Power grid construction management and control method based on three-dimensional digital twinning

Abstract

The invention relates to a power grid construction management and control method based on three-dimensional digital twinning, and relates to the technical field of power grid construction management and control, and the construction management and control method comprises the following steps: S1, a three-dimensional digital twinning model construction stage: constructing a power grid project high-precision three-dimensional model based on BIM, and superposing GIS geographic data to generate a construction scene three-dimensional digital twinning model; s2, in a construction process dynamic mapping stage, processing data of the multi-source data acquisition module in real time through an edge computing node; s3, optimizing a construction management and control decision; s4, a dynamic risk management and control stage; and S5, an execution feedback stage: forming a closed loop of three-dimensional digital twinborn model construction-construction rehearsal analysis-decision-execution-model updating. According to the power grid construction management and control method based on the three-dimensional digital twinning, the construction management and control process can be transformed from extensive experience driving to data intelligent driving, and high-reliability and high-adaptability digital infrastructure support is provided for novel power system construction.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid construction management and control, and in particular to a power grid construction management and control method based on three-dimensional digital twins. Background Art

[0002] A 3D digital twin is a virtualized, three-dimensional, dynamic model of a physical entity or system. Through real-time data synchronization, simulation analysis, and intelligent decision support, it enables precise mapping and dynamic interaction with the physical world. At its core, it uses sensors, the Internet of Things (IoT), big data, and artificial intelligence (AI) to map the geometry, operational status, and environmental data of physical objects into a digital space in real time.

[0003] Power grid construction management and control mainly ensures the safety, efficiency and compliance of the construction process through systematic means, covering the entire life cycle of design, construction and acceptance, and focusing on solving resource coordination, risk prevention and control, and quality control in complex environments.

[0004] The existing power grid construction management and control system has the following major problems: 1. Design conflict detection is delayed. Traditional 2D drawings make it difficult to detect pipeline collisions and equipment installation conflicts, leading to on-site rework. 2. Progress management is extensive, relying on manual data entry, making it difficult to track construction progress in real time. 3. Resource scheduling is inefficient. The allocation of machinery, personnel, and materials relies on experience, which is prone to idleness or shortages. 4. Safety hazards are difficult to predict, and risk assessment in complex environments is insufficient.

[0005] To solve the above technical problems, a power grid construction management and control method based on three-dimensional digital twins is proposed. By integrating high-precision three-dimensional models with real-time data, it can realize the digital reproduction of all elements of the construction scene, such as equipment status, personnel location, and environmental parameters, and support transparent management of the entire life cycle from design to acceptance. It can combine physical engines with AI algorithms for real-time simulation and deduction, dynamically simulate construction processes, mechanical paths, and risk scenarios, detect conflicts in advance, and generate optimization plans. It can realize intelligent scheduling of resources in advance, achieve precise allocation of personnel, machinery, and materials, and reduce idleness and waste. By processing sensor data such as wind speed and inclination through edge computing nodes, it can be combined with digital twins to simulate extreme working conditions such as strong winds and heavy rains, trigger millisecond-level warnings, and improve the safety of the construction process. Through point cloud scanning and image recognition technology, it can realize automatic comparison between construction results and design models, avoid manual acceptance omissions, resolve design conflicts in advance, and correct construction errors in real time, significantly reducing rework costs. Summary of the Invention

[0006] The present invention provides a power grid construction management and control method based on three-dimensional digital twins, which realizes design pre-verification, dynamic progress optimization, intelligent resource scheduling, and real-time risk warning, solving the problems of inefficiency and safety hazards in traditional construction management.

[0007] The present invention solves the above technical problems with the following solution: a power grid construction control method based on three-dimensional digital twins, the construction control method comprising the following steps:

[0008] S1, the 3D digital twin model construction phase, builds a high-precision 3D model of the power grid project based on BIM, overlays GIS geographic data to generate a 3D digital twin model of the construction scene, and embeds construction logic rules (such as process dependencies and mechanical operation radius restrictions);

[0009] S2, the dynamic mapping stage of the construction process, uses edge computing nodes to process data from multi-source data acquisition modules in real time, and uses point cloud scanning and image recognition technology to compare actual construction results with design model deviations;

[0010] S3, optimizes construction management and control decisions. Construction rehearsals are conducted through the construction simulation module to optimize construction management and control decisions. a. Optimize construction progress by dynamically adjusting the construction sequence based on the critical path method (CPM) and reinforcement learning algorithms. b. Optimize resource scheduling by combining digital twin simulation results to optimize machinery paths and material distribution plans. c. Risk warning: Use a three-dimensional digital twin model to simulate the impact of extreme weather (such as wind speeds >10m / s) on high-altitude operations and trigger stop-work orders in advance.

[0011] S4, the dynamic risk management stage, uses the dynamic management and control module to analyze construction data in real time, generate progress optimization suggestions, risk warnings, and resource allocation instructions and decision-making support information, and send the decision-making support information to the on-site mobile terminal;

[0012] S5, the execution feedback stage, manually confirms the final execution decision with reference to the decision-making auxiliary information, feeds back the final execution decision to the three-dimensional model through the visual interactive platform, updates the twin model, and forms a closed loop of "three-dimensional digital twin model construction-construction rehearsal analysis-decision-execution-model update".

[0013] On the basis of the above technical solution, the present invention can also be improved as follows.

[0014] Furthermore, the multi-source data acquisition module collects construction environment, equipment status and personnel behavior data through Internet of Things sensors (such as crane inclination sensors, GPS positioning), drone inspections, and construction site cameras.

[0015] Furthermore, the construction simulation module combines a physical engine with an AI algorithm to simulate construction processes, mechanical motion paths, and potential conflicts.

[0016] Furthermore, the visual interaction platform includes an on-site mobile terminal and a management and control host.

[0017] Furthermore, the risk warning includes simulating the impact of extreme environments on construction through a three-dimensional digital twin model, including detecting the behavior of personnel not wearing safety equipment and the presence of people in dangerous areas through image recognition.

[0018] Furthermore, the edge computing node adopts industrial-grade edge computing equipment, equipped with a multi-core processor, 8GB memory and GPU acceleration module, and supports multiple industrial communication protocol interfaces RS485, CAN bus, Ethernet and wireless connections 4G / 5G, Wi-Fi, and LoRaWAN.

[0019] Furthermore, the IoT sensors include industrial sensors (such as tilt sensors for cranes and tension machine data) and environmental sensors (such as temperature, humidity, and wind speed sensors).

[0020] The beneficial effects of the present invention are as follows: the present invention provides a power grid construction management and control method based on three-dimensional digital twins, which has the following advantages:

[0021] 1. By integrating high-precision 3D models with real-time data, it is possible to digitally reproduce all elements of the construction scene, including equipment status, personnel location, and environmental parameters, supporting transparent management of the entire life cycle from design to acceptance.

[0022] 2. It can combine the physics engine with AI algorithms for real-time simulation and deduction, dynamically simulate construction processes, machinery paths, and risk scenarios, detect conflicts in advance, and generate optimization plans. This can achieve intelligent resource scheduling in advance, achieve precise allocation of personnel, machinery, and materials, and reduce idleness and waste.

[0023] 3. By processing sensor data such as wind speed and tilt angle through edge computing nodes, digital twins can be combined to simulate extreme working conditions such as strong winds and heavy rains, triggering millisecond-level warnings to improve the safety of the construction process;

[0024] 4. Through point cloud scanning and image recognition technology, automatic comparison between construction results and design models can be achieved, avoiding manual acceptance omissions, resolving design conflicts in advance, correcting construction errors in real time, and significantly reducing rework costs;

[0025] 5. This three-dimensional digital twin-based power grid construction management and control method can transform the construction management and control process from extensive experience-driven to data intelligence-driven, providing highly reliable and adaptable digital infrastructure support for the construction of new power systems.

[0026] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the description, the following is a description of the preferred embodiments of the present invention. The specific implementation of the present invention is given in detail by the following examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0028] Figure 1 A flow chart of a method for power grid construction management and control based on three-dimensional digital twins provided in one embodiment of the present invention.

[0029] Figure 2 A system flow chart of a power grid construction management and control method based on three-dimensional digital twins provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following is combined with Figure 1-2 The principles and features of the present invention are described, and the examples given are intended only to illustrate the present invention and are not intended to limit the scope of the present invention. The following paragraphs describe the present invention in more detail by way of example. The advantages and features of the present invention will become more apparent from the following description and claims.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0032] As the instruction manual Figure 1-2 As shown;

[0033] Example 1:

[0034] Transmission line construction;

[0035] S1, the 3D digital twin model construction phase, builds a 3D model of the tower, conductors, and grounding devices based on BIM, overlays GIS geographic data to generate a 3D digital twin model of the construction scene, and sets construction constraints such as a maximum crane lifting weight of 8 tons and a safe operating radius of 15 meters;

[0036] S2, the dynamic mapping stage of the construction process, uses edge computing nodes to process data from multi-source data acquisition modules in real time, and uses point cloud scanning and image recognition technology to compare the deviation between the actual construction site and the design model site;

[0037] S3, optimizes construction management and control decisions. The tower assembly process is simulated on the digital twin platform. The construction simulation module detects collision risks between the crane path and surrounding buildings, optimizing construction management and control decisions. This includes: a. optimizing the construction progress, such as adjusting the crane station position to another coordinate; b. optimizing resource scheduling, combining digital twin simulation results to optimize machinery paths and material distribution plans; c. risk warning, simulating extreme weather conditions through a 3D digital twin model. For example, if a wind speed >10m / s affects high-altitude operations, a stoppage order is triggered in advance.

[0038] S4, the dynamic risk control stage. When the wind speed sensor in the risk warning module detects an instantaneous wind speed of 12 m / s, the dynamic control module triggers an early warning, suspends the high-altitude work instructions, and dispatches construction resources to transfer personnel to the ground cable laying task.

[0039] S5, the execution feedback stage, manually confirms the final execution decision with reference to the decision-making auxiliary information, feeds back the final execution decision to the three-dimensional model through the visual interactive platform, updates the twin model, and forms a closed loop of three-dimensional digital twin model construction-construction rehearsal analysis-decision-execution-model update.

[0040] Example 2:

[0041] 3D design conflict pre-verification, pipeline and equipment layout conflict detection before substation construction;

[0042] S1: Use BIM software to build a 3D model of the substation, including components such as cable trenches, transformers, and switchgear. Attributes such as equipment size and installation requirements are embedded, and GIS geographic data such as terrain elevation and underground rock distribution are superimposed to generate a 3D digital twin model that integrates the geological environment.

[0043] S2, the dynamic mapping stage of the construction process, uses edge computing nodes to process data from multi-source data acquisition modules in real time, and uses point cloud scanning and image recognition technology to compare the deviation between the actual construction site and the design model site;

[0044] S3, optimizes construction management and control decisions, detects physical collisions between cable trenches and foundation piles, simulates equipment hoisting paths, and verifies whether the crane's operating radius covers the installation location;

[0045] S4, the dynamic risk control stage, triggers an alarm when the distance between the cable trench and the foundation pile is less than 0.5m and the crane arm length is ≥20m, and generates a conflict report to the control host, such as adjusting the cable trench position to the new coordinates [X=50, Y=80];

[0046] S5, the execution feedback stage, manually confirms the final execution decision with reference to the decision-making auxiliary information, updates the twin model, and forms a closed loop of three-dimensional digital twin model construction-construction rehearsal analysis-decision-making-execution-model update.

[0047] Example 3:

[0048] Dynamic progress management and resource scheduling optimization, transmission line tower assembly and construction;

[0049] S1: Use BIM software to build a 3D model of the transmission line tower, including the number of assembled towers, overlay GIS geographic data, and generate a 3D digital twin model that integrates the geological environment;

[0050] S2, the dynamic mapping stage of the construction process, collects crane operation data (number of hoisting operations, single operation time) in real time through edge computing nodes to calculate construction efficiency;

[0051] S3 optimizes construction management and control decisions by importing actual progress data into the 3D digital twin model and dynamically adjusting the construction sequence using the critical path method (CPM). If a tower group is delayed due to geological problems, the crane path is replanned to minimize idle time. A supplementary planning scheme is then output, and the material requirements of the new scheme are estimated.

[0052] S5, the execution feedback stage, manually confirms the final execution decision with reference to the decision-making auxiliary information, calculates the material demand (such as bolts and tower materials) for the next three days based on the three-dimensional digital twin model, and triggers the warehousing and logistics system to deliver to the new usage location in advance.

[0053] It should be noted that, in this document, relational terms such as first and second are used solely to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between these entities or operations. Matters not described in detail in this specification are well known to those skilled in the art.

[0054] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in this industry can smoothly implement the present invention as described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with this profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the scope of protection of the technical solution of the present invention.

Claims

1. A power grid construction management and control method based on three-dimensional digital twins, characterized in that: Power grid construction control methods The following steps are involved: S1, the 3D digital twin model construction phase, builds a high-precision 3D model of the power grid project based on BIM, superimposes GIS geographic data to generate a 3D digital twin model of the construction scene, and embeds construction logic rules; S2, the dynamic mapping stage of the construction process, uses edge computing nodes to process data from multi-source data acquisition modules in real time, and uses point cloud scanning and image recognition technology to compare actual construction results with design model deviations; S3, optimizes construction management and control decisions. Construction rehearsals are conducted through the construction simulation module to optimize construction management and control decisions. a. Optimize construction progress by dynamically adjusting the construction sequence based on the critical path method and reinforcement learning algorithm. b. Optimize resource scheduling by combining digital twin simulation results to optimize machinery paths and material distribution plans. c. Risk warning by simulating the impact of extreme weather on high-altitude operations through a three-dimensional digital twin model to trigger a stoppage order in advance. S4, the dynamic risk management stage, uses the dynamic management and control module to analyze construction data in real time, generate progress optimization suggestions, risk warnings, and resource allocation instructions and decision-making support information, and send the decision-making support information to the on-site mobile terminal; S5, the execution feedback stage, manually confirms the final execution decision with reference to the decision-making auxiliary information, feeds back the final execution decision to the three-dimensional model through the visual interactive platform, updates the twin model, and forms a closed loop of three-dimensional digital twin model construction-construction rehearsal analysis-decision-execution-model update.

2. The power grid construction management and control method based on three-dimensional digital twin according to claim 1 is characterized in that: The multi-source data acquisition module collects construction environment, equipment status and personnel behavior data through Internet of Things sensors, drone inspections, and construction site cameras.

3. The power grid construction management and control method based on three-dimensional digital twin according to claim 1 is characterized in that: The construction simulation module combines a physics engine with an AI algorithm to simulate construction processes, mechanical motion paths, and potential conflicts.

4. The power grid construction management and control method based on three-dimensional digital twin according to claim 1 is characterized in that: The visual interaction platform includes an on-site mobile terminal and a management and control host.

5. The power grid construction management and control method based on three-dimensional digital twin according to claim 1 is characterized in that: The risk warning includes simulating the impact of extreme environments on construction through a three-dimensional digital twin model, including detecting the behavior of personnel not wearing safety equipment and the presence of people in dangerous areas through image recognition.

6. The power grid construction management and control method based on three-dimensional digital twin according to claim 1 is characterized in that: The edge computing node adopts industrial-grade edge computing equipment, equipped with a multi-core processor, 8GB memory and GPU acceleration module, and supports multiple industrial communication protocol interfaces RS485, CAN bus, Ethernet and wireless connections 4G / 5G, Wi-Fi, and LoRaWAN.

7. The power grid construction management and control method based on three-dimensional digital twin according to claim 1 is characterized in that: The IoT sensors include industrial sensors and environmental sensors.

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