A method and system for calculating the carbon footprint of a power tower throughout its life cycle

Abstract

The application relates to the technical field of power tower full life cycle carbon footprint accounting, and provides a power tower full life cycle carbon footprint accounting method and system, which are used to solve the problems of low efficiency and poor accuracy of power tower full life cycle carbon footprint accounting in the prior art. The application comprises the following steps: constructing a power tower component library, combining a building information model, a steel type, a welding process and anticorrosion coating parameters to generate a design stage carbon accounting benchmark; capturing component installation deviation and splicing gap in real time, calculating material loss caused by construction error and converting the material loss into carbon emission increment; loading data by using digital twin technology, adjusting the material loss warning threshold, and updating the design data; analyzing icing load deformation and corrosion expansion in the operation and maintenance stage based on the corrected data, and generating a full life cycle carbon footprint report combined with the updated stress distribution data. The technical scheme provided by the application can improve the efficiency and accuracy of power tower full life cycle carbon footprint accounting.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power tower full life cycle carbon footprint accounting, and particularly relates to a power tower full life cycle carbon footprint accounting method and system. BACKGROUND

[0002] With the increasing concern about climate change, various industries are seeking effective ways to reduce carbon emissions. In the power industry, power towers, as an important part of the transmission line, their full life cycle carbon footprint accounting is particularly important. Accurate carbon footprint accounting can help understand the impact on the environment from raw material extraction, manufacturing, transportation, installation, operation and maintenance to retirement disposal at each stage. This not only helps enterprises fulfill their social responsibilities and improve environmental awareness, but also provides a scientific basis for policymakers to promote the development and application of low-carbon technologies to meet the international community's requirements for reducing greenhouse gas emissions.

[0003] There are already some methods and tools for evaluating the full life cycle carbon footprint of power towers. These schemes are usually based on life cycle assessment theory, collecting data from various links such as raw material usage, energy consumption, and waste disposal methods to estimate total carbon emissions. In addition, some research has used model simulation methods combined with actual cases to analyze and try to provide more accurate results. In practical operation, these methods provide support for identifying key influencing factors and finding emission reduction opportunities, and to some extent, promote technological innovation and progress within the industry.

[0004] However, the existing schemes also have obvious shortcomings. First, in terms of data acquisition, due to the involvement of multiple links and the crossing of different regions and departments, it is extremely difficult to collect complete and accurate data, which may result in a large deviation in the final results. Second, existing methods focus more on qualitative description or local optimization, lacking systematic and comprehensive consideration, making it difficult to reflect the complex interaction relationship throughout the life cycle. Finally, due to the large differences in resource endowments in different regions, the adopted technical standards and management measures are not the same, making it challenging to compare across regions and limiting the widespread application of research results. The existence of these problems suggests the need to further explore and improve new methods suitable for power tower full life cycle carbon footprint accounting. SUMMARY

[0005] The present application provides a power tower full life cycle carbon footprint accounting method and system to solve the problem of low efficiency and poor accuracy in the prior art.

[0006] In a first aspect, the present application provides a power tower full life cycle carbon footprint accounting method, comprising:

[0007] constructing a power tower component library through high-precision three-dimensional modeling technology, the high-precision three-dimensional modeling technology being based on building information modeling to perform geometric parameterization modeling on a power tower structure, integrating steel type, welding process and anticorrosion coating parameters, and generating design stage carbon accounting benchmark data;

[0008] deploying a handheld laser radar device at a power tower construction site, and capturing installation angle deviation and component splicing gap of a power tower assembly in real time in a high-speed point cloud scanning manner, calculating material loss amount caused by construction error based on point cloud degree distribution, and converting the material loss amount into construction error carbon emission increment data;

[0009] loading the design stage carbon accounting benchmark data and the construction error carbon emission increment data through digital twinning technology, and comparing a tower design model with on-site construction point cloud data using a synchronous refreshing mechanism to identify insufficient bolt fastening and component redundant cutting phenomena, dynamically adjust a material loss warning threshold, and correct the design stage carbon accounting benchmark data;

[0010] based on the corrected design stage carbon accounting benchmark data, performing collaborative analysis on icing load deformation and corrosion area expansion in a power tower operation and maintenance stage, updating component stress distribution data using the high-precision three-dimensional modeling technology, and generating a full life cycle carbon footprint quantification report.

[0011] Optionally, the comparison of the tower design model with the on-site construction point cloud data using the synchronous refreshing mechanism to identify insufficient bolt fastening and component redundant cutting phenomena, dynamically adjust the material loss warning threshold, and correct the design stage carbon accounting benchmark data, comprises:

[0012] based on the geometric constraint conditions of bolt connection nodes in the tower design model, determining abnormal connection nodes with insufficient bolt fastening according to the difference in spatial density decay slope between adjacent bolt connection node clusters;

[0013] traversing the preset geometric topological relationship of the component splicing joint in the tower design model and the point cloud curvature distribution of the actual component splicing area in the on-site construction point cloud data, and locating the redundant cutting position of the component when the mutation amplitude of the edge point cloud curvature of the splicing joint exceeds the critical value of the plastic deformation of the component material;

[0014] based on the cutting area proportion of the abnormal connection nodes and the redundant cutting position of the component, establishing a nonlinear regression relationship between construction error and material loss amount, and dynamically adjusting the boundary conditions of the material loss warning threshold according to the gradient change trend of the material loss amount with the growth of the construction error;

[0015] The boundary condition is iteratively reversed into the member stress distribution data of the tower design model, the load transfer path of the stress concentration area is cut by weakening the member redundancy cutting phenomenon, and the design stage carbon accounting benchmark data is corrected.

[0016] Optionally, the nonlinear regression relationship between construction error and material loss is established based on the cutting area proportion of the abnormal connection node and the member redundancy cutting position, comprising:

[0017] The gradient of the spatial density attenuation slope difference of the abnormal connection node is calculated, the three-dimensional neighborhood point cloud density radial gradient vector of the bolt connection node is analyzed, the direction deviation degree of the adjacent three-dimensional neighborhood point cloud density radial gradient vector is used to screen each abnormal connection node, and the deviation angle sine value and the spatial density attenuation slope are associated to generate a deviation degree weight factor;

[0018] The eigenvalue of the local coordinate system decomposition curvature tensor of the spliced seam edge point cloud is used to identify the curvature mutation area combined with the material yield strength threshold, and the projection area proportion of the member redundancy cutting position is analyzed by three-dimensional surface parameterization projection;

[0019] The spatio-temporal composite feature is constructed by fusing the deviation degree weight factor and the projection area proportion, and the error accumulation feature of the assembly time sequence is extracted by using a sliding window to generate a high-dimensional regression input set;

[0020] Based on the material plastic deformation critical value, the dynamic reduction coefficient of the construction error to the bearing area is defined, the high-dimensional regression input set is associated to the material loss, and the nonlinear regression relationship between the construction error and the material loss is established.

[0021] Optionally, the boundary condition of dynamically adjusting the material loss warning threshold according to the gradient change trend of the material loss with the growth of the construction error, comprising:

[0022] Based on the spatio-temporal distribution of the construction error, a multidimensional gradient field is constructed, and the main direction module length of the multidimensional gradient field is extracted, wherein the time dimension module length represents the error accumulation rate, and the spatial dimension module length reflects the material loss diffusion intensity;

[0023] A sliding window is set on the member assembly time sequence, the dynamic covariance of the main direction module length is analyzed, when the main direction module length exceeds three times the standard deviation of the historical mean value, it is marked as a gradient direction mutation area, and according to the correlation between the module length of the gradient direction mutation area and the material loss diffusion intensity, a dynamic adjustment factor is obtained;

[0024] Based on the load transfer path topology of the stress concentration area, the dynamic adjustment factor is used to attenuate the load weight of the adjacent nodes of the redundancy cutting area, and the boundary condition of dynamically adjusting the material loss warning threshold is adjusted.

[0025] Optionally, the material loss amount caused by the construction error is calculated based on the point cloud distribution, and the material loss amount is converted into construction error carbon emission increment data, including:

[0026] Based on the power tower component point cloud distribution data obtained by high-speed point cloud scanning, the point cloud normal vector offset corresponding to the component installation angle deviation and the point cloud hollow area corresponding to the component splicing gap are extracted;

[0027] According to the correlation between the point cloud normal vector offset and the steel bending deformation coefficient, the component plastic deformation compensation amount caused by the component installation angle deviation is calculated, and based on the matching degree between the point cloud hollow area and the component welding penetration parameter, the steel redundant filling amount caused by the splicing gap is calculated;

[0028] The component plastic deformation compensation amount and the steel redundant filling amount are classified and superimposed according to the component type to generate a material loss amount corrected by construction error, and based on the steel model carbon footprint coefficient and the welding process unit heat input carbon emission factor, the material loss amount is converted into construction error carbon emission increment data.

[0029] Optionally, the steel redundant filling amount caused by the splicing gap is calculated based on the matching degree between the point cloud hollow area and the component welding penetration parameter, including:

[0030] The point cloud hollow area is analyzed in three dimensions, and the depth distribution gradient field and surface curvature characteristics are extracted, and the depth difference and curvature change rate of the point cloud hollow area are used to generate a penetration requirement distribution field;

[0031] Based on the unit penetration filling amount coefficient in the component welding penetration parameter, a mapping relationship between penetration and filling amount is constructed, and according to the depth gradient direction of each region in the penetration requirement distribution field and the spatial superposition relationship of the welding path, the matching degree difference value between the actual penetration and the theoretical penetration is analyzed;

[0032] In the local coordinate system of the welding path, the matching degree difference value is spatially integrated, the curvature characteristics of the penetration requirement distribution field are used to correct the integration weight, and the three-dimensional spatial distribution is calculated in combination with the material expansion coefficient of the welding heat affected zone.

[0033] Based on the three-dimensional spatial distribution and the geometric topological relationship of the component splicing joint, the steel filling amount cumulative value is segmented and counted, and the molten pool dynamic shrinkage rate in the welding process parameter is associated to generate the steel redundant filling amount.

[0034] Optionally, the icing load deformation and corrosion area expansion in the operation and maintenance stage of the power tower are analyzed in coordination, the component stress distribution data updated by the high-precision three-dimensional modeling technology are combined to generate a carbon footprint quantification report for the whole life cycle, including:

[0035] Based on the space-time distribution of ice thickness, the dynamic stress field of ice load on the surface of the component is established, and through the coupling relationship between the deformation rate of ice load and the curvature change of the component, the stress concentration coefficient and the elastic modulus attenuation gradient are obtained;

[0036] The thickness of the oxide layer and the density of microcracks in the corrosion area are extracted, and the corrosion diffusion rate model is established combined with the environmental temperature and humidity data to generate the material thickness attenuation field;

[0037] The stress concentration coefficient, elastic modulus attenuation gradient and material thickness attenuation field are coupled and related, and based on the stress distribution data of the component, an ice and corrosion synergistic degradation model is established, and when the weight of ice and corrosion synergistic degradation exceeds the material fatigue strength threshold, carbon footprint increment data is generated;

[0038] Based on the space-time distribution characteristics of ice and corrosion synergistic degradation and the carbon footprint increment data, a carbon flow vector matrix is constructed to generate a full life cycle carbon footprint quantification report.

[0039] In the second aspect, the application provides a full life cycle carbon footprint accounting system for a power tower, comprising:

[0040] The integration module constructs a power tower component library through high-precision three-dimensional modeling technology, which parameterizes the geometry of the power tower structure based on the building information model, integrates steel type, welding process and corrosion protection coating parameters, and generates design stage carbon accounting benchmark data;

[0041] The conversion module deploys a handheld laser radar device at the construction site of the power tower, and captures the installation angle deviation and component splicing gap of the power tower assembly in real time in a high-speed point cloud scanning mode, calculates the material loss caused by construction errors based on the point cloud distribution, and converts the material loss into construction error carbon emission increment data;

[0042] The correction module loads the design stage carbon accounting benchmark data and the construction error carbon emission increment data through digital twinning technology, and compares the tower design model with the on-site construction point cloud data using a synchronous refreshing mechanism to identify insufficient bolt tightening and component redundant cutting phenomena, dynamically adjust the material loss warning threshold, and correct the design stage carbon accounting benchmark data;

[0043] The generation module, based on the corrected design stage carbon accounting benchmark data, cooperatively analyzes the ice load deformation and corrosion area expansion in the operation and maintenance stage of the power tower, and generates a full life cycle carbon footprint quantification report combined with the component stress distribution data updated by the high-precision three-dimensional modeling technology.

[0044] In a third aspect, the embodiments of the present application provide a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the power tower full life cycle carbon footprint accounting method according to the first aspect.

[0045] In a fourth aspect, the embodiments of the present application provide a computer storage medium, which stores a computer program, and when the computer program is executed by a computer, a power tower full life cycle carbon footprint accounting method according to the first aspect is implemented.

[0046] In the embodiments of the present application, a power tower component library is constructed by high-precision three-dimensional modeling technology, the high-precision three-dimensional modeling technology is based on building information modeling to perform geometric parameterization modeling on the power tower structure, integrates steel type, welding process and anticorrosion coating parameters, and generates design stage carbon accounting benchmark data; a handheld laser radar device is deployed at the power tower construction site, and the installation angle deviation and component splicing gap of the power tower assembly are captured in real time in a high-speed point cloud scanning mode, the material loss amount caused by construction error is calculated based on point cloud degree distribution, and the material loss amount is converted into construction error carbon emission increment data; the design stage carbon accounting benchmark data and the construction error carbon emission increment data are loaded by digital twin technology, and the synchronous refreshing mechanism is used to compare the tower design model and the on-site construction point cloud data, identify the insufficient bolt fastening degree and the component redundant cutting phenomenon, dynamically adjust the material loss warning threshold and correct the design stage carbon accounting benchmark data; based on the corrected design stage carbon accounting benchmark data, the icing load deformation and corrosion area expansion in the operation and maintenance stage of the power tower are analyzed, the component stress distribution data updated by the high-precision three-dimensional modeling technology are combined, and a full life cycle carbon footprint quantification report is generated.

[0047] The technical scheme of the present application has the following beneficial effects:

[0048] The application can accurately simulate and record the design parameters and environmental influence factors of each component by constructing a power tower component library through high-precision three-dimensional modeling technology, thereby providing reliable data support for subsequent carbon emission accounting. The handheld laser radar device is deployed on the construction site, and the high-speed point cloud scanning mode can monitor the error in the installation process in real time, accurately calculate the additional carbon emissions caused by material loss in the construction process, and enhance the accuracy of carbon footprint accounting. The data of the design stage and the construction stage are integrated by using the digital twin technology, and the synchronous refreshing mechanism is used for comparison and analysis, which not only helps to identify problems in the construction, such as insufficient bolt fastening and component redundant cutting, but also dynamically adjusts the material loss warning threshold, timely corrects the carbon accounting baseline data in the design stage, and improves the overall project execution efficiency and quality control level. Finally, based on the updated carbon accounting baseline data in the design stage, the operation and maintenance stage is comprehensively evaluated, the icing load deformation and corrosion area expansion are combined, and a full life cycle carbon footprint quantification report is generated, realizing transparent management of carbon emissions from design to retirement.

[0049] Further, the method identifies insufficient bolt fastening and component redundant cutting positions by analyzing the spatial density decay slope difference of bolt connection nodes in the tower design model and the point cloud curvature distribution of the actual splicing area, and dynamically adjusts the material loss warning threshold based on the nonlinear regression relationship, and feeds back the information to the design model to correct the component stress distribution data. The corresponding effect is that this method can effectively improve the prediction accuracy of errors and material loss that may occur in the construction process, optimize the carbon accounting baseline data in the design stage, reduce unnecessary material use and the additional carbon emissions caused thereby, and further improve the accurate accounting and management of the full life cycle carbon footprint of the power tower.

[0050] These and other aspects of the application will become more apparent from the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0052] Figure 1 A flowchart of a power tower full life cycle carbon footprint accounting method provided by the application is shown;

[0053] Figure 2 A step diagram of a power tower full life cycle carbon footprint accounting method provided by the application is shown.

[0054] Figure 3 A structural schematic diagram of a power tower full life cycle carbon footprint accounting system provided by the present application is shown.

[0055] Figure 4 A structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0056] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0057] In some processes described in the specification and claims of the present application and the above-described drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations can be executed or in parallel without the order in which they appear in this text, and the serial numbers of the operations such as 101, 102, etc. are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and the operations can be executed in sequence or in parallel. It should be noted that the "first", "second", etc. in this text are used to distinguish different messages, devices, modules, etc., and do not represent the order, nor do "first" and "second" represent different types.

[0058] The present application proposes a method of constructing a power tower component library through high-precision three-dimensional modeling technology, aiming to integrate information such as steel type, welding process and corrosion protection coating parameters, and generate design stage carbon accounting benchmark data. This method emphasizes the accurate simulation and recording of materials and processes at the initial design stage, in order to provide a solid data foundation for subsequent construction, operation and even carbon footprint accounting in the decommissioning stage.

[0059] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0060] Figure 1 And Figure 2 A flowchart and step diagram of a power tower full life cycle carbon footprint accounting method provided by the embodiments of the present application are shown as shown in Figure 2 The method comprises:

[0061] 101. Constructing a power tower component library through high-precision three-dimensional modeling technology, which is based on building information modeling to geometrically parameterize the power tower structure, integrates steel type, welding process, and corrosion protection coating parameters, and generates design stage carbon accounting benchmark data.

[0062] In this step, high-precision three-dimensional modeling technology is a technology that uses computer-aided design software to create detailed virtual models of physical objects, which can accurately represent the geometric shape, size, and material properties of objects.

[0063] The power tower component library is a database that centrally stores all power tower components and their related information, including but not limited to the design parameters, material types, manufacturing processes, and maintenance records of each component, to support management and optimization work throughout the entire life cycle from design to retirement.

[0064] Building information modeling is a method of digitally representing buildings or infrastructure projects, which not only contains geometric data, but also covers multi-dimensional information such as time, cost, construction progress, and facility management.

[0065] The power tower structure refers to a steel structure system used to support power transmission lines, composed of multiple standardized components such as tower bodies, crossarms, and foundations, and its design needs to consider factors such as strength, stability, and durability.

[0066] Geometric parameterization modeling refers to using mathematical models to describe the shape and size of physical objects.

[0067] Steel type includes the specifications and characteristics of different types of steel materials, used to determine structural strength and durability.

[0068] Welding process involves different welding methods and their impact on material properties, ensuring the safety and reliability of the connection.

[0069] Corrosion protection coating parameters focus on the thickness, composition, and durability of the protective layer, used to extend the service life of the structure.

[0070] Design stage carbon accounting benchmark data includes all possible carbon emissions during the design stage.

[0071] In the embodiments of the present application, first, according to the architectural design drawings and relevant standards, a virtual model of the power tower is created using high-precision three-dimensional modeling software. Then, by integrating the steel type, welding process, and corrosion protection coating parameters in the database, detailed material properties are added to the model. These properties not only help to accurately simulate the structural behavior, but also provide necessary input data for subsequent carbon emission assessment. Finally, by integrating all the information, the carbon accounting benchmark data for the entire design phase is calculated. This process not only accurately reproduces the actual structure of the tower, but also provides necessary input data for subsequent construction error analysis.

[0072] Taking a newly built transmission line project as an example, after the preliminary design is completed, engineers use professional software to build a three-dimensional model of the power tower containing detailed component information. This model not only accurately reproduces the actual structure of the tower, but also integrates the specific type of selected steel and its corresponding carbon footprint data, as well as the welding technology and corrosion treatment scheme used. Based on this model, the team can quickly estimate the carbon emissions in the design phase and provide support for the project's sustainable development goals.

[0073] 102、In the power tower construction site, a handheld laser radar device is deployed, and the installation angle deviation and component joint gap of the power tower assembly are captured in real time in a high-speed point cloud scanning mode. The material loss caused by construction error is calculated based on the point cloud degree distribution, and the material loss is converted into construction error carbon emission increment data;

[0074] In this step, the handheld laser radar device is used for high-speed point cloud scanning to capture the installation angle deviation and component joint gap of the power tower assembly in real time.

[0075] High-speed point cloud scanning refers to the use of laser radar or optical sensors to quickly collect a large number of point coordinate data in the environment to form a "point cloud" for generating high-resolution three-dimensional images.

[0076] Power tower components are the basic units that make up the power tower, including but not limited to angle steel, bolts, welded parts, etc. The quality of these components directly affects the safety and service life of the entire power tower structure.

[0077] Installation angle deviation refers to the angle difference between the actual position of the component during installation and the position required by the design drawings. This deviation may affect the overall stability of the structure.

[0078] Component joint gap refers to the size of the gap between two adjacent components. Proper gap is necessary to allow thermal expansion and contraction, but excessive gap may lead to a decrease in structural strength.

[0079] Point cloud degree distribution is a data set composed of a large number of spatial points, which can be used to analyze the surface morphology of an object.

[0080] Material loss due to construction errors refers to the additional material consumption caused by discrepancies between actual construction and design.

[0081] Construction error carbon emission increment data is used to convert the material losses into corresponding carbon emission values ​​to assess the environmental impact of the construction phase.

[0082] In this embodiment, a handheld LiDAR device is deployed at the construction site to scan the power transmission towers being installed and acquire point cloud data. Next, algorithms analyze this data to identify installation angle deviations and component splicing gaps, and calculate the resulting material loss. Subsequently, based on a pre-set material carbon footprint coefficient, the material loss is converted into incremental carbon emissions due to construction errors. The final result reflects the carbon emissions exceeding design expectations during actual construction, helping to optimize the construction process.

[0083] Continuing with the above case, during the tower installation, technicians used handheld LiDAR to periodically scan the site and discovered minor angular deviations and incomplete splicing issues in certain areas. By analyzing the point cloud data of these problem areas, the required amount of additional steel was determined, and the carbon emission records for the construction phase were updated accordingly, ensuring the accuracy of the project's overall carbon footprint management.

[0084] 103. By using digital twin technology to load the carbon accounting baseline data of the design stage and the carbon emission increment data of the construction error, and by using a synchronous refresh mechanism to compare the tower design model with the on-site construction point cloud data, the system identifies insufficient bolt tightness and redundant cutting of components, dynamically adjusts the material loss early warning threshold, and corrects the carbon accounting baseline data of the design stage.

[0085] In this step, digital twin technology creates an exact copy of a physical entity in a virtual environment to monitor and optimize its operation in real time.

[0086] Design-phase carbon accounting baseline data includes a set of data on greenhouse gas emissions estimated from all relevant activities during the design phase, providing a basis for carbon emission assessments in subsequent phases.

[0087] The incremental carbon emissions from construction errors are the additional carbon emissions calculated based on errors that occur during construction. They reflect the material waste and corresponding environmental impacts caused by inaccurate construction.

[0088] The synchronous refresh mechanism allows the design model to keep pace with the actual construction progress.

[0089] A tower design model is a virtual representation of a power transmission tower created based on a design scheme. It contains detailed structural information and is used to guide construction and perform various analyses.

[0090] On-site construction point cloud data is a set of spatial coordinate data obtained through point cloud scanning technology in the actual construction situation, which can be used to compare the differences between the design model and the actual situation.

[0091] Insufficient bolt fastening indicates that the connection point does not meet the specified fastening requirements, which may lead to structural instability.

[0092] Component redundant cutting refers to measurement or operation errors that result in unnecessary material removal, increasing costs and carbon emissions.

[0093] The material loss warning threshold is a standard value set by the system to alert when the actual construction material loss exceeds this value, so that timely measures can be taken to reduce waste.

[0094] In the embodiments of the present application, first, the design stage carbon accounting benchmark data and construction error carbon emission increment data are loaded through the digital twin platform. Then, the synchronous refresh mechanism is used to compare the design model with the on-site construction point cloud data. In this process, the system automatically identifies problems such as insufficient bolt fastening and component redundant cutting, and dynamically adjusts the material loss warning threshold based on these problems. At the same time, the design stage carbon accounting benchmark data is corrected based on the dynamic adjustment results to reflect the latest construction actual situation, thereby achieving precise control and optimization.

[0095] In the aforementioned project, as the construction progresses, the engineering team continuously imports new point cloud data into the digital twin system, timely discovers a number of insufficient bolt fastening cases, and confirms some unplanned component cutting. Based on these feedbacks, the team adjusts the warning system settings and updates the carbon emission prediction model to ensure that the project always moves towards the low-carbon goal.

[0096] 104、Based on the corrected design stage carbon accounting benchmark data, the icing load deformation and corrosion area expansion during the operation and maintenance stage of the electric power tower are analyzed in coordination, and the component stress distribution data updated by the high-precision three-dimensional modeling technology are combined to generate a full life cycle carbon footprint quantification report.

[0097] In this step, the corrected design stage carbon accounting benchmark data is the initial carbon accounting benchmark data adjusted after considering the problems found during construction and the improvement measures.

[0098] Icing load deformation describes the pressure changes caused by snow and ice coverage on the structure, affecting the structural stability.

[0099] Corrosion area expansion focuses on the degree of metal corrosion caused by environmental factors, reducing the safety of the structure.

[0100] The component stress distribution data updated by the high-precision three-dimensional modeling technology is data obtained by recalculating the stress of the power tower component under different working conditions using the latest three-dimensional modeling technology, which helps to more accurately predict the structural performance.

[0101] The carbon footprint quantification report aggregates carbon emission data at each stage from design to decommissioning to assess the overall environmental impact of the project.

[0102] In the embodiments of the present application, firstly, a dynamic model of ice load and corrosion diffusion is established, and then combined with the component stress distribution data, the influence of the two on the long-term performance of the power tower is analyzed. By continuously monitoring the icing condition and the development of corrosion, the stress analysis model is updated using a three-dimensional modeling tool. After a period of data accumulation, a detailed carbon footprint report is formed, showing the trend of the environmental impact of the project throughout its life cycle, providing valuable reference for the planning of future similar projects.

[0103] After the completion of the tower, the maintenance personnel continuously monitor the icing condition and the development of corrosion, and at the same time, the stress analysis model is updated using a three-dimensional modeling tool. After a period of accumulation, a detailed carbon footprint report is formed, showing the trend of the environmental impact of the project throughout its life cycle, providing valuable reference for the planning of future similar projects.

[0104] In summary, steps 101 to 104 realize the fine management of the power tower from design, construction to operation and maintenance, and even decommissioning. This method improves the engineering quality, reduces carbon emissions throughout the life cycle of the project, promotes the development of green energy, and sets an environmental protection benchmark for future infrastructure construction.

[0105] In order to solve the problem of material loss control and accuracy of carbon emission accounting in the construction process of the power transmission tower, the scheme focuses on using a synchronous refreshing mechanism to compare the tower design model with the point cloud data at the construction site to identify the phenomenon of insufficient bolt fastening and component redundant cutting, and accordingly dynamically adjust the material loss warning threshold and correct the design stage carbon accounting baseline data. This method realizes accurate positioning and quantitative evaluation of possible problems in the construction process by analyzing the difference in spatial density decay slope under geometric constraint conditions and the curvature distribution of point cloud in the actual splicing area. In some embodiments, the use of a synchronous refreshing mechanism to compare the tower design model with the point cloud data at the construction site in step 103 to identify the phenomenon of insufficient bolt fastening and component redundant cutting, dynamically adjust the material loss warning threshold and correct the design stage carbon accounting baseline data includes:

[0106] 201、based on the geometric constraint conditions of the bolt connection nodes in the tower design model, determine the abnormal connection nodes with insufficient bolt fastening according to the difference in spatial density decay slope between adjacent bolt connection node clusters;

[0107] In step 201, the geometric constraint refers to the provision of the spatial relationship of the tower component connection position and its surrounding environment based on the design specification, which includes size, shape, and relative position between each other, etc. information, for ensuring the correct assembly between each part. The difference in spatial density decay slope refers to the different speed of the distribution density of point cloud clusters between adjacent bolt connection nodes, which is used to analyze whether the bolt fastening degree meets the requirements. The adjacent bolt connection node cloud cluster refers to the bolt connection node group gathered together according to the point cloud data.

[0108] In the embodiment of the application, first, the relevant data of the bolt connection node is extracted from the tower design model, and the field construction point cloud data is collected. Then, the spatial distribution of each node is determined by using the clustering algorithm, and the spatial density decay slope between adjacent nodes is calculated. Then, according to the preset standard, the slope difference between each node is compared to determine which connection node has the phenomenon of insufficient bolt fastening degree. Finally, these information is integrated to form a data set that comprehensively reflects the bolt fastening state.

[0109] 202, traverse the preset geometric topological relationship of the component splicing joint in the tower design model and the point cloud curvature distribution of the actual splicing area of the component in the field construction point cloud data, and when the point cloud curvature mutation amplitude of the splicing joint edge exceeds the region of the critical value of the plastic deformation of the component material, the redundant cutting position of the component is located;

[0110] In step 202, the preset geometric topological relationship describes the ideal shape and relative position relationship of the component splicing joint in the design model. The point cloud curvature distribution of the actual splicing area reflects the actual splicing condition of the construction site. The plastic deformation critical value is the maximum stress value that the material can withstand without permanent deformation. When the point cloud curvature mutation amplitude of the splicing joint edge exceeds this critical value, it indicates that there is a redundant cutting phenomenon of the component. Based on these information, the redundant cutting position can be accurately located. The point cloud curvature distribution refers to the distribution of the bending degree of each point on the surface obtained by analyzing the three-dimensional point cloud data, which measures the curvature size at each point by calculating the local geometric characteristics around the point, and reflects the smoothness or complexity of the object surface. The critical value of the plastic deformation of the component material refers to the maximum stress value that the material can withstand without permanent deformation. Once the applied stress exceeds this critical value, the material will begin to produce irreversible deformation.

[0111] In the embodiment of the application, first, the component splicing joint information in the tower design model is obtained, and compared with the field construction point cloud data. Then, the curvature analysis technology is used to detect the point cloud curvature change of the splicing joint edge. Then, according to the threshold value of the material plastic deformation critical value, the area exceeding the threshold value is selected as the redundant cutting position. Finally, a report containing all the redundant cutting positions is generated.

[0112] 203. Establish a nonlinear regression relationship between construction error and material loss based on the proportion of cutting area of the abnormal connection node and the redundant cutting position of the component, and dynamically adjust the boundary conditions of the material loss warning threshold according to the gradient change trend of the material loss with the increase of the construction error;

[0113] In step 203, the nonlinear regression relationship is a mathematical model based on abnormal connection nodes and component redundant cutting positions, which is used to quantify the relationship between construction error and material loss. The gradient change trend is to analyze the rate of change of material loss with the increase of construction error. In the construction process, the part that actually cuts beyond the design requirement due to misoperation or other reasons is called component redundant cutting position. This phenomenon may lead to structural strength reduction and material waste. The cutting area ratio refers to the proportion of the total area occupied by the redundant cutting part, which is an important indicator for evaluating construction error, and is used to quantify the deviation between actual cutting and design requirements. Based on these parameters, the boundary conditions of the material loss warning threshold can be dynamically adjusted to ensure that potential problems are discovered and corrected in a timely manner.

[0114] In the embodiments of the present application, first, integrate all the data of abnormal connection nodes and redundant cutting positions obtained from steps 201 and 202 to form a comprehensive data set. Then, a machine learning algorithm is used to analyze this data set to build a nonlinear regression model reflecting the relationship between construction error and material loss. Based on this model, the trend of material loss with the increase of construction error is predicted, and the boundary conditions of the material loss warning threshold are dynamically adjusted accordingly.

[0115] 204. Reverse iteration of the boundary conditions to the component stress distribution data of the tower design model to correct the design stage carbon accounting benchmark data by weakening the load transfer path of the stress concentration area caused by the component redundant cutting phenomenon.

[0116] In step 204, the boundary conditions are the correlation between construction error and material loss derived from the nonlinear regression relationship. The reverse iteration process is to apply these conditions to the component stress distribution data of the tower design model to correct the design stage carbon accounting benchmark data. In particular, adjustments are made to address stress concentration problems caused by component redundant cutting, thereby reducing unnecessary carbon emissions. The material loss warning threshold refers to the upper limit of material loss that triggers a warning. When the actual loss approaches or exceeds this threshold, the system will issue a warning, prompting the need to take measures to avoid further loss. Component stress distribution data refers to the stress state of each component at different points inside under different load conditions. Load transfer path refers to the route of force propagation from one part to another.

[0117] In this embodiment, boundary conditions are first imported into the tower design model to locate stress concentration areas. Next, load transfer paths in these areas are simulated to mitigate their impact on overall structural stability. Then, the carbon accounting baseline data from the design phase is revised based on the simulation results to ensure a more accurate reflection of actual construction conditions. Finally, an optimized design scheme is provided, reducing both material waste and carbon emissions.

[0118] Here is a specific example:

[0119] Taking a newly constructed power transmission line project as an example, firstly, a 3D laser scanner is used to collect detailed point cloud data of the tower construction site. Then, computer-aided design software is used to analyze this data, identifying all locations where bolt tightening is insufficient and where components have been incorrectly cut. Next, based on these findings, a relationship model between construction errors and material waste is established, and the material loss early warning standards are adjusted accordingly. Finally, the tower design is updated based on the new material loss data to ensure that the carbon emissions in the design more accurately reflect the actual situation.

[0120] In summary, steps 201 to 204 not only effectively control construction errors in tower construction projects but also significantly improve the accuracy of material management and carbon emission accounting, making them particularly suitable for the refined management of large-scale infrastructure projects. This not only helps reduce resource waste but also provides strong technical support for promoting green building development. Furthermore, this method helps engineering teams better understand and address various challenges that arise during construction, ensuring timely and high-quality project completion.

[0121] To further improve the accuracy of material loss control and carbon emission accounting in tower construction projects, the scheme further refines the process of establishing a nonlinear regression relationship based on the proportion of the cutting area of ​​abnormal connection nodes and redundant cutting positions of components. This process includes gradient calculation of the difference in spatial density attenuation slope of abnormal connection nodes, identification of curvature abrupt change regions by combining point cloud curvature features, and finally fusion of deviation weight factor and projected area proportion to construct spatiotemporal composite features, thereby improving the accuracy of material loss prediction. In some embodiments, step 203, which establishes a nonlinear regression relationship between construction error and material loss based on the proportion of the cutting area of ​​the abnormal connection nodes and redundant cutting positions of components, includes:

[0122] 301. Perform gradient calculation on the difference in spatial density decay slope for the abnormal connection nodes, analyze the radial gradient vector of the three-dimensional neighborhood point cloud density of the bolt connection node, filter each abnormal connection node by the directional deviation of the radial gradient vector of the adjacent three-dimensional neighborhood point cloud density, and generate a deviation weight factor based on the correlation between the sine value of the deviation angle and the spatial density decay slope.

[0123] In step 301, the spatial density decay slope difference refers to a measure of evaluating the tightness of bolted joints. Bolted joints refer to the key positions in structural design where different components are connected together by bolts. These joints are crucial for ensuring the safety and stability of the overall structure, and their design and construction quality directly affects the performance of the entire structure. The three-dimensional neighborhood point cloud density radial gradient vector describes the direction and magnitude of the change in point cloud density around each node. The direction deviation refers to the angular deviation between adjacent three-dimensional neighborhood point cloud density radial gradient vectors, which is used to measure the directional consistency of the point cloud distribution around the bolted joint. The deviation angle sine value is used to measure the angular deviation between adjacent three-dimensional neighborhood point cloud density radial gradient vectors. The deviation degree weight factor is a quantitative index generated based on the correlation between the deviation angle sine value and the spatial density decay slope.

[0124] In the embodiments of the present application, first, the gradient calculation of the spatial density decay slope difference of the abnormal connection node is performed, and the three-dimensional neighborhood point cloud density radial gradient vector of each node is determined using local density estimation technology (such as kernel density estimation). Then, by comparing the directions of the radial gradient vectors between adjacent nodes, the deviation angle sine value is calculated. Then, according to the relationship between the deviation angle sine value and the spatial density decay slope, the deviation degree weight factor is generated. Finally, these factors reflect the tightening state of each abnormal connection node.

[0125] 302, decompose the curvature tensor eigenvalue of the local coordinate system of the spliced joint edge point cloud, identify the curvature mutation area combined with the material yield strength threshold, and analyze the projection area proportion of the redundant cutting position of the component through three-dimensional surface parameterization projection;

[0126] In step 302, the spliced joint edge point cloud refers to the point cloud data of the edge part formed by actual construction at the spliced joint of large structures such as towers. These data reflect the actual geometric shape and surface characteristics of the spliced joint, and are an important basis for analyzing the splicing quality. The curvature tensor eigenvalue is the surface bending characteristic obtained by decomposing the local coordinate system of the spliced joint edge point cloud. The material yield strength threshold is the maximum stress value that the material can withstand without permanent deformation. The curvature mutation area refers to the area in the point cloud data where the curvature (i.e., the degree of surface bending) changes significantly. Usually, such mutations may indicate redundant cutting or other construction errors. The projection area proportion refers to the proportion of the projection area of the redundant cutting position on the three-dimensional surface to the total area.

[0127] In the embodiments of the present application, first, the local coordinate system transformation technique is used to convert the splicing seam edge point cloud to a unified coordinate system, and the curvature tensor eigenvalue is calculated. Then, the curvature mutation area is identified in combination with the material yield strength threshold, indicating that there may be redundant cutting. Then, through three-dimensional surface parameterization projection analysis, the projection area ratio of the redundant cutting position is calculated. This step helps to accurately identify and quantify the component redundant cutting phenomenon.

[0128] 303. A spatiotemporal composite feature is constructed by fusing the deviation weight factor and the projection area ratio, and an error accumulation feature in the assembly sequence is extracted using a sliding window to generate a high-dimensional regression input set;

[0129] In step 303, the spatiotemporal composite feature is a comprehensive index constructed by fusing the deviation weight factor and the projection area ratio, which is used to comprehensively describe the construction error. The sliding window is a data processing technique used to extract the error accumulation feature in the assembly sequence. The assembly sequence refers to the time sequence in which various components are installed in a certain order during construction. Understanding the assembly sequence helps to analyze the error accumulation in the construction process, thereby optimizing the construction process and quality management. The error accumulation feature refers to the small errors caused by various reasons (such as manufacturing tolerance, installation error, etc.) that gradually accumulate during the assembly process, forming the error accumulation feature. The high-dimensional regression input set is a data set composed of the above features, which provides input for establishing a nonlinear regression model.

[0130] In the embodiments of the present application, first, the deviation weight factor and the projection area ratio are fused to construct a spatiotemporal composite feature. Then, the sliding window technique is used to extract the error accumulation feature in the assembly process to form time series data. Then, these features are integrated into a high-dimensional regression input set as the basis for subsequent nonlinear regression analysis. This process ensures comprehensive capture and quantification of construction errors.

[0131] 304. Based on the material plastic deformation critical value, a dynamic reduction coefficient of the construction error on the bearing area is defined, and the high-dimensional regression input set is associated with the material loss to establish a nonlinear regression relationship between the construction error and the material loss.

[0132] In step 304, the material plastic deformation critical value refers to the maximum stress value that the material can withstand without permanent deformation. Once this critical value is exceeded, the material will undergo irreversible deformation. The dynamic reduction coefficient is defined based on the material plastic deformation critical value, which reflects the influence of construction error on the bearing area. The process of associating the high-dimensional regression input set with the material loss is achieved by establishing a nonlinear regression relationship, which can accurately predict the material loss under different construction error levels. Material loss refers to the total amount of material loss caused by various reasons (such as cutting error, excessive processing, etc.) during the entire construction process.

[0133] In the embodiments of the present application, first, a dynamic reduction coefficient of construction error to bearing area is defined based on the critical value of material plastic deformation. Then, the high-dimensional regression input set is associated with the actual material loss, and a nonlinear regression model is established using machine learning algorithms such as random forest or support vector machine. Then, the model is trained and verified to ensure its prediction accuracy. Finally, a nonlinear regression model is generated that accurately reflects the relationship between construction error and material loss.

[0134] Here is a specific example:

[0135] Taking a new transmission line project as an example, the technical personnel first calculate the gradient of the spatial density decay slope difference of the abnormal connection nodes in the tower design model, identify the nodes with insufficient bolt tightening. Then, by analyzing the curvature tensor eigenvalue of the edge point cloud of the splicing joint, identify the possible redundant cutting position, and calculate its projection area proportion. Subsequently, the error accumulation features in the assembly time sequence are extracted using the sliding window technique to form a high-dimensional regression input set. Finally, a nonlinear regression relationship between construction error and material loss is established to optimize the material management strategy.

[0136] In summary, steps 301 to 304 not only achieve effective control of construction errors in tower construction projects, but also significantly improve the accuracy of material management and carbon emission accounting. This method not only helps to reduce resource waste, but also provides strong technical support for promoting green building development. At the same time, through the fine management of construction error and material loss, the sustainability and economic benefits of engineering projects can be greatly improved.

[0137] To solve the problem that the material loss warning threshold is difficult to adjust dynamically according to the construction error, the present scheme proposes a method of dynamically adjusting the boundary conditions of the material loss warning threshold according to the gradient change trend of the material loss with the growth of the construction error. This involves constructing a multi-dimensional gradient field and extracting its main direction module length, using a sliding window to analyze the error accumulation rate, and adjusting the load weight of adjacent nodes in the redundant cutting area according to the load transfer path topology of the stress concentration area, to realize the optimization adjustment of the warning threshold. In some embodiments, step 203 of dynamically adjusting the boundary conditions of the material loss warning threshold according to the gradient change trend of the material loss with the growth of the construction error comprises:

[0138] 401、Based on the spatio-temporal distribution of the construction error, a multi-dimensional gradient field is constructed, and the main direction module length of the multi-dimensional gradient field is extracted, wherein the time dimension module length represents the error accumulation rate, and the spatial dimension module length represents the material loss diffusion intensity;

[0139] In step 401, the construction error refers to the deviation between the actual construction situation and the design drawings. The multi-dimensional gradient field is a data field constructed based on the spatio-temporal distribution of the construction error, used to describe the change trend of the construction error with time and space. The main direction module length is the length value of the main change direction extracted from the multi-dimensional gradient field, where the time dimension module length represents the error accumulation rate, reflecting the growth speed of the error over time; the spatial dimension module length reflects the material loss diffusion intensity, indicating the expansion degree of material loss in space.

[0140] In the embodiments of the present application, first, error data in the construction process is collected, and a multi-dimensional gradient field containing time and space dimensions is constructed. Then, the main direction module length is extracted from the gradient field using mathematical analysis methods such as Fourier transform. Then, the module lengths of the time dimension and the space dimension are calculated to evaluate the error accumulation rate and the material loss diffusion intensity, respectively. Finally, these parameters help identify the key problem areas that may exist in the construction process.

[0141] 402. Set a sliding window on the component assembly time sequence, analyze the dynamic covariance of the main direction module length, and mark the gradient direction mutation area when the main direction module length exceeds three times the standard deviation of the historical mean. According to the correlation between the module length of the gradient direction mutation area and the material loss diffusion intensity, a dynamic adjustment factor is obtained;

[0142] In step 402, the sliding window is a data analysis technique that analyzes rolling by setting a certain time window size on the component assembly time sequence. Dynamic covariance refers to the change of the main direction module length in different time windows. When the main direction module length exceeds three times the standard deviation of the historical mean, it is marked as a gradient direction mutation area. According to the correlation between the module length of these mutation areas and the material loss diffusion intensity, a dynamic adjustment factor can be obtained.

[0143] In the embodiments of the present application, first, a sliding window is set on the component assembly time sequence, and the dynamic covariance of the main direction module length is analyzed using statistical methods. Then, when the main direction module length in a certain period is found to deviate significantly from three times the standard deviation of the historical mean, it is marked as a gradient direction mutation area. Then, based on the relationship between the module length of these mutation areas and the material loss diffusion intensity, the corresponding dynamic adjustment factor is calculated, which is used to adjust the material loss warning threshold in the subsequent step. This step helps to discover and respond to abnormal situations in the construction process in a timely manner.

[0144] 403. Based on the load transfer path topology of the stress concentration area, the dynamic adjustment factor is used to attenuate the load weight of the adjacent nodes of the redundant cutting area, and the boundary conditions of the material loss warning threshold are dynamically adjusted.

[0145] In step 403, the load transfer path topology of the stress concentration area describes the part of the structure where the stress is more concentrated and the way the load is transferred. The dynamic adjustment factor is an adjustment coefficient calculated based on the data obtained in the previous step. The load transfer path topology describes how the load is transferred through the structure, and the load weight decay is an optimization strategy that can dynamically adjust the boundary conditions of the material loss warning threshold to ensure more accurate reflection of the actual construction conditions.

[0146] In the embodiments of the present application, the load transfer path topology of the stress concentration area is first determined. Then, the load weight decay processing is performed on the adjacent nodes of the redundant cutting area using the dynamic adjustment factor to simulate its impact on the overall structural stability. Then, the boundary conditions of the material loss warning threshold are dynamically adjusted based on these simulation results to ensure timely discovery of potential risks. Finally, an optimized warning system is provided to improve construction quality and efficiency.

[0147] The following is a specific example:

[0148] Taking a new transmission line project as an example, first, the error space-time distribution model is constructed according to the error data recorded during construction, and the main direction module length is extracted as the evaluation basis. Then, during the assembly of the transmission tower, the sliding window technique is used to analyze the error trend, mark the gradient direction mutation area, and calculate the dynamic adjustment factor accordingly. Finally, according to the characteristics of the stress concentration area of the transmission tower, the above factor is used to optimize the potential high-loss risk points, and the dynamic adjustment of the warning threshold is realized, thereby effectively preventing excessive material loss.

[0149] In summary, steps 401 to 403 accurately analyze the space-time distribution characteristics of construction errors, combine dynamic covariance analysis and stress analysis, and realize intelligent adjustment of the material loss warning threshold, greatly improving resource utilization during construction, reducing cost increases due to excessive loss, and enhancing the safety and reliability of the project. This method is not only suitable for transmission line projects, but also can be widely applied to other types of construction projects, and has strong universality and practicality.

[0150] In order to solve the problem of difficult accurate calculation of material loss caused by errors during construction, the scheme introduces a technical path for calculating the material loss caused by construction errors based on point cloud degree distribution, and converting it into construction error carbon emission increment data. This method accurately calculates the construction error compensation and filling amount by analyzing the point cloud normal vector offset and the area of the hollow region, combined with the steel bending deformation coefficient and the welding penetration parameter, and then converts it into carbon emission data. In some embodiments, the step 102 described above for calculating the material loss caused by construction errors based on point cloud degree distribution, and converting the material loss into construction error carbon emission increment data, comprises:

[0151] 501、based on the power tower component point cloud distribution data obtained by high-speed point cloud scanning, the point cloud normal vector offset corresponding to the component installation angle deviation and the point cloud hollow area corresponding to the component splicing gap are extracted;

[0152] In step 501, the power tower component point cloud distribution data refers to the data set of the spatial position information of each component of the power tower obtained by the high-speed three-dimensional laser scanning device. The point cloud normal vector offset refers to the change amount of the point cloud data direction caused by the deviation of the actual installation angle of the component from the design angle, which is used to quantify the angle deviation of the component installation. The point cloud hollow area refers to the size of the missing area in the point cloud data caused by the loose splicing of the components, which is used to evaluate the component splicing gap. The component splicing gap refers to the gap or incomplete fit between two or more components caused by manufacturing precision, installation error and other factors when splicing.

[0153] In the embodiment of the application, first, the installed power tower components are scanned by a high-speed three-dimensional laser scanning device to obtain high-density point cloud data. Then, computer vision algorithms are used to analyze these data to identify parts that do not conform to the design model, thereby obtaining the point cloud normal vector offset corresponding to the component installation angle deviation and the point cloud hollow area corresponding to the component splicing gap. Finally, the specific values of the construction error are determined according to these parameters.

[0154] 502、According to the correlation between the point cloud normal vector offset and the steel bending deformation coefficient, the component plastic deformation compensation amount caused by the component installation angle deviation is calculated, and based on the matching degree between the point cloud hollow area and the component welding penetration parameter, the steel redundant filling amount caused by the splicing gap is calculated;

[0155] In step 502, the component plastic deformation compensation amount refers to the deformation compensation amount that needs to be performed in order to restore the original design shape or meet new functional requirements after the component has been deformed plastically under the action of external force. This value is calculated based on the steel bending deformation coefficient and is used to adjust the influence of construction error. The steel bending deformation coefficient is an index that measures the permanent deformation capacity of steel under the action of external force, and the component welding penetration parameter reflects the depth of the welding material penetrating into the base material during the welding process, which is used to control the welding quality. The steel redundant filling amount refers to the additional welding material usage amount caused by the splicing gap, which is determined according to the point cloud hollow area and the welding penetration parameter.

[0156] In the embodiments of the present application, first, a mathematical model between the point cloud normal vector offset and the steel bending deformation coefficient is established, and then the component plastic deformation compensation amount caused by the installation angle deviation is calculated according to the model. Then, by analyzing the relationship between the point cloud hollow area and the pre-set welding penetration parameter, the amount of steel that needs to be additionally filled is estimated. By comprehensively considering the above two aspects, the total steel loss amount is obtained.

[0157] 503、The component plastic deformation compensation amount and the steel redundant filling amount are classified and superimposed according to the component type to generate the material loss amount of construction error correction, and the material loss amount is converted into construction error carbon emission increment data based on the steel type carbon footprint coefficient and the welding process unit heat input carbon emission factor.

[0158] In step 503, the steel type carbon footprint coefficient refers to the amount of greenhouse gas emissions per unit mass of a specific type of steel from production to scrap throughout the life cycle, and the welding process unit heat input carbon emission factor represents the amount of greenhouse gases such as carbon dioxide generated per unit energy input in the welding process. The material loss amount is the total loss amount obtained by classifying and summarizing the component plastic deformation compensation amount and the steel redundant filling amount. The construction error carbon emission increment data refers to the conversion of the material loss amount into the corresponding carbon emission amount, that is, the greenhouse gas emissions generated in the process of producing additional materials and taking remedial measures (such as welding).

[0159] In the embodiments of the present application, the plastic deformation compensation amount and the steel redundant filling amount of different component types are first summarized to form a comprehensive material loss report. Then, the material loss is converted into carbon emission data by applying the corresponding steel type carbon footprint coefficient and the welding process unit heat input carbon emission factor, and the quantitative conversion from physical loss to environmental impact is completed.

[0160] The following is a specific example:

[0161] Taking a newly built power transmission line project as an example, in the project, first, a high-speed three-dimensional laser scanner is used to scan the newly built power tower components in all directions to obtain detailed point cloud distribution data. Then, the data is analyzed by a computer program to identify the installation angle deviation of all components and the point cloud normal vector offset caused by the installation angle deviation, and the point cloud hollow area formed by the joint gap between components. Based on the above analysis results, an accurate mathematical model is established to calculate the required component plastic deformation compensation amount and steel redundant filling amount. Finally, combined with specific steel types and welding process parameters, these material loss amounts are converted into additional carbon emissions caused by construction errors.

[0162] In summary, steps 501 to 503, through in-depth analysis of point cloud data during the construction of power transmission towers, not only accurately quantify material losses caused by construction errors but also further convert them into specific carbon emissions, providing a scientific basis for optimizing construction processes and reducing resource waste. This method helps improve the environmental protection level of engineering construction projects, reduce carbon footprints, and promote the development of green buildings.

[0163] To further improve the precise control of steel redundancy during steel structure welding, this solution details the specific steps for calculating steel redundancy based on the matching degree between the area of ​​the point cloud void region and the welding penetration parameters of the component. This method performs three-dimensional morphological analysis of the point cloud void region, combines the spatial superposition relationship of the welding path, statistically calculates the cumulative value of steel filling in segments, and considers the material expansion coefficient of the welding heat-affected zone to accurately calculate the steel redundancy. In some embodiments, step 502, which calculates the steel redundancy caused by the splicing gap based on the matching degree between the area of ​​the point cloud void region and the welding penetration parameters of the component, includes:

[0164] 601. Perform three-dimensional morphological analysis on the area of ​​the point cloud cavity region, and extract the depth distribution gradient field and surface curvature features. Generate the melting depth requirement distribution field through the axial depth range and curvature change rate of the point cloud cavity region.

[0165] In step 601, the area of ​​the point cloud void region refers to the region in the component surface data acquired through 3D scanning technology where there are missing or incomplete parts. The depth distribution gradient field describes the depth variation at different locations within these regions; the surface curvature feature reflects the degree of curvature of the surface in this region, helping to understand its geometry. The axial depth range represents the maximum depth difference from one end of the void region to the other. The rate of change of curvature measures the speed at which the degree of surface curvature changes, used to analyze the complexity within the region. These parameters are used together to generate the weld penetration requirement distribution field, i.e., to determine the ideal weld penetration depth to be achieved during welding based on the specific morphology of the void region.

[0166] In this embodiment, a high-precision 3D laser scanning device is first used to scan the component, collecting a dataset containing point cloud void regions. Next, image processing algorithms (such as filtering and edge detection) and geometric analysis methods are used to extract the depth distribution gradient field and surface curvature features. Based on the extracted information, the axial depth range and curvature change rate are further calculated, and this information is combined to construct the melt depth requirement distribution field. This process ensures a comprehensive analysis of the void regions, providing accurate basic data for subsequent steps.

[0167] 602、Based on the unit penetration filling amount coefficient in the component welding penetration parameter, a penetration-filling amount mapping relationship is constructed, and according to the spatial superposition relationship between the depth gradient direction of each region in the penetration requirement distribution field and the welding path, the matching degree difference between the actual penetration and the theoretical penetration is analyzed;

[0168] In step 602, the unit penetration filling amount coefficient refers to the amount of material required per unit depth. The penetration-filling amount mapping relationship is a corresponding rule pre-set based on the welding process characteristics. The actual penetration is the real penetration depth measured after welding. The theoretical penetration is the ideal value required by the design. The matching degree difference reflects the gap between the two, helping to evaluate the welding quality.

[0169] In the embodiments of the present application, first, the unit penetration filling amount coefficient is set according to the welding process standard, and a penetration-filling amount mapping model is established. Then, the penetration requirement distribution field obtained in step 601 is applied to the model to calculate the matching degree difference between the actual penetration and the theoretical penetration of each region. Here, numerical simulation method is used in combination with the spatial superposition relationship of the welding path to analyze, ensuring the accuracy and reliability of the calculation results.

[0170] 603、In the local coordinate system of the welding path, the matching degree difference is spatially integrated, the integral weight is corrected by the curvature characteristics of the penetration requirement distribution field, and the three-dimensional spatial distribution is calculated in combination with the material expansion coefficient of the welding heat affected zone.

[0171] In step 603, the local coordinate system defines a reference frame for the welding path, which facilitates spatial integration operation. Spatial integration is a process of cumulative calculation of the matching degree difference in this framework. The correction of the integral weight by the curvature characteristics means that the importance of integration is adjusted according to the surface curvature, making the result more close to the actual situation. The material expansion coefficient of the welding heat affected zone reflects the proportion of material volume change due to temperature rise in the welding process, which is crucial for predicting deformation.

[0172] In the embodiments of the present application, first, the spatial integration operation of the matching degree difference is performed in the local coordinate system, and the integral weight is corrected by the surface curvature characteristics. In addition, the material expansion coefficient of the welding heat affected zone is also considered, and the three-dimensional spatial distribution map is obtained through thermodynamic analysis. This step comprehensively uses geometric modeling and thermodynamic analysis technology to ensure the accuracy of the calculation results.

[0173] 604、Based on the three-dimensional spatial distribution and the geometric topological relationship of the component splicing joint, the steel filling amount cumulative value is segmented and counted, and the molten pool dynamic shrinkage rate in the welding process parameter is associated to generate the steel redundant filling amount.

[0174] In step 604, the three-dimensional spatial distribution refers to the spatial distribution information of the filling amount obtained after the above steps. The geometric topological relationship involves the position, shape, and other geometric properties of the component joint. The dynamic shrinkage rate of the molten pool describes the proportion of the size reduction during the cooling process of the welding pool, which is crucial for determining the final filling amount.

[0175] In the embodiments of the present application, first, the three-dimensional spatial distribution data obtained in step 603 is matched with the geometric topological information of the component joint. Next, the expected filling amount of each segment is adjusted according to the dynamic shrinkage rate of the molten pool in the welding process parameters to compensate for the volume loss during the cooling process. For this purpose, numerical simulation technology is used to predict the behavior of the molten pool under different welding conditions and its morphological changes after cooling. In addition, the finite element analysis method is used to process the entire welding area in segments, and the cumulative value of steel filling amount is calculated segment by segment to ensure that each local area is fully considered. Finally, based on these cumulative values and the corrected filling requirements, the overall distribution map of steel redundant filling amount is generated.

[0176] The following is a specific example:

[0177] Taking a new transmission line project as an example, first, high-precision three-dimensional laser scanning equipment is used to capture the detailed geometric shape of the tower components, especially those with defects or irregularities. Next, the unit penetration filling amount coefficient is set according to the welding process characteristics, and a mapping model of penetration and filling amount is established. Then, considering the influence of the material expansion coefficient of the welding heat-affected zone, a three-dimensional spatial distribution map is obtained through thermodynamic analysis. Finally, based on the obtained three-dimensional spatial distribution and the geometric topological relationship of the component joint, the cumulative value of steel filling amount is obtained. Through the above steps, the welding quality of the tower components of the new transmission line project is significantly improved, the redundant filling amount of steel during the welding process is accurately controlled, the structural safety hazards caused by insufficient or excessive filling are effectively avoided, and the overall construction efficiency is improved.

[0178] In summary, steps 601 to 604 can effectively predict and control the redundant filling amount of steel during the welding process through in-depth analysis and processing of the point cloud hollow area, significantly improve the welding quality and efficiency, reduce the cost, and reduce the structural safety hazards, which is of great significance to improving the overall construction level of large steel structure projects. This method not only improves the economic benefits of engineering projects, but also enhances safety and sustainability.

[0179] To further improve the accuracy of the whole life cycle carbon footprint quantification analysis, the scheme sets forth a method for collaborative analysis of ice load deformation and corrosion area expansion in the operation and maintenance stage of the power iron tower, and generates a whole life cycle carbon footprint quantification report combined with the component stress distribution data updated by the high-precision three-dimensional modeling technology. The method establishes a dynamic model of ice load and corrosion diffusion, generates a whole life cycle carbon footprint quantification report combined with the stress concentration coefficient, the elastic modulus attenuation gradient and the material thickness attenuation field. In some embodiments, the collaborative analysis of ice load deformation and corrosion area expansion in the operation and maintenance stage of the power iron tower in step 104, combined with the component stress distribution data updated by the high-precision three-dimensional modeling technology, generates a whole life cycle carbon footprint quantification report, including:

[0180] 701. Based on the spatio-temporal distribution of ice thickness, a dynamic stress field of ice load on the surface of the component is established, and through the coupling relationship between the deformation rate of ice load and the curvature change of the component, the stress concentration coefficient and the elastic modulus attenuation gradient are obtained;

[0181] In step 701, the spatio-temporal distribution data of ice thickness is used to establish a dynamic stress field of ice load on the surface of the component. The ice load deformation rate represents the shape change speed caused by ice per unit time, and the component curvature change describes the change of the component shape over time. The stress concentration coefficient reflects the phenomenon that the stress of the specific area of the component increases significantly, and the elastic modulus attenuation gradient shows the speed of the weakening of the material rigidity over time.

[0182] In the embodiments of the present application, first, the spatio-temporal distribution data of ice thickness is obtained by using the high-precision three-dimensional modeling technology, and the dynamic stress field is calculated by the finite element analysis algorithm. Then, based on the historical data and the physical model, the relationship between the ice load deformation rate and the curvature change of the component is determined, so as to obtain the stress concentration coefficient and the elastic modulus attenuation gradient. The final result is to accurately evaluate the influence of ice on the component by coupling the above parameters.

[0183] 702. Extract the oxide layer thickness and micro-crack density of the corrosion area, establish a corrosion diffusion rate model combined with the environmental temperature and humidity data, and generate a material thickness attenuation field;

[0184] In step 702, the oxide layer thickness refers to the thickness of the oxide layer formed on the metal surface due to the oxidation reaction, which is an important indicator for evaluating the corrosion degree of the material surface. The micro-crack density is the number of small cracks per unit area, which is used to measure the degree of internal damage of the material. The environmental temperature and humidity data cover the information of temperature and humidity changing over time, which have a significant impact on the corrosion rate. The corrosion diffusion rate model is a mathematical model based on environmental factors, material properties and existing corrosion conditions to predict the future corrosion expansion speed.

[0185] In this embodiment, non-destructive testing techniques (such as ultrasonic testing or magnetic particle testing) are first used to obtain specific parameters of the corroded areas on the tower components, including oxide layer thickness and microcrack density. Simultaneously, a sensor network deployed around the power tower collects ambient temperature and humidity data in real time. Next, this data is input into a pre-trained machine learning model. This model combines materials science principles with extensive historical data to accurately simulate the corrosion process over time. Based on the model's output, a material thickness attenuation field is generated, representing the spatial distribution of material thickness reduction due to corrosion. This entire process ensures accurate prediction of corrosion propagation and provides a solid data foundation for subsequent steps.

[0186] 703. The stress concentration factor, elastic modulus attenuation gradient and material thickness attenuation field are coupled and correlated, and a synergistic degradation model of icing and corrosion is established based on the component stress distribution data. When the weight of icing and corrosion synergistic degradation exceeds the material fatigue strength threshold, carbon footprint incremental data is generated.

[0187] In step 703, the stress concentration factor describes the degree to which structural discontinuities cause a significant increase in local stress and is an important parameter for assessing structural weaknesses. The elastic modulus decay gradient represents the rate at which a material's ability to recover its original shape gradually weakens after long-term deformation under stress, reflecting the degree of material aging. The material thickness decay field shows the spatial distribution of material thickness reduction caused by corrosion and other factors. The synergistic degradation model of icing and corrosion comprehensively considers the degradation of material properties under the combined effects of icing loads and corrosion. The material fatigue strength threshold refers to the maximum stress level that a material can withstand under repeated stress; exceeding this value may lead to material failure.

[0188] In this embodiment, the stress concentration factor, elastic modulus attenuation gradient obtained in step 701, and the material thickness attenuation field generated in step 702 are first integrated to form a comprehensive database reflecting the health status of the component. Next, a synergistic degradation model of icing and corrosion is developed. This model considers not only the impact of icing loads on the component but also the weakening effect of corrosion on material properties. Numerical simulation methods are used to analyze the combined effects of icing and corrosion on material properties under different conditions. In particular, when the stress caused by this synergistic effect exceeds the material fatigue strength threshold, the system automatically calculates the corresponding carbon footprint increment data. This step uses advanced finite element analysis software to simulate the stress distribution under various working conditions and is corrected using actual monitoring data to ensure the accuracy of the results. Finally, maintenance strategies are formulated based on these data to prevent potential failures caused by icing and corrosion, while quantifying their environmental impact.

[0189] 704. Construct a carbon flow vector matrix based on the spatiotemporal distribution characteristics of icing and corrosion synergistic degradation and the carbon footprint increment data, and generate a full life cycle carbon footprint quantification report.

[0190] In step 704, the spatiotemporal distribution characteristics of icing and corrosion synergistic degradation refer to the characteristics of the influence of icing and corrosion on power tower components changing over time and space. The carbon footprint increment data is the newly added carbon emissions based on the decline in material performance caused by the synergistic effect of icing and corrosion. The carbon flow vector matrix is a data structure used to systematically record and analyze the entire process of these carbon emissions from generation to diffusion.

[0191] In the embodiments of the present application, first, the spatiotemporal distribution characteristics of icing load deformation and corrosion area expansion are integrated, combined with the previously calculated stress concentration coefficient, elastic modulus attenuation gradient, and material thickness attenuation field data, to construct a comprehensive icing and corrosion synergistic degradation model. Then, whether the synergistic degradation weight evaluated by the model exceeds the material fatigue strength threshold is determined to generate carbon footprint increment data. Next, based on these carbon footprint increment data, a carbon flow vector matrix is constructed, which not only contains the specific values of carbon emissions, but also records detailed information such as the location, time, and possible impact range of the carbon emissions. Finally, using all the above information, a full life cycle carbon footprint quantification report is generated.

[0192] The following is a specific example:

[0193] Taking a newly built transmission line project as an example, in the actual operation process, first, deploy a sensor network to monitor the icing condition of the power tower in real time, and use non-destructive testing technology to regularly check the corrosion condition of the tower components. Through numerical simulation and machine learning algorithm processing of the collected data, a dynamic stress field of icing load and a corrosion diffusion rate model are established. Subsequently, these data are input into the icing and corrosion synergistic degradation model to evaluate the overall health status of the power tower and its impact on the environment. Based on this, a full life cycle carbon footprint quantification report is generated, providing a scientific basis for subsequent maintenance plans.

[0194] In summary, steps 701 to 704 achieve precise monitoring and analysis of the deformation of icing load and the expansion of corrosion area during the operation and maintenance phase of power towers by integrating advanced sensing technology, numerical simulation technology, and machine learning algorithms. Not only can potential safety hazards be discovered in a timely manner, but the carbon footprint of power facilities throughout their entire life cycle can also be effectively quantified, providing strong support for optimizing operation and maintenance strategies and greatly improving the sustainable management level of power infrastructure.

[0195] Figure 3 A structural diagram of a power tower full life cycle carbon footprint accounting system is provided for the embodiments of the present application, as shown in Figure 3 The system comprises:

[0196] The integration module 31 constructs a power tower component library through high-precision three-dimensional modeling technology, which is based on building information modeling to perform geometric parameterization modeling on the power tower structure, integrates steel type, welding process, and corrosion coating parameters, and generates design stage carbon accounting benchmark data;

[0197] The conversion module 32 deploys a handheld laser radar device at the power tower construction site and captures installation angle deviation and component splicing gap of the power tower assembly in real time in a high-speed point cloud scanning manner, calculates material loss caused by construction error based on point cloud degree distribution, and converts the material loss into construction error carbon emission increase data.

[0198] The correction module 33 loads the design stage carbon accounting benchmark data and the construction error carbon emission increase data through digital twinning technology, compares the tower design model with the on-site construction point cloud data using a synchronous refreshing mechanism, identifies insufficient bolt fastening and component redundant cutting phenomena, dynamically adjusts the material loss warning threshold, and corrects the design stage carbon accounting benchmark data.

[0199] The generation module 34 generates a full life cycle carbon footprint quantification report based on the corrected design stage carbon accounting benchmark data, cooperatively analyzes icing load deformation and corrosion area expansion in the operation and maintenance stage of the power tower, and updates the component stress distribution data using the high-precision three-dimensional modeling technology.

[0200] Figure 3 The power tower full life cycle carbon footprint accounting system can perform Figure 1 and Figure 2 The implementation principle and technical effects of the power tower full life cycle carbon footprint accounting method described in the embodiments are not repeated. The specific operation of each module and unit of the power tower full life cycle carbon footprint accounting system described in the above embodiments has been described in detail in the embodiments related to the method, and will not be described in detail here.

[0201] In one possible design, Figure 3 The power tower full life cycle carbon footprint accounting system described in the embodiments can be implemented as a computing device, such as Figure 4 The computing device can include a storage component 41 and a processing component 42.

[0202] The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 42.

[0203] The processing component 42 is used for the above Figure 1 and Figure 2The embodiment provides a power tower full life cycle carbon footprint accounting method.

[0204] The processing component 42 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, for executing the above method.

[0205] The storage component 41 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0206] Of course, the computing device can also include other components, such as input / output interfaces, display components, communication components, etc.

[0207] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0208] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0209] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component, the storage component, etc. can be a basic server resource rented or purchased from the cloud computing platform.

[0210] The embodiment of the application further provides a computer storage medium storing a computer program, and the computer program can implement the above method when executed by a computer. Figure 1 And Figure 2 The embodiment provides a power tower full life cycle carbon footprint accounting method.

[0211] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0212] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0213] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software products can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and include a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0214] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for calculating the carbon footprint of power transmission towers throughout their entire life cycle, characterized in that, include: A power transmission tower component library is constructed using high-precision 3D modeling technology. This technology uses building information modeling (BIM) to perform geometric parametric modeling of the power transmission tower structure, integrates steel type, welding process, and anti-corrosion coating parameters, and generates carbon accounting baseline data for the design phase. Handheld lidar devices are deployed at the construction site of power towers, and the installation angle deviation and splicing gap of power tower components are captured in real time by high-speed point cloud scanning. The material loss caused by construction errors is calculated based on the point cloud degree distribution, and the material loss is converted into carbon emission increment data of construction errors. By loading the carbon accounting baseline data of the design phase and the carbon emission increment data of the construction error using digital twin technology, and by using a synchronous refresh mechanism to compare the tower design model with the on-site construction point cloud data, insufficient bolt tightness and redundant component cutting phenomena are identified, the material loss early warning threshold is dynamically adjusted, and the carbon accounting baseline data of the design phase is corrected. Based on the revised carbon accounting baseline data in the design phase, a collaborative analysis of icing load deformation and corrosion area expansion in the operation and maintenance phase of power transmission towers is conducted. Combined with the component stress distribution data updated by the high-precision three-dimensional modeling technology, a full life cycle carbon footprint quantification report is generated. The method of using a synchronous refresh mechanism to compare the tower design model with the on-site construction point cloud data, identifying insufficient bolt tightening and redundant component cutting, dynamically adjusting the material loss early warning threshold, and correcting the carbon accounting baseline data in the design phase includes: Based on the geometric constraints of bolted connection nodes in the tower design model, abnormal connection nodes with insufficient bolt tightness are identified according to the difference in the spatial density attenuation slope between cloud clusters of adjacent bolted connection nodes. Traverse the preset geometric topology of the component splicing joints in the tower design model and the point cloud curvature distribution of the actual splicing area of ​​the component in the on-site construction point cloud data. When the abrupt change in the curvature of the point cloud at the edge of the splicing joint exceeds the critical value of the plastic deformation of the component material, locate the redundant cutting position of the component. Based on the proportion of the cutting area between the abnormal connection node and the redundant cutting position of the component, a nonlinear regression relationship between construction error and material loss is established, and the boundary conditions of the material loss warning threshold are dynamically adjusted according to the gradient change trend of the material loss as the construction error increases. The boundary conditions are iterated in reverse to the stress distribution data of the components in the tower design model. By reducing the load transfer path of the stress concentration area due to the redundant cutting phenomenon of components, the carbon accounting baseline data in the design stage is corrected.

2. The method according to claim 1, characterized in that, The establishment of a nonlinear regression relationship between construction error and material loss based on the proportion of the cutting area between the abnormal connection node and the redundant cutting position of the component includes: The gradient calculation of the difference in spatial density decay slope of the abnormal connection nodes is performed, the radial gradient vector of the three-dimensional neighborhood point cloud density of the bolt connection node is analyzed, and each abnormal connection node is screened by the directional deviation of the radial gradient vector of the adjacent three-dimensional neighborhood point cloud density. The deviation weight factor is generated based on the correlation between the sine value of the deviation angle and the spatial density decay slope. The curvature tensor eigenvalues ​​are decomposed using the local coordinate system of the point cloud at the splice seam edge. Combined with the material yield strength threshold, curvature abrupt change regions are identified. The proportion of the projected area of ​​the redundant cutting position of the component is analyzed by parametric projection of the three-dimensional surface. By fusing the deviation weighting factor and the projection area ratio, a spatiotemporal composite feature is constructed, and the error accumulation feature of the assembly sequence is extracted using a sliding window to generate a high-dimensional regression input set. Based on the critical value of material plastic deformation, a dynamic reduction coefficient of construction error on the bearing area is defined. The high-dimensional regression input set is associated with the material loss to establish a nonlinear regression relationship between construction error and material loss.

3. The method according to claim 1, characterized in that, The boundary conditions for dynamically adjusting the material loss early warning threshold based on the gradient change trend of the material loss amount with the increase of the construction error include: A multi-dimensional gradient field is constructed based on the spatiotemporal distribution of the construction error, and the principal direction modulus of the multi-dimensional gradient field is extracted. The temporal dimension modulus represents the error accumulation rate, and the spatial dimension modulus reflects the material loss diffusion intensity. A sliding window is set on the component assembly sequence to analyze the dynamic covariance of the main direction modulus. When the main direction modulus exceeds three times the standard deviation of the historical mean, it is marked as a gradient direction abrupt change region. Based on the correlation between the modulus of the gradient direction abrupt change region and the material loss diffusion intensity, a dynamic adjustment factor is obtained. Based on the load transfer path topology of the stress concentration region, the dynamic adjustment factor is used to attenuate the load weight of adjacent nodes in the redundant cutting region, and the boundary conditions of the material loss warning threshold are dynamically adjusted.

4. The method according to claim 1, characterized in that, The calculation of material loss due to construction errors based on point cloud degree distribution, and the conversion of the material loss into incremental carbon emissions data due to construction errors, includes: Based on the point cloud distribution data of power tower components obtained by high-speed point cloud scanning, the point cloud normal vector offset corresponding to the component installation angle deviation and the point cloud void area corresponding to the component splicing gap are extracted. Based on the correlation between the point cloud normal vector offset and the bending deformation coefficient of the steel, the compensation amount for the plastic deformation of the component caused by the installation angle deviation of the component is calculated. At the same time, based on the matching degree between the area of ​​the point cloud void region and the welding penetration parameter of the component, the amount of steel redundancy filling caused by the splicing gap is calculated. The plastic deformation compensation amount of the component and the redundant filling amount of steel are superimposed according to the component type to generate the material loss amount for construction error correction. Based on the carbon footprint coefficient of steel type and the carbon emission factor of unit heat input of welding process, the material loss amount is converted into carbon emission increment data of construction error.

5. The method according to claim 4, characterized in that, The calculation of the redundant steel filling amount caused by the splicing gap based on the matching degree between the area of ​​the point cloud void region and the welding penetration parameters of the component includes: A three-dimensional morphological analysis is performed on the area of ​​the point cloud cavity region, and the depth distribution gradient field and surface curvature features are extracted. The melting depth requirement distribution field is generated by the axial depth range and curvature change rate of the point cloud cavity region. Based on the unit melt depth filling coefficient in the component welding melt depth parameters, a mapping relationship between melt depth and filling amount is constructed. According to the spatial superposition relationship between the depth gradient direction of each region in the melt depth demand distribution field and the welding path, the difference in matching degree between actual melt depth and theoretical melt depth is analyzed. In the local coordinate system of the welding path, the matching degree difference is spatially integrated, the integral weight is corrected by the curvature characteristics of the weld penetration requirement distribution field, and the three-dimensional spatial distribution is calculated by combining the material expansion coefficient of the weld heat-affected zone. Based on the geometric topological relationship between the three-dimensional spatial distribution and the component splice seam, the cumulative value of steel filling is statistically analyzed segment by segment, and associated with the dynamic shrinkage rate of the molten pool in the welding process parameters to generate redundant steel filling.

6. The method according to claim 1, characterized in that, The aforementioned collaborative analysis of icing load deformation and corrosion zone expansion during the operation and maintenance phase of power transmission towers, combined with component stress distribution data updated by the high-precision 3D modeling technology, generates a full life-cycle carbon footprint quantification report, including: Based on the spatiotemporal distribution of ice thickness, a dynamic stress field of ice load on the surface of the component is established, and the stress concentration factor and elastic modulus attenuation gradient are obtained by coupling the deformation rate of ice load with the curvature change of the component. The oxide layer thickness and microcrack density of the rusted area are extracted, and a rust diffusion rate model is established by combining environmental temperature and humidity data to generate a material thickness attenuation field. The stress concentration factor, elastic modulus attenuation gradient and material thickness attenuation field are coupled and correlated, and a synergistic degradation model of icing and corrosion is established based on component stress distribution data. When the weight of icing and corrosion synergistic degradation exceeds the material fatigue strength threshold, incremental carbon footprint data is generated. Based on the spatiotemporal distribution characteristics of the synergistic degradation of icing and corrosion and the incremental carbon footprint data, a carbon flow vectorization matrix is ​​constructed to generate a full life cycle carbon footprint quantification report.

7. A system for calculating the carbon footprint of power transmission towers throughout their entire life cycle, used to implement the method for calculating the carbon footprint of power transmission towers throughout their entire life cycle as described in any one of claims 1 to 6, characterized in that, include: The integration module constructs a power tower component library using high-precision 3D modeling technology. This high-precision 3D modeling technology performs geometric parametric modeling of the power tower structure based on Building Information Modeling (BIM), integrates steel type, welding process, and anti-corrosion coating parameters, and generates carbon accounting baseline data for the design phase. The conversion module deploys a handheld lidar device at the power tower construction site and captures the installation angle deviation and component splicing gap of the power tower components in real time using high-speed point cloud scanning. Based on the point cloud degree distribution, it calculates the material loss caused by construction errors and converts the material loss into carbon emission increment data of construction errors. The correction module loads the carbon accounting baseline data from the design phase and the carbon emission increment data from the construction error using digital twin technology. It also uses a synchronous refresh mechanism to compare the tower design model with the on-site construction point cloud data, identify insufficient bolt tightening and redundant component cutting, dynamically adjust the material loss early warning threshold, and correct the carbon accounting baseline data from the design phase. The generation module, based on the modified carbon accounting baseline data in the design phase, performs a collaborative analysis of the icing load deformation and corrosion area expansion in the operation and maintenance phase of power transmission towers. Combined with the component stress distribution data updated by the high-precision three-dimensional modeling technology, it generates a quantitative report on the carbon footprint of the entire life cycle.

8. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the carbon footprint accounting method for the entire life cycle of power transmission towers as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a method for calculating the carbon footprint of a power transmission tower throughout its entire life cycle, as described in any one of claims 1 to 6.

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