Sluice group collaborative automatic control and monitoring system fusing BIM and real-time working conditions

Abstract

This invention relates to the field of water conservancy project scheduling and intelligent monitoring technology, specifically disclosing a sluice gate group collaborative automatic control monitoring system integrating BIM and real-time operating conditions. The system includes: locating water flow intersection areas by building a high-precision BIM hydraulic model; conducting deviation analysis between theoretical and measured hydraulic characteristics to identify deviation areas; quantifying dynamic hydraulic disturbances based on historical data of sluice gate group flow switching; calculating the comprehensive intensity of the disturbances; and inverting the critical disturbance trigger threshold for flow switching; performing refined grid modeling for stable deviation areas through multi-condition deviation stability analysis; establishing a condition-deviation mapping model for unstable deviation areas and correcting BIM prediction data; and finally, combining monitoring priorities and critical thresholds to achieve collaborative automatic control of the sluice gate group. This improves the simulation accuracy of water flow intersection areas, distinguishes between static deviations and dynamic disturbances, quantifies the disturbance propagation law, and delineates scheduling safety red lines.

Description

Technical Field

[0001] This invention relates to the field of water conservancy project scheduling and intelligent monitoring technology, specifically to a sluice gate group collaborative automatic control and monitoring system that integrates BIM and real-time operating conditions. Background Technology

[0002] In plain river networks, urban water systems, water diversion projects, and irrigation district water conservancy hubs, sluice gate groups undertake multiple functions such as flood control and drainage, water resource scheduling, ecological water replenishment, and water environment improvement, serving as core infrastructure for watershed water security. With the networking of water conservancy projects and the increasing complexity of scheduling conditions, the joint operation of multiple sluice gates has become the norm. Frequent occurrences of water flow convergence, diversion, and countercurrent phenomena lead to highly nonlinear, non-constant, and strongly disturbed hydraulic characteristics in areas where water flows intersect. During dynamic operations such as sluice gate group flow switching, gate opening adjustment, and pump station start-up and shutdown, dynamic hydraulic disturbances such as sudden rises and falls in local water levels, drastic changes in flow velocity, continuous generation of backflow vortices, and flow field instability can easily be induced. These disturbances can range from affecting scheduling accuracy and water resource utilization efficiency to causing bank erosion, hydraulic structure vibration, riverbed siltation, abnormal water exchange, and even localized flooding risks, directly threatening the operational safety of water conservancy projects and the flood control safety of the watershed.

[0003] Currently, BIM (Building Information Modeling) technology has been gradually applied to 3D modeling, hydraulic simulation, and scheduling visualization in water conservancy projects, providing an intuitive and efficient means for water flow simulation and spatial analysis. However, existing technologies have the following shortcomings:

[0004] 1. Traditional BIM hydraulic simulation often uses linear vector superposition to calculate the merging characteristics, which lacks an accurate characterization of complex hydraulic mechanisms such as turbulent mixing, momentum exchange, pressure gradient and vortex generation in the cross-flow area. It has not built a mechanism model that can reflect nonlinear interaction, resulting in significant deviations between theory and actual measurement, making it difficult to directly guide fine scheduling.

[0005] 2. Although the current industrial data acquisition system at water conservancy project sites has deployed a large number of water level and flow velocity sensors, the massive amounts of real-time data collected are mostly limited to simple storage and display. There is a lack of systematic industrial data analysis and big data mining capabilities. It is difficult to automatically extract the deviation evolution patterns and turbulence transmission characteristics from historical flow switching operations. Existing methods cannot effectively distinguish between the static inherent deviations in the water flow intersection area and the dynamic hydraulic turbulence caused by flow switching, which can easily lead to misjudgment and over-control. There is a lack of systematic calculation methods for the spatial zoning, intensity quantification, transmission velocity, and attenuation patterns of dynamic turbulence, making it difficult to locate the source, core area, and diffusion path.

[0006] 3. The scheduling of sluice gate groups relies heavily on experience, lacks a quantitative mapping relationship between flow switching operations and disturbance triggers, and lacks executable critical thresholds and safety red lines. Targeted and refined modeling of stable deviation areas has not been carried out, and a real-time correction mechanism has not been established for operating condition-related deviations. Monitoring deployment and scheduling control lack priority. Overall, a complete data-knowledge-control closed loop has not been formed, from industrial data acquisition, big data analysis, mechanism model-driven processes to process control execution. This results in a long-term disconnect between BIM simulation and the actual flow field, making it difficult to guarantee scheduling safety.

[0007] To address this, the present invention provides a collaborative automatic control and monitoring system for sluice gate groups that integrates BIM and real-time operating conditions. The system aims to systematically solve the above problems by introducing a hydraulic mechanism model, constructing a deviation knowledge graph, and establishing an operating condition-deviation mapping reasoning model. Summary of the Invention

[0008] The purpose of this invention is to provide a sluice gate group collaborative automatic control and monitoring system that integrates BIM and real-time operating conditions to solve the aforementioned background problems.

[0009] The objective of this invention can be achieved through the following technical solution: a sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions, comprising the following modules:

[0010] Deviation Analysis Module: By building a BIM hydraulic model, it simulates the water flow intersection area in the target water area, and performs deviation analysis on the theoretical hydraulic characteristics and actual hydraulic characteristics of the water flow intersection area to determine whether there is a deviation between the theoretical hydraulic characteristics and actual hydraulic characteristics of the water flow intersection area.

[0011] Turbulence determination module: If it exists, locate the deviation area and analyze the historical flow switching monitoring data of the sluice gate group to determine whether there is dynamic hydraulic turbulence in the deviation area;

[0012] Turbulence Analysis Module: If present, calculate the overall turbulence intensity of each dynamic hydraulic turbulence region, quantify the propagation velocity attenuation law, and calculate the critical turbulence trigger threshold for the flow switching of the sluice gate group;

[0013] Model correction module: Performs multi-condition deviation stability analysis on the deviation area to determine whether the deviation is stable. If stable, it performs refined modeling on the stable deviation area.

[0014] Control execution module: If unstable, determine whether the deviation changes with the operating conditions. If so, determine the monitoring priority based on the comprehensive turbulence intensity of each dynamic hydraulic turbulence zone, correct the BIM model prediction data by establishing an operating condition-deviation mapping model, and identify high deviation operating conditions by combining the critical turbulence trigger threshold.

[0015] Furthermore, the method for determining whether there is a deviation between the theoretical and actual hydraulic characteristics of the water flow intersection area is as follows:

[0016] Hydraulic characteristics include the velocity and direction of water flow;

[0017] Import high-precision topography, hydraulic facilities, and basic hydraulic parameters of the target water area to build a basic BIM hydraulic simulation model;

[0018] Input the water flow boundary conditions under different working conditions, namely the flow rate, velocity, and direction of a single water flow, and simulate the water flow process in the model. Identify and locate the water flow intersection area through the superposition of flow field vectors and the sudden change of velocity gradient.

[0019] Record the geographic coordinates, extent, and simulated hydraulic characteristics of a single water flow in the intersection area, and mark it as the water flow intersection area;

[0020] The simulated velocity and direction of the single flow in the water flow intersection area are extracted from the BIM simulation results, and the theoretical combined velocity and direction of the water flow intersection area are calculated by the vector linear superposition method.

[0021] The actual hydraulic characteristics of the water flow intersection area were measured on-site at the same location, at the same time, and under the same working conditions as the BIM model, and the actual hydraulic characteristics dataset was obtained.

[0022] Calculate the deviation between the theoretical hydraulic characteristics and the actual hydraulic characteristics at each monitoring point. If the deviation meets the requirements, then there is a deviation between the theoretical hydraulic characteristics and the actual hydraulic characteristics in the cross-flow area.

[0023] Furthermore, the method for determining whether dynamic hydraulic turbulence exists in the deviation region is as follows:

[0024] The deviation region is the area where there is a discrepancy between the theoretical and actual hydraulic characteristics of the water flow intersection area.

[0025] Extract historical data on the flow switching of the sluice gate group within the historical period from the dispatch and control system, including switching type, switching magnitude, switching rate, switching sequence, and corresponding operating conditions;

[0026] For each set of flow switching conditions, extract the time series data of hydraulic characteristics and dynamic changes in flow field morphology of the deviation area and its surroundings;

[0027] The time series was divided into three stages: before traffic switching, during switching, and after switching, and the differences between each stage and the static stable baseline value were compared.

[0028] Establish quantitative judgment criteria for dynamic hydraulic disturbances, with the core distinction between static inherent deviations and dynamic disturbances caused by flow switching. If any indicator exceeds the limit, dynamic hydraulic disturbances are judged to exist.

[0029] Furthermore, the quantification criteria for dynamic hydraulic disturbance are as follows:

[0030] The velocity change rate = the maximum change in velocity during flow switching / the static steady velocity. If the velocity change rate meets the requirements, then there is dynamic hydraulic turbulence.

[0031] The angle of sudden change in flow direction is the maximum deflection angle of the flow direction during flow switching. If the angle of sudden change in flow direction meets the requirements, dynamic hydraulic turbulence exists.

[0032] Abnormal flow field morphology: After the flow rate is switched, a new backflow / countercurrent zone is generated or the original deviation zone is expanded, which indicates dynamic hydraulic turbulence.

[0033] Hydraulic parameter recovery time: If the time required for the hydraulic parameters to recover to the static stable range after the flow rate switch meets the requirements, then dynamic hydraulic disturbance exists.

[0034] Furthermore, the calculation method for the overall disorder intensity is as follows:

[0035] The overall turbulence intensity is obtained by weighting and summing the water level rise / fall values, flow velocity change amplitude, and backflow duration at each monitoring node.

[0036] Furthermore, the quantification process of the propagation speed attenuation law is as follows:

[0037] From the results of the disordered region localization, the starting disordered node (the earliest disordered node) and subsequent disordered nodes are extracted and sorted in chronological order.

[0038] Based on the river topology in the BIM model, nodes that are not hydraulically conductive are removed, and only the node sequence along the main flow direction is retained;

[0039] The retained nodes are paired up to their upstream and downstream locations, with each pair of nodes corresponding to a sub-river segment;

[0040] The length of the river centerline for each pair of nodes is exported from the BIM model. The time of turbulence occurrence for each pair of nodes is extracted from the fused data and substituted into the formula to calculate the turbulence propagation speed of each sub-river segment.

[0041] The calculation formula is: the turbulence propagation speed of sub-segment ij = the straight-line distance between upstream node i and downstream node j / the time difference between the turbulence propagation from node i to node j, that is, the time when the turbulence occurs at node j - the time when the turbulence occurs at node i;

[0042] The turbulence attenuation coefficient of sub-river segment ij = the comprehensive turbulence intensity of downstream node j / the comprehensive turbulence intensity of upstream node i.

[0043] Furthermore, the critical disorder triggering threshold is calculated as follows:

[0044] Extract historical data from multiple traffic switching operations, including:

[0045] Independent variables: gate adjustment rate, single-step gate adjustment steps, total flow adjustment range;

[0046] Dependent variable: Whether it causes dynamic hydraulic disturbance;

[0047] A binary logistic regression model was constructed, with a critical condition of a probability of 90% or greater inducing severe disorder. The thresholds of each operational parameter were then deduced by substituting the values ​​into the regression model.

[0048] Extract historical disturbance data for each sub-river section, including water level rise / fall values, overall disturbance intensity, and whether it has caused widespread disturbance.

[0049] The water level rise / fall data are grouped by sub-river segments, and the 90th percentile is calculated. That is, in 90% of the turbulence events, the water level rise / fall value is less than or equal to the water level rise / fall value and there is no diffusion. The 90th percentile is the water level carrying capacity threshold of the sub-river segment.

[0050] Furthermore, the method for determining whether the deviation is stable is as follows:

[0051] Select multiple scheduling conditions that cover the actual operating range. The operating condition parameters include pump station flow and gate opening. Use orthogonal experimental design to combine multiple operating condition parameters to cover the entire operating condition range.

[0052] For each set of working conditions, linear superposition calculations are performed using the BIM model to obtain the theoretical flow rate for each set of working conditions.

[0053] Under the same operating conditions, the actual composite flow velocity in the intersection area is obtained through on-site monitoring;

[0054] Calculate the relative deviation of the flow velocity and the deviation of the flow direction angle for each set of working conditions;

[0055] Calculate the percentage of operating conditions where the deviation meets the requirements across all operating conditions, and compare it with the preset percentage. If the percentage is greater than or equal to the preset percentage, the deviation is considered to be generally significant.

[0056] Calculate the coefficient of variation for the deviation values ​​of all operating conditions. If the coefficient of variation meets the requirements, it means that the deviation fluctuates very little between different operating conditions and is stable; otherwise, it is unstable.

[0057] Furthermore, the method for determining the monitoring priority is as follows:

[0058] The deviation of each working condition is correlated with the scheduling parameters, and the Pearson correlation coefficient is calculated. If the Pearson correlation coefficient meets the requirements, the deviation changes with the working condition.

[0059] The overall turbulence intensity of each dynamic hydraulic turbulence region is obtained, and after normalization, they are arranged in descending order to obtain the monitoring priority of the dynamic hydraulic turbulence region.

[0060] Furthermore, the method for correcting the BIM model prediction data by establishing the working condition-deviation mapping model is as follows:

[0061] Select working condition characteristic parameters and core deviation indicators, and divide the dataset into training set and test set according to the proportion;

[0062] The random forest algorithm was selected to construct the mapping model, and the hyperparameters were optimized by the grid search method to minimize the model prediction error.

[0063] The BIM model takes real-time operating condition characteristic parameters as input, first outputs theoretical prediction values, then calls the mapping model to obtain deviation prediction values, and completes the correction according to the formula:

[0064] Corrected merging velocity: ;

[0065] Corrected merging direction: ;

[0066] Among them, E v E represents the flow rate predicted by the BIM model. θ The corrected data, which is the flow direction predicted by the BIM model, is the actual flow field prediction value.

[0067] The beneficial effects of this invention are as follows:

[0068] Accurately identify theoretical-measurement deviations in water flow intersection zones, distinguish between static inherent deviations and dynamic hydraulic turbulence, avoid overall analysis and misjudgments, and improve the targeting of scheduling;

[0069] To achieve precise zoning of disordered spaces, quantify the overall intensity, transmission speed, and attenuation patterns of disorder, and clearly locate the source, core impact area, and spread range, providing a basis for prevention and control;

[0070] Establish a quantitative relationship between the flow switching of sluice gate groups and the triggering of disturbances, invert the critical threshold and the carrying capacity of river sections, delineate the safety red line for scheduling, and suppress disturbances from the source;

[0071] By refining the model with a denser mesh in stable deviation areas and establishing a mapping model for real-time correction of deviations related to operating conditions, the accuracy of BIM simulation is greatly improved.

[0072] Based on the graded monitoring and control of disturbance intensity, high-risk areas can be managed frequently and low-risk areas can be monitored routinely, thereby improving resource utilization efficiency.

[0073] This forms a complete closed loop of modeling, deviation identification, disorder determination, propagation quantification, model correction, and automatic control, promoting the upgrading of sluice gate group scheduling from experience-driven to data-driven and prediction-driven. Attached Figure Description

[0074] The invention will now be further described with reference to the accompanying drawings.

[0075] Figure 1 This is a functional module diagram of the water gate group collaborative automatic control and monitoring system that integrates BIM and real-time operating conditions according to the present invention.

[0076] Figure 2 This is a logical judgment diagram used in this invention to determine whether there is a deviation between the theoretical hydraulic characteristics and the actual hydraulic characteristics of the water flow intersection area. Detailed Implementation

[0077] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0078] Example: Please refer to Figure 1 As shown, the sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions described in this invention specifically includes the following modules:

[0079] Deviation Analysis Module: By building a BIM hydraulic model, it simulates the water flow intersection area in the target water area, and performs deviation analysis on the theoretical hydraulic characteristics and actual hydraulic characteristics of the water flow intersection area to determine whether there is a deviation between the theoretical hydraulic characteristics and actual hydraulic characteristics of the water flow intersection area.

[0080] Please see Figure 2 As shown, the process of locating the water flow intersection area includes:

[0081] Import high-precision topography, hydraulic facilities, and basic hydraulic parameters of the target water area to build a basic BIM hydraulic simulation model;

[0082] Input the water flow boundary conditions under different working conditions, namely the flow rate, velocity, and direction of a single water flow, and simulate the water flow process in the model. Identify and locate the water flow intersection area through the superposition of flow field vectors and the sudden change of velocity gradient.

[0083] Record the geographic coordinates, extent, and simulated hydraulic characteristics of a single water flow in the intersection area, and mark it as the water flow intersection area;

[0084] The hydraulic characteristics include the flow velocity and direction of the water flow;

[0085] The simulated velocity and direction of the single flow in the water flow intersection area are extracted from the BIM simulation results. The theoretical velocity and theoretical confluence direction of the water flow intersection area are calculated using the vector linear superposition method.

[0086] For example, taking two streams of water as an example, multiple streams of water are superimposed in sequence:

[0087] Combined velocity component: , ;

[0088] Theoretical confluence velocity: ;

[0089] Theoretical convergence direction: ;

[0090] In the BIM model, the velocity and direction of the single water flow at each monitoring point are calculated separately. The velocity and direction of each single water flow are then substituted into the vector linear superposition formula. The BIM model automatically calculates the theoretical combined velocity and theoretical combined direction of each monitoring point and outputs the theoretical hydraulic characteristics of each monitoring point.

[0091] The actual hydraulic characteristics of the water flow intersection area were measured on-site at the same location, at the same time, and under the same working conditions as the BIM model, and the actual hydraulic characteristics dataset was obtained.

[0092] The deviation between the theoretical hydraulic characteristics and the actual hydraulic characteristics at each monitoring point is calculated and compared with the preset deviation. If the deviation is greater than the preset deviation, then there is a deviation.

[0093] It is understandable that if there is a deviation, the physical meaning is that linear superposition ignores the interaction between water flows, such as turbulent mixing, momentum exchange, pressure gradient changes, vortex generation, etc. In the actual confluence zone, energy dissipation (reduction in flow velocity) or directional deflection often occurs, resulting in the composite flow velocity not matching the linear superposition result.

[0094] It should be noted that the purpose of determining whether there is a deviation between the theoretical hydraulic characteristics and the actual hydraulic characteristics of the water flow intersection area is to: delineate the spatial objects of subsequent analysis (the deviation intersection area), avoid the waste of resources in the whole-domain analysis, provide a preliminary basis for the dynamic hydraulic disturbance judgment of the deviation area, terminate the process if there is no deviation, and directly use the BIM model to guide the scheduling, and proceed to the next step if there is a deviation.

[0095] Turbulence determination module: If it exists, locate the deviation area and analyze the historical flow switching monitoring data of the sluice gate group to determine whether there is dynamic hydraulic turbulence in the deviation area;

[0096] The deviation region refers to the area where there is a discrepancy between the theoretical hydraulic characteristics and the actual hydraulic characteristics of the water flow intersection region.

[0097] The process of determining whether dynamic hydraulic turbulence exists in the deviation region includes:

[0098] Historical data on the flow switching of the sluice gate group within the historical period are extracted from the dispatch and control system, including switching type (single gate / multiple gate linkage, water replenishment / water discharge), switching magnitude, switching rate, switching sequence, and corresponding operating conditions (basic flow / water level).

[0099] For each set of flow switching conditions, extract the time series data of hydraulic characteristics (flow velocity / flow direction / water level change curves over time) and dynamic change data of flow field morphology in the deviation area and its surroundings;

[0100] The time series was divided into three stages: before traffic switching, during switching, and after switching, and the differences between each stage and the static stable baseline value were compared.

[0101] Establish quantitative judgment criteria for dynamic hydraulic disturbances, focusing on distinguishing between static inherent deviations and dynamic disturbances caused by flow switching. Select the following indicators; if any indicator exceeds the specified value, dynamic hydraulic disturbances are determined to exist:

[0102] The velocity change rate = the maximum change in velocity during flow switching / the static stable velocity. If the velocity change rate is greater than or equal to the preset change rate, then there is dynamic hydraulic turbulence.

[0103] The flow direction change angle is the maximum deflection angle of the flow direction during flow switching. The flow direction change angle is greater than or equal to the preset change angle.

[0104] Abnormal flow field morphology: After flow rate switching, a new backflow / countercurrent zone is generated or the morphology of the original deviation zone expands by ≥50%;

[0105] Hydraulic parameter recovery time: After flow rate switching, the time for hydraulic parameters to recover to the static stable range is ≥1 hour (weak self-stabilizing ability).

[0106] It is understandable that the deviations are distinguished into inherent static deviations of the flow field and dynamic turbulence deviations caused by flow switching. By comparing the time-series monitoring data of historical flow switching with the static stability benchmark, the existence of dynamic turbulence is determined by quantitative indicators, so as to avoid misjudging static deviations as dynamic turbulence caused by scheduling.

[0107] It should be noted that the purpose of determining whether there is dynamic hydraulic turbulence in the deviation area is to: accurately locate the dynamic turbulence deviation area, clarify the core object of subsequent turbulence propagation analysis and threshold calculation, realize the classification of static / dynamic attributes of deviation, and define the direction for subsequent control strategies (static deviation focuses on model optimization, while dynamic turbulence focuses on turbulence propagation quantification and trigger threshold calculation).

[0108] Turbulence Analysis Module: If present, calculate the overall turbulence intensity of each dynamic hydraulic turbulence region, quantify the propagation velocity attenuation law, and calculate the critical turbulence trigger threshold for the flow switching of the sluice gate group;

[0109] The calculation process for the overall turbulence intensity of each dynamic hydraulic turbulence region is as follows:

[0110] By analyzing the occurrence time, intensity, and transmission characteristics of disturbances at spatial nodes, the initiation region, core influence region, and transmission and diffusion region of the disturbance can be precisely located.

[0111] Find the monitoring node where the earliest dynamic disorder occurs, and confirm the cause of the disorder by combining the operational characteristics. The area where this node is located is the disorder initiation zone.

[0112] Calculate the overall turbulence intensity of each node (water level rise / fall value + velocity change amplitude + backflow duration, weighted sum). The area where the nodes with the highest overall turbulence intensity are located and the turbulence duration is ≥30 minutes is the core impact area.

[0113] Arrange the order in which the disorder occurred at each node according to time, and draw the disorder transmission path in combination with the river topology. The area on the path other than the starting area and the core area is the transmission and diffusion area.

[0114] Nodes that show no disturbance or have very weak disturbance intensity throughout the entire process are identified and marked as steady-state zones. Their topographic / hydrological characteristics (such as wide river channel cross-section and weak connectivity with the main water system) are analyzed.

[0115] Output a dynamic disorder distribution map, clearly defining the specific location and extent of the initiation zone, core zone, and conduction zone, as well as the dominant disorder type in each zone;

[0116] For each disorder node, the overall disorder intensity I is calculated using a weighted method to eliminate the one-sidedness of a single indicator;

[0117] The quantification process of the propagation speed attenuation law includes:

[0118] The first point to clarify is the actual transmission path of the disturbance from the initiation region to the diffusion region, specifically:

[0119] From the results of localizing the disordered region, extract the initial disordered node (the earliest node to show disorder) and subsequent disordered nodes, and sort them in chronological order.

[0120] Based on the river topology (natural flow direction) in the BIM model, nodes that are not hydraulically conductive (such as tributary blind end nodes) are removed, and only the node sequence along the main flow direction is retained.

[0121] The retained nodes are paired up to their upstream and downstream locations, with each pair of nodes corresponding to a sub-river segment;

[0122] Secondly, it should be noted that calculating the turbulence propagation speed of each sub-river segment, quantifying the rate of turbulence transmission in different river sections, and identifying fast-propagating segments (which require focused early interception) are as follows:

[0123] The calculation formula is: the turbulence propagation speed of sub-segment ij = the straight-line distance between upstream node i and downstream node j / the time difference between the turbulence propagation from node i to node j, that is, the time when the turbulence occurs at node j - the time when the turbulence occurs at node i;

[0124] The implementation process is as follows:

[0125] The length of the river centerline for each pair of nodes is exported from the BIM model. The time of turbulence occurrence for each pair of nodes is extracted from the fused data and substituted into the formula to calculate the turbulence propagation speed of each sub-river segment.

[0126] Thirdly, it should be noted that calculating the propagation attenuation coefficient of each sub-river segment quantifies the degree of turbulence attenuation during the conduction process and identifies attenuation-free propagation segments. Specifically:

[0127] The turbulence attenuation coefficient of sub-river segment ij = the comprehensive turbulence intensity of downstream node j / the comprehensive turbulence intensity of upstream node i;

[0128] The calculation process for the critical disturbance trigger threshold of the sluice gate group flow switching operation is as follows:

[0129] The first point to clarify is the critical disturbance trigger threshold for traffic switching operations, specifically:

[0130] Extract historical data from multiple traffic switching operations, including:

[0131] Independent variables: gate adjustment rate, single-step gate adjustment steps, total flow adjustment range;

[0132] Dependent variable: Whether it causes dynamic hydraulic disturbance;

[0133] A binary logistic regression model was constructed, with a critical condition of a probability of 90% or greater inducing severe disorder. The thresholds of each operational parameter were then deduced by substituting the values ​​into the regression model.

[0134] Secondly, it should be noted that the turbulence carrying capacity threshold for each river section is calculated to determine the maximum turbulence intensity / water level change that the river can withstand. If this threshold is exceeded, the turbulence will spread. Specifically:

[0135] Extract historical disturbance data for each sub-river section, including water level rise / fall values, overall disturbance intensity, and whether it has caused widespread disturbance.

[0136] The water level rise / fall data are grouped by sub-river segments, and the 90th percentile is calculated. That is, in 90% of the turbulence events, the water level rise / fall value is less than or equal to the water level rise / fall value and there is no diffusion. This value is the water level carrying capacity threshold of the river segment.

[0137] The overall turbulence intensity is grouped by sub-river segment, and the 90th percentile is calculated, which is the intensity carrying capacity threshold of that river segment.

[0138] It is understandable that the logic of quantifying the propagation speed decay law and determining the critical turbulence triggering threshold is as follows: dynamic hydraulic turbulence is caused by the flow switching of the sluice gate group. Its propagation and intensity are affected by both the inherent characteristics of the flow field and the flow switching operation parameters. By quantifying the propagation decay law, the influence range of the turbulence is clarified. By calculating the critical threshold of the operation parameters through the coupled model, the turbulence triggering is transformed into a quantifiable scheduling constraint.

[0139] It should be noted that the purpose of quantifying the attenuation law of propagation speed and determining the critical disturbance trigger threshold is to: define a quantitative safety red line (critical disturbance trigger threshold) for scheduling operations to avoid dynamic disturbances caused by traffic switching; and provide a basis for early prevention and control of disturbances (such as predicting the scope of disturbance impact based on propagation speed and taking control measures in downstream areas in advance).

[0140] Model correction module: Performs multi-condition deviation stability analysis on the deviation area to determine whether the deviation is stable. If stable, it performs refined modeling on the stable deviation area.

[0141] Select multiple scheduling conditions that cover the actual operating range, for example:

[0142] Pump station flow rates: low (2m³ / s), medium (5m³ / s), and high (8m³ / s);

[0143] Gate opening: different opening combinations (e.g., 30%, 60%, 90% for Huangjinba Gate Station);

[0144] Orthogonal experimental design was adopted, combining multiple parameters to cover the entire operating condition range;

[0145] For each set of working conditions, linear superposition calculations are performed using the BIM model to obtain the theoretical flow rate for each set of working conditions.

[0146] Under the same operating conditions, the actual composite flow velocity in the intersection area is obtained through on-site monitoring;

[0147] Calculate the relative deviation of the flow velocity and the deviation of the flow direction angle for each set of working conditions;

[0148] Based on engineering requirements and sensor accuracy, a threshold is set, and the percentage of working conditions where the deviation exceeds the threshold is calculated. This percentage is then compared with a preset percentage. If the percentage is greater than or equal to the preset percentage, the deviation is considered to be generally significant.

[0149] Calculate the coefficient of variation for the deviation values ​​of all working conditions and compare it with the preset coefficient of variation. If the coefficient of variation is less than or equal to the preset coefficient of variation, it means that the deviation fluctuates very little between different working conditions and can be regarded as stable. Otherwise, it is unstable.

[0150] It should be noted that if the deviation is significant and stable, it indicates that there is a fixed nonlinear effect in the intersection area (such as permanent vortices caused by terrain). The area should be modeled in detail in the BIM model and listed as a high-risk area in the scheduling to avoid extreme working conditions that may induce strong nonlinearity.

[0151] If stable, the BIM model mesh in the stable deviation area will be refined from the conventional level to 2m×2m to improve spatial resolution and accurately restore local terrain features;

[0152] It is understandable that stability deviation is an inherent nonlinear characteristic of the flow field, and a purely linear BIM model cannot accurately simulate it. It is necessary to achieve refined modeling through mesh refinement, model optimization, and precise parameter calibration to make the model fit the actual flow field.

[0153] It should be noted that the purpose of refining the modeling of the stable deviation region is to: improve the BIM simulation accuracy of the stable deviation region, solve the problem of simulation-measurement disconnect, and provide accurate model prediction basis for scheduling; visualize and restore the nonlinear hydraulic characteristics of the stable deviation region, providing model support for engineering modifications (such as the deployment of diversion dams); and provide a high-precision sub-model for BIM simulation of coordinated scheduling of sluice gate groups, improving the accuracy of the whole-domain simulation.

[0154] Control execution module: If unstable, determine whether the deviation changes with the working conditions. If so, determine the monitoring priority based on the comprehensive intensity of the turbulence in each dynamic hydraulic turbulence area. Correct the BIM model prediction data by establishing a working condition-deviation mapping model, and identify high deviation working conditions by combining the critical turbulence trigger threshold.

[0155] The process of determining whether the deviation changes with operating conditions includes:

[0156] The deviation of each operating condition was correlated with the main scheduling parameters (such as pump station flow rate Q and gate opening G).

[0157] Calculate the Pearson correlation coefficient. If the Pearson correlation coefficient is greater than or equal to the preset correlation coefficient, it indicates that the deviation is significantly linearly correlated with the parameter, that is, the deviation changes with the operating conditions.

[0158] It should be noted that if the deviation changes with the operating conditions, it indicates that the nonlinear effect is closely related to the scheduling parameters. It is necessary to establish an operating condition-deviation mapping model (such as multinomial regression or neural network) to correct the model prediction in real time or as a feedforward compensation. At the same time, high deviation operating conditions can be actively avoided by optimizing the scheduling or incorporated into the control objectives.

[0159] The process of determining the monitoring priority includes:

[0160] The overall turbulence intensity of each dynamic hydraulic turbulence region is obtained, and after normalization, they are arranged in descending order to obtain the monitoring priority of the dynamic hydraulic turbulence region.

[0161] The construction process of the operating condition-deviation mapping model includes:

[0162] Select working condition characteristic parameters and core deviation indicators, and divide the dataset into training set and test set in a 7:3 ratio;

[0163] The random forest algorithm (strong anti-interference ability and suitable for small sample data in water conservancy) is preferred to build the mapping model. The hyperparameters (number of decision trees and learning rate) are optimized by grid search method to minimize the model prediction error RMSE.

[0164] The process of correcting the BIM model prediction data includes:

[0165] The BIM model takes real-time operating condition characteristic parameters as input, first outputs theoretical prediction values, then calls the mapping model to obtain deviation prediction values, and completes the correction according to the formula:

[0166] Corrected merging velocity: ;

[0167] Corrected merging direction: ;

[0168] Among them, E v E represents the flow rate predicted by the BIM model. θ The corrected data is the flow field prediction value that matches the actual flow direction, based on the flow direction predicted by the BIM model.

[0169] The process of identifying high-deviation operating conditions by combining critical disturbance triggering thresholds includes:

[0170] Using the condition-deviation mapping model, the deviation values ​​of all possible conditions are predicted. Conditions with deviation indices exceeding the threshold are marked as high-deviation conditions and included in the restricted / prohibited scheduling condition set.

[0171] Establish operating condition avoidance scheduling rules:

[0172] When scheduling, priority should be given to low deviation conditions (such as medium base flow rate + small flow angle).

[0173] If scheduling must be carried out under high deviation conditions, the flow switching amplitude / rate should be reduced in combination with the critical disturbance trigger threshold, and a small-amplitude, high-frequency adjustment method should be adopted.

[0174] When operating condition avoidance conflicts with water / replenishment demand, a multi-objective optimization algorithm is adopted to solve for the optimal scheduling scheme with the dual objectives of minimizing deviation and maximizing water demand satisfaction rate, so as to achieve a balance between the two.

[0175] The technical solution and advantages of this application are as follows: By building a BIM hydraulic model, the cross-flow area in the target water area is simulated and located. By analyzing the deviation between the theoretical and actual hydraulic characteristics of the cross-flow area, it is determined whether there is a deviation between the theoretical and actual hydraulic characteristics. If so, the deviation area is located, and the historical flow switching monitoring data of the sluice gate group is analyzed to determine whether there is dynamic hydraulic turbulence in the deviation area. If so, the comprehensive turbulence intensity of each dynamic hydraulic turbulence area is calculated, the propagation velocity attenuation law is quantified, and the critical turbulence trigger threshold for the flow switching of the sluice gate group is calculated. Multi-condition deviation stability analysis is performed on the deviation area to determine whether the deviation is stable. If stable, the stable deviation area is modeled in detail. If unstable, it is determined whether the deviation changes with the operating conditions. If so, the monitoring priority is determined according to the comprehensive turbulence intensity of each dynamic hydraulic turbulence area. The predicted data of the BIM model is corrected by establishing an operating condition-deviation mapping model, and high deviation operating conditions are identified by combining the critical turbulence trigger threshold. This application establishes a high-precision BIM hydraulic model to locate water flow intersection areas, conducts deviation analysis between theoretical and measured hydraulic characteristics, and identifies deviation areas. Based on historical data of sluice gate group flow switching, it quantifies and determines dynamic hydraulic turbulence, calculates the comprehensive intensity of turbulence, and inverts the critical turbulence trigger threshold for flow switching. Through multi-condition deviation stability analysis, it implements refined grid modeling for stable deviation areas, establishes a condition-deviation mapping model for unstable deviation areas, and corrects BIM prediction data. Finally, by combining monitoring priorities and critical thresholds, it achieves coordinated automatic control of the sluice gate group, improving the simulation accuracy of water flow intersection areas, distinguishing between static deviations and dynamic turbulence, quantifying turbulence propagation patterns, and delineating scheduling safety red lines.

[0176] The embodiments of the present invention have been described in detail above, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent changes and improvements made in accordance with the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions, characterized by: Includes the following modules: Deviation Analysis Module: By building a BIM hydraulic model, it simulates the water flow intersection area in the target water area, and performs deviation analysis on the theoretical hydraulic characteristics and actual hydraulic characteristics of the water flow intersection area to determine whether there is a deviation between the theoretical hydraulic characteristics and actual hydraulic characteristics of the water flow intersection area. Turbulence determination module: If it exists, locate the deviation area and analyze the historical flow switching monitoring data of the sluice gate group to determine whether there is dynamic hydraulic turbulence in the deviation area; Turbulence Analysis Module: If present, calculate the overall turbulence intensity of each dynamic hydraulic turbulence region, quantify the propagation velocity attenuation law, and calculate the critical turbulence trigger threshold for the flow switching of the sluice gate group; Model correction module: Performs multi-condition deviation stability analysis on the deviation area to determine whether the deviation is stable. If stable, it performs refined modeling on the stable deviation area. Control execution module: If unstable, determine whether the deviation changes with the operating conditions. If so, determine the monitoring priority based on the comprehensive turbulence intensity of each dynamic hydraulic turbulence zone, correct the BIM model prediction data by establishing an operating condition-deviation mapping model, and identify high deviation operating conditions by combining the critical turbulence trigger threshold.

2. The sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions as described in claim 1, characterized in that: The method for determining whether there is a deviation between the theoretical and actual hydraulic characteristics of the water flow intersection area is as follows: Hydraulic characteristics include the velocity and direction of water flow; Import high-precision topography, hydraulic facilities, and basic hydraulic parameters of the target water area to build a basic BIM hydraulic simulation model; Input the water flow boundary conditions under different working conditions, namely the flow rate, velocity, and direction of a single water flow, and simulate the water flow process in the model. Identify and locate the water flow intersection area through the superposition of flow field vectors and the sudden change of velocity gradient. Record the geographic coordinates, extent, and simulated hydraulic characteristics of a single water flow in the intersection area, and mark it as the water flow intersection area; The simulated velocity and direction of the single flow in the water flow intersection area are extracted from the BIM simulation results, and the theoretical combined velocity and direction of the water flow intersection area are calculated by the vector linear superposition method. The actual hydraulic characteristics of the water flow intersection area were measured on-site at the same location, at the same time, and under the same working conditions as the BIM model, and the actual hydraulic characteristics dataset was obtained. Calculate the deviation between the theoretical hydraulic characteristics and the actual hydraulic characteristics at each monitoring point. If the deviation meets the requirements, then there is a deviation between the theoretical hydraulic characteristics and the actual hydraulic characteristics in the cross-flow area.

3. The sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions as described in claim 1, characterized in that: The method for determining whether dynamic hydraulic disturbance exists in the deviation region is as follows: The deviation region is the area where there is a discrepancy between the theoretical and actual hydraulic characteristics of the water flow intersection area. Extract historical data on the flow switching of the sluice gate group within the historical period from the dispatch and control system, including switching type, switching magnitude, switching rate, switching sequence, and corresponding operating conditions; For each set of flow switching conditions, extract the time series data of hydraulic characteristics and dynamic changes in flow field morphology of the deviation area and its surroundings; The time series was divided into three stages: before traffic switching, during switching, and after switching, and the differences between each stage and the static stable baseline value were compared. Establish quantitative judgment criteria for dynamic hydraulic disturbances, with the core distinction between static inherent deviations and dynamic disturbances caused by flow switching. If any indicator exceeds the limit, dynamic hydraulic disturbances are judged to exist.

4. The sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions as described in claim 3, characterized in that: The criteria for determining dynamic hydraulic disturbance are as follows: The velocity change rate = the maximum change in velocity during flow switching / the static steady velocity. If the velocity change rate meets the requirements, then there is dynamic hydraulic turbulence. The angle of sudden change in flow direction is the maximum deflection angle of the flow direction during flow switching. If the angle of sudden change in flow direction meets the requirements, dynamic hydraulic turbulence exists. Abnormal flow field morphology: After the flow rate is switched, a new backflow / countercurrent zone is generated or the original deviation zone is expanded, which indicates dynamic hydraulic turbulence. Hydraulic parameter recovery time: If the time required for the hydraulic parameters to recover to the static stable range after the flow rate switch meets the requirements, then dynamic hydraulic disturbance exists.

5. The sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions as described in claim 1, characterized in that: The method for calculating the overall intensity of the disorder is as follows: The overall turbulence intensity is obtained by weighting and summing the water level rise / fall values, flow velocity change amplitude, and backflow duration at each monitoring node.

6. The sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions as described in claim 5, characterized in that: The quantification process of the propagation speed attenuation law is as follows: From the results of the disordered region localization, the starting disordered node (the earliest disordered node) and subsequent disordered nodes are extracted and sorted in chronological order. Based on the river topology in the BIM model, nodes that are not hydraulically conductive are removed, and only the node sequence along the main flow direction is retained; The retained nodes are paired up to their upstream and downstream locations, with each pair of nodes corresponding to a sub-river segment; The length of the river centerline for each pair of nodes is exported from the BIM model. The time of turbulence occurrence for each pair of nodes is extracted from the fused data and substituted into the formula to calculate the turbulence propagation speed of each sub-river segment. The calculation formula is: the turbulence propagation speed of sub-segment ij = the straight-line distance between upstream node i and downstream node j / the time difference between the turbulence propagation from node i to node j, that is, the time when the turbulence occurs at node j - the time when the turbulence occurs at node i; The turbulence attenuation coefficient of sub-river segment ij = the comprehensive turbulence intensity of downstream node j / the comprehensive turbulence intensity of upstream node i.

7. The sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions as described in claim 6, characterized in that: The critical disorder trigger threshold is calculated as follows: Extract historical data from multiple traffic switching operations, including: Independent variables: gate adjustment rate, single-step gate adjustment steps, total flow adjustment range; Dependent variable: Whether it causes dynamic hydraulic disturbance; A binary logistic regression model was constructed, with a critical condition of a probability of 90% or greater inducing severe disorder. The thresholds of each operational parameter were then deduced by substituting the values ​​into the regression model. Extract historical disturbance data for each sub-river section, including water level rise / fall values, overall disturbance intensity, and whether it has caused widespread disturbance. The water level rise / fall data are grouped by sub-river segments, and the 90th percentile is calculated. That is, in 90% of the turbulence events, the water level rise / fall value is less than or equal to the water level rise / fall value and there is no diffusion. The 90th percentile is the water level carrying capacity threshold of the sub-river segment.

8. The sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions as described in claim 1, characterized in that: The method for determining whether the deviation is stable is as follows: Select multiple scheduling conditions that cover the actual operating range. The operating condition parameters include pump station flow and gate opening. Use orthogonal experimental design to combine multiple operating condition parameters to cover the entire operating condition range. For each set of working conditions, linear superposition calculations are performed using the BIM model to obtain the theoretical flow rate for each set of working conditions. Under the same operating conditions, the actual composite flow velocity in the intersection area is obtained through on-site monitoring; Calculate the relative deviation of the flow velocity and the deviation of the flow direction angle for each set of working conditions; Calculate the percentage of operating conditions where the deviation meets the requirements across all operating conditions, and compare it with the preset percentage. If the percentage is greater than or equal to the preset percentage, the deviation is considered to be generally significant. Calculate the coefficient of variation for the deviation values ​​of all operating conditions. If the coefficient of variation meets the requirements, it means that the deviation fluctuates very little between different operating conditions and is stable; otherwise, it is unstable.

9. The sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions as described in claim 1, characterized in that: The method for determining the monitoring priority is as follows: The deviation of each working condition is correlated with the scheduling parameters, and the Pearson correlation coefficient is calculated. If the Pearson correlation coefficient meets the requirements, the deviation changes with the working condition. The overall turbulence intensity of each dynamic hydraulic turbulence region is obtained, and after normalization, they are arranged in descending order to obtain the monitoring priority of the dynamic hydraulic turbulence region.

10. The sluice gate group collaborative automatic control and monitoring system integrating BIM and real-time operating conditions as described in claim 9, characterized in that: The method for correcting the BIM model prediction data by establishing a working condition-deviation mapping model is as follows: Select working condition characteristic parameters and core deviation indicators, and divide the dataset into training set and test set according to the proportion; The random forest algorithm was selected to construct the mapping model, and the hyperparameters were optimized by the grid search method to minimize the model prediction error. The BIM model takes real-time operating condition characteristic parameters as input, first outputs theoretical prediction values, then calls the mapping model to obtain deviation prediction values, and completes the correction according to the formula: Corrected merging velocity: ; Corrected merging direction: ; Among them, E v E represents the flow rate predicted by the BIM model. θ The corrected data, which is the flow direction predicted by the BIM model, is the actual flow field prediction value.

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