An intelligent flood prevention monitoring system for a substation based on three-dimensional digital twinning and multi-source perception fusion

Abstract

This invention proposes an intelligent flood control monitoring system for substations based on 3D digital twins and multi-source sensing fusion. The system includes a 3D digital twin model construction module, a multi-source data acquisition and fusion module, an AI intelligent analysis engine, a dynamic flood control plan management module, and a closed-loop emergency response control module. The system constructs a full-chain, intelligent, and closed-loop substation flood control monitoring system through 3D digital twins, multi-source sensing fusion, AI intelligent analysis, dynamic plans, and closed-loop response technologies. The system is implemented through data acquisition, AI analysis, plan triggering, automatic execution, and closed-loop feedback, solving problems such as incomplete sensing, delayed response, and reliance on manual decision-making in existing systems. The system combines substation operation information with artificial intelligence algorithms to achieve panoramic visualization of flood conditions, intelligent early warning, dynamic simulation, automatic linkage, and closed-loop handling, thereby improving the intelligence level of substation flood control.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation and maintenance and disaster prevention and control technology for power systems, specifically to an intelligent monitoring system for flood control in substations based on the fusion of three-dimensional digital twins and multi-source sensing. Background Technology

[0002] Currently, flood control at substations mainly relies on manual inspections, which are inefficient, risky, and unable to respond in real time. Single-sensor monitoring can only obtain point data such as water level and rainfall, lacking global perception. Two-dimensional map displays cannot intuitively reflect terrain elevation differences, water spread paths, and drainage capacity. Passive alarms only trigger once a threshold is exceeded, resulting in delayed response. Contingency plans are static, existing in paper or Excel documents, and cannot be dynamically adjusted according to real-time conditions. Existing technologies lack spatial visualization, making it difficult to determine the trend of water spread. Multi-source data is fragmented, with water level, video, and meteorological data unable to be linked. Analysis reveals that the emergency response is delayed, making it impossible to predict and initiate measures in advance; there is a lack of closed-loop management and no automatic execution mechanism after an alarm is triggered. Therefore, the applicant proposes a substation flood control intelligent monitoring system based on 3D digital twins and multi-source sensing fusion. This system is a 3D visualization intelligent linkage monitoring method and system for substation flood control scenarios. It combines GIS aerial photography, 3D modeling, multi-source data fusion, video patrol, AI analysis, and dynamic contingency plan response technologies to overcome the limitations of traditional monitoring systems and achieve a new generation of intelligent flood control system with one-map perception, intelligent early warning, automatic linkage, and closed-loop handling. Summary of the Invention

[0003] To address the aforementioned technical challenges, this invention proposes an intelligent substation flood control monitoring system based on 3D digital twins and multi-source sensing fusion. The system constructs a full-chain, intelligent, and closed-loop substation flood control monitoring system through 3D digital twins, multi-source sensing fusion, AI intelligent analysis, dynamic contingency plans, and closed-loop response technologies. The system is implemented through data acquisition, AI analysis, contingency plan triggering, automatic execution, and closed-loop feedback, solving problems such as incomplete sensing, delayed response, and reliance on manual decision-making in existing systems. The system integrates substation operation information with artificial intelligence algorithms to achieve panoramic visualization of flood conditions, intelligent early warning, dynamic simulation, automatic linkage, and closed-loop handling, thereby improving the intelligence level of substation flood control.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] A substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is characterized by the following: the system includes a 3D digital twin model construction module, a multi-source data acquisition and fusion module, an AI intelligent analysis engine, a dynamic flood control plan management module, and a closed-loop emergency response control module; the 3D digital twin model construction module is based on UAV aerial photography and LiDAR data, and includes information on building structure, terrain elevation, drainage network, and equipment location; the multi-source data acquisition and fusion module uses multi-source heterogeneous data fusion sensing technology to integrate substation operation phases... The data is fused and overlaid onto the model constructed by the 3D digital twin model building module to form a three-dimensional fusion perception framework of "space-time-state", breaking down data silos; the AI ​​intelligent analysis engine adopts an AI + digital twin intelligent decision-making mechanism to achieve a qualitative leap from "passive alarm" to "active intervention"; the dynamic flood control plan management module can pre-set plans under different conditions and trigger them by setting different substation operating conditions, and supports users to customize the trigger conditions, which has high flexibility; the closed-loop emergency response control module realizes the emergency response control of the substation by performing feedback verification, effect evaluation and self-learning optimization.

[0006] Furthermore, the construction of the 3D digital twin model in the 3D digital twin model construction module of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion specifically involves:

[0007] 1) Utilize drone aerial photography and lidar to collect data from substations, generate a high-precision three-dimensional digital twin model of the substation, and achieve accurate mapping between the physical world and the virtual world;

[0008] 2) Model construction includes: building structure and elevation, drainage network topology, flood control gate, water barrier, water pump location, and terrain elevation;

[0009] 3) The display supports three levels of view switching: "Aerial View → Floor Plan → 3D Model" to meet the needs of different scenarios.

[0010] Furthermore, the implementation of the multi-source data acquisition and fusion module in the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is specifically as follows:

[0011] 1) Employing multi-source heterogeneous data fusion sensing technology, relevant substation operation data is fused and overlaid onto a model constructed using a 3D digital twin model building module;

[0012] 2) Substation operation-related data includes:

[0013] Video stream: Real-time monitoring of key areas such as walls, gates, and water collection wells;

[0014] Sensor data: water immersion, liquid level, rainfall, wind speed, temperature and humidity;

[0015] Meteorological data: Rainfall intensity, wind direction and speed for the next hour;

[0016] Equipment status: Pump start / stop, gate open / close;

[0017] 3) Display all data on a three-dimensional digital twin model in space to form a "single map" and a three-dimensional fusion perception framework of "space-time-state" to break down data silos.

[0018] Furthermore, the specific implementation process of the AI ​​intelligent analysis engine in the substation flood control intelligent monitoring system based on 3D digital twin and multi-source perception fusion is as follows:

[0019] 1) Waterlogging simulation: Based on topographic elevation and rainfall, predict the extent and depth of waterlogging;

[0020] 2) Structural toughness assessment: Calculate the inherent flood resistance score of the substation, with a score range of 0~100;

[0021] 3) Emergency response recommendations: Automatically generate the optimal scheduling strategy.

[0022] Furthermore, the AI ​​intelligent analysis engine of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source perception fusion is equipped with a dynamic simulation model for water accumulation and a substation flood control structural resilience assessment algorithm, specifically:

[0023] Dynamic simulation model of water diffusion;

[0024] Based on digital elevation models and simplified fluid dynamics equations, the model predicts the spread path, speed, and depth of water accumulation under continuous rainfall. The model combines the actual terrain and drainage facilities of the substation to achieve high-precision, real-time water accumulation simulation. Unlike general GIS flood models, it has scene-specificity and engineering applicability.

[0025] The dynamic simulation model for water accumulation diffusion is based on the assumption that the water flow is laminar, neglecting the inertial term, constant surface roughness, and uniform rainfall distribution. Its core calculation formula is:

[0026]

[0027] Where: E(x,y,t) represents evaporation and infiltration losses, ignoring short-term forecasts;

[0028] h(x,y,t) is the depth of water accumulation at position (x,y) at time t, in meters.

[0029] R(x,y,t) represents the rainfall intensity in m / s, converted from hourly rainfall (mm / h) data from meteorological monitoring.

[0030] v(x,y) is the water flow velocity vector, determined by the terrain slope, and is calculated using the following formula:

[0031]

[0032] z(x,y) represents the terrain elevation;

[0033] K is the hydroconductivity, which is related to the surface material. For conventional concrete, K≈0.01m / s, and for grass, K≈0.002m / s.

[0034] The dynamic simulation model for water diffusion uses a computational equation solution method:

[0035] The finite difference method is used to iteratively solve the problem on the 3D model mesh:

[0036] 1) Divide the substation area into N×M grids;

[0037] 2) Initially, h(x,y,0)=0;

[0038] 3) With a time step of Δt = 60s, iteratively update the water depth of each grid.

[0039] 4) Boundary conditions: The wall is an "impermeable boundary", and the drainage outlet is a "sink point" (h=0);

[0040] Output:

[0041] 1) The "water accumulation heat map" is updated every 5 minutes and overlaid on the 3D model;

[0042] 2) Areas where the water depth is predicted to exceed 0.1m within the next 30 minutes;

[0043] Algorithm for assessing the resilience of substation flood control structures;

[0044] The substation flood control structural resilience assessment algorithm proposes the concept of "substation flood control structural resilience" and constructs a quantifiable scoring model to evaluate the inherent flood resistance capacity of substations under current meteorological and geographical conditions; the calculation formula is as follows:

[0045]

[0046]

[0047]

[0048]

[0049] Where: w1+w2+w3=1, and the default weighting coefficients are: w1=0.4, w2=0.4, w3=0.2;

[0050] k1 is the steepness coefficient, with a parameter configuration of 2.0;

[0051] hthreshold is the critical elevation difference, with a parameter setting of 0.3m; when Δh>hthreshold, the terrain advantage is obvious, and feeling→1;

[0052] Qactual represents the actual drainage volume;

[0053] Qdesign represents the design drainage capacity of the substation.

[0054] I represents the local rainfall intensity predicted by the rain gauge / radar at the station site;

[0055] A represents the actual catchment area of ​​the substation, excluding the non-seepage area, i.e., the substation plan, the area of ​​the plant area plus the building roof area. Here, dividing by 1000 indicates unit conversion, from ㎡ to m³.

[0056] Δh represents the terrain elevation difference, the lowest point inside the station vs. the external ground, in meters, and the data comes from a 3D model.

[0057] hdoor refers to the height of the flood control gate, in meters (m). Data source: equipment ledger.

[0058] hwater_pred is the external water depth predicted by AI; if hdoor>hwater_pred+0.1m, the flood control barrier structure is considered safe, and fstructure=1;

[0059] Color mapping:

[0060] IRI≥80: Green - Safe; 60≤IRI<79: Yellow - Concern; IRI<60: Red - High Risk.

[0061] Furthermore, the basic conditions for triggering the dynamic flood control plan management module of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion are as follows:

[0062] Start the main water pump: Qactual<0.8*Qdesign&&I>30mm / h;

[0063] Close the flood control gate: IRI<70&&hwater_pred>hdoor-0.2;

[0064] Red alert issued: IRI < 60.

[0065] Furthermore, the closed-loop emergency response mechanism of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is specifically as follows:

[0066] Execution feedback verification: The system uses video AI analysis to confirm whether "the flood control gate has been closed" and "the water pumps are running";

[0067] Effectiveness evaluation: Recalculate the IRI after implementing the contingency plan to assess the effectiveness of the measures;

[0068] Self-learning optimization: Record the effect of each response and optimize the weights w1, w2, and w3.

[0069] Furthermore, the specific steps of the method for implementing the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion are as follows:

[0070] Step 1: Data Acquisition; After system installation, data on the substation is collected through drone aerial photography and LiDAR, including information such as building structure, terrain elevation, drainage network and equipment location. Then, a model is built using a 3D digital twin model building module. The basic data is input by the personnel during the initial use.

[0071] Step 2: AI Analysis; The AI ​​intelligent analysis engine uses an AI + digital twin intelligent decision-making mechanism to analyze the real-time input data of the substation. During AI analysis, the multi-source data acquisition and fusion module uses multi-source heterogeneous data fusion and perception technology to fuse and overlay the substation operation-related data onto the model constructed by the three-dimensional digital twin model construction module, forming a spatial-time-state three-dimensional fusion perception framework, breaking down data silos. After the AI ​​analysis is completed, the output results are used for subsequent contingency plan triggering.

[0072] Step 3: Contingency Plan Triggering; The dynamic flood control contingency plan management module pre-sets contingency plans under different conditions and triggers them by setting different substation operating conditions. After the system receives the AI ​​analysis results and outputs them, the corresponding contingency plan is automatically triggered. It supports user-defined trigger conditions, offering high flexibility.

[0073] Step 4: Automatic Execution; After the emergency plan is triggered, the system automatically controls the relevant equipment according to the plan to ensure the safe operation of the substation and promptly reports back to the system.

[0074] Step 5: Closed-loop feedback is implemented. During the execution process, emergency response control of the substation is achieved through execution feedback verification, effect evaluation, and self-learning optimization. Real-time execution status can be obtained, which facilitates management and decision-making by managers.

[0075] The benefits of this application are:

[0076] 1. The substation flood control intelligent monitoring system based on 3D digital twin and multi-source perception fusion realizes full-area visualization of the substation through 3D model, improves situational awareness, and provides panoramic visibility;

[0077] 2. The AI ​​model of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source perception fusion can predict flood conditions in advance, realize "early detection and early warning", and provide intelligent early warning;

[0078] 3. The substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion automatically executes the emergency plan, reduces human intervention delays, and automatically links the system.

[0079] 4. The substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion realizes full-process recording and traceability from alarm to response, achieving closed-loop management;

[0080] 4. The substation flood control intelligent monitoring system based on 3D digital twin and multi-source perception fusion supports the access of multiple substations, forming a regional flood control collaborative network with strong scalability. Attached Figure Description

[0081] Figure 1 This is a schematic diagram of the system architecture of the present invention;

[0082] Figure 2 This is a schematic diagram of the substation flood control structure toughness assessment algorithm of the present invention;

[0083] Figure 3 This is a schematic diagram of the three-dimensional digital twin model of the present invention;

[0084] Figure 4 This is a schematic diagram of the multi-source data fusion display system interface of the present invention. Figure 1 ;

[0085] Figure 5 This is a schematic diagram of the multi-source data fusion display system interface of the present invention. Figure 2 ;

[0086] Figure 6 This is a schematic diagram of the interface of the dynamic flood control plan configuration system of the present invention;

[0087] Figure 7 This is a schematic diagram of the boundary of the flood control ledger management system of the present invention;

[0088] Figure 8 This is a schematic diagram of the dynamic simulation model for water diffusion in this invention. Detailed Implementation

[0089] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0090] like Figure 1-8As shown, this is a substation flood control intelligent monitoring system based on the fusion of three-dimensional digital twin and multi-source sensing. Figure 1 As shown, the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion includes a 3D digital twin model construction module, a multi-source data acquisition and fusion module, an AI intelligent analysis engine, a dynamic flood control plan management module, and a closed-loop emergency response control module. The 3D digital twin model construction module is built based on UAV aerial photography and LiDAR data, and includes information on building structure, terrain elevation, drainage network, and equipment location. The multi-source data acquisition and fusion module uses multi-source heterogeneous data fusion sensing technology to fuse and overlay substation operation-related data onto the 3D digital twin model. The model built on the construction module forms a three-dimensional fusion perception framework of space-time-state, breaking down data silos; the AI ​​intelligent analysis engine adopts an AI + digital twin intelligent decision-making mechanism to achieve a qualitative leap from "passive alarm" to "active intervention"; the dynamic flood control plan management module allows for the pre-setting of plans under different conditions and the setting of different substation operating conditions for triggering, supporting user-defined trigger conditions and possessing high flexibility; the closed-loop emergency response control module achieves emergency response control of the substation through feedback verification, effect evaluation, and self-learning optimization.

[0091] like Figure 3 As shown, the construction of the 3D digital twin model of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is specifically as follows:

[0092] 1) Utilize drone aerial photography and lidar to collect data from substations, generate a high-precision three-dimensional digital twin model of the substation, and achieve accurate mapping between the physical world and the virtual world;

[0093] 2) Model construction includes: building structure and elevation, drainage network topology, flood control gate, water barrier, water pump location, and terrain elevation;

[0094] 3) The display supports three levels of view switching: "Aerial View → Floor Plan → 3D Model" to meet the needs of different scenarios.

[0095] like Figure 4-5 As shown, the implementation of the multi-source data acquisition and fusion module in the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is as follows:

[0096] 1) Employing multi-source heterogeneous data fusion sensing technology, relevant substation operation data is fused and overlaid onto a model constructed using a 3D digital twin model building module;

[0097] 2) Substation operation-related data includes:

[0098] Video stream: Real-time monitoring of key areas such as walls, gates, and water collection wells;

[0099] Sensor data: water immersion, liquid level, rainfall, wind speed, temperature and humidity;

[0100] Meteorological data: Rainfall intensity, wind direction and speed for the next hour;

[0101] Equipment status: Pump start / stop, gate open / close;

[0102] 3) Display all data on a three-dimensional digital twin model in space to form a "single map" and a three-dimensional fusion perception framework of "space-time-state" to break down data silos.

[0103] The specific implementation process of the AI ​​intelligent analysis engine in the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is as follows:

[0104] 1) Waterlogging simulation: Based on topographic elevation and rainfall, predict the extent and depth of waterlogging;

[0105] 2) Structural toughness assessment: Calculate the inherent flood resistance score of the substation, with a score range of 0~100;

[0106] 3) Emergency response recommendations: Automatically generate the optimal scheduling strategy.

[0107] The AI ​​intelligent analysis engine of the substation flood control intelligent monitoring system, based on 3D digital twin and multi-source sensing fusion, is equipped with a dynamic simulation model for water accumulation and a substation flood control structural resilience assessment algorithm. Figure 7 As shown, specifically:

[0108] Dynamic simulation model of water diffusion;

[0109] Based on digital elevation models and simplified fluid dynamics equations, the model predicts the spread path, speed, and depth of water accumulation under continuous rainfall. The model combines the actual terrain and drainage facilities of the substation to achieve high-precision, real-time water accumulation simulation. Unlike general GIS flood models, it has scene-specificity and engineering applicability.

[0110] The dynamic simulation model for water accumulation diffusion is based on the assumption that the water flow is laminar, neglecting the inertial term, constant surface roughness, and uniform rainfall distribution. Its core calculation formula is:

[0111]

[0112] Where: E(x,y,t) represents evaporation and infiltration losses, ignoring short-term forecasts;

[0113] h(x,y,t) is the depth of water accumulation at position (x,y) at time t, in meters.

[0114] R(x,y,t) represents the rainfall intensity in m / s, converted from hourly rainfall (mm / h) data from meteorological monitoring.

[0115] v(x,y) is the water flow velocity vector, determined by the terrain slope, and is calculated using the following formula:

[0116]

[0117] z(x,y) represents the terrain elevation;

[0118] K is the hydroconductivity, which is related to the surface material. For conventional concrete, K≈0.01m / s, and for grass, K≈0.002m / s.

[0119] The dynamic simulation model for water diffusion uses a computational equation solution method:

[0120] The finite difference method is used to iteratively solve the problem on the 3D model mesh:

[0121] 1) Divide the substation area into N×M grids;

[0122] 2) Initially, h(x,y,0)=0;

[0123] 3) With a time step of Δt = 60s, iteratively update the water depth of each grid.

[0124] 4) Boundary conditions: The wall is an "impermeable boundary", and the drainage outlet is a "sink point" (h=0);

[0125] Output:

[0126] 1) The "water accumulation heat map" is updated every 5 minutes and overlaid on the 3D model;

[0127] 2) Areas where the water depth is predicted to exceed 0.1m within the next 30 minutes;

[0128] Algorithm for assessing the resilience of substation flood control structures;

[0129] The substation flood control structural resilience assessment algorithm proposes the concept of "substation flood control structural resilience" and constructs a quantifiable scoring model to evaluate the inherent flood resistance capacity of substations under current meteorological and geographical conditions; the calculation formula is as follows:

[0130]

[0131]

[0132]

[0133]

[0134] Where: w1+w2+w3=1, and the default weighting coefficients are: w1=0.4, w2=0.4, w3=0.2;

[0135] k1 is the steepness coefficient, with a parameter configuration of 2.0;

[0136] hthreshold is the critical elevation difference, with a parameter setting of 0.3m; when Δh>hthreshold, the terrain advantage is obvious, and feeling→1;

[0137] Qactual represents the actual drainage volume;

[0138] Qdesign represents the design drainage capacity of the substation.

[0139] I represents the local rainfall intensity predicted by the rain gauge / radar at the station site;

[0140] A represents the actual catchment area of ​​the substation, excluding the non-seepage area, i.e., the substation plan, the area of ​​the plant area plus the building roof area. Here, dividing by 1000 indicates unit conversion, from ㎡ to m³.

[0141] Δh represents the terrain elevation difference, the lowest point inside the station vs. the external ground, in meters, and the data comes from a 3D model.

[0142] hdoor refers to the height of the flood control gate, in meters (m). Data source: equipment ledger.

[0143] hwater_pred is the external water depth predicted by AI; if hdoor>hwater_pred+0.1m, the flood control barrier structure is considered safe, and fstructure=1;

[0144] Color mapping:

[0145] IRI≥80: Green - Safe; 60≤IRI<79: Yellow - Concern; IRI<60: Red - High Risk.

[0146] The basic conditions for triggering the dynamic flood control plan management module of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion are as follows:

[0147] Start the main water pump: Qactual<0.8*Qdesign&&I>30mm / h;

[0148] Close the flood control gate: IRI<70&&hwater_pred>hdoor-0.2;

[0149] Red alert issued: IRI < 60.

[0150] The closed-loop emergency response mechanism of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is as follows:

[0151] Execution feedback verification: The system uses video AI analysis to confirm whether "the flood control gate has been closed" and "the water pumps are running";

[0152] Effectiveness evaluation: Recalculate the IRI after implementing the contingency plan to assess the effectiveness of the measures;

[0153] Self-learning optimization: Record the effect of each response and optimize the weights w1, w2, and w3.

[0154] The specific steps of the implementation method of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion are as follows:

[0155] Step 1: Data Acquisition; After system installation, data on the substation is collected through drone aerial photography and LiDAR, including information such as building structure, terrain elevation, drainage network and equipment location. Then, a model is built using a 3D digital twin model building module. The basic data is input by the personnel during the initial use.

[0156] Step 2: AI Analysis; The AI ​​intelligent analysis engine uses an AI + digital twin intelligent decision-making mechanism to analyze the real-time input data of the substation. During AI analysis, the multi-source data acquisition and fusion module uses multi-source heterogeneous data fusion and perception technology to fuse and overlay the substation operation-related data onto the model constructed by the three-dimensional digital twin model construction module, forming a spatial-time-state three-dimensional fusion perception framework, breaking down data silos. After the AI ​​analysis is completed, the output results are used for subsequent contingency plan triggering.

[0157] Step 3: Contingency Plan Triggering; The dynamic flood control contingency plan management module pre-sets contingency plans under different conditions and triggers them by setting different substation operating conditions. After the system receives the AI ​​analysis results and outputs them, the corresponding contingency plan is automatically triggered. It supports user-defined trigger conditions, offering high flexibility.

[0158] Step 4: Automatic Execution; After the emergency plan is triggered, the system automatically controls the relevant equipment according to the plan to ensure the safe operation of the substation and promptly reports back to the system.

[0159] Step 5: Closed-loop feedback is implemented. During the execution process, emergency response control of the substation is achieved through execution feedback verification, effect evaluation, and self-learning optimization. Real-time execution status can be obtained, which facilitates management and decision-making by managers.

[0160] The implementation method of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is further illustrated by describing the specific situation under a rainstorm scenario:

[0161] 1. Initial data acquisition and status data acquisition fusion:

[0162] Rainfall intensity: 45 mm / h

[0163] External water accumulation prediction: 0.25m;

[0164] Water pump not started:

[0165] 2. Calculation of the resilience assessment algorithm for substation flood control structures:

[0166] felevation=0.6 (Δh=0.2m);

[0167] fdrainage=0 (Qactual=0);

[0168] fstructure=0.5 (hdoor=0.5m, hwater=0.25m);

[0169] IRI=100×(0.4×0.6+0.4×0+0.2×0.5)=34;

[0170] 3. Prediction based on dynamic model of water accumulation diffusion:

[0171] Ten minutes later, the water depth outside the substation perimeter wall will reach 0.3m, and backflow may occur.

[0172] 4. System Response:

[0173] 1) Trigger a red alert (IRI<60) and provide a smart prompt;

[0174] 2) Automatically start the water pumps and close the flood control gates;

[0175] 3) After 5 minutes, the IRI rose back to 78, and the system alarm returned to normal.

[0176] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any modifications or equivalent changes made based on the technical essence of the present invention shall still fall within the scope of protection claimed by the present invention.

Claims

1. A substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion, characterized in that: The substation flood control intelligent monitoring system based on 3D digital twin and multi-source perception fusion includes a 3D digital twin model construction module, a multi-source data acquisition and fusion module, an AI intelligent analysis engine, a dynamic flood control plan management module, and a closed-loop emergency response control module. The 3D digital twin model construction module is built based on UAV aerial photography and LiDAR data, and includes information on building structure, terrain elevation, drainage network, and equipment location. The multi-source data acquisition and fusion module uses multi-source heterogeneous data fusion perception technology to fuse and overlay substation operation-related data onto the model constructed by the 3D digital twin model construction module, forming a spatial-temporal-state 3D fusion perception framework, breaking down data silos. The AI ​​intelligent analysis engine adopts an AI+digital twin intelligent decision-making mechanism, achieving a qualitative leap from "passive alarm" to "active intervention." The dynamic flood control plan management module allows for pre-setting plans under different conditions and triggering different substation operating conditions, supporting user-defined trigger conditions and providing high flexibility. The closed-loop emergency response control module achieves substation emergency response control through feedback verification, effect evaluation, and self-learning optimization.

2. The substation flood control intelligent monitoring system based on three-dimensional digital twin and multi-source sensing fusion as described in claim 1, characterized in that: The construction of the 3D digital twin model in the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is specifically as follows: 1) Utilize drone aerial photography and lidar to collect data from substations, generate a high-precision three-dimensional digital twin model of the substation, and achieve accurate mapping between the physical world and the virtual world; 2) Model construction includes: building structure and elevation, drainage network topology, flood control gate, water barrier, water pump location, and terrain elevation; 3) The display supports three levels of view switching: "Aerial View → Floor Plan → 3D Model" to meet the needs of different scenarios.

3. The substation flood control intelligent monitoring system based on three-dimensional digital twin and multi-source sensing fusion as described in claim 1, characterized in that: The implementation of the multi-source data acquisition and fusion module in the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is as follows: 1) Employing multi-source heterogeneous data fusion sensing technology, relevant substation operation data is fused and overlaid onto a model constructed using a 3D digital twin model building module; 2) Substation operation-related data includes: Video stream: Real-time monitoring of key areas such as walls, gates, and water collection wells; Sensor data: water immersion, liquid level, rainfall, wind speed, temperature and humidity; Meteorological data: Rainfall intensity, wind direction and speed for the next hour; Equipment status: Pump start / stop, gate open / close; 3) Display all data on a three-dimensional digital twin model in space to form a "single map" and a three-dimensional fusion perception framework of "space-time-state" to break down data silos.

4. The substation flood control intelligent monitoring system based on three-dimensional digital twin and multi-source sensing fusion as described in claim 1, characterized in that: The specific implementation process of the AI ​​intelligent analysis engine in the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is as follows: 1) Waterlogging simulation: Based on topographic elevation and rainfall, predict the extent and depth of waterlogging; 2) Structural toughness assessment: Calculate the inherent flood resistance score of the substation, with a score range of 0~100; 3) Emergency response recommendations: Automatically generate the optimal scheduling strategy.

5. The substation flood control intelligent monitoring system based on three-dimensional digital twin and multi-source sensing fusion as described in claim 1, characterized in that: The AI ​​intelligent analysis engine of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source perception fusion includes a dynamic simulation model for water accumulation and a substation flood control structural resilience assessment algorithm, specifically: Dynamic simulation model of water diffusion; Based on digital elevation models and simplified fluid dynamics equations, the model predicts the spread path, speed, and depth of water accumulation under continuous rainfall. The model combines the actual terrain and drainage facilities of the substation to achieve high-precision, real-time water accumulation simulation. Unlike general GIS flood models, it has scene-specificity and engineering applicability. The dynamic simulation model for water accumulation diffusion is based on the assumption that the water flow is laminar, neglecting the inertial term, constant surface roughness, and uniform rainfall distribution. Its core calculation formula is: ； Where: E(x,y,t) represents evaporation and infiltration losses, ignoring short-term forecasts; h(x,y,t) is the depth of water accumulation at position (x,y) at time t, in meters. R(x,y,t) represents the rainfall intensity in m / s, converted from hourly rainfall (mm / h) data from meteorological monitoring. v(x,y) is the water flow velocity vector, determined by the terrain slope, and is calculated using the following formula: ； z(x,y) represents the terrain elevation; K is the hydroconductivity, which is related to the surface material. For conventional concrete, K≈0.01m / s, and for grass, K≈0.002m / s. The dynamic simulation model for water diffusion uses a computational equation solution method: The finite difference method is used to iteratively solve the problem on the 3D model mesh: 1) Divide the substation area into N×M grids; 2) Initially, h(x,y,0)=0; 3) With a time step of Δt = 60s, iteratively update the water depth of each grid. 4) Boundary conditions: The wall is an "impermeable boundary", and the drainage outlet is a "sink point" (h=0); Output: 1) The "water accumulation heat map" is updated every 5 minutes and overlaid on the 3D model; 2) Areas where the water depth is predicted to exceed 0.1m within the next 30 minutes; Algorithm for assessing the resilience of substation flood control structures; The substation flood control structural resilience assessment algorithm proposes the concept of "substation flood control structural resilience" and constructs a quantifiable scoring model to assess the inherent flood resistance capacity of substations under current meteorological and geographical conditions; the calculation formula is as follows: ； ； ； ； Where: w1+w2+w3=1, and the default weighting coefficients are: w1=0.4, w2=0.4, w3=0.2; k1 is the steepness coefficient, with a parameter configuration of 2.0; hthreshold is the critical elevation difference, with a parameter setting of 0.3m; when Δh>hthreshold, the terrain advantage is obvious, and feeling→1; Qactual represents the actual drainage volume; Qdesign represents the design drainage capacity of the substation. I represents the local rainfall intensity predicted by the rain gauge / radar at the station site; A represents the actual catchment area of ​​the substation, excluding the non-seepage area, i.e., the substation plan, the area of ​​the plant area plus the building roof area. Here, dividing by 1000 indicates unit conversion, from ㎡ to m³. Δh represents the terrain elevation difference, the lowest point inside the station vs. the external ground, in meters, and the data comes from a 3D model. hdoor refers to the height of the flood control gate, in meters (m). Data source: equipment ledger. hwater_pred is the external water depth predicted by AI; if hdoor>hwater_pred+0.1m, the flood control barrier structure is considered safe, and fstructure=1; Color mapping: IRI≥80: Green - Safe; 60≤IRI<79: Yellow - Concern; IRI<60: Red - High Risk.

6. The substation flood control intelligent monitoring system based on three-dimensional digital twin and multi-source sensing fusion as described in claim 1, characterized in that: The basic conditions for triggering the dynamic flood control plan management module of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion are as follows: Start the main water pump: Qactual<0.8*Qdesign&&I>30mm / h; Close the flood control gate: IRI<70&&hwater_pred>hdoor-0.2; Red alert issued: IRI < 60.

7. The substation flood control intelligent monitoring system based on three-dimensional digital twin and multi-source sensing fusion as described in claim 1, characterized in that: The closed-loop emergency response mechanism of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion is as follows: Execution feedback verification: The system uses video AI analysis to confirm whether "the flood control gate has been closed" and "whether the water pumps are running"; Effectiveness evaluation: Recalculate the IRI after implementing the contingency plan to assess the effectiveness of the measures; Self-learning optimization: Record the effect of each response and optimize the weights w1, w2, and w3.

8. The substation flood control intelligent monitoring system based on three-dimensional digital twin and multi-source sensing fusion as described in claim 1, characterized in that: The specific steps of the substation flood control intelligent monitoring system based on 3D digital twin and multi-source sensing fusion are as follows: Step 1: Data Acquisition; After system installation, data on the substation is collected through drone aerial photography and LiDAR, including information such as building structure, terrain elevation, drainage network and equipment location. Then, a model is built through the 3D digital twin model building module. The basic data is input by the personnel during the initial use. Step 2: AI Analysis; The AI ​​intelligent analysis engine uses an AI + digital twin intelligent decision-making mechanism to analyze the real-time input data of the substation. During AI analysis, the multi-source data acquisition and fusion module uses multi-source heterogeneous data fusion and perception technology to fuse and overlay the substation operation-related data onto the model constructed by the three-dimensional digital twin model construction module, forming a three-dimensional fusion perception framework of space-time-state, breaking down data silos. After the AI ​​analysis is completed, the output results are used for subsequent contingency plan triggering. Step 3: Contingency Plan Triggering; The dynamic flood control contingency plan management module pre-sets contingency plans under different conditions and triggers them by setting different substation operating conditions. After the system receives the AI ​​analysis results and outputs them, the corresponding contingency plan is automatically triggered. It supports user-defined trigger conditions, offering high flexibility. Step 4: Automatic Execution; After the emergency plan is triggered, the system automatically controls the relevant equipment according to the plan to ensure the safe operation of the substation and promptly reports back to the system. Step 5: Closed-loop feedback is implemented. During the execution process, emergency response control of the substation is achieved through execution feedback verification, effect evaluation, and self-learning optimization. Real-time execution status can be obtained, which facilitates management and decision-making by managers.

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