. The abnormality degree value is then converted into an integer code. After completing the above comparison, check whether the virtual node already has an anomaly identifier. If it does not exist and the comparison has generated at least one anomaly identifier, write the generated anomaly identifier into the status identifier field of the virtual node. If an existing anomaly identifier exists and the comparison has generated a new anomaly identifier, merge the new anomaly identifier with the existing one. The merging process is as follows: if the existing anomaly identifier and the new anomaly identifier are of different types, write a composite anomaly identifier into the status identifier field of the virtual node. The composite anomaly identifier uses a multi-segment concatenation format, storing the anomaly type code and anomaly severity value of each anomaly identifier in sequence, with each segment separated by a delimiter. If the existing anomaly identifier and the new anomaly identifier are of the same type, retain the identifier with the larger anomaly severity value and overwrite the smaller value with the larger value. If no abnormality is generated in this comparison, but there is already an abnormality in the status identifier field of the virtual node, then clear all abnormality in the status identifier field of the virtual node and mark the virtual node as normal. After traversing all virtual nodes, an anomaly detection report is generated. The anomaly detection report includes the dynamic mapping identifier code, anomaly type, anomaly severity, and detection timestamp of all virtual nodes marked with an anomaly.

[0025] Further, S5 includes: Obtain all abnormal virtual nodes marked with storage anomaly identifier or pollution anomaly identifier, assign a traversal flag bit to each abnormal virtual node and initialize the traversal flag bit to the untraversed state; Select one of the abnormal virtual nodes as the current search node, and recursively search upstream along the virtual channel in the dynamic digital mapping structure. When an upstream node is found, check whether the upstream node exists in the abnormal virtual nodes. If it exists and the level number of the upstream node is less than the level number of the current search node, mark the upstream node as the new current search node and continue searching upstream until no upstream node with a smaller level number that exists in the abnormal virtual nodes can be found. Record the node that can no longer be searched upstream as a candidate starting node, and update the traversal flag of all abnormal virtual nodes passed in this search process to the traversed state. Repeat until all abnormal virtual nodes are in the traversal flag state, and obtain at least one candidate starting node. When there is only one candidate starting node, the candidate starting node is determined as the starting intervention node. When there are multiple candidate starting nodes, the comprehensive value of the abnormal urgency of each candidate starting node is calculated, and the candidate starting node with the largest comprehensive value of the abnormal urgency is determined as the starting intervention node. The calculation process of the comprehensive value of the abnormal urgency is as follows: For each candidate starting node, obtain the first deviation difference between the water level mapping value of the candidate starting node and the minimum ecological water level threshold, and the second deviation difference between the water level mapping value of the candidate starting node and the maximum flood control limit water level threshold. Obtain the third deviation difference between the water quality mapping value of the candidate starting node and the water quality safety threshold. Take the maximum value among the first deviation difference, the second deviation difference, and the third deviation difference as the benchmark deviation value. According to the abnormal identification type marked on the candidate starting node, query the corresponding weight coefficient from the preset urgency weight table. Multiply the benchmark deviation value by the weight coefficient to obtain the comprehensive value of the abnormal urgency of the candidate starting node.

[0026] In this embodiment, the status identifier fields of all virtual nodes are read from the mapping database of the dynamic digital mapping structure. Virtual nodes whose status identifier fields are not empty are filtered out and treated as abnormal virtual nodes. A traversal record is created for each abnormal virtual node. The traversal record contains the dynamic mapping identifier code, the level number and a traversal flag of the node. All traversal flags are initialized to the untraversed state. Create a list of candidate starting nodes, initialized to empty; Enter the loop search phase, with the loop condition being the existence of abnormal virtual nodes whose traversal flag is in an untraversed state; In each iteration, arbitrarily select one of the remaining untraversed abnormal virtual nodes as the current search node, initialize a search path stack, and push the current search node onto the search path stack. Starting from the current search node, a recursive search is performed upstream along the virtual channel in the dynamic digital mapping structure. The specific implementation of the recursive search is as follows: obtain the dynamic mapping identifier of all upstream neighboring nodes of the current search node. For each upstream neighboring node, check whether the upstream neighboring node exists in the abnormal virtual node set. If it exists and the level number of the upstream neighboring node is less than the level number of the current search node, then the upstream neighboring node is taken as the new current search node and pushed onto the search path stack. Then, the operation of recursively searching upstream is repeated. If no such upstream neighboring node exists, the upstream search is stopped. At this time, the top node of the search path stack is the upstream abnormal virtual node that can be reached in this search. Record the top node of the search path stack as a candidate starting node, and add the candidate starting node to the candidate starting node list; Update the traversal flag of all nodes in the search path stack to the traversed state; Clear the search path stack and enter the next loop until the traversal flag of all abnormal virtual nodes is in the traversed state. After the loop ends, obtain the list of candidate starting nodes and check the number of nodes in the list of candidate starting nodes; If there is only one node in the candidate starting node list, then that node is determined as the starting intervention node. If there are multiple nodes in the candidate starting node list, the multi-candidate starting node selection process will proceed, which includes: For each candidate starting node in the candidate starting node list, perform the following operations: Read the minimum ecological water level threshold and the maximum flood control limit water level threshold from the storage security threshold storage area of ​​the candidate starting node, and read the water quality safety threshold from the pollution safety threshold storage area; Read the current water level mapping value from the water level mapping value storage area of ​​the candidate starting node, and read the current water quality mapping value from the water quality mapping value storage area; Calculate the first deviation difference between the water level mapping value and the minimum ecological water level threshold. The first deviation difference is the minimum ecological water level threshold minus the water level mapping value. If the result is negative, take zero. Calculate the second deviation difference between the water level mapping value and the highest flood control limit water level threshold. The second deviation difference is the water level mapping value minus the highest flood control limit water level threshold. If the result is negative, take zero. Calculate the third deviation difference between the water quality mapping value and the water quality safety threshold. The third deviation difference is the water quality mapping value minus the water quality safety threshold. If the result is negative, take zero. The maximum value among the first deviation difference, the second deviation difference, and the third deviation difference is taken as the benchmark deviation value; Get all the abnormal identifier types contained in the current status identifier field of the candidate starting node, and query the corresponding weight coefficient from the preset urgency weight table according to the abnormal identifier type. The urgency weight table stores the correspondence between different abnormal type combinations and weight coefficients. If there are multiple abnormal types, the maximum value of the weight coefficients corresponding to each abnormal type is taken as the final weight coefficient. Multiply the baseline deviation value by the final weighting coefficient to obtain the comprehensive value of the abnormal urgency of the candidate starting node; Compare the comprehensive abnormal urgency values ​​of all candidate starting nodes, and select the candidate starting node with the largest comprehensive abnormal urgency value as the starting intervention node. If there are multiple maximum values, select the one with the smallest hierarchical number as the starting intervention node.

[0027] Furthermore, S5 also includes: In the dynamic digital mapping structure, the first downstream neighboring node of the starting intervention node is obtained, and both the starting intervention node and the first downstream neighboring node are added to the set of intervention-affected nodes. Starting from the first downstream adjacent node, continue traversing downstream. When traversing to a downstream node, determine whether the downstream node has a branch channel. If it has a branch channel, add all nodes on the branch channel to the set of nodes affected by the intervention. During the traversal, for each node added to the set of nodes affected by the intervention, the upstream node identifier and downstream node identifier list are recorded to form a tree-structured influence path diagram; When the downstream node encountered is the last node of the cascade water source, the search for downstream nodes stops, and the construction of the set of nodes affected by the intervention is completed. All nodes in the set of nodes affected by the intervention are sorted in ascending order of hierarchical number to generate a sequence of nodes affected by the intervention. Read the water level mapping value, water quality mapping value, minimum ecological water level threshold, maximum flood control limit water level threshold, and water quality safety threshold for each node in the intervention impact node sequence from the mapping database, and package the read data into an intervention node data table.

[0028] In this embodiment, the dynamic mapping identifier of the starting intervention node is obtained, a queue is created for breadth-first search, and the dynamic mapping identifier of the starting intervention node is added to the queue. Create an intervention-affected node set, initialized to empty, and create an impact path record table to store the upstream and downstream node relationships for each node; If the queue is not empty, perform the following operations: Take the dynamic mapping identifier of the current node from the head of the queue and add the node to the set of nodes affected by the intervention; In the dynamic digital mapping structure, query the dynamic mapping identifiers of all downstream neighboring nodes of the current node to obtain the list of downstream nodes; For each downstream node in the downstream node list, perform the following operations: Add the downstream node to the tail of the queue; Establish a relationship between the current node and the downstream node in the impact path record table, record the current node as the upstream node of the downstream node, and append the downstream node as its downstream node to the record of the current node. Check whether the downstream node has a branch channel, that is, whether the downstream node has multiple downstream nodes at the same time. If so, mark the downstream node as a branch node and create a branch identifier for the branch node in the affected path record table. Continue the loop until the queue is empty; When the queue is empty, it means that all downstream nodes reachable from the starting intervention node have been traversed. At this time, the set of nodes affected by the intervention includes the starting intervention node and all its downstream nodes. Check if the set of nodes affected by the intervention contains the last node. The last node is a node without downstream nodes. If it is not included, it means that the traversal is incomplete and the search is re-executed. All nodes in the set of nodes affected by the intervention are sorted in ascending order according to the pre-assigned hierarchical number to obtain the sequence of nodes affected by the intervention. Nodes with the same hierarchical number are arranged according to their order of appearance in the set. Based on the sequence of nodes affected by the intervention, the water level mapping value, water quality mapping value, minimum ecological water level threshold, maximum flood control limit water level threshold, and water quality safety threshold of each node are read in batches from the mapping database. The read data is organized into an intervention node data table according to the node order. The intervention node data table includes node identifier, water level mapping value, water quality mapping value, minimum ecological water level threshold, maximum flood control limit water level threshold, water quality safety threshold, and a list of upstream node identifiers and a list of downstream node identifiers for that node. The intervention node data table is stored in a temporary storage area for use in subsequent scheduling instruction generation steps.

[0029] Further, S6 includes: The starting intervention node is taken as the first-level processing object. Based on the first comparison relationship between the water level mapping value of the starting intervention node and the highest flood control limit water level threshold, and the second comparison relationship between the water quality mapping value of the starting intervention node and the water quality safety threshold, the first water release demand level of the starting intervention node is determined. Following the sequence of intervention impact nodes, each subsequent intervention impact node is taken as the current treatment node. For each current treatment node, the estimated water release flow of the upstream node is obtained. The minimum water level maintenance requirement of the current treatment node is determined based on the third comparison relationship between the water level mapping value of the current treatment node and the minimum ecological water level threshold. The water quality improvement requirement of the current treatment node is determined based on the fourth comparison relationship between the water quality mapping value of the current treatment node and the water quality safety threshold. Based on the minimum water level maintenance requirement, the water quality improvement requirement, and the estimated water release flow of the upstream node, the upstream water inflow capacity that the current treatment node can accept is calculated. Convert the acceptable upstream water inflow capacity into the expected water discharge flow rate of the current treatment node; After traversal, the expected water release flow rate corresponding to each node in the sequence of nodes affected by the intervention is obtained; based on the node identifier of each node and the corresponding expected water release flow rate, a scheduling instruction for the water source unit corresponding to each node is generated. The scheduling instruction includes an instruction type field, a target node identifier field, a water release flow rate field, and an execution time window field.

[0030] In this embodiment, the intervention node data table is read to obtain the water level mapping value, water quality mapping value, maximum flood control limit water level threshold, and water quality safety threshold of the starting intervention node; The water level mapping value of the initial intervention node is compared with the highest flood control limit water level threshold. If the water level mapping value is greater than the highest flood control limit water level threshold, the excess amount is calculated, which is the water level mapping value minus the highest flood control limit water level threshold. If the water level mapping value is not greater than the highest flood control limit water level threshold, the excess amount is set to zero. The water quality mapping value of the initial intervention node is compared with the water quality safety threshold. If the water quality mapping value is greater than the water quality safety threshold, the excess amount is calculated, which is the water quality mapping value minus the water quality safety threshold. If the water quality mapping value is not greater than the water quality safety threshold, the excess amount is set to zero. The excess amount and the excess amount are respectively input into the first water release demand level determination logic. This logic includes: pre-setting multiple water release demand levels, each level corresponding to an excess amount range and an excess amount range, matching the actual excess amount with the excess amount range of each level, matching the actual excess amount with the excess amount range of each level, and taking the higher level of the two matching results as the first water release demand level of the starting intervention node. Based on the first water release demand level, the corresponding basic water release flow is queried from the preset water release flow mapping table. The water release flow mapping table stores the correspondence between different water release demand levels and basic water release flow. The queried basic water release flow is used as the expected water release flow of the starting intervention node and recorded in the intervention node data table. Next, following the sequence of nodes affected by the intervention, each node affected by the intervention is processed sequentially, starting from the node following the initial intervention node. For the currently processed node, the following operations are performed: retrieve the upstream node identifier of the node from the intervention node data table, and retrieve the already determined expected water discharge flow of the upstream node. Read the water level mapping value, water quality mapping value, minimum ecological water level threshold, and water quality safety threshold of the intervention node from the intervention node data table; The water level mapping value of this node is compared with the minimum ecological water level threshold. If the water level mapping value is lower than the minimum ecological water level threshold, the water level deficit is calculated as the minimum ecological water level threshold minus the water level mapping value. If the water level mapping value is not lower than the minimum ecological water level threshold, the water level deficit is set to zero. The water quality mapping value of this node is compared with the water quality safety threshold. If the water quality mapping value is higher than the water quality safety threshold, the water quality improvement requirement is calculated. The water quality improvement requirement is calculated as follows: based on the difference between the water quality mapping value and the water quality safety threshold, combined with the total water volume of the water source unit, the amount of water that needs to be replaced is estimated. Specifically, the difference is multiplied by the current water storage volume of the water source unit and then divided by the water quality safety threshold. If the water quality mapping value is not higher than the water quality safety threshold, the water quality improvement requirement is set to zero. The basic water demand of the node is obtained by adding the water level deficit of the node and the water demand for water quality improvement. Read the highest flood control limit water level threshold of the node from the intervention node data table, calculate the remaining reservoir capacity of the node. The remaining reservoir capacity is the highest flood control limit water level threshold minus the current water level mapping value. If the result is negative, take zero. Compare the remaining reservoir capacity with the node's basic water demand, and take the smaller value as the node's acceptance base. The expected water discharge flow of the upstream node is compared with the receiving base of the node. If the expected water discharge flow of the upstream node is less than or equal to the receiving base, the acceptable upstream water capacity of the node is set to be equal to the expected water discharge flow of the upstream node; if the expected water discharge flow of the upstream node is greater than the receiving base, the acceptable upstream water capacity of the node is set to be equal to the receiving base. The upstream inflow capacity that the node can accept is used as the expected discharge flow rate of the node and recorded in the intervention node data table; After determining the expected discharge flow of the node, check whether the node has multiple downstream nodes. If so, the flow needs to be allocated to each downstream node. The allocation method is as follows: based on the proportion of the acceptance base of each downstream node to the total acceptance base, the expected discharge flow of the current node is allocated to each downstream node proportionally, and the allocation result is used as the expected discharge flow of the upstream node of each downstream node for subsequent downstream node calculations. After traversing all the nodes affected by the intervention, the expected water discharge flow rate for each node is obtained; For each node in the sequence of nodes affected by the intervention, a scheduling instruction is generated. The format of the scheduling instruction includes: instruction start symbol, instruction type field which is fixed as water release instruction, target node identifier field which is the dynamic mapping identifier code of the node, water release flow field which is the expected water release flow of the node, execution time window field which is determined by adding a preset delay time to the current time, and instruction end symbol. All generated scheduling instructions are stored in the instruction queue in the order of node hierarchical number.

[0031] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0032] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0033] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A method for intelligent scheduling of cascade water resources in mountainous areas based on dynamic digital mapping, characterized in that, include: S1, deploy sensor nodes in the physical space of the cascade water source in the mountainous area to obtain the water level parameters and water quality parameters of each water source unit at the current moment; S2, input the water level parameters and the water quality parameters into a pre-constructed dynamic digital mapping structure. The dynamic digital mapping structure includes virtual nodes that are consistent with the topology of each water source unit in the mountain cascade water source and virtual channels connecting each virtual node. Each virtual node stores the dynamic mapping identifier code of the water source unit. S3, based on the water level parameter, update the water level mapping value of the corresponding virtual node in the dynamic digital mapping structure, and based on the water quality parameter, update the water quality mapping value of the corresponding virtual node; S4. Traverse all virtual nodes in the dynamic digital mapping structure, extract the water level mapping value and water quality mapping value of each virtual node, compare the water level mapping value of each virtual node with the preset storage safety threshold range, compare the water quality mapping value of each virtual node with the preset pollution safety threshold, and mark the storage abnormality mark or pollution abnormality mark on the virtual node according to the comparison result. S5, determine the starting intervention node that needs to perform scheduling operations from the virtual nodes marked with storage anomaly identifiers or pollution anomaly identifiers, and determine the set of intervention-affected nodes along the virtual channel downstream, starting from the starting intervention node; S6. Based on the correlation between the water level mapping value and water quality mapping value of each virtual node in the set of starting intervention nodes and intervention impact nodes, generate scheduling instructions for each water source unit.

2. The intelligent scheduling method for cascade water resources in mountainous areas based on dynamic digital mapping according to claim 1, characterized in that, The construction process of the dynamic digital mapping structure includes: Collect geographic information system data of cascade water sources in mountainous areas, and extract the latitude and longitude coordinates, elevation data and upstream and downstream connection relationships between each water source unit; The mapping coordinates of each water source unit in the digital space are determined based on the latitude and longitude coordinates and the elevation data, and a directed connection channel between virtual nodes is established based on the upstream and downstream connection relationship. A unique dynamic mapping identifier is generated for each virtual node, and the dynamic mapping identifier is associated with the mapping coordinates, initial water level mapping value, and initial water quality mapping value of the virtual node and stored in the mapping database. In the mapping database, a storage area for safety thresholds and a storage area for pollution safety thresholds are independently allocated for each virtual node. The storage area for safety thresholds is used to store the minimum ecological water level threshold and the maximum flood control limit water level threshold of the water source unit corresponding to the virtual node. The storage area for pollution safety thresholds is used to store the water quality safety threshold of the water source unit corresponding to the virtual node.

3. The intelligent scheduling method for cascade water resources in mountainous areas based on dynamic digital mapping according to claim 2, characterized in that, The S3 includes: Receive the data packet uploaded by the sensor node, and parse the data packet to obtain the device code of the sensor node that sent the data packet; The dynamic mapping identifier of the target virtual node that is bound to the device code is queried in the pre-established device-node lookup table. The device-node lookup table stores the one-to-one correspondence between the device codes of each sensing node in the physical space and the dynamic mapping identifiers of each virtual node in the digital space. Locate the corresponding target virtual node in the dynamic digital mapping structure based on the retrieved dynamic mapping identifier code; The water level parameter is converted into a first numerical stream that matches the data format of the water level mapping value of the target virtual node, and the first numerical stream is written into the water level mapping storage area of ​​the target virtual node to overwrite the original water level mapping value in the storage area. The water quality parameters are converted into a second numerical stream that matches the data format of the water quality mapping value of the target virtual node, and the second numerical stream is written into the water quality mapping storage area of ​​the target virtual node to overwrite the original water quality mapping value in the storage area. After updating the water level mapping value and water quality mapping value, a write success feedback signal is generated and sent to the sensing node; If no data packet is received from a certain sensor node within a preset time window, a retransmission request is sent to the gateway device corresponding to that sensor node. The retransmission request includes the device code of the sensor node and the timestamp information of the missing data.

4. The intelligent scheduling method for cascade water resources in mountainous areas based on dynamic digital mapping according to claim 3, characterized in that, S4 includes: In the dynamic digital mapping structure, all virtual nodes are traversed in ascending order of hierarchical number. For each virtual node currently traversed, the minimum ecological water level threshold and the maximum flood control limit water level threshold are read from the storage safety threshold storage area of ​​the virtual node, and the water quality safety threshold is read from the pollution safety threshold storage area of ​​the virtual node. The minimum ecological water level threshold and the maximum flood control limit water level threshold read are used to form the storage safety threshold range. It is then determined whether the water level mapping value of the virtual node is lower than the minimum ecological water level threshold or higher than the maximum flood control limit water level threshold. If so, the virtual node is determined to be in a storage abnormal state. A storage abnormality identifier is written into the status identifier field of the virtual node. The storage abnormality identifier includes an abnormality type code and an abnormality degree code. The abnormality degree code is determined based on the difference between the water level mapping value and the corresponding threshold. Determine whether the water quality mapping value of the virtual node is higher than the water quality safety threshold. If so, determine that the virtual node is in an abnormal pollution state. Write an abnormal pollution identifier into the status identifier field of the virtual node. The abnormal pollution identifier includes a pollutant code and a pollution degree code. The pollutant code is determined according to the specific pollutant exceeding the standard in the water quality parameters. The pollution degree code is determined according to the ratio range of the water quality mapping value and the water quality safety threshold. When the same virtual node is simultaneously marked with both storage anomaly and pollution anomaly, a composite anomaly identifier is written into the status identifier field of the virtual node. The composite anomaly identifier is a concatenation of the storage anomaly identifier and the pollution anomaly identifier.

5. The intelligent scheduling method for cascade water resources in mountainous areas based on dynamic digital mapping according to claim 4, characterized in that, S5 includes: Obtain all abnormal virtual nodes marked with storage anomaly identifier or pollution anomaly identifier, assign a traversal flag bit to each abnormal virtual node and initialize the traversal flag bit to the untraversed state; Select one of the abnormal virtual nodes as the current search node, and recursively search upstream along the virtual channel in the dynamic digital mapping structure. When an upstream node is found, check whether the upstream node exists in the abnormal virtual nodes. If it exists and the level number of the upstream node is less than the level number of the current search node, mark the upstream node as the new current search node and continue searching upstream until no upstream node with a smaller level number that exists in the abnormal virtual nodes can be found. Record the node that can no longer be searched upstream as a candidate starting node, and update the traversal flag of all abnormal virtual nodes passed in this search process to the traversed state. Repeat until all abnormal virtual nodes are in the traversal flag state, and obtain at least one candidate starting node. When there is only one candidate starting node, the candidate starting node is determined as the starting intervention node. When there are multiple candidate starting nodes, the comprehensive value of the abnormal urgency of each candidate starting node is calculated, and the candidate starting node with the largest comprehensive value of the abnormal urgency is determined as the starting intervention node. The calculation process of the comprehensive value of the abnormal urgency is as follows: For each candidate starting node, obtain the first deviation difference between the water level mapping value of the candidate starting node and the minimum ecological water level threshold, and the second deviation difference between the water level mapping value of the candidate starting node and the maximum flood control limit water level threshold. Obtain the third deviation difference between the water quality mapping value of the candidate starting node and the water quality safety threshold. Take the maximum value among the first deviation difference, the second deviation difference, and the third deviation difference as the benchmark deviation value. According to the abnormal identification type marked on the candidate starting node, query the corresponding weight coefficient from the preset urgency weight table. Multiply the benchmark deviation value by the weight coefficient to obtain the comprehensive value of the abnormal urgency of the candidate starting node.

6. The intelligent scheduling method for cascade water resources in mountainous areas based on dynamic digital mapping according to claim 5, characterized in that, The S5 also includes: In the dynamic digital mapping structure, the first downstream neighboring node of the starting intervention node is obtained, and both the starting intervention node and the first downstream neighboring node are added to the set of intervention-affected nodes. Starting from the first downstream adjacent node, continue traversing downstream. When traversing to a downstream node, determine whether the downstream node has a branch channel. If it has a branch channel, add all nodes on the branch channel to the set of nodes affected by the intervention. During the traversal, for each node added to the set of nodes affected by the intervention, the upstream node identifier and downstream node identifier list are recorded to form a tree-structured influence path diagram; When the downstream node encountered is the last node of the cascade water source, the search for downstream nodes stops, and the construction of the set of nodes affected by the intervention is completed. All nodes in the set of nodes affected by the intervention are sorted in ascending order of hierarchical number to generate a sequence of nodes affected by the intervention. Read the water level mapping value, water quality mapping value, minimum ecological water level threshold, maximum flood control limit water level threshold, and water quality safety threshold for each node in the intervention impact node sequence from the mapping database, and package the read data into an intervention node data table.

7. The intelligent scheduling method for cascade water resources in mountainous areas based on dynamic digital mapping according to claim 6, characterized in that, The S6 includes: The starting intervention node is taken as the first-level processing object. Based on the first comparison relationship between the water level mapping value of the starting intervention node and the highest flood control limit water level threshold, and the second comparison relationship between the water quality mapping value of the starting intervention node and the water quality safety threshold, the first water release demand level of the starting intervention node is determined. Following the sequence of intervention impact nodes, each subsequent intervention impact node is taken as the current treatment node. For each current treatment node, the estimated water release flow of the upstream node is obtained. The minimum water level maintenance requirement of the current treatment node is determined based on the third comparison relationship between the water level mapping value of the current treatment node and the minimum ecological water level threshold. The water quality improvement requirement of the current treatment node is determined based on the fourth comparison relationship between the water quality mapping value of the current treatment node and the water quality safety threshold. Based on the minimum water level maintenance requirement, the water quality improvement requirement, and the estimated water release flow of the upstream node, the upstream water inflow capacity that the current treatment node can accept is calculated. Convert the acceptable upstream water inflow capacity into the expected water discharge flow rate of the current treatment node; After traversal, the expected water release flow rate corresponding to each node in the sequence of nodes affected by the intervention is obtained; based on the node identifier of each node and the corresponding expected water release flow rate, a scheduling instruction for the water source unit corresponding to each node is generated. The scheduling instruction includes an instruction type field, a target node identifier field, a water release flow rate field, and an execution time window field.

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