Water conservancy equipment data correlation mapping processing method and system based on geographic information of check district

Abstract

This invention relates to the field of water conservancy engineering informatization and geographic information data processing, specifically to a method and system for processing water conservancy equipment data association mapping based on polder geographic information. The method includes: a spatial topology construction step: acquiring geospatial data and constructing a spatial topology graph data structure containing a set of nodes and edges; a damping real-time update step: receiving control node operating status data and calculating and updating the connectivity damping attribute values ​​of the node data; a dynamic connectivity calculation step: responding to trigger commands by calling a graph traversal algorithm, calculating dynamic connectivity domains based on damping values, and generating a subset of effective influence domain data; and an association mapping rendering step: using the subset to retrieve logically connected target data objects and generating a visual interactive mapping view containing dynamic association information. This invention constructs a dynamic spatial topology graph that can respond to changes in the physical equipment status, achieving accurate association and visualization of water conservancy equipment data in geographic space.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering informatization and geographic information data processing, specifically to a method and system for data association and mapping of water conservancy equipment based on polder geographic information. Background Technology

[0002] In the current information management environment of water conservancy in polder areas, a large number of control nodes such as pumping stations and sluice gates, as well as sensing devices such as water level and video, are distributed in a complex geographical space, continuously generating heterogeneous geographical attribute data and real-time operation status data.

[0003] To monitor these facilities, existing solutions typically use static geographic information system layer overlays or fixed database associations to present the distribution of equipment and basic information. Although this method has basic positioning functions in static display scenarios, it fails to effectively integrate hydraulic logic and spatial topology, resulting in a disconnect between data presentation and physical flow. Since the connectivity of the polder river network changes dynamically with the opening and closing of control nodes, relying solely on static maps cannot represent the actual logical path of water flow and topological passage costs in real time.

[0004] This static and rigid data association pattern results in a lack of dynamic mapping between control commands and perception feedback. The system cannot automatically filter out video streams or sensor data within the effective influence domain based on the current connectivity damping properties, causing operators to face information redundancy or blind spots in supervision, and making it difficult to intuitively obtain effective data objects related to the current operation logic. At the same time, the lack of a pre-drill verification mechanism based on dynamic topology makes it difficult to effectively identify non-physical dead loop paths or node load overflow risks before the command is issued.

[0005] Therefore, how to construct a dynamic spatial topology data structure that can respond to changes in physical state in order to achieve real-time and accurate mapping and logical verification between control nodes and associated data objects has become an urgent technical problem to be solved. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method and system for data association and mapping of water conservancy equipment based on polder area geographic information. Specifically, the technical solution of this invention is as follows:

[0007] Obtain geospatial data of the target polder area, and construct a spatial topology map data structure that maps the geospatial data in the computer memory. The spatial topology map data structure includes an edge data set representing the transmission channel and a node data set representing the control node.

[0008] The system receives the operating status data of the control node in real time through the data interface, and uses the processor to calculate and update the connectivity damping attribute value of the corresponding node data in the spatial topology graph data structure based on the operating status data. The connectivity damping attribute value is configured as a numerical weight to quantify the topology passage cost.

[0009] In response to a trigger command for a target control node, a graph traversal algorithm is invoked to dynamically calculate the connected component based on the preset logical state of the trigger command and the current connectivity damping attribute value of the entire graph.

[0010] Based on the results of the dynamic connectivity calculation, an effective influence domain data subset centered on the target control node is generated in memory. The effective influence domain data subset includes nodes and edges whose topological reachability satisfies preset conditions.

[0011] Using the effective influence domain data subset as search criteria, target data objects in a logically connected state are retrieved and filtered from a pre-set associated data object library;

[0012] The rendering engine is invoked to generate a visual interactive mapping view containing dynamic association information based on the target data object and output it to the display terminal.

[0013] Preferably, the graph traversal algorithm is invoked to perform dynamic connectivity calculation based on the preset logical state of the triggering instruction and the current connectivity damping attribute value of the entire graph, including:

[0014] Set the target control node as the starting pointer for the topology graph search;

[0015] Read the connectivity damping attribute values ​​of each node in the current spatial topology graph data structure and perform breadth-first search (BFS) logical operation;

[0016] Traverse and calculate all downstream node data and associated edge data that can establish a logical path with the starting pointer under the current topological weight state;

[0017] All downstream node data and associated edge data calculated through traversal are marked as a subset of reachable topology data.

[0018] Preferably, the step of using the effective influence domain data subset as retrieval conditions to retrieve and filter target data objects in a logically connected state from a pre-set associated data object library includes:

[0019] Parse the spatial index coordinates of the metadata of each sensor data stream in the associated data object library;

[0020] Compare the spatial inclusion relationship between the spatial index coordinates and the topological path geometric range covered by the effective influence domain data subset;

[0021] If the comparison result shows that the spatial index coordinates fall within the geometric range of the topological path, then the index of the sensor data stream is retained in memory as the target data object;

[0022] If the comparison result shows that the spatial index coordinates do not fall within the geometric range of the topological path, a masking command is generated to hide the sensor data stream in the current interactive mapping view.

[0023] Preferably, the step of invoking the rendering engine to generate a visual interactive mapping view containing dynamic association information based on the target data object and outputting it to the display terminal includes:

[0024] Calculate the frustum projection range of the video stream data contained in the target data object in the virtual 3D space;

[0025] Map the projection range of the view frustum onto the corresponding edge element of the spatial topology graph data structure;

[0026] When it is detected that the corresponding edge element belongs to the subset of the effective influence domain data, a highlight rendering instruction is generated to display the video stream data in the interactive mapping view;

[0027] When the connectivity damping attribute value of the corresponding edge element associated node is detected to be greater than the preset blocking threshold, the association binding between the video stream data and the current view level is released.

[0028] Preferred options also include:

[0029] Based on the triggering command, the closed-loop logic state of the spatial topology data structure at the next time step is pre-simulated in the simulation environment;

[0030] Execute a logic verification program to detect whether there is a dead loop data flow path in the closed loop logic state, and determine whether the node load value is greater than the preset load limit value.

[0031] If the existence of the infinite loop data stream path is detected or the node load value is greater than the preset load limit value, an abnormal interruption signal is generated, and a warning mark layer and correction operation prompt information are rendered at the corresponding coordinate position in the interactive mapping view.

[0032] A data association and mapping processing system for water conservancy equipment based on polder area geographic information includes:

[0033] The topology modeling module is used to acquire geospatial data of the target polder area and construct a spatial topology map data structure that maps the geospatial data in the computer memory. The spatial topology map data structure includes an edge data set representing the transmission channel and a node data set representing the control node.

[0034] The data update module is used to receive the running status data of the control node in real time through the data interface, and use the processor to calculate and update the connectivity damping attribute value of the corresponding node data in the spatial topology graph data structure based on the running status data. The connectivity damping attribute value is configured as a numerical weight to quantify the topology passage cost.

[0035] The calculation engine module is used to respond to the trigger command for the target control node, call the graph traversal algorithm, perform dynamic connectivity domain calculation based on the preset logical state of the trigger command and the connectivity damping attribute value of the current full graph, and generate an effective influence domain data subset centered on the target control node based on the calculation results. The effective influence domain data subset includes nodes and edges whose topological reachability meets the preset conditions.

[0036] The dynamic mapping rendering module is used to retrieve and filter target data objects in a logically connected state from a preset associated data object library using the effective influence domain data subset as retrieval conditions, and call the rendering engine to generate a visual interactive mapping view containing dynamic association information based on the target data objects and output it to the display terminal.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. This invention constructs a dynamic spatial topology map data structure that responds to changes in the state of physical equipment, deeply integrating the discretization of geographic space with the real-time operating status of water conservancy facilities, thereby achieving real-time quantification of water flow transmission damping. By combining node opening damping with the frictional resistance weight derived from the river channel geometric attributes, it can accurately calculate the dynamic connectivity domain in complex river networks, effectively solving the problem that traditional static mapping cannot reflect the actual impact range of water level changes and gate actions on water flow. This ensures that the data objects presented in the visualization view strictly correspond to the physical hydraulic connectivity area, thus achieving dynamic consistency between geographic information and hydraulic logic.

[0039] 2. This invention, through virtual state injection technology and the construction of logical shadow graphs, enables the pre-simulation of the state of the water conservancy system at the next moment before the command is issued. It can predict the damping value based on control parameters and redirect the mapping relationship. By introducing a path cumulative damping calculation model that includes Manning roughness and flow velocity gravity compensation, the dimensionless nodal damping and the river channel geometric impedance with physical dimensions are unified. This allows for the comprehensive quantification of energy loss of water flow during transmission in the graph traversal algorithm, avoiding path search errors caused by simply relying on nodal states. It significantly improves the physical authenticity of the association mapping and ensures the accuracy of path calculation under complex conditions of plain tidal river networks.

[0040] 3. This invention constructs a geometric coverage range that precisely matches the current hydraulic topology path by parsing the spatial index coordinates of sensor metadata and combining them with a dynamic buffering algorithm, thus realizing intelligent filtering of massive monitoring data. By introducing Boolean union operations of device positioning drift tolerance and river width, and performing containment tests of spatial points and polygons under a unified projected coordinate system, it can automatically shield sensor interference in logically blocked areas or non-connected tributaries, effectively solving the contradiction between information silos and global blind spots that are common in water conservancy information systems. This allows operators to focus on key data within the effective influence domain, improving the efficiency and accuracy of human-computer interaction.

[0041] 4. This invention establishes a depth mapping mechanism between video stream data and virtual 3D geographic space by dynamically intersecting the projection range of the view frustum with the river centerline elements. It also utilizes connectivity damping properties to control the binding state of the video and view in real time, preventing videos that obstruct the river from misleading the operator. By performing a depth-first search with hydraulic constraints and node flow balance verification in a simulation environment, it can identify dead-loop data stream paths and node load overflow risks in advance based on the physical laws of gravity flow. It promptly generates abnormal interruption signals and warning markers in the interactive view, thereby ensuring the safety of water conservancy dispatch instructions and the reliability of logical closed loops, and avoiding potential hazards caused by non-physical flow states to actual engineering projects. Attached Figure Description

[0042] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0043] Figure 1 This is a flowchart of the method of the present invention;

[0044] Figure 2 This is a structural diagram of the system of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0046] Example 1:

[0047] Please see Figure 1 A data association and mapping processing method for water conservancy equipment based on polder area geographic information includes:

[0048] Obtain geospatial data of the target polder area, and construct a spatial topology map data structure that maps the geospatial data in the computer memory. The spatial topology map data structure includes an edge data set representing the transmission channel and a node data set representing the control node.

[0049] The system receives the operating status data of the control node in real time through the data interface, and uses the processor to calculate and update the connectivity damping attribute value of the corresponding node data in the spatial topology graph data structure based on the operating status data. The connectivity damping attribute value is configured as a numerical weight to quantify the topology passage cost.

[0050] In response to a trigger command for a target control node, a graph traversal algorithm is invoked to dynamically calculate the connected component based on the preset logical state of the trigger command and the current connectivity damping attribute value of the entire graph.

[0051] Based on the results of the dynamic connectivity calculation, an effective influence domain data subset centered on the target control node is generated in memory. The effective influence domain data subset includes nodes and edges whose topological reachability satisfies preset conditions.

[0052] Using the effective influence domain data subset as search criteria, target data objects in a logically connected state are retrieved and filtered from a pre-set associated data object library;

[0053] The rendering engine is invoked to generate a visual interactive mapping view containing dynamic association information based on the target data object and output it to the display terminal.

[0054] This embodiment details the specific execution logic of the data association mapping processing method for water conservancy equipment based on polder area geographic information. The core of this method lies in constructing a dynamic spatial topology map that can respond to changes in the physical equipment status. The system executes the steps of constructing the spatial topology map data structure, aiming to discretize the continuous geographic space into a computer-processable logical graph. In this process, the system obtains the geographic spatial data of the target polder area through a GIS interface and instantiates the structure as a directed graph in the computer memory. ;in, A unique index identifier for a node in a topology graph; a set of node data. Each node object in Defined as a structure containing both static and dynamic properties: static properties include spatial coordinates. Riverbed elevation Design current capacity and the design head of the pumping station For ordinary river nodes, this value is... For pump station nodes, this value is the value specified on the equipment nameplate.

[0055] Dynamic attributes include real-time water level Opening degree and connectivity damping Edge data set The transmission channel between mapping nodes, each edge Represents the node arrive The water flow path, and the edge data structure explicitly stores the river channel design width obtained from the GIS layer. River length Geometric properties and spatial vector coordinate sequence describing the trajectory of the river centerline In this step, the directionality initialization of the edge data is based on automatic resolution using the digital elevation model: the processor reads the nodes. and Riverbed elevation data ,like ,in, If a preset plains flow threshold is set, for example, 0.1 meters, then a one-way edge is constructed. Otherwise, construct bidirectional edges to simulate the potential reversible flow characteristics of plain tidal river networks;

[0056] The system performs real-time calculation and update of the connectivity damping attribute value. The system uses logical parameters designed to quantify the ease with which water flows through a control node. Through data interfaces such as OPCUA or ModbusTCP, the system receives real-time operational status data from the control nodes. The processor parses the data packets to extract real-time water level and opening values, and updates the corresponding nodes accordingly. of and The field is updated with the connectivity damping property value according to the following logic. :

[0057]

[0058] in, : Data acquired in real time by the PLC, with the physical meaning of the first The real-time opening normalized value of each control node; The value is derived from hydraulic experience and is specifically set as the critical cavitation opening ratio of the control gate, usually set as follows: ; : Derived from system preset, its physical meaning is a tiny constant to prevent the denominator from being zero; Derived from the definition of computer systems, its physical meaning is positive infinity for double-precision floating-point numbers as defined by the IEEE 754 standard;

[0059] In response to a trigger command targeting the control node, the system invokes a graph traversal algorithm; based on the pre-set logical state of the trigger command and the current state of other nodes in the entire graph... The value is used to calculate the dynamic connectivity component; the result of this calculation generates a subset of data representing the effective influence domain. This subset contains only nodes and edges that, under the current hydraulic conditions, are physically connected to the target node; the system utilizes As search criteria, target data objects are filtered from a pre-set library of related data objects, and the rendering engine is invoked to generate a visual interactive mapping view.

[0060] Example 2:

[0061] The graph traversal algorithm is invoked to dynamically calculate the connected components based on the preset logical state of the triggering instruction and the current connectivity damping attribute value of the entire graph, including:

[0062] Set the target control node as the starting pointer for the topology graph search;

[0063] Read the connectivity damping attribute values ​​of each node in the current spatial topology graph data structure and perform breadth-first search (BFS) logical operation;

[0064] Traverse and calculate all downstream node data and associated edge data that can establish a logical path with the starting pointer under the current topological weight state;

[0065] All downstream node data and associated edge data calculated through traversal are marked as a subset of reachable topology data.

[0066] This embodiment further specifies the calculation process of dynamic connected domains, focusing on how to map the preset logical state of the triggering command to the graph traversal algorithm, and solving the problem that simply relying on node damping cannot reflect the resistance along the river.

[0067] The system performs the virtual state injection step; setting the start pointer Previously, the processor parsed the control parameters contained in the trigger instruction, such as the target activation degree. Using the formula described in Example 1, the predicted damping value of the target node after instruction execution is calculated. ; Create a temporary state lookup table In terms of specific programming implementation, a hash dictionary structure is used, such as std::unordered_map in C++ or Dict in Python, with node ID as the key and damping value as the value, mapping the damping value of the target node to a hash value. Temporary redirection to That is, in subsequent queries, the hash table is retrieved first. If a key-value match is found, the predicted value stored in the table is returned. Otherwise, the global real-time data is read back to construct a logical shadow graph that reflects the state at the next moment.

[0068] The processor performs a breadth-first search operation based on this logical shadow graph; the queue is initialized. and access tag set During the traversal, in order to fully quantify the transmission cost of water flow in the complex river network, the distance from the starting point to the current node is calculated. Path cumulative damping :

[0069]

[0070] in, : Represents the ordered set of indices of all nodes traversed on the current search path; : Represents the set of all edge objects traversed on the current search path; function If node Returns to the target object that triggered the instruction. Otherwise, return the real-time monitoring value. ;function The aim is to calculate the topological weight of frictional hydraulic resistance, i.e., the normalized surrogate value of frictional head loss, using the geographical attributes of edge data; the specific logic is to read the edges. River length With the design width of the river channel The data is derived from the spatial topology graph data structure described in Example 1, and dimensional correction logic is applied to perform calculations.

[0071]

[0072] Regarding the clarity of the physical meaning and derivation process of this formula: In this embodiment... The physical essence of this is normalized friction loss, used to characterize the obstruction weight of river geometry on water flow during topological graph traversal; its derivation logic is based on Manning's formula. , The hydraulic gradient is a deformation of ; given that this invention is applied to plain polder areas, where river channels are mostly artificially dredged trapezoidal or rectangular regular cross-sections with relatively stable width-to-depth ratios, an assumption about the polder area river morphology is introduced, namely, within the allowable error range of engineering, the hydraulic radius ... With the width of the river There is a statistically significant linear mapping relationship. , This is a shape correction factor, typically taken as 0.75-0.85 for a typical trapezoidal cross-section; substituting this relationship into the head loss formula... Then, geometric variables are extracted to obtain core terms related to geometric dimensions. An index of 1.33 is... The decimal approximation;

[0073] Regarding parameters The accuracy of the definition and dimensional balance: to ensure the accuracy of the calculation results Ability to interact with dimensionless nodal damping Direct algebraic superposition; parameters are explicitly defined in this embodiment. The dimensionless hydraulic-topology mapping synthesis coefficients are constructed as follows: ;in, Manning roughness, in units of ; It is the flow velocity-gravity compensation constant, and its physical essence is the acceleration due to gravity. Values ;because The dimensions are ,and After multiplying, we get , making have The dimensions of the composite quantity; due to the geometric terms in the formula The dimensions are Multiply the two Ensure that the output results are verified It is a dimensionless value; physically, it represents the relative head loss share corresponding to a unit geometric resistance, thus solving the technical problem that different physical quantities cannot be superimposed in the same topological weight.

[0074] The logic for determining path reachability is as follows: A maximum cumulative damping threshold is introduced. For example, it can be set to 15.0; this threshold is obtained through statistical analysis of historical hydraulic data of the polder area, specifically by calculating the total average head loss from the main intake to the farthest control point during the dry season, and then raising it by 20% as the maximum resistance boundary; the damping value is determined in response to the detection of any node in the path. or If the resistance is too great and the potential energy is exhausted, the search for that path branch should be immediately cut off.

[0075] Ultimately, all will be satisfied. The nodes and associated edges are added to the reachable topology data subset. This embodiment integrates node opening damping and river geometric resistance to ensure that the connected domain calculation results can accurately reflect the hydraulic influence range under geographic constraints.

[0076] Example 3:

[0077] Using the effective influence domain data subset as search criteria, target data objects in a logically connected state are retrieved and filtered from a pre-set associated data object library, including:

[0078] Parse the spatial index coordinates of the metadata of each sensor data stream in the associated data object library;

[0079] Compare the spatial inclusion relationship between the spatial index coordinates and the topological path geometric range covered by the effective influence domain data subset;

[0080] If the comparison result shows that the spatial index coordinates fall within the geometric range of the topological path, then the index of the sensor data stream is retained in memory as the target data object;

[0081] If the comparison result shows that the spatial index coordinates do not fall within the geometric range of the topological path, a masking command is generated to hide the sensor data stream in the current interactive mapping view.

[0082] This embodiment details the specific mechanism for filtering sensor data using a subset of effective influence domain data, namely, the matching logic between spatial indexes and topological paths. The system executes the spatial index coordinate parsing step, as the associated data object library stores metadata for various sensors, such as water level gauges and flow meters. The system parses the spatial index coordinates of the metadata of each sensor's data stream. Typically latitude and longitude To ensure geometric accuracy of the comparison and eliminate coordinate system differences between different data sources, the system invokes a projection transformation service to convert the coordinates... The coordinates are uniformly converted to a planar projective coordinate system consistent with the topology graph data structure, such as CGCS2000 or UTM projection, to obtain standardized coordinates. ;

[0083] The system execution space includes a relation comparison step; extracting the topological path geometry covered by the effective influence domain data subset. This is a wide polygon buffer generated by connected river channels; to ensure the accuracy of the comparison and to resolve the dimensional differences between latitude and longitude and metric units, the system employs a dynamic buffering algorithm. The specific logic is as follows: traverse each edge in the effective influence domain. Read the river channel design width from its GIS attributes. Meters, and introduce a preset equipment positioning drift tolerance. For example, it can be set to 15 meters; this value is determined based on the product of the nominal accuracy CEP value of the sensor's GPS module and the multipath effect coefficient of the field environment, to cover more than 95% of positioning drift scenarios; when the edge After projecting the coordinates onto the plane coordinate system, set the buffer radius parameter. The geographic information processing engine is invoked to generate corresponding polygonal regions. Finally, a Boolean union operation is performed on all polygonal regions with connected edges to obtain the result. Based on this, the system performs an inclusion test between spatial points and polygons:

[0084]

[0085] in, : Derived from the comparison logic output, its physical meaning is the display status flag bit; : Planar coordinates derived from sensor metadata after projection transformation; The buffer zone is derived from topology calculation and dynamic buffering algorithm, and its physical meaning is the geometric coverage area of ​​the current water flow;

[0086] Execute dynamic masking and retention logic; in response to The system retains the index of the sensor data stream in memory as the target data object and renders it normally on the UI; in response to The system generates a blocking command, which will force the sensor's icon transparency to be set to 0 or remove it from the rendering list, even if the sensor is within the map's field of view.

[0087] This embodiment effectively solves the contradiction between information silos and global blind spots by dynamically comparing the geometric range of the topological path with the spatial index coordinates, ensuring that the operator focuses only on the data of the actual connected area of ​​the water flow.

[0088] Example 4:

[0089] The rendering engine is invoked to generate a visual interactive mapping view containing dynamically related information based on the target data object and output it to the display terminal, including:

[0090] Calculate the frustum projection range of the video stream data contained in the target data object in the virtual 3D space;

[0091] Map the projection range of the view frustum onto the corresponding edge element of the spatial topology graph data structure;

[0092] When it is detected that the corresponding edge element belongs to the subset of the effective influence domain data, a highlight rendering instruction is generated to display the video stream data in the interactive mapping view;

[0093] When the connectivity damping attribute value of the corresponding edge element associated node is detected to be greater than the preset blocking threshold, the association binding between the video stream data and the current view level is released.

[0094] This embodiment involves the deep fusion of video stream data and 3D geospatial data, particularly the dynamic mapping mechanism of the view frustum. It focuses on solving the precise intersection calculation between the view frustum and linear river features. The system executes the view frustum projection range calculation step; the processor acquires the real-time pose matrix of the camera and calculates the view matrix. With projection matrix And construct the view-projection composite matrix. ;based on Extracting the components of the visual cone The six cutting planes are left, right, bottom, top, near, and far. Among them, the first... a plane equation coefficients pass The linear combination of row vectors is obtained, for example, the coefficients in the left plane are... The right plane is And so on; specifically, let's assume... The elements in are Then, for the left plane, the formula for calculating the coefficients of the equation is: ;

[0095] To ensure the explicitness of the algorithm implementation, the coefficients of each of the other clipping planes... The calculation logic is as follows: the Right coefficient for the right plane is... The bottom coefficient of the lower plane is The top coefficient of the upper plane is The near coefficient of the near plane is The far-plane Far coefficient is ;in, represent The first of the matrix Row vectors, the calculated four-dimensional vectors correspond to the plane equations. Parameters;

[0096] The system performs edge element mapping and state detection steps; it traverses the edge elements in the spatial topology graph. Read the associated three-dimensional coordinate sequence of the river centerline. For each line segment Perform a view frustum intersection test: calculate the distances from the endpoints of the line segments to the six planes. Directed distance If there exists any plane Make and If the line segment passes through the inner side of all planes, then it is determined that the line segment is outside the visual cone; otherwise, if the line segment passes through the inner side of all planes, then it is determined that the line segment is outside the visual cone. With the cone There is a spatial mapping relationship;

[0097] Based on this, the system detects edges in real time. The associated connectivity state; in response to detection If the data belongs to a subset of the effective influence domain, meaning it is connected in the current hydraulic topology, the system generates a highlight command, rendering the river segment as a highlight color in the interactive view and associating it with the video window; in response to detection... Connectivity damping property value of the upstream controlled node The status is marked as blocked, that is The system automatically unbinds the video stream from the current view to prevent misleading the operator.

[0098] Example 5:

[0099] Also includes:

[0100] Based on the triggering command, the closed-loop logic state of the spatial topology data structure at the next time step is pre-simulated in the simulation environment;

[0101] Execute a logic verification program to detect whether there is a dead loop data flow path in the closed loop logic state, and determine whether the node load value is greater than the preset load limit value.

[0102] If the existence of the infinite loop data stream path is detected or the node load value is greater than the preset load limit value, an abnormal interruption signal is generated, and a warning mark layer and correction operation prompt information are rendered at the corresponding coordinate position in the interactive mapping view.

[0103] This embodiment details the logic verification procedure based on the pre-simulation mechanism, focusing on identifying non-physical abnormal flow states; the system performs simulation environment initialization, constructs the logic state for the next time step; and replaces the physical parameters of the target nodes involved in the triggering command with the command set values. The remaining nodes maintain real-time values. ;

[0104] Two core verification algorithms are implemented: Infinite loop data flow path detection: a depth-first search strategy with hydraulic constraints is employed to avoid false topology reports; a global access flag set is defined. Recursive stack of the current path ; Traverse each node in the graph ,like Then the recursive function is called. Inside the function, join in and ; Traversal All outgoing edges The search continues only if the hydraulic recursion condition is met: that is, the downstream node. It is in a physically conductive state, that is And there exists an effective driving head, i.e. ,in, For example, the preset minimum driving head threshold. This is used to filter out spurious micro-pressure differential flows; in this simulation preview, to eliminate unknown prediction errors, it is explicitly specified that... and Directly mapped from the real-time water level collected by the sensor at the current moment. That is, it is assumed that the water level field remains quasi-static at the instant of execution of the single-step control command, thereby providing a definite input basis for the hydraulic recursion conditions;

[0105] like For pump stations that are connected to the system, the criterion is revised to: Under these strict physical constraints: if If a dead loop is found that violates the physical laws of gravity flow, i.e., a non-physical logical loop, then return... ;like If so, the recursive call is made; this logic effectively eliminates false alarms caused by the bidirectional edge structure in the plain river network and only captures real logical anomalies.

[0106] Node load overflow detection: Calculate node traffic balancing margin ;

[0107]

[0108] in, To design overcurrent capacity; to input flow The calculation logic is as follows: traverse the set of incoming edges. By using a modified hydraulic formula, square root calculations were added to conform to Torricelli's law:

[0109]

[0110] in, These are the upstream and current water levels, respectively. and This is a lookup table function based on openness; the specific lookup table data structure disclosed in this embodiment is as follows, illustrated in C++ Map structure: Key is the openness interval. Value contains a structure Interpolation calculation is ,in, The lower boundary value defined for the current lookup interval, in this example, is... This explicit linear interpolation parameter ensures the reproducibility of flow calculations;

[0111] Execute exception interrupt logic; in response to True or It generates an abnormal interrupt signal, intercepts physical control commands, and renders a warning marker layer at the corresponding coordinate position in the interactive mapping view.

[0112] Example 6:

[0113] Please see Figure 2 A data association and mapping processing system for water conservancy equipment based on polder area geographic information, including:

[0114] The topology modeling module is used to acquire geospatial data of the target polder area and construct a spatial topology map data structure that maps the geospatial data in the computer memory. The spatial topology map data structure includes an edge data set representing the transmission channel and a node data set representing the control node.

[0115] The data update module is used to receive the running status data of the control node in real time through the data interface, and use the processor to calculate and update the connectivity damping attribute value of the corresponding node data in the spatial topology graph data structure based on the running status data. The connectivity damping attribute value is configured as a numerical weight to quantify the topology passage cost.

[0116] The calculation engine module is used to respond to the trigger command for the target control node, call the graph traversal algorithm, perform dynamic connectivity domain calculation based on the preset logical state of the trigger command and the connectivity damping attribute value of the current full graph, and generate an effective influence domain data subset centered on the target control node based on the calculation results. The effective influence domain data subset includes nodes and edges whose topological reachability meets the preset conditions.

[0117] The dynamic mapping rendering module is used to retrieve and filter target data objects in a logically connected state from a preset associated data object library using the effective influence domain data subset as retrieval conditions, and call the rendering engine to generate a visual interactive mapping view containing dynamic association information based on the target data objects and output it to the display terminal.

[0118] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for processing water conservancy equipment data association mapping based on geographic information of a check district, characterized in that, include: Obtain geospatial data of the target polder area, and construct a spatial topology map data structure that maps the geospatial data in the computer memory. The spatial topology map data structure includes an edge data set representing the transmission channel and a node data set representing the control node. The system receives the operating status data of the control node in real time through the data interface, and uses the processor to calculate and update the connectivity damping attribute value of the corresponding node data in the spatial topology graph data structure based on the operating status data. The connectivity damping attribute value is configured as a numerical weight to quantify the topology passage cost. In response to a trigger command for a target control node, a graph traversal algorithm is invoked to dynamically calculate the connected component based on the preset logical state of the trigger command and the current connectivity damping attribute value of the entire graph. Based on the results of the dynamic connectivity calculation, an effective influence domain data subset centered on the target control node is generated in memory. The effective influence domain data subset includes nodes and edges whose topological reachability satisfies preset conditions. Using the effective influence domain data subset as search criteria, target data objects in a logically connected state are retrieved and filtered from a pre-set associated data object library; The rendering engine is invoked to generate a visual interactive mapping view containing dynamic association information based on the target data object and output it to the display terminal. The graph traversal algorithm, based on the preset logical state of the triggering instruction and the current connectivity damping attribute value of the entire graph, performs dynamic connectivity component calculation, including: Set the target control node as the starting pointer for the topology graph search; Read the connectivity damping attribute values ​​of each node in the current spatial topology graph data structure and perform a breadth-first search logical operation; Traverse and calculate all downstream node data and associated edge data that can establish a logical path with the starting pointer under the current topological weight state; All downstream node data and associated edge data calculated through traversal are marked as a subset of reachable topology data.

2. The method according to claim 1, wherein, The step of using the effective influence domain data subset as search criteria to retrieve and filter target data objects in a logically connected state from a pre-set associated data object library includes: Parse the spatial index coordinates of the metadata of each sensor data stream in the associated data object library; Compare the spatial inclusion relationship between the spatial index coordinates and the topological path geometric range covered by the effective influence domain data subset; If the comparison result shows that the spatial index coordinates fall within the geometric range of the topological path, then the index of the sensor data stream is retained in memory as the target data object; If the comparison result shows that the spatial index coordinates do not fall within the geometric range of the topological path, a masking command is generated to hide the sensor data stream in the current interactive mapping view.

3. The method for data association and mapping of water conservancy equipment based on polder geographic information according to claim 1, characterized in that, The process of calling the rendering engine to generate a visual interactive mapping view containing dynamic association information based on the target data object and outputting it to the display terminal includes: Calculate the frustum projection range of the video stream data contained in the target data object in the virtual 3D space; Map the projection range of the view frustum onto the corresponding edge element of the spatial topology graph data structure; When it is detected that the corresponding edge element belongs to the subset of the effective influence domain data, a highlight rendering instruction is generated to display the video stream data in the interactive mapping view; When the connectivity damping attribute value of the corresponding edge element associated node is detected to be greater than the preset blocking threshold, the association binding between the video stream data and the current view level is released. 4.The method of claim 1, wherein, Also includes: Based on the triggering command, the closed-loop logic state of the spatial topology data structure at the next time step is pre-simulated in the simulation environment; Execute a logic verification program to detect whether there is a dead loop data flow path in the closed loop logic state, and determine whether the node load value is greater than the preset load limit value. If the existence of the infinite loop data stream path is detected or the node load value is greater than the preset load limit value, an abnormal interruption signal is generated, and a warning mark layer and correction operation prompt information are rendered at the corresponding coordinate position in the interactive mapping view.

5. A water conservancy equipment data association mapping processing system based on geographic information of a check district, characterized in that, include: The topology modeling module is used to acquire geospatial data of the target polder area and construct a spatial topology map data structure that maps the geospatial data in the computer memory. The spatial topology map data structure includes an edge data set representing the transmission channel and a node data set representing the control node. The data update module is used to receive the running status data of the control node in real time through the data interface, and use the processor to calculate and update the connectivity damping attribute value of the corresponding node data in the spatial topology graph data structure based on the running status data. The connectivity damping attribute value is configured as a numerical weight to quantify the topology passage cost. The calculation engine module is used to respond to the trigger command for the target control node, call the graph traversal algorithm, perform dynamic connectivity domain calculation based on the preset logical state of the trigger command and the connectivity damping attribute value of the current full graph, and generate an effective influence domain data subset centered on the target control node based on the calculation results. The effective influence domain data subset includes nodes and edges whose topological reachability meets preset conditions. The dynamic mapping rendering module is used to retrieve and filter target data objects in a logically connected state from a preset associated data object library using the effective influence domain data subset as retrieval conditions, and call the rendering engine to generate a visual interactive mapping view containing dynamic association information based on the target data objects and output it to the display terminal.

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