Substation hole digging pile construction supervision method and system based on GIM and digital twinning

Abstract

The invention discloses a transformer substation hole digging pile construction supervision method and system based on GIM and digital twinning, and relates to the field of transformer substation digital twinning. In the method, environment parameters, construction progress and position information are collected in real time by deploying an Internet of Things sensor network, a three-dimensional digital model is constructed based on a power grid information model standard, and the construction speed of a hole digging pile is improved; and mapping the acquired real-time data to a corresponding position of the model through a sensor identifier and a space coordinate to form a time sequence database, and continuously updating the state of the model by using a digital twin engine to realize dynamic supervision and risk early warning of the construction process.

Description

Technical Field

[0001] This application belongs to the field of digital twins for substations, and in particular relates to a method and system for supervising the construction of bored piles in substations based on GIM and digital twins. Background Technology

[0002] Drilled pile construction is a crucial step in substation construction, and its quality directly impacts the safe operation and service life of the substation. Currently, supervision of drilled pile construction in substations is primarily conducted through on-site inspections. However, conventional inspection methods suffer from low monitoring frequency, delayed data collection, and untimely information transmission, making real-time monitoring of the construction site difficult. Furthermore, the lack of quantitative evaluation standards for on-site inspection results hinders the creation of standardized data records, easily leading to the omission of potential construction hazards.

[0003] While related technologies employ single-point sensors for localized monitoring, the scattered sensor distribution and inconsistent data formats prevent the formation of a complete construction status perception network. Furthermore, existing monitoring systems generally lack the capability for in-depth analysis and coordinated processing of multi-source monitoring data, making it difficult to promptly detect and warn of potential construction risks.

[0004] However, as the construction environment of substations becomes increasingly complex, existing regulatory methods are no longer sufficient to meet the needs of refined and intelligent construction management. Especially under complex geological conditions, the lack of comprehensive monitoring and effective early warning mechanisms at the construction site easily leads to construction quality problems and safety accidents, a situation that requires further improvement. Summary of the Invention

[0005] This application provides a method for supervising the construction of bored piles in substations based on GIM and digital twins, addressing the technical challenges of achieving comprehensive perception, data correlation, and risk early warning in existing monitoring systems. This method deploys an IoT sensor network to collect environmental parameters, construction progress, and location information in real time. A three-dimensional digital model is constructed based on the power grid information model standard. The collected real-time data is mapped to corresponding locations on the model using sensor identifiers and spatial coordinates, forming a time-series database. A digital twin engine is used to continuously update the model's state, enabling dynamic supervision and risk early warning of the construction process.

[0006] Firstly, this application provides a method for supervising the construction of bored piles in substations based on GIM and digital twins, including: Receive real-time monitoring data collected by an Internet of Things (IoT) sensor network. The real-time monitoring data includes environmental parameter data, construction progress data, and personnel location data. The real-time monitoring data includes sensor identification information and spatial coordinate information. A three-dimensional model of substation construction is established based on the power grid information model standard. The three-dimensional model includes spatial layout information of the construction site and preset sensor installation location information. Based on the sensor identification information, the spatial coordinate information, and the spatial layout information, a mapping relationship is established between the real-time monitoring data and the corresponding preset sensor installation positions in the three-dimensional model, and the data is recorded in chronological order to form time series data. Based on the time series data, the digital twin engine program is invoked to dynamically update the state information of the 3D model; Based on the status information, a construction risk analysis is performed, and an early warning signal is generated when an anomaly is detected.

[0007] By adopting the above technical solution, the system first deploys an IoT sensor network to collect real-time monitoring data, including environmental parameter data, construction progress data, and construction personnel location data. Each data point contains the corresponding sensor's identification information and spatial coordinate information. Then, based on the power grid information model standard, a three-dimensional model of the substation construction is constructed. This model not only includes the spatial layout information of the construction site but also pre-sets the sensor installation location information. Based on the sensor identification information, spatial coordinate information, and spatial layout information, a mapping relationship is established between the collected real-time monitoring data and the corresponding preset sensor locations in the three-dimensional model, and the data is recorded in chronological order to form time-series data. The digital twin engine program dynamically updates the state information of the three-dimensional model based on this time-series data. Finally, the system performs construction risk analysis based on the updated state information, and generates early warning signals in a timely manner when an anomaly is detected. This achieves multi-dimensional real-time monitoring of the construction site, establishes a spatial mapping relationship between monitoring data and the construction scene, and forms a time-series-based construction state analysis mechanism, improving the supervision efficiency and risk warning capability of substation bored pile construction. In conjunction with some embodiments of the first aspect, in some embodiments, after the step of receiving real-time monitoring data collected by the Internet of Things sensor network, the method further includes: Acquire the temperature, humidity, and vibration data from the environmental parameter data; Based on the temperature data, humidity data, and vibration data, the changing trend of environmental parameters is calculated. The environmental risk level is determined based on the changing trends of the environmental parameters and the preset alarm threshold. The environmental risk level is used as the input parameter for the construction risk analysis.

[0008] By adopting the above technical solution, after receiving real-time monitoring data, the system acquires key environmental parameters such as temperature, humidity, and vibration, calculates their changing trends, and compares them with preset thresholds to promptly identify abnormal environmental conditions. This provides an accurate basis for environmental risk assessment for subsequent construction risk analysis, effectively improving the early warning capability of environmental risks at the construction site and reducing construction safety hazards caused by environmental factors. In conjunction with some embodiments of the first aspect, in some embodiments, after the step of receiving real-time monitoring data collected by the Internet of Things sensor network, the method further includes: Obtain the pile foundation construction depth data, concrete pouring volume data, and steel cage installation location data from the construction progress data; Based on the pile foundation construction depth data and the concrete pouring volume data, the single pile construction progress completion rate is calculated. The single pile construction progress completion rate includes the ratio of the pile foundation excavation depth to the design depth and the ratio of the actual concrete pouring volume to the design pouring volume. Based on the installation position data of the rebar cage, the installation compliance of the rebar cage is determined, including the verticality of the centerline of the rebar cage, the thickness of the protective layer, and the deviation value of the joint position. Input the single pile construction progress completion rate and the steel cage installation compliance into the construction progress prediction model to calculate the progress deviation; When the progress deviation exceeds a preset threshold, construction optimization suggestions are generated.

[0009] By adopting the above technical solution, based on real-time data such as pile foundation construction depth, concrete pouring volume, and steel cage installation position, the single pile construction progress completion rate and installation compliance are calculated. Combined with the construction progress prediction model, progress deviations are detected in a timely manner, realizing precise monitoring and dynamic optimization of each stage of pile foundation construction, effectively improving the accuracy of construction quality and progress management. In some embodiments, in conjunction with the first aspect, after the step of establishing a three-dimensional model of substation construction based on the power grid information model standard, the method further includes: Obtain the structural layout information and equipment installation location information from the three-dimensional model; Based on the structural layout information and the equipment installation location information, a construction site spatial constraint model is established. The structural layout information is divided into construction operation areas, and the equipment installation locations are marked within the construction operation areas. Based on the construction operation areas and the equipment installation locations, the spatial constraints for equipment movement are determined. Based on the spatial constraint model, the spatial positional relationship between construction equipment is monitored in real time. When the distance between devices is detected to be less than the safe distance, a safe avoidance path is generated.

[0010] By adopting the above technical solution, a spatial constraint model is established based on the structural layout and equipment installation location information in the three-dimensional model. This enables real-time monitoring of the spatial positional relationship between construction equipment and automatic generation of safe avoidance paths, effectively avoiding the risk of equipment collision during construction and improving the efficiency and safety of spatial management at the construction site. In conjunction with some implementations of the first aspect, in some implementations, after the step of dynamically updating the state information of the 3D model by invoking a digital twin engine program, the method further includes: Obtain the component installation position deviation information and process completion progress information from the status information; The component installation position deviation information is compared with the construction quality control standard. When the deviation exceeds the allowable value, the adjustment parameters are calculated. This includes extracting the spatial coordinate data from the component installation position deviation information, comparing the spatial coordinate data with the design position, and calculating the required displacement and adjustment angle. Based on the progress information of the aforementioned process, the execution time of subsequent processes is generated; The adjustment parameters and the execution time are updated in the three-dimensional model.

[0011] By adopting the above technical solution, the displacement and adjustment angle are automatically calculated by comparing the component installation position with the quality control standards in real time, and subsequent construction plans are generated in combination with the process progress. This achieves the control of construction quality and process connection, and improves the collaborative efficiency and controllability of construction quality. In conjunction with some implementations of the first aspect, in some implementations, the step of performing construction risk analysis based on the state information and generating an early warning signal when an anomaly is detected specifically includes: Obtain the equipment operating status information and personnel location data from the status information; Based on the device operating status information, calculate the real-time distance between the devices; When the real-time distance is less than the preset safe distance, a collision warning signal is generated, wherein the device movement direction is predicted according to the preset motion trajectory model, and the minimum safe distance between devices is calculated. Based on the personnel location data, determine whether any personnel have entered the hazardous work area; When unauthorized entry is detected, a control command is output.

[0012] By adopting the above technical solutions, and by calculating equipment spacing and predicting equipment movement trajectories in real time, combined with personnel location monitoring, the risk of equipment collisions and unauthorized personnel entry can be detected in a timely manner, thereby achieving safety early warning and dynamic control at the construction site and reducing the risk of safety accidents during the construction process. Secondly, embodiments of this application provide a substation bored pile construction monitoring system based on GIM and digital twins, comprising: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to cause the system to perform the method described in the first aspect and any possible implementation thereof. Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a system, cause the system to perform the method described in the first aspect and any possible implementation thereof. Fourthly, embodiments of this application provide a computer program product that, when run on a system, causes the system to execute the method described in any possible implementation of the first aspect. One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application provides a method for supervising the construction of bored piles in substations based on GIM and digital twins. By deploying an Internet of Things sensor network to collect environmental parameters, construction progress and location information in real time, a three-dimensional digital model is constructed based on the power grid information model standard. The collected real-time data is mapped to the corresponding location in the model through sensor identification and spatial coordinates to form a time series database. The model status is continuously updated using a digital twin engine to achieve dynamic supervision and risk warning of the construction process.

[0013] 2. This application provides a method for supervising the construction of bored piles in substations based on GIM and digital twins. Based on real-time data such as pile foundation construction depth, concrete pouring volume, and rebar cage installation position, the method calculates the single pile construction progress completion rate and installation compliance. Combined with a construction progress prediction model, it promptly detects progress deviations, realizing precise monitoring and dynamic optimization of each stage of pile foundation construction, effectively improving the accuracy of construction quality and progress management.

[0014] 3. This application provides a method for supervising the construction of bored piles in substations based on GIM and digital twins. A spatial constraint model is established based on the structural layout and equipment installation location information in the three-dimensional model, which realizes real-time monitoring of the spatial positional relationship between construction equipment and automatic generation of safe avoidance paths. This effectively avoids the risk of equipment collision during construction and improves the efficiency and safety of spatial management at the construction site. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a substation bored pile construction supervision method based on GIM and digital twins, as described in this application.

[0016] Figure 2 This is another flowchart illustrating a substation bored pile construction supervision method based on GIM and digital twins, as described in this application.

[0017] Figure 3 This is a schematic diagram of the physical device structure of a substation bored pile construction monitoring system based on GIM and digital twin provided in an embodiment of this application. Detailed Implementation

[0018] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0019] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more. In the field of substation construction, bored pile foundation construction is an important foundation project, and its construction quality and progress directly affect the construction cycle and operational safety of the substation.

[0020] In related technologies, the construction management of bored piles in substations mainly relies on manual inspection and experience-based judgment, lacking real-time monitoring and intelligent analysis methods for the construction process. This makes it difficult to detect construction anomalies in a timely manner and take effective optimization measures, resulting in a high risk of construction delays and quality hazards.

[0021] This application is primarily applied to the construction supervision of bored piles in substations, including key processes such as pile excavation, reinforcement cage installation, and concrete pouring. In these scenarios, comprehensive monitoring and early warning of construction progress, quality parameters, and construction risks are required. To address these technical issues, this application provides a method for supervising the construction of bored piles in substations based on GIM and digital twins. An embodiment is described below, combined with… Figure 1 The present application describes a method for supervising the construction of bored piles in substations based on GIM and digital twins. Please see Figure 1 This is a flowchart illustrating a substation bored pile construction supervision method based on GIM and digital twins in an embodiment of this application.

[0022] S101 Receives real-time monitoring data collected by the Internet of Things sensor network.

[0023] The real-time monitoring data includes environmental parameter data, construction progress data, and personnel location data. Environmental parameter data refers to environmental monitoring data such as temperature, humidity, noise levels, geological parameters, and vibration. Construction progress data includes data on the pile foundation construction depth, concrete pouring volume, and rebar cage installation location. Personnel location data is the real-time location information of construction personnel collected through personnel positioning terminals. Each monitoring data point contains a unique identifier and spatial coordinates for the corresponding sensor.

[0024] Specifically, the system collects various monitoring data in real time through an IoT sensor network deployed at the construction site. Environmental monitoring sensors are arranged at key locations in the construction area according to specifications, collecting environmental parameters at regular intervals; construction progress monitoring equipment is installed on construction machinery to record construction process data in real time; personnel positioning terminals are carried by construction workers and continuously upload location information. The system receives this data through a wireless communication network and stores it according to sensor identification.

[0025] In some embodiments, the system employs a multi-level data acquisition architecture: Optionally, edge computing nodes are deployed at the construction site to preprocess and temporarily store raw data, reducing data transmission pressure. Optionally, a distributed data acquisition server is used to achieve data load balancing and fault-tolerant backup.

[0026] In some embodiments, a data quality control mechanism is implemented to ensure the reliability of data acquisition. This involves validating the data from each sensor, including checking the numerical range, sampling frequency, and data continuity. When abnormal data is detected, the system flags the data and triggers a sensor fault diagnosis process, ensuring that subsequent analyses use valid and reliable monitoring data.

[0027] S102. Establish a three-dimensional model of the substation construction according to the power grid information model standard.

[0028] The 3D model includes spatial layout information of the construction site and pre-set sensor installation location information. Spatial layout information refers to the structural layout of permanent facilities such as the substation main transformer foundation, power distribution equipment area, and cable interlayer, as well as the zoning of temporary facilities such as material processing areas, temporary storage areas, and construction operation areas. Sensor installation location information includes the spatial coordinates, installation angles, and monitoring ranges of various sensors.

[0029] Specifically, the system employs a modular modeling approach to construct the 3D model. First, CAD drawings of the substation project are imported to extract the geometric information of key structures. Then, according to construction specifications, the construction area is divided, and the installation locations of various equipment are marked. Finally, the sensor network layout scheme is overlaid onto the model to form a complete 3D scene. The system uses the power grid information model standard to define the model's data structure, ensuring interoperability with other systems. In some embodiments, different modeling strategies can be employed: Optionally, parametric modeling techniques can be used to quickly generate 3D models of standard components using preset templates. Optionally, laser scanning technology can be used to acquire on-site measured data to improve the accuracy of the model. Optionally, existing standard component models can be reused using a BIM technology library.

[0030] In some embodiments, to ensure the model's usability, the system implements a model quality control mechanism. This involves interference checks, integrity verification, and standard compliance checks. When problems are found in the model, the system generates correction suggestions and re-verifies the model after modification to ensure it accurately reflects the actual conditions at the construction site.

[0031] S103. Based on sensor identification information, spatial coordinate information, and spatial layout information, establish a mapping relationship between real-time monitoring data and the corresponding preset sensor installation positions in the three-dimensional model, and record them in chronological order to form time series data.

[0032] The mapping relationship refers to the association between real-time monitoring data and the corresponding sensor locations in the 3D model. Time series data refers to a sequence of monitoring data with timestamps, organized chronologically.

[0033] Specifically, the system first locates the corresponding sensor nodes in the 3D model using sensor identification information to verify the deviation between the actual installation location and the preset location. Then, it establishes a correlation between the monitoring data and the sensor nodes, forming a data mapping table. The system uses a time-series database to store the mapped data, supporting rapid retrieval by time, spatial location, and data type.

[0034] S104. Based on the time series data, call the digital twin engine program to dynamically update the state information of the 3D model.

[0035] The digital twin engine program refers to a software system used to synchronize the physical construction site with the digital model. Status information includes component installation position deviations, process completion progress, equipment operating status, and personnel distribution.

[0036] Specifically, the system employs a real-time data processing mechanism to update the 3D model. First, the engine program reads the latest monitoring data from the time-series database; then, based on the data type, it calls the corresponding processing module to calculate various state parameters; finally, the calculation results are updated in the 3D model, achieving dynamic synchronization between the physical world and the digital model. For example, when new component position data is received, the system updates the spatial position and attitude information of the corresponding component in the model.

[0037] In some embodiments, different update strategies can be adopted: Optionally, a real-time update mechanism can be used for parameters that change frequently to ensure real-time synchronization between the model state and the field state. Optionally, a periodic update mechanism can be used for parameters that change slowly to reduce system resource consumption. Optionally, different update priorities can be set according to the importance of the data.

[0038] S105. Conduct construction risk analysis based on status information, and generate early warning signals when an anomaly is detected.

[0039] Construction risk analysis refers to the process of identifying potential safety hazards based on status information. Early warning signals include equipment collision warnings, personnel violation warnings, schedule delay warnings, and quality substandard warnings.

[0040] Specifically, the system employs a multi-dimensional risk assessment mechanism. First, based on equipment operating status information, the system calculates the real-time distance between devices using a motion trajectory model, triggering a collision warning when the distance is less than a safety threshold. Second, based on personnel location data, the system monitors in real time whether personnel are entering dangerous areas, issuing control instructions immediately upon detection of unauthorized entry. Third, the system compares the completion status of work processes with the planned progress, generating warning information when a delay risk is detected.

[0041] In some embodiments, data transmission delays or sensor malfunctions may lead to incomplete or inaccurate status information. For example, delayed device location data uploads can cause collision warnings to lag; lost personnel location information can prevent timely detection of individuals illegally entering dangerous areas; and incomplete progress data can cause errors in construction delay warnings. To address this, the system sets different warning levels based on the degree of risk and employs corresponding push notification methods. For high-risk warnings, the system simultaneously notifies relevant personnel through multiple channels; for low-risk warnings, the system logs the information for subsequent analysis.

[0042] In the above embodiments, a real-time data collection system is implemented to gather environmental parameters, construction progress, and personnel locations at the construction site via an IoT sensor network. A 3D model incorporating spatial layout and sensor locations is established based on the power grid information model standard. The monitoring data is mapped to the model to form time-series data, and a digital twin engine is used to dynamically update the model's state, enabling real-time monitoring of the construction site. Simultaneously, the system performs multi-dimensional risk analysis based on the updated status information, promptly identifying safety hazards such as equipment collisions, personnel violations, and schedule delays, effectively improving the safety management level of substation bored pile construction.

[0043] In the above embodiments, the system achieves basic supervision of the construction site by collecting and processing real-time monitoring data, combined with 3D models and digital twin technology. However, to ensure that the quality and progress of pile foundation construction meet the requirements, relying solely on basic monitoring data is insufficient. To further improve the accuracy and controllability of construction progress management, this application also provides another method for supervising the construction of bored piles in substations based on GIM and digital twins. The following is a combination of... Figure 2 Another method for supervising the construction of bored piles in substations based on GIM and digital twins, as described in this application, is as follows: Please see Figure 2 This is another flowchart illustrating a substation bored pile construction supervision method based on GIM and digital twins in an embodiment of this application.

[0044] S201. Receive real-time monitoring data collected by the Internet of Things sensor network.

[0045] In this embodiment, when monitoring construction progress data in real time, the system deploys a dedicated sensor network to collect key parameters during the pile foundation construction process. Data acquisition terminals are installed on construction machinery such as drilling rigs and concrete pumping equipment to record construction process data in real time; total stations and other surveying equipment are deployed at the construction site to continuously track the installation position of components. The system receives this data through a wireless communication network and establishes a construction progress database.

[0046] S202. Obtain data on pile foundation construction depth, concrete pouring volume, and rebar cage installation location from the construction progress data.

[0047] Among them, the pile foundation construction depth data refers to the excavation depth value of the pile foundation collected in real time by a depth sensor. The concrete pouring volume data refers to the actual volume of concrete pumped, as counted by a flow meter. The reinforcement cage installation location data includes the spatial coordinates, tilt angle, and elevation information of the reinforcement cage.

[0048] Specifically, the system employs a multi-source data acquisition method to obtain construction progress data. Depth sensors are installed at the drill bit location on the drilling rig, collecting excavation depth data every minute; flow meters are deployed on the concrete delivery pipeline to accumulate the pouring volume in real time; and total stations are set up at fixed measurement points to dynamically track and measure the rebar cage installation process. The system categorizes the collected data according to station number and stores it in the construction progress database.

[0049] In some embodiments, different data acquisition methods can be employed: Optionally, an acoustic depth sounder can be used to verify the excavation depth. Optionally, laser scanning can be used to obtain the spatial position of the reinforcing cage. Optionally, a pressure sensor can be used to monitor the concrete pouring volume.

[0050] In some embodiments, data acquisition may be interrupted due to equipment failure or inclement weather. For example, rain can affect measurement accuracy, water ingress into sensors can cause data anomalies, and signal interference can lead to data loss. To address this, the system has a data backup mechanism that supplements automatically acquired data with manually measured data, ensuring the integrity of construction progress data.

[0051] S203. Based on the pile foundation construction depth data and concrete pouring volume data, calculate the single pile construction progress completion rate.

[0052] Among them, the single pile construction progress completion rate refers to the ratio of the actual construction progress to the design requirements during the construction of a single pile foundation. The pile foundation excavation depth ratio is the percentage obtained by dividing the measured depth by the design depth, and the concrete pouring volume ratio is the percentage obtained by dividing the actual pouring volume by the design volume.

[0053] Specifically, the system first acquires the design parameters for a single pile foundation, including the design depth and design concrete volume. Then, it reads real-time collected depth and pouring volume data, calculating the excavation and pouring completion rates respectively. The system uses a weighted average to combine the two rates and obtain the overall progress completion rate of the single pile construction.

[0054] In some embodiments, different calculation strategies can be adopted: Optionally, the weights of the excavation depth ratio and the pouring volume ratio can be dynamically adjusted according to the importance of the process. Optionally, the completion rate can be corrected by considering the construction difficulty coefficient. Optionally, the calculation model can be optimized by combining historical data statistical patterns.

[0055] In some embodiments, deviations between the actual construction volume and the design value may occur due to changes in geological conditions. For example, encountering special strata may require deepening the pile foundation, or the soil may be soft, necessitating an increase in the amount of concrete. To address this, the system employs a dynamic adjustment mechanism for design parameters, updating the design values ​​based on construction approval documents to ensure the accuracy of the progress completion rate calculation.

[0056] S204. Determine the compliance of the rebar cage installation based on the rebar cage installation location data.

[0057] Installation compliance refers to the degree to which the actual installation state of the reinforcing cage matches the design requirements. Centerline verticality refers to the angle between the axis of the reinforcing cage and the vertical direction. Protective layer thickness refers to the distance from the outer edge of the reinforcing bar to the concrete surface. Joint position deviation refers to the offset of the reinforcing bar connection from the design position.

[0058] Specifically, the system uses total station measurement data to calculate the spatial orientation of the reinforcing cage. First, it determines the coordinates of the center points at the top and bottom of the cage and calculates the verticality of the axis. Then, it obtains the protective layer thickness using laser ranging. Finally, it performs location measurements on the joints. The system compares the measurement results with the specifications and generates an installation compliance score.

[0059] In some embodiments, different measurement methods can be used: Optionally, a tilt sensor can be used to directly measure verticality. Optionally, ultrasonic testing can be used to detect the protective layer thickness. Optionally, photogrammetry can be used to obtain the joint position.

[0060] In some embodiments, data inaccuracies may occur due to deformation of the rebar cage or limitations in the measurement angle. For example, bending may occur during the hoisting of the rebar cage, obstructing the measurement line of sight and preventing the acquisition of complete data. To address this, the system employs a multi-point measurement method and utilizes a data fusion algorithm to improve positioning accuracy.

[0061] S205. Input the single pile construction progress completion rate and the steel cage installation compliance into the construction progress prediction model to calculate the progress deviation.

[0062] The construction schedule prediction model refers to a schedule prediction algorithm trained based on historical construction data, used to assess the trend of schedule changes and predict schedule deviations during the construction process. Schedule deviations include excavation schedule deviations, pouring schedule deviations, and overall schedule deviations, which respectively reflect the degree of difference between each construction stage and the planned schedule.

[0063] Specifically, the system employs machine learning methods to construct a multi-layered prediction model. First, the single-pile construction progress completion rate is used as the primary input parameter for schedule prediction, comprehensively reflecting the completion status of pile foundation excavation depth and concrete pouring volume. Second, the compliance of the rebar cage installation is used as a process quality correction factor to adjust the prediction results; higher installation accuracy leads to more ideal predicted construction efficiency. The model analyzes historical construction data to learn the mapping relationship between progress completion rate, installation quality, and actual schedule, establishing a prediction function that incorporates construction efficiency, quality impact, and environmental factors. The system uses this function to calculate the estimated completion time of remaining processes and compares it with the planned schedule to obtain the schedule deviation value.

[0064] In some embodiments, different prediction methods can be employed: Optionally, a deep neural network model can be used for prediction, learning the nonlinear relationship between construction parameters and construction period through a multilayer perceptron network to improve prediction accuracy. Optionally, a Long Short-Term Memory (LSTM) network can be used for time series analysis to fully utilize the temporal characteristics of the construction process and improve prediction accuracy. Optionally, a fuzzy rule system can be constructed by combining expert experience to transform engineers' construction experience into prediction rules, enhancing the model's practicality. Optionally, an ensemble learning method can be used to integrate the results of multiple prediction models and reduce the prediction bias of a single model.

[0065] S206. When the schedule deviation exceeds the preset threshold, generate construction optimization suggestions.

[0066] The preset threshold refers to the maximum allowable delay time according to construction management specifications. Construction optimization suggestions include measures such as adjusting construction techniques, optimizing resource allocation, and improving workflow coordination.

[0067] Specifically, the system generates targeted suggestions based on the reasons for schedule deviations. When low excavation efficiency is detected, it suggests adjusting drilling parameters; when slow pouring speed is detected, it proposes an optimized pumping process; and when poor installation accuracy is detected, it provides correction operation guidance. The system pushes the optimization suggestions to on-site management personnel to guide construction adjustments.

[0068] In some embodiments, different suggestion generation strategies can be employed: Optionally, recommending optimized solutions based on historical success cases. Optionally, matching solutions through an expert knowledge base. Optionally, dynamically generating suggestions based on on-site conditions.

[0069] In some embodiments, after receiving monitoring data, the system acquires temperature data, humidity data, and vibration data from the environmental parameter data, calculates the environmental parameter change trend based on the temperature data, humidity data, and vibration data, determines the environmental risk level according to the environmental parameter change trend and the preset alarm threshold, and uses the environmental risk level as an input parameter for construction risk analysis.

[0070] Temperature data refers to the sequence of temperature values ​​measured by an array of temperature sensors deployed at key locations on the construction site, including the temperature of the concrete pouring area, the temperature of the curing area, and the ambient temperature. Humidity data refers to the relative humidity values ​​of the air collected by a humidity sensor network, used to assess the dampness of the construction environment and the curing conditions of the concrete. Vibration data refers to the sequence of vibration acceleration obtained by an array of acceleration sensors installed on the ground and key structures, reflecting the degree of impact of construction operations on the surrounding environment. The trend of environmental parameter changes refers to the pattern characteristics of these parameters changing over time, including the fluctuation amplitude, rate of change, periodicity, and correlation between parameters.

[0071] Specifically, in the data preprocessing stage, the raw data is first subjected to wavelet filtering to eliminate high-frequency noise and abnormal fluctuations; then, data normalization is performed to transform parameters of different dimensions to a unified scale. In the trend analysis stage, the system uses a sliding window algorithm to calculate characteristic parameters such as temperature change rate, humidity fluctuation range, and vibration intensity, while simultaneously employing Fourier transform to analyze the spectral characteristics of the vibration signal to identify potential abnormal vibration sources. In the risk assessment stage, the system compares the extracted characteristic parameters with preset multi-level alarm thresholds. For example, when the temperature change rate in the concrete pouring area exceeds 3℃ / hour and lasts for more than 30 minutes, the system raises the environmental risk level to a level two warning; when a sudden increase of 50% in vibration intensity is detected and abnormal peaks appear in the spectrum, the system triggers a level three warning; when multiple parameters are abnormal simultaneously, the system comprehensively assesses the risk level. Finally, the system inputs the determined environmental risk level and its associated abnormal parameter information into the construction risk analysis module as an important basis for assessing the overall safety status of the construction site. In this way, the system can achieve comprehensive dynamic monitoring of the construction environment, promptly detecting and warning of potential environmental risks. Specifically, the system continuously collects environmental data through a distributed sensor network, uses data analysis algorithms to identify abnormal change patterns, and assesses risk levels according to preset rules, thus providing a reliable basis for construction management decisions. For example, when the system detects abnormal fluctuations in temperature and humidity, it can adjust concrete curing measures in advance; when excessive vibration is detected, it can adjust construction procedures or take vibration reduction measures in a timely manner. This risk management method based on real-time monitoring and intelligent analysis significantly improves the safety and controllability of the construction process.

[0072] In some embodiments, after establishing a 3D model of the substation construction, the system acquires structural layout information and equipment installation location information from the model and executes a spatial constraint analysis process. The structural layout information refers to the spatial division and layout characteristics of various functional areas within the substation, including the location of permanent facilities such as the main transformer foundation area, power distribution equipment area, and cable interlayer area, as well as the area division of temporary facilities such as material storage yards, equipment storage areas, and construction access roads. The equipment installation location information refers to the target installation coordinates, working range, and clearance space of various construction equipment, used to plan equipment movement paths and prevent collisions. The spatial constraint model refers to a 3D meshed model of the construction site constructed based on the above information, used for real-time calculation and evaluation of the spatial relationships between equipment.

[0073] Specifically, the system employs a multi-level spatial analysis method to construct the constraint model. In the spatial partitioning phase, the construction site is first divided into different functional areas based on structural layout information, with access permissions and passage rules set for each area. Then, the installation location and working range of equipment are marked within each area, forming boundary constraints for equipment activities. In the constraint modeling phase, the system discretizes the site space into a three-dimensional mesh, with each mesh cell recording its passability, load-bearing capacity, and temporal constraints. In the path planning phase, the system constructs a path planner for equipment movement based on the A* algorithm, calculating the optimal avoidance path in real time. For example, when hoisting equipment needs to cross a material storage yard, the system considers ground bearing capacity, air clearance, and construction procedure requirements to plan a safe and efficient movement path; when multiple pieces of equipment are operating simultaneously, the system calculates the spacing between equipment in real time to ensure that their operating radii do not interfere with each other.

[0074] In some embodiments, changes in construction procedures or alterations in site conditions may cause the spatial constraint model to fail. For example, the addition of temporary construction equipment may alter the site layout, rain may render some passageways impassable, or an emergency task may require equipment to enter unplanned areas. To address this, the system employs a dynamic constraint update mechanism. By adjusting grid attributes through real-time monitoring data, the system ensures that the constraint model always reflects the actual site conditions. Simultaneously, the system continuously tracks equipment positions. When a potential collision is predicted, it immediately calculates a new avoidance scheme and prompts operators to perform avoidance maneuvers via a visual interface. In this way, when site conditions change, the system can quickly adjust the spatial constraint model and replan equipment movement paths, ensuring the continuity and safety of construction activities.

[0075] In some embodiments, after dynamically updating the state information of the 3D model, the system acquires component installation position deviation information and process completion progress information, and executes a construction quality analysis process. Component installation position deviation information refers to the difference between the actual installation position and the designed position of the component, obtained through 3D scanning and measurement equipment, including spatial coordinate deviation, tilt angle, and elevation error. Process completion progress information refers to the actual completion status of each construction process, including completion time, quality rating, and resource consumption data. Adjustment parameters refer to the spatial position adjustments required to correct installation deviations, including displacement in three directions and rotation angle.

[0076] Specifically, the system employs precise measurement and intelligent analysis methods to process construction quality data. In the deviation analysis phase, the system first collects the actual spatial coordinates of components using a total station and a 3D laser scanner to establish a point cloud model. Then, it registers the point cloud data with the design model and calculates the positional deviations of each key point. In the parameter calculation phase, the system optimizes the calculation of adjustment amounts based on the least squares method to ensure that the adjusted component positions meet design requirements to the greatest extent possible. In the process planning phase, the system combines the logical relationships between processes and resource constraints, using the critical path method to calculate the optimal execution time for subsequent processes. For example, when the system detects that the verticality deviation of a rebar cage exceeds the allowable value specified in the code, it accurately calculates the tilt angle and displacement that need to be adjusted; when there is a deviation in the installation position of a precast component, the system generates detailed correction parameters based on the relative positions of surrounding components.

[0077] Furthermore, the system implements a tiered adjustment strategy. For critical nodes, high-precision measuring equipment is used for verification, while for minor deviations, the adjustment precision is appropriately relaxed within permissible limits. Simultaneously, the system updates the adjustment process in the 3D model in real time, providing visual guidance to on-site construction personnel to accurately execute the adjustment operations.

[0078] In some embodiments, when performing construction risk analysis, the system acquires real-time equipment operating status information and personnel location data, and executes a safety monitoring process. The equipment operating status information includes real-time location, speed, working status, and load of various construction machinery; the personnel location data is real-time spatial coordinates obtained through positioning terminals worn by workers, used to monitor personnel movement trajectories and aggregation patterns on the construction site. The safety monitoring process analyzes this data to promptly identify potential safety risks and trigger corresponding early warning and control measures.

[0079] Specifically, the system employs multi-dimensional safety analysis algorithms to process monitoring data. For equipment monitoring, the system uses a Kalman filter algorithm to predict equipment movement trajectories in real time and calculates the minimum distance between devices; when the predicted distance between devices is less than a safety threshold, a collision avoidance warning is immediately generated. For personnel management, the system divides the construction site into different levels of hazardous areas and tracks personnel locations in real time; when unauthorized personnel enter a restricted area, the system immediately sends control instructions to on-site management personnel. For example, when the working areas of tower cranes and excavators may overlap, the system calculates the collision risk in advance and plans avoidance strategies; when construction personnel are detected approaching high-voltage equipment areas, the system immediately triggers audible and visual alarms.

[0080] In some embodiments, incomplete monitoring data may occur due to signal obstruction or equipment malfunction. For example, metal structures may interfere with positioning signals, equipment sensors may temporarily fail, or unforeseen events may create monitoring blind spots. To address this, the system employs data fusion technology, comprehensively utilizing multi-source information for location estimation and risk assessment to ensure the continuity and reliability of safety monitoring. Simultaneously, the system maintains focused monitoring of high-risk areas, ensuring the timely implementation of safety management measures through multiple early warning mechanisms. Therefore, during the installation of large components, the system can monitor installation accuracy in real time, promptly detect positional deviations, and generate optimized adjustment plans to ensure that construction quality meets design requirements.

[0081] The system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of a substation bored pile construction monitoring system based on GIM and digital twin, provided in an embodiment of this application.

[0082] It should be noted that, Figure 3 The structure of the system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0083] like Figure 3 As shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 302 or a program loaded from storage portion 308 into Random Access Memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0084] The following components are connected to I / O interface 305: input section 306 including a camera, infrared sensor, etc.; output section 307 including a liquid crystal display (LCD) and speakers, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0085] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0086] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0088] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or it may exist independently and not assembled into the system. The storage medium carries one or more computer programs that, when executed by a processor of a system, cause the system to implement the methods provided in the above embodiments.

[0089] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0090] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0091] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0092] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for supervising the construction of bored piles in substations based on GIM and digital twins, characterized in that, include: Receive real-time monitoring data collected by an Internet of Things (IoT) sensor network. The real-time monitoring data includes environmental parameter data, construction progress data, and personnel location data. The real-time monitoring data includes sensor identification information and spatial coordinate information. A three-dimensional model of substation construction is established based on the power grid information model standard. The three-dimensional model includes spatial layout information of the construction site and preset sensor installation location information. Based on the sensor identification information, the spatial coordinate information, and the spatial layout information, a mapping relationship is established between the real-time monitoring data and the corresponding preset sensor installation positions in the three-dimensional model, and the data is recorded in chronological order to form time series data. Based on the time series data, the digital twin engine program is invoked to dynamically update the state information of the 3D model; Based on the status information, a construction risk analysis is performed, and an early warning signal is generated when an anomaly is detected.

2. The method according to claim 1, characterized in that, After the step of receiving real-time monitoring data collected by the Internet of Things sensor network, the method further includes: Acquire the temperature, humidity, and vibration data from the environmental parameter data; Based on the temperature data, humidity data, and vibration data, the changing trend of environmental parameters is calculated. The environmental risk level is determined based on the changing trends of the environmental parameters and the preset alarm threshold. The environmental risk level is used as the input parameter for the construction risk analysis.

3. The method according to claim 1, characterized in that, After the step of receiving real-time monitoring data collected by the Internet of Things sensor network, the method further includes: Obtain the pile foundation construction depth data, concrete pouring volume data, and steel cage installation location data from the construction progress data; Based on the pile foundation construction depth data and the concrete pouring volume data, the single pile construction progress completion rate is calculated. The single pile construction progress completion rate includes the ratio of the pile foundation excavation depth to the design depth and the ratio of the actual concrete pouring volume to the design pouring volume. Based on the installation position data of the rebar cage, the installation compliance of the rebar cage is determined, including the verticality of the centerline of the rebar cage, the thickness of the protective layer, and the deviation value of the joint position. Input the single pile construction progress completion rate and the steel cage installation compliance into the construction progress prediction model to calculate the progress deviation; When the progress deviation exceeds a preset threshold, construction optimization suggestions are generated.

4. The method according to claim 1, characterized in that, Following the step of establishing a three-dimensional model of substation construction based on the power grid information model standard, the method further includes: Obtain the structural layout information and equipment installation location information from the three-dimensional model; Based on the structural layout information and the equipment installation location information, a construction site spatial constraint model is established. The structural layout information is divided into construction operation areas, and the equipment installation locations are marked within the construction operation areas. Based on the construction operation areas and the equipment installation locations, the spatial constraints for equipment movement are determined. Based on the spatial constraint model, the spatial positional relationship between construction equipment is monitored in real time. When the distance between devices is detected to be less than the safe distance, a safe avoidance path is generated.

5. The method according to claim 1, characterized in that, After the step of dynamically updating the state information of the 3D model by calling the digital twin engine program, the method further includes: Obtain the component installation position deviation information and process completion progress information from the status information; The component installation position deviation information is compared with the construction quality control standard. When the deviation exceeds the allowable value, the adjustment parameters are calculated. This includes extracting the spatial coordinate data from the component installation position deviation information, comparing the spatial coordinate data with the design position, and calculating the required displacement and adjustment angle. Based on the progress information of the aforementioned process, the execution time of subsequent processes is generated; The adjustment parameters and the execution time are updated in the three-dimensional model.

6. The method according to claim 1, characterized in that, The step of performing construction risk analysis based on the status information and generating an early warning signal when an anomaly is detected specifically includes: Obtain the equipment operating status information and personnel location data from the status information; Based on the device operating status information, calculate the real-time distance between the devices; When the real-time distance is less than the preset safe distance, a collision warning signal is generated, wherein the device movement direction is predicted according to the preset motion trajectory model, and the minimum safe distance between devices is calculated. Based on the personnel location data, determine whether any personnel have entered the hazardous work area; When unauthorized entry is detected, a control command is output.

7. A substation bored pile construction monitoring system based on GIM and digital twin, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the system to perform the method as described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the system, the system performs the method as described in any one of claims 1-6.

9. A computer program product, characterized in that, When the computer program product is run on the system, the system performs the method as described in any one of claims 1-6.

Images