Power distribution network project prefabricated foundation assembly optimization and construction progress intelligent management method

Abstract

The invention relates to a power distribution network engineering prefabricated foundation assembly optimization and construction progress intelligent management method. According to the method, terrain data and environment data of a construction area are collected, a digital model is established and used for describing terrain features and construction environment conditions, and design parameters of a prefabricated foundation are determined based on the model. According to design parameters, the prefabricated foundation is divided into a main body bearing section and an auxiliary leveling section, the main body bearing section bears the weight of equipment, and the auxiliary leveling section adapts to topographic changes by adjusting the levelness and the elevation. And generating an optimal installation scheme by adopting a genetic algorithm in combination with topographic data and design parameters in the digital model. In the construction process, the laser distance measuring sensor is used for collecting spatial position data of the installed foundation in real time, the spatial position data is compared with the optimal installation scheme, and a deviation value is calculated. And when the deviation value exceeds a threshold value, a leveling scheme is generated according to the measured data. According to the method, construction optimization and experience accumulation are realized, and the construction efficiency and precision are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network construction management, and particularly to a method for optimizing the prefabricated foundation assembly of a distribution network project and intelligent management of the construction progress. Background Art

[0002] In the prior art, the prefabricated foundation construction in distribution network projects usually adopts the methods of standardized design and manual measurement, and manually adjusts the foundation structure according to the topographic conditions at the construction site. These methods rely on empirical judgment and the operation skills of on-site construction personnel, and determine the design parameters and installation positions of the foundation through simple topographic survey data. In the foundation installation stage, the levelness and elevation of the foundation are usually corrected by traditional measuring tools and manual adjustment methods to meet the installation requirements of the equipment.

[0003] However, in the prior art, there are multiple limitations in the foundation installation process. First, the construction site conditions are complex, and it is difficult for traditional methods to quickly adapt to different topographic changes, resulting in a relatively high risk of mismatch between the foundation design and the actual terrain. Second, due to the lack of intelligent optimization tools, the installation accuracy depends on manual experience, with low efficiency and large errors. In addition, during the foundation installation adjustment process, it is difficult to effectively guarantee the connection strength and flatness, which is likely to cause potential hazards to the subsequent stable operation of the equipment.

[0004] To solve the above problems, the present invention proposes a method for optimizing the prefabricated foundation assembly of a distribution network project and intelligent management of the construction progress. Summary of the Invention

[0005] The present application provides a method for optimizing the prefabricated foundation assembly of a distribution network project and intelligent management of the construction progress to improve the construction efficiency and accuracy.

[0006] The present application provides a method for optimizing the prefabricated foundation assembly of a distribution network project and intelligent management of the construction progress, including:

[0007] Collect the topographic data and environmental data of the construction area, establish a digital model of the construction area, the digital model is used to describe the topographic features and construction environmental conditions, and determine the design parameters of the prefabricated foundation according to the digital model, the design parameters include the foundation type, weight and geometric dimensions;

[0008] According to the design parameters, divide the prefabricated foundation into a main load-bearing section and an auxiliary leveling section, the main load-bearing section is used to bear the weight of the equipment, and the auxiliary leveling section is used to adapt to the topographic changes of the installation position to adjust the levelness and elevation of the prefabricated foundation;

[0009] Combined with the terrain data and design parameters in the digital model, a genetic algorithm is used to generate the optimal installation plan for precast foundations. The optimal installation plan includes the installation location, installation direction of each precast foundation, and the adjustment height of the auxiliary leveling section. The optimization goal is to minimize the adjustment amount of the auxiliary leveling section while ensuring that the connection strength between adjacent precast foundations meets the design requirements;

[0010] During the construction process, the spatial position data of the installed precast foundations is collected in real time through laser distance sensors, and the spatial position data is compared with the optimal installation plan to calculate the deviation values of the actual installation position, direction, and height;

[0011] When the deviation value exceeds the preset threshold, a leveling plan is generated based on the real-time collected spatial position data. The leveling plan includes the compensation height and adjustment angle of the auxiliary leveling section, which are used to correct the installation deviation and ensure the flatness and structural stability between adjacent precast foundations.

[0012] Furthermore, the terrain data and environmental data of the construction area are collected, and a digital model of the construction area is established, including:

[0013] Automated surveying equipment is deployed in the construction area, including a laser scanner carried by a drone, a total station, and a high-precision GNSS positioning device, which are used to collect the three-dimensional terrain data of the construction area;

[0014] By deploying geological drilling equipment and sensor arrays, the geological parameters and environmental conditions of the construction area are collected;

[0015] The collected data is integrated and processed using 3D modeling software to establish a digital model of the construction area.

[0016] Furthermore, the integration and processing of the collected data using 3D modeling software to establish a digital model of the construction area includes:

[0017] The three-dimensional point cloud data collected by the laser scanner carried by the drone and the total station is fused and calibrated with the geographical coordinate data obtained by the high-precision GNSS positioning device to generate a high-resolution terrain model of the construction area. During the calibration process, the GNSS positioning data is used as the global reference to ensure that the accuracy of the point cloud model is consistent with the actual terrain;

[0018] The formation data obtained by the geological drilling equipment is matched with the geographical location corresponding to the point cloud model, and the geological layer structure information is embedded into the three-dimensional terrain model through a multi-level superposition method to ensure that the model can reflect the underground soil and geological characteristics while describing the surface features;

[0019] The environmental data collected by the sensor array is attached as a dynamic label to the corresponding area of the model to describe the change trend of the environmental conditions during the construction period.

[0020] Furthermore, the use of 3D modeling software to integrate and process the collected data to establish a digital model of the construction area also includes:

[0021] Introducing a dynamic update mechanism to collect new terrain, geological and environmental data in real time during the construction process, integrate it with the existing model, and update the areas in the model related to the construction progress to ensure that the digital model always reflects the latest status of the construction site;

[0022] The model’s topography, geology and environmental characteristics are presented in a graphical form through 3D visualization tools, allowing construction personnel to intuitively understand construction conditions and assist in optimizing the design and implementation of prefabricated foundation installation solutions.

[0023] Furthermore, during the construction process, the spatial position data of the installed prefabricated foundation is collected in real time by a laser ranging sensor, including:

[0024] After each prefabricated foundation is initially installed, a laser range finder is used to measure the spatial coordinates of key points in real time, including the four corner points and the top center point of the prefabricated foundation, to fully describe the plane position, height and levelness of the foundation.

[0025] The laser ranging sensor acquires three-dimensional position data by multi-point scanning, and converts the measurement results into numerical expressions in the global reference coordinate system in combination with the digital model coordinate system of the construction area;

[0026] During the measurement process, the sensor’s measurement angle and range are controlled by a high-precision pan-tilt head to ensure that all key points of the foundation are covered.

[0027] The beneficial effects of the technical solution provided by this application include:

[0028] (1) The present invention generates the optimal installation plan by utilizing digital models and genetic algorithms, and collects and analyzes spatial position data in real time during the construction process, effectively reducing deviations during the installation process, thereby ensuring the installation accuracy of the prefabricated foundation and avoiding errors caused by relying on manual experience in traditional methods. (2) Through automated data collection and analysis, and real-time generation of leveling plans, the present invention can quickly adapt to complex terrain changes at the construction site, significantly reduce the time required for construction adjustments, improve construction efficiency, and meet the needs of project progress. (3) The present invention divides the prefabricated foundation into a main load-bearing section and an auxiliary leveling section, and specifically designs a leveling plan, which can ensure the flatness and connection strength between adjacent foundations, improve the stability of the overall structure of the foundation, and meet the long-term reliability requirements of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1It is a flowchart of a method for optimizing precast foundation assembly and intelligent management of construction progress in a distribution network project provided by the first embodiment of this application. Detailed implementation manners

[0030] Many specific details are set forth in the following description in order to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the connotation of this application. Therefore, this application is not limited by the specific implementations disclosed below.

[0031] The first embodiment of this application provides a method for optimizing precast foundation assembly and intelligent management of construction progress in a distribution network project. Please refer to Figure 1 , this figure is a schematic diagram of the first embodiment of this application. The following combines Figure 1 to detail a method for optimizing precast foundation assembly and intelligent management of construction progress provided by the first embodiment of this application.

[0032] Step S101: Collect topographic data and environmental data of the construction area, establish a digital model of the construction area, the digital model is used to describe topographic features and construction environmental conditions, and determine the design parameters of the precast foundation according to the digital model, the design parameters include foundation type, weight and geometric dimensions.

[0033] Step S101 involves collecting topographic data and environmental data of the construction area, establishing a digital model of the construction area, and determining the design parameters of the precast foundation according to the digital model. The specific implementation method of this step is as follows:

[0034] First, use professional surveying equipment such as total stations, laser scanners carried by unmanned aerial vehicles or three-dimensional topographic scanners to comprehensively collect data on the topography of the construction area. The data should include information such as height differences, slopes, ground undulations within the area, and elevations of key positions to accurately describe topographic features. In addition, by arranging survey points or using geological drilling methods, collect geological data related to the topography, such as soil bearing capacity, soil layer distribution, groundwater level, etc., for supporting the mechanical analysis of precast foundation design.

[0035] Next, collect construction environmental data, which includes but is not limited to climate conditions (such as rainfall, wind speed and temperature changes), distribution of surrounding obstacles on the construction site, and material transportation routes. This data can be obtained through weather stations, site survey records, and geographic information systems (GIS).

[0036] The collected data needs to be integrated through data fusion processing technology. Using modeling software such as AutoCAD, Revit, or other 3D modeling tools, a digital model of the construction area is established. The digital model should accurately reflect the undulation of the terrain, the location of obstacles, and the surrounding environmental conditions in 3D form. For the geometric accuracy of the model, centimeter-level or millimeter-level resolution can be selected to meet the high-precision requirements of precast foundation design.

[0037] Based on the established digital model, key parameters affecting the foundation design are further extracted. For example, calculate the terrain slope at the equipment installation location through the digital model to evaluate the impact of the terrain on the foundation; analyze the soil bearing capacity in combination with geological parameters to ensure that the foundation design meets the requirements of structural stability.

[0038] Based on the digital model, determine the design parameters of the precast foundation, including the type, weight, and geometric dimensions of the foundation. The type can select different foundation styles according to the types of equipment to be carried and construction conditions, such as block foundations or strip foundations; the weight is calculated based on the equipment load and the density of the foundation material; the geometric dimensions need to be combined with the installation requirements of the equipment and the terrain conditions, and the specific dimensions of the foundation are determined by simulating the installation scenario through the digital model.

[0039] Finally, the digital model and design parameters should be stored in the form of electronic data for use in the optimization calculation of the installation plan and actual construction guidance in subsequent steps. This step ensures that the subsequent division, installation optimization, and construction adjustment of the precast foundation have reliable data support and accurate model reference, guaranteeing the feasibility and efficiency of the entire method.

[0040] Furthermore, the collection of topographic data and environmental data of the construction area and the establishment of a digital model of the construction area include:

[0041] Automated surveying equipment is deployed in the construction area, including laser scanners, total stations, and high-precision GNSS positioning devices carried by drones, for collecting 3D topographic data of the construction area;

[0042] By deploying geological drilling equipment and sensor arrays, collect geological parameters and environmental conditions of the construction area;

[0043] Use 3D modeling software to integrate and process the collected data to establish a digital model of the construction area.

[0044] Collecting topographic data and environmental data of the construction area and establishing a digital model of the construction area are the basic steps to achieve the optimization of precast foundation assembly. To ensure the accuracy and comprehensiveness of the model, the entire process involves multiple links of data collection, processing, and integration, and each link needs to be specifically implemented in combination with the actual situation of the construction site.

[0045] GNSS (Global Navigation Satellite System) is a system that uses satellites for positioning, navigation, and timekeeping. It sends signals to the ground through a group of navigation satellites deployed in Earth's orbit. User devices receive these signals and calculate the precise position of the device on the Earth's surface by measuring the time difference of signal arrival.

[0046] First, automated surveying equipment is deployed within the construction area. The drone-mounted laser scanner is one of the key devices. By flying along a predetermined route, it conducts three-dimensional scanning of the terrain in the construction area, generating high-precision point cloud data. The point cloud data contains information such as the height, slope, and contour of the surface within the area, which can clearly describe the three-dimensional characteristics of the construction area. To ensure full coverage of the scanning results, the drone flight path needs to be carefully planned, including height settings, overlap rate requirements, and flight speed control. In addition, to enhance measurement accuracy, total stations are deployed at key nodes of the terrain, such as near area boundaries, sudden changes in terrain, and equipment installation locations, to record the elevation and coordinate values of these points. The data from the total stations serves as a reference for calibrating and integrating with the drone point cloud data. The GNSS positioning device further provides support for the global coordinate system to ensure the geographical accuracy of the entire digital model. The GNSS device combines a base station set up on-site with a mobile receiver to obtain the precise position of each measurement point in real-time.

[0047] Next, geological drilling equipment and sensor arrays are deployed to collect geological parameters and environmental data. The geological drilling equipment can obtain information about the underground soil layers in the construction area, including key parameters such as soil type, bearing capacity, compressibility, and groundwater level. These data are crucial for the design of precast foundations to ensure that the design parameters match the actual bearing capacity of the foundation. At the same time, environmental sensor arrays are deployed at representative locations in the construction area to monitor environmental conditions such as temperature, humidity, wind speed, precipitation, and surface humidity in real-time. These data can reflect the possible environmental changes during the construction process and provide support for the dynamic update of the digital model.

[0048] Subsequently, the collected data is integrated and processed using 3D modeling software to establish a digital model of the construction area. The point cloud data generates a 3D surface model of the terrain through the modeling software, which is calibrated in combination with total station and GNSS positioning data to ensure the overall accuracy of the model. Geological data forms a layered description of the underground structure by projecting the parameters of each drilling point to the corresponding positions in the model, such as indicating the thickness and characteristics of soil layers at different depths. Environmental data, as dynamic parameters, is superimposed on the model according to time nodes to describe the changing construction environmental conditions over time. After the model integration is completed, the partition management technology is used to divide the construction area into multiple sub-areas, each of which contains independent terrain, geological, and environmental hierarchical data for further basic design and optimization calculations.

[0049] In this process, to ensure the integrity and real-time nature of data processing, a dynamic update mechanism needs to be introduced. During the construction process, if new measurement data is added or significant changes occur in the on-site conditions, such as local adjustments to the terrain or sudden changes in environmental conditions, the new data can be integrated into the digital model in real time to ensure that the model always reflects the latest state of the construction area. In addition, through linkage with 3D visualization tools, the generated digital model can be presented in a visual form, including contour maps, cross-sectional views, and geological stratification maps, facilitating the construction team to intuitively understand the regional characteristics and use them to guide the optimization design of precast foundations and the construction progress planning.

[0050] Through the above steps, the method provided in this embodiment realizes the efficient collection and integration of multi-dimensional data in the construction area. The established digital model is comprehensive, accurate, and dynamic, providing strong technical support for the design and construction optimization of precast foundations.

[0051] Furthermore, the use of 3D modeling software to integrate and process the collected data to establish a digital model of the construction area includes:

[0052] Fusing and calibrating the 3D point cloud data collected by the laser scanner and total station carried by the unmanned aerial vehicle with the geographic coordinate data obtained by the high-precision GNSS positioning device to generate a high-resolution terrain model of the construction area. During the calibration process, the GNSS positioning data is used as the global benchmark to ensure that the accuracy of the point cloud model is consistent with the actual terrain;

[0053] Matching the formation data obtained by the geological drilling equipment with the corresponding geographical positions of the point cloud model, and embedding the geological layer structure information into the 3D terrain model through a multi-level superposition method to ensure that the model can reflect the underground soil and geological characteristics while describing the surface features;

[0054] Taking the environmental data collected by the sensor array as a dynamic label and attaching it to the corresponding area of the model to describe the changing trend of environmental conditions during construction.

[0055] Integrate and process the collected data using 3D modeling software to establish a digital model of the construction area. This digital model integrates multi-source and multi-dimensional data, capable of accurately describing the terrain, geology, and environmental characteristics of the construction area, providing comprehensive support for subsequent construction optimization.

[0056] First, generate a terrain model of the construction area using the 3D point cloud data collected by the laser scanner and total station carried by the unmanned aerial vehicle (UAV). The point cloud data contains information such as the height, slope, and boundary features of the ground surface, featuring high resolution and rich details. However, due to the possible influence of wind speed changes and equipment vibrations during the UAV flight, there may be certain errors in the directly generated point cloud data. Therefore, use the geographic coordinate data obtained by the high-precision GNSS positioning device as the global reference, and fuse and calibrate the point cloud data with the GNSS coordinates. During the calibration process, adjust the spatial distribution of the point cloud data through coordinate registration algorithms to make it consistent with the geographical location of the actual terrain. The accuracy of GNSS positioning data is usually at the centimeter level, which can effectively improve the overall accuracy and reliability of the point cloud model.

[0057] Based on the generated terrain model, embed the formation data collected by geological drilling equipment into the model. Parameters such as the soil layer thickness, soil type, bearing capacity, and groundwater level recorded by the geological drilling equipment are matched with the corresponding geographical locations in the point cloud model to generate a multi-level model containing surface and subsurface information. To achieve this matching, the geographical coordinates of the drilling points need to be corresponded one by one with the specific locations in the point cloud model, and then through the method of multi-layer superposition, the geological parameters are represented in a layered form. For example, the surface layer in the model can be represented as elevation data, and the distribution and characteristics of each subsurface soil layer are described by superimposed vector or volume data. This method can not only visually present the terrain features but also reflect the soil bearing characteristics and groundwater distribution at different depths within the construction area, providing comprehensive support for the design of precast foundations.

[0058] In addition, integrate the environmental data collected by the sensor array into the model and attach it to the corresponding area of the model in the form of dynamic labels. The environmental data includes parameters such as the real-time monitored temperature and humidity, wind speed, and precipitation. These data are superimposed on the 3D model in a time series manner through the mapping relationship with the geographical locations in the model. The design of dynamic labels can reflect the changing trends of the construction environmental conditions. For example, in the label displayed in a certain area of the model, it is possible to directly view the precipitation situation in the past week and the current surface humidity. This information is of great significance for construction progress planning and adjustment of the installation plan of precast foundations.

[0059] Through the above integration process, the digital model of the construction area established can not only accurately reflect the three-dimensional structure of the terrain, but also cover the underground geological conditions and dynamic environmental parameters. During the data processing, the model combines a variety of calibration and optimization techniques to ensure the relevance and accuracy of the data. This comprehensively integrated data model can be used not only for the preliminary design of the construction plan, but also for dynamic updates during the construction process, providing strong technical support for optimizing construction efficiency and ensuring project quality. The implementation of this method can effectively improve the accuracy of the foundation design and the level of construction intelligence in complex construction environments.

[0060] Furthermore, the use of three-dimensional modeling software to integrate and process the collected data to establish a digital model of the construction area further includes:

[0061] Introduce a dynamic update mechanism to collect new terrain, geological, and environmental data in real time during the construction process, and fuse it with the existing model to update the areas related to the construction progress in the model, ensuring that the digital model always reflects the latest state of the construction site;

[0062] Present the terrain, geological, and environmental characteristics of the model in a graphical form through three-dimensional visualization tools for construction personnel to intuitively understand the construction conditions and assist in optimizing the design and implementation of the precast foundation installation plan.

[0063] Based on the use of three-dimensional modeling software to integrate data and establish a digital model of the construction area in this embodiment, a dynamic update mechanism and three-dimensional visualization tools are introduced to achieve the real-time, intuitive, and operable nature of the model. This method can ensure that the digital model always reflects the latest state of the construction site, providing a reliable basis for construction decision-making and optimized design.

[0064] The introduction of the dynamic update mechanism is to cope with the continuous changes in the construction site conditions. During the construction process, devices such as drones, laser scanners, geological drilling equipment, and environmental sensors are used to collect new terrain, geological, and environmental data in real time. For example, after partial foundation installation is completed in the construction area, the new terrain elevation data may change, and the model needs to be re-scanned and updated. In addition, changes in geological conditions during construction, such as fluctuations in the groundwater level, also need to be dynamically monitored through a sensor array. The data collected in real time is transmitted to the three-dimensional modeling system through wireless transmission or the storage module of on-site devices for fusion processing with the data in the existing model.

[0065] During the integration process, the newly added data is compared with the existing model through a differential analysis method to locate the changed areas, and only the model data in these areas is updated. This method of local update can significantly improve the processing efficiency while avoiding interference with the areas that have not changed. For example, when the soil layer thickness in a specific area within the construction area is adjusted due to construction, only the geological data related to this area is updated, while the rest of the area remains unchanged. For the newly added environmental data, such as the change in precipitation, through the method of time series superposition, it is mapped to the corresponding geographical location in the model, enabling the model to display the change trend of environmental conditions in real time.

[0066] Through a three-dimensional visualization tool, the terrain, geology, and environmental characteristics in the digital model are presented in an intuitive graphical form. The visualization tool can generate three-dimensional contour maps, geological stratification profiles, and trend maps of dynamic environmental conditions. Construction personnel can intuitively understand the construction conditions through these visualization results. For example, the elevation change in a certain area can be displayed through color gradients, and the soil type and bearing capacity of the underground strata can be represented by different materials in the profile. In terms of environmental changes, the model can display the historical data and future trend predictions of precipitation, wind speed, temperature, and humidity by overlaying dynamic layers. This intuitive presentation method helps construction personnel quickly understand the on-site situation and assist in optimizing the installation plan of precast foundations.

[0067] In addition, the three-dimensional visualization tool also supports interactive functions. Construction personnel can view the detailed data of a specific area in the model by selecting it, including terrain elevation, soil type, and environmental conditions. This interactivity can meet the specific needs of different construction stages. For example, view the geological conditions before foundation installation, monitor environmental changes during installation, and evaluate the stability of the constructed area after installation.

[0068] By introducing a dynamic update mechanism and a three-dimensional visualization tool, the digital model of the construction area not only achieves real-time and accuracy but also significantly improves the readability and application value of information through graphical presentation. This method ensures the dynamic adjustment of data and the optimization of the plan during the construction process, and at the same time provides technical support and reliable guarantee for the project implementation in a complex construction environment.

[0069] Step S102: According to the design parameters, divide the precast foundation into a main load-bearing section and an auxiliary leveling section. The main load-bearing section is used to bear the weight of the equipment, and the auxiliary leveling section is used to adapt to the terrain changes at the installation location to adjust the levelness and elevation of the precast foundation.

[0070] Step S102 involves dividing the precast foundation into a main load-bearing section and an auxiliary leveling section according to the design parameters, where the main load-bearing section is used to bear the weight of the equipment, and the auxiliary leveling section is used to adapt to the terrain changes at the installation location to adjust the levelness and elevation of the precast foundation. The following is the specific implementation of this step.

[0071] First, according to the design parameters determined in step S101, including foundation type, weight, geometric dimensions, etc., conduct an overall mechanical property analysis of the precast foundation. Use finite element analysis software (such as ANSYS) to simulate the stress distribution of the precast foundation when bearing the weight of the equipment, so as to clarify the main action area and auxiliary action area where the equipment weight is concentrated. The main load-bearing section should cover the main application range of the equipment load and be designed to have a high load-bearing capacity, for example, by increasing the cross-sectional size or using high-strength materials to enhance its mechanical properties.

[0072] Subsequently, determine the design requirements for the auxiliary leveling section according to the variation characteristics of the terrain data and the terrain slope information in the digital model. The role of the auxiliary leveling section is to adapt to the terrain undulation at the foundation installation location and adjust the overall levelness and elevation of the foundation through height adjustment. In the design, the auxiliary leveling section usually adopts a screw adjustment structure or a nested adjustment unit to accurately adjust the height and angle during the installation process. The adjustment range should be calculated based on the terrain data to ensure effective adaptation in construction areas with large terrain changes.

[0073] Next, verify the stability of the foundation in combination with the geological parameters, especially whether the force condition of the main load-bearing section will be affected during the adjustment of the auxiliary leveling section. By simulating the adjustment states under different terrain conditions, verify whether the connection strength and anti-overturning ability of the overall structure of the foundation meet the design requirements after the levelness and elevation adjustment are completed.

[0074] In terms of physical structure division, the main load-bearing section and the auxiliary leveling section should adopt a detachable or modular design to facilitate transportation and installation at the construction site. The bottom of the main load-bearing section is designed as a flat grounding surface, and the contact area with the foundation is optimized through materials or geometric characteristics to enhance grounding stability. The connection between the auxiliary leveling section and the main load-bearing section should be designed as an adjustable and fixed combined structure, for example, fixed by bolts and supplemented with adjustable gaskets to ensure that the two sections can stably maintain an integrated state after adjustment.

[0075] Finally, record the division results of the main load-bearing section and the auxiliary leveling section in the digital model, including detailed parameters such as the geometric dimensions, load-bearing capacity, and adjustment range of each section. These parameters will be used for dynamic analysis and real-time adjustment in the subsequent optimization of the installation plan and construction guidance. Through such division design, the precast foundation can balance the load-bearing performance and terrain adaptability, ensuring both mechanical stability and flexibility in dealing with the terrain complexity of the construction site during the installation process.

[0076] Step S103: Combine the terrain data and design parameters in the digital model, and use the genetic algorithm to generate the optimal installation plan for the precast foundation. The optimal installation plan includes the installation position, installation direction of each precast foundation, and the adjustment height of the auxiliary leveling section. The optimization goal is to minimize the adjustment amount of the auxiliary leveling section while ensuring that the connection strength between adjacent precast foundations meets the design requirements.

[0077] Step S103 involves combining the terrain data and design parameters in the digital model and using the genetic algorithm to generate the optimal installation plan for the precast foundation. The specific implementation method of this step is as follows.

[0078] First, extract the terrain data of the construction area from the digital model established in step S101. The terrain data should include the elevation, slope of the possible installation positions of each precast foundation, and the horizontal distance between adjacent installation points. At the same time, obtain the geometric dimensions, weight, and maximum adjustment range of the leveling section of each precast foundation from the design parameters. These parameters are used as input variables for the optimization calculation. To ensure the accuracy of the results, necessary normalization processing should be performed on these input variables to eliminate the influence of different dimensions on the calculation results.

[0079] Next, based on the optimization framework of the genetic algorithm, set the objective function and constraint conditions. The main optimization goal of the objective function is to minimize the adjustment amount of the auxiliary leveling section to reduce the workload and error accumulation of the installation adjustment. The constraint conditions include ensuring that the connection strength between the precast foundations meets the design requirements, which can be specifically achieved by restricting the horizontal distance of the installation positions and the contact area between adjacent foundations. At the same time, it is also necessary to ensure that the adjustment height of the foundation does not exceed the design range of the auxiliary leveling section to avoid structural instability.

[0080] In the initialization stage of the genetic algorithm, randomly generate several initial populations of installation plans. The encoding of each installation plan should cover parameters such as the installation position, installation direction, and adjustment height of the precast foundation. In each iteration, calculate the fitness value of each installation plan according to the objective function, and generate a new population through selection, crossover, and mutation operations. The selection operation preferentially retains high-quality individuals according to the fitness value. The crossover operation generates new plans by combining the high-quality parts of different installation plans. The mutation operation is used to introduce random changes to increase the diversity of the plans.

[0081] To accelerate convergence and improve the quality of the solution, a constraint handling strategy can be adopted, such as adding a penalty factor to the fitness function to penalize the solutions that violate the constraint conditions. In addition, a step size dynamic adjustment mechanism can be combined. A larger search step size is adopted at the initial stage of iteration to quickly explore the solution space, and the step size is reduced at the later stage of iteration to accurately locate the optimal solution.

[0082] After a certain number of iterations or when the preset convergence condition is reached, the installation solution with the highest fitness value is selected as the optimal solution. This optimal installation solution includes the specific installation positions, installation directions of each precast foundation, and the adjustment heights of the auxiliary leveling sections. These parameters will be used to guide the actual installation of the foundation during the construction stage.

[0083] Finally, the generated optimal installation solution is associated and stored with the digital model for deviation comparison and dynamic adjustment in subsequent steps. This method effectively balances the contradiction between minimizing the adjustment amount and meeting the design requirements of the connection strength through optimization calculations, ensuring the optimality of the installation solution in theory and the operability of actual construction.

[0084] Furthermore, the genetic algorithm includes the following steps:

[0085] Step F101: Initialize a population that contains multiple individuals, and each individual represents a specific precast foundation installation solution. The installation solution includes the installation position, installation direction of the foundation, and the adjustment height of the auxiliary leveling section;

[0086] Step F102: Conduct performance evaluation on each individual in the population. The performance evaluation is based on a predefined multi-objective fitness function. The fitness function aims to minimize the adjustment amount of the auxiliary leveling section while ensuring that the connection strength between adjacent precast foundations meets the design requirements;

[0087] Step F103: Perform non-dominated sorting on the individuals in the population according to the fitness function values, and select the individuals with better performance to enter the next generation population;

[0088] Step F104: Perform crossover and mutation operations on the next generation population to generate new precast foundation installation solutions;

[0089] Step F105: Repeat steps F102 to F104 until the predetermined number of iterations is reached or the specified performance index meets the stop condition.

[0090] The implementation method of the genetic algorithm includes steps of initializing the population, performance evaluation, non-dominated sorting, crossover and mutation operations, and iterative optimization. The following is a detailed description of each step.

[0091] First, in the initialization stage of the genetic algorithm, a population containing multiple individuals is created. Each individual corresponds to an installation plan for a precast foundation. The parameters of the installation plan include the installation location, installation direction, and adjustment height of the auxiliary leveling section. During initialization, the parameters of the installation location are randomly generated according to the predetermined foundation distribution area in the digital model, ensuring that the installation locations of each individual conform to the actual terrain conditions of the construction area. The installation direction is generated through random distribution within a preset range, and the specific angle can be restricted in combination with the slope information of the terrain to avoid generating unreasonable direction values. The adjustment height of the auxiliary leveling section is randomly generated, and the range is limited within the maximum allowable adjustment range of the leveling section to ensure the feasibility of the design plan. The population size is usually set according to computing resources and optimization requirements. For example, it contains 50 to 100 individuals to provide sufficient solution space exploration ability.

[0092] In the performance evaluation stage, a predefined multi-objective fitness function is used to evaluate each individual. The fitness function should comprehensively consider the adjustment amount of the auxiliary leveling section, the connection strength between adjacent foundations, and the overall construction efficiency of the foundation installation plan. For example, by calculating the actual adjustment height of the auxiliary leveling section, the performance of the individual in minimizing the adjustment amount is evaluated; the connection strength between adjacent foundations is evaluated through a finite element analysis model to determine whether it meets the design requirements; the overall efficiency of each plan is calculated by combining construction time and energy consumption. The results of the performance evaluation quantitatively measure the quality of each individual in numerical form, providing a basis for subsequent sorting and selection.

[0093] Subsequently, based on the results of the performance evaluation, a non-dominated sorting method is used to sort the individuals in the population. The principle of non-dominated sorting is to select individuals with better performance in each objective according to the Pareto optimization theory of multi-objective optimization. For each individual, its fitness function value is compared with the performance of other individuals to determine its sorting level in the population. Individuals with better performance (i.e., non-dominated individuals) are preferentially retained to generate the next generation of the population. This method can effectively balance the contradictions between various optimization objectives and improve the diversity and quality of the solutions.

[0094] After entering the crossover and mutation stage, new individuals are generated through genetic operations. The crossover operation selects two parent individuals and generates new offspring individuals by combining their installation location, direction, and adjustment height parameters. The crossover method can adopt single-point crossover, multi-point crossover, or uniform crossover methods to improve the diversity of offspring individuals. The mutation operation introduces small-scale random perturbations. For example, a random offset is added to the installation location, Gaussian noise is applied to the direction parameter, or a new value is generated for the adjustment height using a non-linear random distribution. These operations can expand the search space and prevent the algorithm from falling into local optimal solutions.

[0095] Finally, the algorithm enters the iterative stage, starting from performance evaluation, repeating the operations of sorting, crossover, and mutation, gradually optimizing the individuals in the population until the termination condition is met. The termination condition can be reaching a predetermined number of iterations or the fitness value of the population converging within a certain range. When the condition is satisfied, the individual with the highest fitness value is selected as the final optimal installation plan to guide the actual installation of the precast foundation.

[0096] Through the comprehensive application of the above steps, the genetic algorithm can efficiently generate precast foundation installation plans suitable for complex terrains and construction conditions, taking into account both construction accuracy and efficiency, and has high technical feasibility and engineering practical value.

[0097] Furthermore, the fitness function in the performance evaluation adopts the following formula 1:

[0098]

[0099] Among them, F is the fitness function value, used to comprehensively evaluate the quality of the precast foundation installation plan;

[0100] H d represents the adjustment height of the auxiliary leveling section, in meters, used to reflect the ability of the foundation to adapt to the terrain. Its value is derived from the difference between the designed height of the foundation calculated from the digital model and the actual terrain elevation. An excessive adjustment height will lead to a decrease in construction complexity and stability. Therefore, the optimization goal is to minimize H d ;

[0101] ΔL represents the horizontal position deviation between adjacent precast foundations, in meters, used to measure the flatness of the foundation. Its value is calculated by actually measuring the deviation between the horizontal position of the foundations and the target position using a laser rangefinder sensor.

[0102] S c represents the connection strength between adjacent precast foundations, in Newtons, used to measure the stability of the foundation at the connection. Its value is derived from the calculation of the normal pressure P n and the friction coefficient μ. The formula is S c =P n ·μ. P n can be simulated by the finite element method.

[0103] T f represents the construction time of the current plan, in hours, estimated by the construction simulation system, considering the operation time of foundation transportation, installation, and leveling. A shorter construction time is generally better than a longer one, but it needs to be balanced with other factors.

[0104] T max represents the known maximum construction time, used to normalize the time efficiency;

[0105] σ is a normal distribution parameter used to adjust the decay rate of the horizontal position deviation ΔL in the fitness, and the recommended value is 0.01.

[0106] E f represents the construction energy consumption efficiency, with the unit of kilowatt-hour / ton. It is calculated through the construction equipment power P, construction time T, and precast foundation weight W, and the formula is E f =(P·T) / W.

[0107] k 1 and k 2 are empirical coefficients related to the specific terrain and foundation design, used to balance the influence of the height adjustment amount, horizontal deviation, and connection strength; k 1 The recommended value is from 0.1 to 0.5, used to control the interaction effect of H d and ΔL; k 2 The recommended value is from 0.1 to 1, used to reflect the influence of the horizontal deviation on the connection strength.

[0108] w 1 、w 2 、w 3 and w 4 are weight coefficients, and the recommended values are 0.1, 0.3, 0.2, and 0.4 respectively.

[0109] The first term in Formula 1 strongly constrains the adjustment amount by adjusting the weighted term of the square of the height and the square root of the deviation, giving priority to optimizing the small adjustment amount scheme. The second term adjusts the connection stability with the square of the connection strength and the horizontal deviation term k ·ΔL to ensure the priority of the connection strength. The third term combines the exponential decay function 2 ·ΔL to adjust the connection stability and ensure the priority of the connection strength. The third term combines the exponential decay function and the time normalization term to optimize the construction efficiency. The fourth term limits the high-energy consumption scheme through the logarithmic function of the energy consumption ln(1 + E f ).

[0110] Through the above formula, the genetic algorithm can achieve an all-round optimization of construction efficiency, structural stability, and construction economy, ensuring the technical feasibility and practicality of the engineering plan.

[0111] Furthermore, the mutation operation in the genetic algorithm includes the following steps:

[0112] For the individuals in the current population, mutate according to the set mutation probability P m , specifically including:

[0113] For the installation position of the precast foundation, calculate the mutation offset (Δx, Δy) according to the following Formulas 2 and 3:

[0114]

[0115] wherein, σ x and σ y are the standard deviations in the x and y directions of the installation position, respectively, calculated from the local change rate of the terrain data; specifically, these two values are determined by the statistical results of the terrain change rate in the digital model.

[0116] θ represents the initial azimuth angle of the precast foundation in the digital model, which is derived from the reference direction of the foundation design in the digital model of the construction area.

[0117] α x and α y are attenuation coefficients used to balance the terrain complexity and the offset amplitude, and their recommended values are 0.01 and 0.02, respectively.

[0118] d represents the distance between adjacent precast foundations;

[0119] For the installation direction, the generation of the variation angle Δθ is calculated using the following formula 4:

[0120] Δθ = σ θ ·(1 + ln(1 + k θ ·RMS slope )) (4)

[0121] wherein, σ θ is the standard deviation of the direction variation, obtained by analyzing the statistical distribution of the terrain slope change, and the recommended value of 0.5 can also be used.

[0122] k θ is the direction adjustment coefficient used to amplify or reduce the sensitivity of the direction variation, and its recommended value is 0.1.

[0123] RMS slope represents the root mean square value of the slope, used to quantify the overall complexity of the terrain in the construction area. Its calculation method is: take the square of the slope values of all sampling points in the construction area, average them, and then take the square root, which is extracted from the digital model.

[0124] For the adjustment height of the auxiliary leveling section, the variation value Δh is calculated based on the following formula 5:

[0125]

[0126] wherein, κ h is the gain coefficient of the adjustment range, and the recommended value is 0.2;

[0127] λ is the height growth rate, and the recommended value is 0.1.

[0128] h t$H_0$ is the current target height of the precast foundation, which is the theoretical height of the foundation design and can be directly obtained from the elevation of the foundation design in the digital model.

[0129] $\eta$ is the amplitude of the random noise, and the recommended value is 0.02.

[0130] $\beta$ is the adjustment coefficient, and the recommended value is 0.7.

[0131] $H$ d1 is the current actual height of the auxiliary leveling section.

[0132] The design of the above mutation formula can explore a wider solution space during the optimization process by combining actual terrain data, geometric design parameters of the foundation, and construction conditions, while ensuring the physical and engineering feasibility of the mutation results. This method of multi-parameter constraint and dynamic adjustment enhances the adaptability of the genetic algorithm, enabling it to effectively cope with the complex and changeable construction environment in the distribution network project.

[0133] The following is the detailed reference code for implementing the genetic algorithm:

[0134]

[0135]

[0136]

[0137]

[0138]

[0139] Step S104: During the construction process, the spatial position data of the installed precast foundation is collected in real time through a laser distance sensor, and the spatial position data is compared with the optimal installation plan to calculate the deviation values of the actual installation position, direction, and height.

[0140] Step S104 involves collecting the spatial position data of the installed precast foundation through a laser distance sensor during the construction process, comparing it with the optimal installation plan, and calculating the deviation values of the actual installation position, direction, and height. The specific implementation method of this step is as follows.

[0141] In the actual construction site, first install the laser distance sensor at an appropriate reference point. The reference point should have sufficient stability and field of view coverage to cover the entire installation area of the precast foundation within the sensor measurement range. Total station or high-precision 3D laser scanner can be used as the measurement equipment, which has high spatial resolution and measurement accuracy and can capture the position data of the installed foundation in real time. The measurement equipment needs to be initialized and calibrated, including the definition of the coordinate system and the verification of the ranging accuracy, to ensure the reliability of the collected data.

[0142] After the installation of each precast foundation is completed, the laser distance sensor obtains its three-dimensional coordinate data by scanning the outer surface of the foundation. To accurately describe the position and attitude of the foundation, the key points collected should include the four corner points of the foundation and its top center point, so as to be able to completely define the plane position, height and levelness of the foundation. The data collected by the sensor is stored in the form of a point cloud and converted into available spatial coordinate information through point cloud processing software.

[0143] Compare the collected spatial position data with the optimal installation plan generated in step S103. First, the coordinate systems of the two need to be unified. By matching the origin of the digital model with the reference point of the sensor and adopting a feature point-based registration algorithm, ensure that the comparison results of the actual measurement data and the optimal installation plan are consistent. The deviation calculation in the comparison process includes position deviations in three dimensions: position, direction and height. The position deviation is obtained by calculating the Euclidean distance between the actual coordinates and the target coordinates. The direction deviation is determined by calculating the included angle between the plane angle of the foundation and the target angle. The height deviation is directly obtained by comparing the difference between the actual height and the target height.

[0144] In the process of deviation calculation, in order to improve the accuracy, the measurement data can be filtered. For example, the Kalman filtering method is used to smooth the dynamically collected data to remove the noise interference in the construction environment. The deviation values in the calculation results need to be stored in the form of specific numerical values, and deviation reports are generated according to the specific installation conditions of different foundations, specifying the deviation amounts of each foundation and possible adjustment requirements.

[0145] In this way, the construction personnel can timely master the actual installation status of each precast foundation and provide accurate input data for the subsequent leveling plan. The measurement and comparison process in step S104 realizes the real-time monitoring of the construction accuracy and provides technical guarantee for ensuring the installation quality and construction efficiency.

[0146] Furthermore, during the construction process, the spatial position data of the installed precast foundation is collected in real time through a laser distance sensor, including:

[0147] After the preliminary installation of each precast foundation, the laser distance sensor is used to measure the spatial coordinates of its key points in real time. The key points include the four corner points and the top center point of the precast foundation to completely describe the plane position, height and levelness of the foundation;

[0148] The laser distance sensor obtains three-dimensional position data through multi-point scanning and combines it with the digital model coordinate system of the construction area to convert the measurement results into numerical expressions in the global reference coordinate system;

[0149] During the measurement process, the measurement angle and range of the sensor are controlled by a high-precision rotating turntable to ensure that all key points of the foundation are covered.

[0150] In this embodiment, the spatial position data of the pre-installed precast foundation is collected in real time by a laser distance sensor during the construction process to ensure that the installation accuracy of each precast foundation meets the design requirements. This process involves multiple refined steps to ensure the comprehensiveness and accuracy of the measurement data.

[0151] After the preliminary installation of each precast foundation, the spatial coordinates of the key points of the foundation are first measured using a laser distance sensor. The selection of key points includes the four corner points and the top center point of the precast foundation. The measurement data of these points can completely describe the plane position, elevation, and levelness of the foundation, and provide necessary reference information for subsequent deviation analysis. Through the precise positioning of these key points, problems such as inclination, displacement, or height mismatch that may exist during the installation process can be identified.

[0152] The laser distance sensor collects the three-dimensional position data of the foundation through multi-point scanning. In actual measurement, the sensor gradually covers the entire surface of the foundation with a certain scanning step size and angular step. During the measurement process, by matching the collected data with the digital model coordinate system of the construction area, the spatial position data of the foundation is converted from the local coordinate system of the sensor to the global reference coordinate system. This coordinate conversion process is based on the known global reference points and the designed position of the foundation in the digital model of the construction area to ensure that the measurement results can accurately reflect the actual position of the foundation in the entire construction area.

[0153] To ensure the comprehensiveness of the measurement data, the laser distance sensor is installed on a high-precision rotating turntable. The rotating turntable can flexibly adjust the measurement angle and range of the sensor to ensure that all key points are covered. Through rotation control, the sensor can sequentially cover the top and side surfaces of the foundation along a predetermined scanning path. For foundations with complex shapes or limited installation positions, the rotating turntable can cover areas that are difficult to directly measure by adjusting the angle, thereby ensuring the integrity of the data.

[0154] During the entire measurement process, the accuracy of the sensor directly affects the reliability of the data. To improve the measurement accuracy, the sensor needs to be automatically calibrated before each measurement. The calibration process measures the known reference points in the construction area and compares them with the standard coordinate values in the digital model to calculate and correct the measurement error of the sensor. In addition, in actual measurement, to eliminate possible interference signals (such as vibration or dust) in the construction environment, the measurement data is denoised and filtered to ensure that the final spatial position data used for analysis has high precision and stability.

[0155] Through the above measurement method, the collected spatial position data can comprehensively reflect the actual installation status of the installed precast foundation and provide reliable input data for subsequent deviation analysis and leveling scheme generation.

[0156] Furthermore, the comparison of the spatial position data with the optimal installation scheme to calculate the deviation values of the actual installation position, direction, and height includes:

[0157] Point-by-point comparison of the spatial position data obtained by the laser distance sensor with the target position parameters of the corresponding foundation in the optimal installation scheme; specifically, it includes extracting the actual three-dimensional coordinates of each key point and the target coordinates of the optimal scheme, and calculating the displacement differences in each direction, including the horizontal offset of the planar position and the height difference in the vertical direction;

[0158] For the installation direction, by calculating the offset angle of the foundation key points in the plane, the included angle difference between its actual direction and the target direction in the optimal installation scheme is evaluated to ensure the integrity of the deviation calculation covering the installation attitude;

[0159] In the height direction, by comparing the actual elevation of the center point at the top of the foundation with the target elevation, the vertical deviation of the auxiliary leveling section is determined;

[0160] After completing the calculation of the position and direction deviations, the deviation values are stored in the digital model of the construction area, and a deviation analysis report is generated, which includes the specific deviation values of all key points and their relative positions.

[0161] In this embodiment, by comparing the spatial position data collected by the laser distance sensor with the optimal installation scheme, the deviation values of the position, direction, and height of the precast foundation during the actual installation process can be accurately calculated, thereby providing a reliable basis for optimizing the installation. The entire process requires a detailed analysis of the spatial position data and presenting the calculation results in a systematic manner.

[0162] First, extract the key points of the installed foundation from the spatial position data collected by the laser distance sensor, including the four corner points and the center point at the top of the foundation. The three-dimensional coordinates of these key points represent the actual installation status of the foundation. Compare these actual coordinates with the preset target coordinates in the optimal installation scheme point by point to calculate the deviation of each key point in the three-dimensional space. The deviation analysis is divided into two parts: the horizontal offset of the planar position and the vertical difference in the height direction. In the planar position, by comparing the x and y components of the actual and target coordinates, the displacement amounts of the foundation in the east-west and north-south directions are determined. In the height direction, by comparing the actual elevation of the center point at the top of the foundation with the target elevation, the height deviation of the auxiliary leveling section is evaluated. This multi-directional deviation calculation method can comprehensively reflect whether the installation position of the foundation meets the design requirements.

[0163] For the installation direction, by calculating the offset angle of the base key points in the plane, the angular difference between the actual direction and the target direction is evaluated. Specifically, through vector analysis of the actual and target positions of the corner points, the included angle between the two is calculated to determine whether the base has deflected during installation. The evaluation of the direction deviation can not only reflect the deviation degree of the base attitude, but also further provide a clear directional reference for adjustment.

[0164] After calculating the deviations in position and direction, all deviation values are recorded in the digital model of the construction area to achieve dynamic update of the model and visualization of the deviations. The deviation values are stored in numerical and graphical forms, including the specific deviation values, deviation types and their relative positions in the construction area for each key point. Based on these data, a deviation analysis report is generated, which details the actual deviation values of each base key point and the potential impact of these deviations on the overall base stability and connection strength.

[0165] The deviation analysis report is an important tool for construction quality control. It can help construction personnel intuitively understand the current installation state of the base and provide clear operation guidance for the areas that need adjustment. In addition, these deviation data can also be used in the subsequent process of generating the leveling plan to ensure that the installation of each base finally meets the design standards.

[0166] Through the above methods, the comparative analysis of the actual installation data and the design scheme can be efficiently implemented to ensure the installation accuracy and construction quality of the base. This detailed and systematic deviation calculation and analysis method provides strong technical support for intelligent management and optimization in the construction process.

[0167] Step S105: When the deviation value exceeds the preset threshold, a leveling plan is generated based on the real-time collected spatial position data. The leveling plan includes the compensation height and adjustment angle of the auxiliary leveling section, which are used to correct the installation deviation and ensure the flatness and structural stability between adjacent precast bases.

[0168] Step S105 involves generating a leveling plan based on the real-time collected spatial position data when the deviation value exceeds the preset threshold to correct the installation deviation and ensure the flatness and structural stability between adjacent precast bases. The implementation of this process includes the following specific contents.

[0169] First, the deviation values calculated in step S104 are evaluated. The deviation values include position deviation, direction deviation and height deviation, and each deviation is compared with the preset threshold respectively. If any one of the deviation values exceeds the threshold, the process of generating the leveling plan needs to be entered immediately. The preset threshold can be determined according to the engineering design requirements, such as the maximum allowable height difference between bases, the allowable deviation angle of levelness, and the maximum offset distance of the installation position.

[0170] Next, based on the collected spatial position data and combined with the terrain features in the digital model, the specific deviation types are classified and processed. For height deviation, the focus is on calculating the height compensation value that needs to be adjusted to ensure that the top of the foundation is consistent with the target elevation; for direction deviation, it is necessary to analyze the inclination direction and angle of the foundation and calculate the rotation angle required for adjustment; for position deviation, by determining the horizontal distance difference between the foundation and the target position, the displacement required for adjustment is generated. All adjustments need to be verified within the adjustment range of the auxiliary leveling section to ensure the feasibility of the design.

[0171] When generating a leveling scheme, digital calculation tools or intelligent control software are preferred to improve the accuracy and efficiency of the scheme. The generation of the scheme is usually based on the principle of reverse calculation, that is, the adjustment parameters of the leveling section are derived according to the deviation value of the basic current state and the target state. These parameters include the height value that needs to be increased or decreased by the auxiliary leveling section, the angle value of the adjustment, and the necessary support and fixing methods. For example, for height adjustment, it can be achieved by simulating the spiral lifting device of the leveling section; for angle adjustment, it can be achieved by changing the contact point angle between the leveling section and the main load-bearing section.

[0172] After the leveling plan is generated, it is transmitted to the construction equipment or operators on site. In specific implementation, the foundation can be adjusted by hydraulic equipment, mechanical arms or manual assistance. For example, during the height adjustment process, the operator controls the spiral lifting device of the auxiliary leveling section to reach the specified compensation height; during the direction adjustment process, the target tilt angle can be achieved by adjusting the angle of the wedge block under the foundation. After each adjustment is completed, the new position of the foundation needs to be re-measured using a laser rangefinder sensor to verify whether the adjustment has achieved the expected effect.

[0173] During the entire leveling process, in order to ensure the stability and connection strength after adjustment, the connection between adjacent foundations needs to be further checked. For example, by applying test loads, the shear strength and tensile strength of the foundation connection are evaluated to see if they meet the design requirements. If it is found that there are still errors between the foundations that exceed the standard, the leveling scheme can be iteratively optimized based on real-time measurement data until the design standard is met.

[0174] The final adjustment results and optimization data will be recorded and integrated into the digital model of the construction area. This dynamic update can transform the experience and data accumulated during the construction process into a reference for subsequent installation optimization, thereby realizing the intelligent and precise management of the entire project. Through this process, the installation accuracy and stability of the prefabricated foundation are effectively guaranteed, and the construction efficiency and quality are significantly improved.

[0175] A second embodiment of the present application provides an electronic device, the electronic device comprising:

[0176] Processor;

[0177] A memory for storing a program which, when read and executed by the processor, executes a method for optimizing the prefabricated foundation assembly and intelligent management of the construction progress in a distribution network project provided in the first embodiment of the present application.

[0178] The third embodiment of the present application provides a computer-readable storage medium with a computer program stored thereon, which, when executed by a processor, executes a method for optimizing the prefabricated foundation assembly and intelligent management of the construction progress in a distribution network project provided in the first embodiment of the present application.

[0179] Although the present application is disclosed above in preferred embodiments, it is not intended to limit the present application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application should be determined by the scope defined by the claims of the present application.

Claims

1. A method for optimizing the assembly of prefabricated foundations and intelligently managing the construction progress of a distribution network project, characterized in that: include: Collecting topographic data and environmental data of the construction area, establishing a digital model of the construction area, the digital model is used to describe the topographic features and construction environmental conditions, and determining the design parameters of the prefabricated foundation according to the digital model, the design parameters including the foundation type, weight and geometric dimensions; According to the design parameters, the prefabricated foundation is divided into a main load-bearing section and an auxiliary leveling section, wherein the main load-bearing section is used to bear the weight of the equipment, and the auxiliary leveling section is used to adapt to the terrain changes at the installation location to adjust the levelness and elevation of the prefabricated foundation; Combined with the terrain data and design parameters in the digital model, the optimal installation scheme of the precast foundation is generated by using a genetic algorithm. The optimal installation scheme includes the installation position, installation direction and adjustment height of the auxiliary leveling section of each precast foundation. The optimization goal is to minimize the adjustment amount of the auxiliary leveling section while ensuring that the connection strength between adjacent precast foundations meets the design requirements. During the construction process, the spatial position data of the installed prefabricated foundation is collected in real time through a laser ranging sensor, and the spatial position data is compared with the optimal installation plan to calculate the deviation value of the actual installation position, direction and height; When the deviation value exceeds a preset threshold, a leveling scheme is generated based on the spatial position data collected in real time. The leveling scheme includes a compensation height and an adjustment angle of the auxiliary leveling section, which are used to correct the installation deviation and ensure the flatness and structural stability between adjacent prefabricated foundations.

2. The method for optimizing the assembly of prefabricated foundations and intelligently managing the construction progress of distribution network engineering according to claim 1 is characterized in that: The collecting of topographic data and environmental data of the construction area and establishing a digital model of the construction area includes: Deploy automated surveying equipment in the construction area, including laser scanners, total stations and high-precision GNSS positioning devices carried by drones, to collect three-dimensional terrain data of the construction area; By deploying geological drilling equipment and sensor arrays, geological parameters and environmental conditions in the construction area are collected; The collected data are integrated and processed using 3D modeling software to create a digital model of the construction area.

3. The method for optimizing the assembly of prefabricated foundations and intelligently managing the construction progress of distribution network engineering according to claim 2 is characterized in that: The three-dimensional modeling software is used to integrate and process the collected data to establish a digital model of the construction area, including: The 3D point cloud data collected by the laser scanner and total station carried by the drone are fused and calibrated with the geographic coordinate data obtained by the high-precision GNSS positioning device to generate a high-resolution terrain model of the construction area. During the calibration process, the GNSS positioning data is used as the global benchmark to ensure that the accuracy of the point cloud model is consistent with the actual terrain; Match the stratigraphic data obtained by the geological drilling equipment with the geographical location corresponding to the point cloud model, and embed the geological layer structure information into the three-dimensional terrain model through multi-layer superposition, ensuring that the model can reflect the underground soil and geological characteristics while describing the surface characteristics; The environmental data collected by the sensor array are attached to the corresponding areas of the model as dynamic tags to describe the changing trends of environmental conditions during construction.

4. The method for optimizing the assembly of prefabricated foundations and intelligently managing the construction progress of distribution network engineering according to claim 3 is characterized in that: The method of integrating and processing the collected data using 3D modeling software to establish a digital model of the construction area also includes: Introducing a dynamic update mechanism to collect new terrain, geological and environmental data in real time during the construction process, integrate it with the existing model, and update the areas in the model related to the construction progress to ensure that the digital model always reflects the latest status of the construction site; The model’s topography, geology and environmental characteristics are presented in a graphical form through 3D visualization tools, allowing construction personnel to intuitively understand construction conditions and assist in optimizing the design and implementation of prefabricated foundation installation solutions.

5. The method for optimizing the assembly of prefabricated foundations and intelligently managing the construction progress of distribution network engineering according to claim 1, characterized in that: During the construction process, the spatial position data of the installed prefabricated foundation is collected in real time by using a laser ranging sensor, including: After each prefabricated foundation is initially installed, a laser range finder is used to measure the spatial coordinates of key points in real time, including the four corner points and the top center point of the prefabricated foundation, to fully describe the plane position, height and levelness of the foundation. The laser ranging sensor acquires three-dimensional position data by multi-point scanning, and converts the measurement results into numerical expressions in the global reference coordinate system in combination with the digital model coordinate system of the construction area; During the measurement process, the sensor’s measurement angle and range are controlled by a high-precision pan-tilt head to ensure that all key points of the foundation are covered.

6. The method for optimizing the assembly of prefabricated foundations and intelligently managing the construction progress of distribution network engineering according to claim 1, characterized in that: The step of comparing the spatial position data with the optimal installation solution and calculating the deviation values ​​of the actual installation position, direction and height includes: Compare the spatial position data obtained by the laser ranging sensor with the target position parameters of the corresponding foundation in the optimal installation solution point by point; specifically, extract the actual three-dimensional coordinates of each key point and the target coordinates of the optimal solution, and calculate the displacement differences in each direction, including the horizontal offset of the plane position and the height difference in the vertical direction; For the installation direction, the offset angle of the key points in the plane is calculated to evaluate the angle difference between the actual direction and the target direction in the optimal installation solution, ensuring that the deviation calculation covers the integrity of the installation posture. In the height direction, the vertical deviation of the auxiliary leveling section is determined by comparing the actual elevation of the center point of the top of the foundation with the target elevation; After completing the position and direction deviation calculations, the deviation values ​​are stored in the digital model of the construction area and a deviation analysis report is generated, which includes the specific deviation values ​​and relative positions of all key points.

7. The method for optimizing the assembly of prefabricated foundations and intelligently managing the construction progress of distribution network engineering according to claim 1, characterized in that: When the deviation value exceeds a preset threshold, a leveling solution is generated based on the spatial position data collected in real time, including: According to the digital model of the construction area and the spatial position data collected in real time, the deviation values ​​are classified and processed to determine the compensation requirements for plane position, installation direction and height respectively. Specifically, for plane position deviation, the required horizontal displacement compensation is calculated by analyzing the connection status between adjacent foundations, and the compensation effect is simulated in the digital model to verify its rationality. For installation direction deviation, the recommended value of the adjustment angle is generated in combination with the current inclination angle of the foundation and the terrain characteristics of the construction area to ensure that the foundation can maintain the connection strength with the adjacent foundation after the direction adjustment. For height deviation, the current height and remaining adjustment range of the auxiliary leveling section are analyzed to generate accurate height compensation to ensure that the top of the foundation reaches the designed elevation. After the compensation parameters are generated, specific leveling execution steps are formulated in combination with the operating characteristics of the construction equipment, including the adjustment of the plane position by changing the position of the support point at the bottom of the foundation or by using wedge adjustment; the adjustment of the installation direction is completed by the construction equipment controlling the inclination angle of the auxiliary leveling section; the height adjustment is completed by accurately controlling the lifting device of the auxiliary leveling section to match its compensation height with the generated recommended value; After the leveling plan is completed, it is uploaded to the construction monitoring system, and the real-time monitoring equipment is used to guide the construction personnel or automated equipment to perform the adjustment operations.

8. The method for optimizing the assembly of prefabricated foundations and intelligently managing the construction progress of distribution network engineering according to claim 1, characterized in that: The genetic algorithm comprises the following steps: Step F101: Initializing a population, the population including a plurality of individuals, each of which represents a specific prefabricated foundation installation scheme, the installation scheme including the installation position, installation direction and adjustment height of the auxiliary leveling section of the foundation; Step F102: performing a performance evaluation on each individual in the population, wherein the performance evaluation is performed based on a predefined multi-objective fitness function, wherein the fitness function takes minimizing the adjustment amount of the auxiliary leveling section as an optimization goal, while ensuring that the connection strength between adjacent prefabricated foundations meets the design requirements; Step F103: Perform non-dominated sorting on the individuals in the population according to the fitness function value, and select individuals with better performance to enter the next generation population; Step F104: performing crossover and mutation operations on the next generation population to generate a new prefabricated foundation installation scheme; Step F105: Repeat steps F102 to F104 until a predetermined number of iterations is reached or a specified performance indicator satisfies a stop condition.

9. The method for optimizing the assembly of prefabricated foundations and intelligently managing the construction progress of distribution network engineering according to claim 8, characterized in that: The fitness function in the performance evaluation adopts the following formula 1: Among them, F is the fitness function value, which is used to comprehensively evaluate the advantages and disadvantages of the prefabricated foundation installation scheme; H d represents the adjustment height of the auxiliary leveling section; ΔL represents the horizontal position deviation between adjacent precast foundations; S c Indicates the connection strength between adjacent precast foundations; T f Indicates the construction time of the current solution; T max represents the known maximum construction time, which is used to normalize the time efficiency; v is the normal distribution parameter, which is used to adjust the decay rate of the deviation in fitness; E f It represents the construction energy efficiency; k1 and k2 are empirical coefficients related to the specific terrain and foundation design, which are used to weigh the influence of height adjustment, horizontal deviation and connection strength; w1, w2, w3 and w4 are weight coefficients.

10. The method for optimizing the assembly of prefabricated foundations and intelligently managing the construction progress of distribution network engineering according to claim 8, characterized in that: The mutation operation in the genetic algorithm includes the following steps: For individuals in the current population, according to the set mutation probability P m Mutations include: The installation position of the precast foundation is calculated according to the following formulas 2 and 3 to calculate the variation offset (Δx, Δy): Among them, σ x and v y are the standard deviations of the installation position in the x and y directions, respectively, calculated from the local rate of change of the terrain data; θ represents the initial azimuth of the precast foundation in the digital model; α x and α y is the attenuation coefficient, which is used to balance the terrain complexity and the offset amplitude; d represents the distance between adjacent precast foundations; For the installation direction, the variation angle Δθ is generated using the following formula 4: Δθ=σ θ ·(1+ln(1+k θ ·RMS slope )) (4) Among them, σ θ is the standard deviation of directional variation; k θ is the direction adjustment coefficient; RMS slope represents the root mean square value of slope; For the adjustment height of the auxiliary leveling section, the variation value Δh is calculated based on the following formula 5: Among them, κ h is the gain coefficient of the adjustment range; λ is the height growth rate; h t is the current target height of the precast foundation; η is the random noise amplitude; β is the adjustment coefficient; H d1 The actual current height of the auxiliary leveling section.

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