Foundation carbon emission calculation method for overhead transmission line construction

Abstract

The invention belongs to the technical field of basic carbon emission calculation of power transmission line construction, and discloses a basic carbon emission calculation method of overhead power transmission line construction. The method comprises the following steps: S3, carrying out data preprocessing on a fixed emission data set and a dynamic emission data set to obtain a feature vector for carbon emission prediction; s4, analyzing and calculating the feature vector to obtain a carbon emission predicted value; and S5, processing the carbon emission predicted value, and obtaining an emission early warning report according to a processing result. S6, identifying the parameter with the maximum carbon emission value according to the carbon emission predicted value, and obtaining an accurate emission reduction suggestion report; s7, performing analysis in combination with the carbon emission predicted value, the construction cost data and the target construction period data to obtain a construction optimization combination report; s8, the construction environment change is simulated according to the carbon emission predicted value, and a carbon cost influence report is obtained.In general, the method has the remarkable advantages of being high in carbon emission prediction accuracy, large in reality auxiliary effect and good in data use and storage safety.

Description

Technical Field

[0001] The present invention relates to the technical field of basic carbon emission calculation for power transmission line construction, and more specifically, to a basic carbon emission calculation method for overhead power transmission line construction. Background Art

[0002] Transmission lines are constructed by using transformers to boost the voltage of electricity generated by generators, and then connecting them to the transmission lines through control devices such as circuit breakers. Structurally, transmission lines are divided into overhead transmission lines and cable lines. Carbon emissions refer to the greenhouse gas emissions generated during the production, transportation, use, and recycling of a product. Both overhead transmission lines and cable tunnel lines generate significant carbon emissions during construction. Therefore, accurately calculating and utilizing the carbon emissions required for construction is a key step in power grid management.

[0003] The patent application with the publication number CN119623834A discloses a method for calculating the basic carbon emissions of overhead transmission line construction. By combining the emission factor method with the characteristics of transmission line foundation engineering, an effective method for calculating the basic carbon emissions of overhead transmission line construction is proposed. The method is then applied to the calculation of carbon emissions from slab-column foundations and bored foundations, thereby exploring potential links and methods for energy conservation and emission reduction.

[0004] However, although the above-mentioned basic carbon emission calculation method for overhead transmission line construction has achieved the calculation of carbon emissions during the construction process to a certain extent through the emission factor method combined with the characteristics of the transmission line foundation project, the existing carbon emission calculation methods mostly rely on real-time calculations. In project construction, carbon emission values are one of the important construction costs of the project. Relying solely on real-time calculations often makes related companies passive and difficult to carry out advance cost planning. In addition, most existing carbon emission calculations are single calculations of carbon emission values without in-depth data mining of carbon emission values.

[0005] In view of this, the present invention proposes a basic carbon emission calculation method for overhead transmission line construction to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solution, the method comprising: S1: Collecting a fixed emission data set based on a sensor network. This step specifically involves collecting a fixed emission data set related to construction through the sensor network. The fixed emission data set includes line length data, tower quantity data, material weight data, and tower type data. S2: Collect dynamic emission data sets based on the sensor network. This step specifically involves collecting dynamic emission data sets related to construction through the sensor network. The dynamic emission data sets include terrain difficulty data, construction process data, and construction complexity data. S3: Preprocess the fixed and dynamic emission datasets to obtain feature vectors for carbon emission prediction. This step specifically involves quantifying the basic data items in the fixed and dynamic emission datasets to reduce the computational burden of subsequent carbon emission predictions and provide multiple interactive data points for system calculations, thereby further improving the accuracy of system predictions. S4: Analyze and calculate the eigenvectors to obtain a carbon emission forecast value. This step specifically involves fusing the eigenvectors to obtain a carbon emission forecast value for the next time period, enabling the system to predict carbon emissions in future time periods and providing core data support for the system to guide construction planning based on carbon emissions, thereby achieving the system's goal of predicting future carbon emissions, making pre-adjustments, and avoiding passive responses. S5: Process the carbon emission forecast values and obtain an emission warning report based on the processing results. This step specifically includes grading the carbon emission forecast values according to the carbon emission budget threshold, and providing a corresponding warning report based on the grading results. This enables the system to convert the system forecast data and grading results into auxiliary and forward-looking decision-making information reports, effectively reducing the time and workload required for data calculation and information conversion for staff, thereby achieving an overall enhancement of the data's actual feedback capabilities and auxiliary guidance capabilities. S6: Identify the parameter with the largest carbon emission value based on the carbon emission prediction value and obtain an accurate emission reduction recommendation report; this step specifically includes calculating the contribution of all sub-data items in the fixed emission data set and the dynamic emission data set based on the carbon emission prediction value, so that the system can quickly identify and locate the key links of emission reduction. Combined with the corresponding contribution threshold comparison, the system can quickly issue corresponding emission reduction optimization reports based on the different emission reduction links after identification and positioning, thereby achieving the purpose of effectively reducing the costs required in the carbon emission reduction process; S7: Analyze the carbon emission forecast, construction cost data, and target construction period data to obtain a construction optimization combination report. This step specifically involves performing multi-objective optimization on construction costs and construction dates based on the carbon emission forecast. This allows the system to not only balance the quantitative carbon emission values, construction costs, and construction dates, but also supports differentiated processing of decision-making objectives through the construction optimization algorithm, enabling the system to quickly find the optimal combination and reduce the time required for decision-making. S8: Simulate construction environment changes based on carbon emission forecasts to generate a carbon cost impact report. This step specifically involves further quantifying carbon emission forecasts based on construction environment changes, enabling the system to adjust individual carbon costs based on dynamic market changes, thereby assisting staff in quickly completing pre-specified hedging strategies. S9: Store key data sets in the database, display emission warning reports and construction optimization combination reports through a visualization panel, and output precise emission reduction recommendation reports and construction optimization combination reports; Furthermore, step S1 includes: Collect fixed emission data sets based on sensor networks; The vehicle-mounted laser rangefinder is used to collect the total length of the planned path in the specified area to obtain the route length data; Through drone aerial photography, the number of towers that need to be built in the designated area is collected to obtain tower quantity data; Through intelligent floor scales and RFID tag tools, the weight values and material type data of material transport vehicles in the designated area are collected to obtain material weight data; Through the BIM database, the structural types of the towers required to be built in the specified area are collected, and the structural types are assigned values to obtain the tower type data; Furthermore, step S2 includes: Collect dynamic emission data sets based on sensor networks; Using drone laser radar, terrain data within a specified area is collected to generate a digital elevation model, and the average slope is automatically calculated. Based on the average slope, a value is assigned to obtain terrain difficulty data. Through the construction machinery control sensor, the construction signals of the construction equipment in the designated area are collected and assigned values to obtain the construction process data; By using UWB positioning badges in conjunction with construction management tools, the distribution of personnel in a designated area is collected and assigned values to obtain construction complexity data. Furthermore, step S3 includes: S3.1: Eliminate line length data using a data normalization formula and material weight data dimensional influence of S3.2: By applying terrain difficulty data Multiply by construction process data , get coupling characteristic data ; S3.3: Composite feature extraction is performed on the material weight data and construction complexity data. The specific calculation formula for composite feature extraction is: ; Get composite feature data ,in, is the construction complexity data; S3.4: Quantify the comprehensive load of the towers using the tower quantity data, tower type data, and terrain difficulty data. The specific calculation formula for quantification is: ; Get comprehensive feature data ,in, is the number of towers, is the tower type data, It is the terrain difficulty data; S3.5: By squaring the terrain difficulty data and performing logarithmic calculation on the construction complexity data, nonlinear terrain difficulty data and nonlinear construction complexity data are obtained. The expression of nonlinear construction complexity data is: ; S3.6: Pack the coupled feature data, the composite feature data, the comprehensive feature data, the standardized line length data, the standardized material weight data, the nonlinear terrain difficulty data, and the nonlinear construction complexity data to obtain a feature vector; Furthermore, step S4 includes: A carbon emission prediction algorithm is constructed based on the eigenvector. The eigenvector is input and the output is a carbon emission prediction value that reflects the carbon emission value in the next time period. The specific formula of the carbon emission prediction algorithm is: ; Get carbon emission prediction value ,in, is the standard carbon emission constant, For the The weight factor of the eigenvector sub-vector, For the subvectors of the eigenvectors, is the feature weight factor, It is the interaction term between coupled characteristic data and comprehensive characteristic data; Furthermore, step S5 includes: Based on the carbon emission budget threshold, when the carbon emission forecast value is less than the carbon emission budget threshold, an emission safety report is generated; when the carbon emission forecast value is greater than or equal to the carbon emission budget threshold, an emission hazard report is generated; The emission safety report includes a statement that the predicted carbon emission values are normal and that workers are requested to proceed with construction according to the preset work schedule; The emission risk report includes an explanation of abnormal predicted carbon emission values, and requests that staff reduce the terrain difficulty data by optimizing transportation routes, reduce the construction process data by increasing the use of prefabrication and assembly processes, and reduce the material weight data by reducing the use of high-carbon materials; Package emission safety reports and emission hazard reports to obtain emission early warning reports; Furthermore, step S6 includes: The contribution of the sub-data items in the fixed emission dataset and the dynamic emission dataset is calculated based on the carbon emission prediction value. The specific calculation formula for the contribution is: ; Get the first Contribution of each sub-data item ,in, is the partial derivative of the carbon emission prediction value, For the The partial derivative of the sub-data items, It is the collection of all sub-data items in the fixed emission dataset and the dynamic emission dataset; Output the corresponding data optimization report based on the comparison results of the contribution value and the corresponding data contribution threshold. The specific comparison method is: when Greater than When Data optimization report, which includes: For the The contribution threshold of each sub-data item; Pack Data optimization report to obtain accurate emission reduction recommendation report; Furthermore, step S7 includes: Based on the target optimization algorithm, the carbon emission prediction value, construction cost data and target construction period data are calculated. The specific expression of the target optimization algorithm is: ; in, is the decision vector, is the standard carbon emission data, is the construction cost data, is the standard construction cost data, Target duration data, is the standard construction period data, 、 and To optimize the weight factor, and satisfy ; Output low-carbon solutions by minimizing carbon emission predictions; Output economic plan by minimizing construction cost data; Output quick-fix solutions by minimizing target duration data; Package low-carbon solutions, economic solutions, and quick-fix solutions to obtain a construction optimization combination report; Furthermore, step S8 includes: The final carbon cost simulation is calculated based on the carbon emission forecast value combined with the construction environment changes. And steel prices increased %, the specific formula for simulation calculation is: ; Get the final carbon cost value ,in, The tax rate increase, is the ratio of steel to material cost; Packaged carbon tax rate increases , steel prices increase % and the final carbon cost value to obtain the carbon cost impact report; Furthermore, key data sets include fixed emission data sets, dynamic emission data sets, feature vectors, carbon emission prediction values, emission early warning reports, precise emission reduction recommendation reports, construction optimization combination reports, and carbon cost impact reports; Methods for outputting precise emission reduction recommendation reports and construction optimization combination reports include: The precise emission reduction recommendation report and construction optimization combination report will be sent to the staff's email address via email.

[0007] The technical effects and advantages of the basic carbon emission calculation method for overhead transmission line construction of the present invention are as follows: The present invention collects fixed emission data sets based on a sensor network, collects dynamic emission data sets based on a sensor network, performs data preprocessing on the fixed emission data sets and the dynamic emission data sets to obtain characteristic vectors for carbon emission prediction, analyzes and calculates the characteristic vectors to obtain carbon emission prediction values, processes the carbon emission prediction values, obtains an emission warning report based on the processing results, identifies the parameters with the largest carbon emission values based on the carbon emission prediction values, obtains an accurate emission reduction recommendation report, combines the carbon emission prediction values, construction cost data and target construction period data for analysis to obtain a construction optimization combination report, simulates construction environment changes based on the carbon emission prediction values to obtain a carbon cost impact report, stores key data sets in a database, and uses a visualization panel to view the emission warning report and The construction optimization combination report is displayed, the precise emission reduction recommendation report and the construction optimization combination report are processed, and output according to the processing results, so that the system can accurately predict the carbon emission value in the future time period, and provide core data support for the cost accounting and advance response of the staff. In addition, the present invention also provides the staff with emission warning, precise emission reduction, construction direction optimization and carbon cost impact based on the carbon emission prediction value, thereby effectively improving the system's auxiliary function for decision-making measures in the physical world. At the same time, through the storage and transmission of important data, the security and usability of the data can be effectively guaranteed. Generally speaking, the present invention has the significant advantages of high carbon emission prediction accuracy, large practical auxiliary role and good data storage security. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a schematic diagram of a basic carbon emission calculation method for overhead transmission line construction according to the present invention. DETAILED DESCRIPTION

[0009] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0010] Example 1 See also Figure 1 As shown, the basic carbon emission calculation method for overhead transmission line construction described in this embodiment includes: S1: Collecting a fixed emission data set based on a sensor network. This step specifically involves collecting a fixed emission data set related to construction through the sensor network. The fixed emission data set includes line length data, tower quantity data, material weight data, and tower type data. S2: Collect dynamic emission data sets based on the sensor network. This step specifically involves collecting dynamic emission data sets related to construction through the sensor network. The dynamic emission data sets include terrain difficulty data, construction process data, and construction complexity data. S3: Preprocess the fixed and dynamic emission datasets to obtain feature vectors for carbon emission prediction. This step specifically involves quantifying the basic data items in the fixed and dynamic emission datasets to reduce the computational burden of subsequent carbon emission predictions and provide multiple interactive data points for system calculations, thereby further improving the accuracy of system predictions. S4: Analyze and calculate the eigenvectors to obtain a carbon emission forecast value. This step specifically involves fusing the eigenvectors to obtain a carbon emission forecast value for the next time period, enabling the system to predict carbon emissions in future time periods and providing core data support for the system to guide construction planning based on carbon emissions, thereby achieving the system's goal of predicting future carbon emissions, making pre-adjustments, and avoiding passive responses. S5: Process the carbon emission forecast values and obtain an emission warning report based on the processing results. This step specifically includes grading the carbon emission forecast values according to the carbon emission budget threshold, and providing a corresponding warning report based on the grading results. This enables the system to convert the system forecast data and grading results into auxiliary and forward-looking decision-making information reports, effectively reducing the time and workload required for data calculation and information conversion for staff, thereby achieving an overall enhancement of the data's actual feedback capabilities and auxiliary guidance capabilities. S6: Identify the parameter with the largest carbon emission value based on the carbon emission prediction value and obtain an accurate emission reduction recommendation report; this step specifically includes calculating the contribution of all sub-data items in the fixed emission data set and the dynamic emission data set based on the carbon emission prediction value, so that the system can quickly identify and locate the key links of emission reduction. Combined with the corresponding contribution threshold comparison, the system can quickly issue corresponding emission reduction optimization reports based on the different emission reduction links after identification and positioning, thereby achieving the purpose of effectively reducing the costs required in the carbon emission reduction process; S7: Analyze the carbon emission forecast, construction cost data, and target construction period data to obtain a construction optimization combination report. This step specifically involves performing multi-objective optimization on construction costs and construction dates based on the carbon emission forecast. This allows the system to not only balance the quantitative carbon emission values, construction costs, and construction dates, but also supports differentiated processing of decision-making objectives through the construction optimization algorithm, enabling the system to quickly find the optimal combination and reduce the time required for decision-making. S8: Simulate construction environment changes based on carbon emission forecasts to generate a carbon cost impact report. This step specifically involves further quantifying carbon emission forecasts based on construction environment changes, enabling the system to adjust individual carbon costs based on dynamic market changes, thereby assisting staff in quickly completing pre-specified hedging strategies. S9: Store key data sets in the database, display emission warning reports and construction optimization combination reports through a visualization panel, and output precise emission reduction recommendation reports and construction optimization combination reports; The core of the present invention is to obtain fixed and dynamic data sets related to carbon emissions through a sensor network, perform corresponding feature extraction, and achieve the purpose of accurately predicting the carbon emissions required for construction based on the extracted feature vectors. The basic data collection in steps S1 and S2 provides basic data support for the data fusion in step S3. Based on the feature vectors after data fusion, the carbon emission value for the future time period is calculated by the carbon emission prediction algorithm in step S4. The carbon emission prediction value in step S4 provides core data support for steps S5, S6, S7, and S8. The emission warning report obtained in step S5 can intuitively display whether the predicted carbon emission value for the future time period exceeds the carbon emission budget. The precise emission reduction recommendation report obtained in step S6 can accurately identify the links where carbon emissions exceed the budget. The construction optimization combination report obtained in step S7 can assist staff in achieving a balance between carbon emissions, construction costs, and construction dates. The carbon cost impact report obtained in step S8 can provide early feedback on cost increases after policy changes and market changes. Finally, in step S9, the results obtained in all steps are processed accordingly, so that the system can have predictive, auxiliary, and data security related to carbon emissions. Step S1 includes: Collect fixed emission data sets based on sensor networks; The vehicle-mounted laser rangefinder is used to collect the total length of the planned path in the specified area to obtain the route length data; Through drone aerial photography, the number of towers that need to be built in the designated area is collected to obtain tower quantity data; Through intelligent floor scales and RFID tag tools, the weight values and material type data of material transport vehicles in the designated area are collected to obtain material weight data; Through the BIM database, the structural types of the towers required to be built in the specified area are collected, and the structural types are assigned values to obtain the tower type data; It should be explained that the structure type assignment means that, for example, when the structure type of the tower is a right-angle tower, the tower type data value is 1; when the structure type of the tower is a corner tower, the tower type data value is 1.5; The core of this implementation method is to collect basic carbon emission data required for overhead transmission line construction through a sensor network, providing solid basic data support for subsequent carbon emission forecasts; Step S2 includes: Collect dynamic emission data sets based on sensor networks; Using drone laser radar, terrain data within a specified area is collected to generate a digital elevation model, and the average slope is automatically calculated. Based on the average slope, a value is assigned to obtain terrain difficulty data. It should be explained that the assignment includes: when the average slope is less than 5 degrees, it is identified as flat land, and the terrain difficulty data output value is 1; when the average slope is greater than or equal to 5 degrees and less than 15 degrees, it is identified as hilly, and the terrain difficulty data output value is 1.3; when the average slope is greater than or equal to 15 degrees, it is identified as mountainous, and the terrain difficulty data output value is 1.8; Through the construction machinery control sensor, the construction signals of the construction equipment in the designated area are collected and assigned values to obtain the construction process data; It should be explained that assigning values to construction signals means, for example, when the construction machinery control sensor detects a concrete vibrating rod signal, it is determined to be a traditional pouring construction process, and the construction process data is assigned a value of 1.2; when the construction machinery control sensor detects a lifting machinery hydraulic signal, it is determined to be a prefabricated assembly construction process, and the construction process data is assigned a value of 0.9; By using UWB positioning badges in conjunction with construction management tools, the distribution of personnel in a designated area is collected and assigned values to obtain construction complexity data. It should be explained that assigning values based on the distribution situation means that when the distribution situation is that the personnel are dispersed, the construction complexity data is assigned a value of 1; when the distribution situation is that the personnel are highly concentrated, the construction complexity data is assigned a value of 1.4; when the distribution situation is that the personnel are linearly distributed, the construction complexity data is assigned a value of 1.6; The core of this implementation is to collect dynamic carbon emission impact data required for overhead transmission line construction through a sensor network, providing dynamic impact-related data support for subsequent carbon emission forecasts, thereby further improving the accuracy of carbon emission forecasts. Step S3 includes: S3.1: Eliminate line length data using a data normalization formula and material weight data dimensional influence of It should be explained that the data standardization formula is, taking line length data as an example, subtracting the mean of the line length data from the line length data and dividing it by the standard deviation of the line length data to obtain the standardized line length data; S3.2: By applying terrain difficulty data Multiply by construction process data , get coupling characteristic data ; S3.3: Composite feature extraction is performed on the material weight data and construction complexity data. The specific calculation formula for composite feature extraction is: ; Get composite feature data ,in, is the construction complexity data; S3.4: Quantify the comprehensive load of the towers using the tower quantity data, tower type data, and terrain difficulty data. The specific calculation formula for quantification is: ; Get comprehensive feature data ,in, is the number of towers, is the tower type data, It is the terrain difficulty data; S3.5: By squaring the terrain difficulty data and performing logarithmic calculation on the construction complexity data, nonlinear terrain difficulty data and nonlinear construction complexity data are obtained. The expression of nonlinear construction complexity data is: ; S3.6: Pack the coupled feature data, the composite feature data, the comprehensive feature data, the standardized line length data, the standardized material weight data, the nonlinear terrain difficulty data, and the nonlinear construction complexity data to obtain a feature vector; The core of this implementation lies in standardizing and extracting data interaction features based on fixed and dynamic emission datasets, enabling the system to achieve the data purpose of quantitative forecasting calculations by calculating basic data items, providing a more accurate, convenient, and interactive data foundation for subsequent forecasting calculations. Specifically, standardized formulas are used to eliminate dimensional differences in basic data, data interaction calculations are used to reveal hidden relationships between data, and nonlinear transformations are used to increase the nonlinear fitting capabilities of basic data. This enables the system to make the contribution of basic data explicit, laying a data foundation for improving forecasting accuracy. Step S4 includes: A carbon emission prediction algorithm is constructed based on the eigenvector. The eigenvector is input and the output is a carbon emission prediction value that reflects the carbon emission value in the next time period. The specific formula of the carbon emission prediction algorithm is: ; Get carbon emission prediction value ,in, is the standard carbon emission constant, For the The weight factor of the eigenvector sub-vector, For the subvectors of the eigenvectors, is the feature weight factor, It is the interaction term between coupled characteristic data and comprehensive characteristic data; The core of this implementation is to obtain a carbon emission prediction value that reflects the carbon emission value in the next time period by calculating the characteristic vector, so that the system has the ability to predict the carbon emission value in the future time period. Specifically, this implementation is to capture complex effects based on the input of the characteristic vector and the interaction between data items by constructing a carbon emission prediction algorithm. For example, the interaction between the integrated coupled characteristic data and the integrated characteristic data quantifies the carbon emissions based on the construction environment and construction process and the total carbon emissions between the construction of a single tower. Compared with traditional single data calculations, the system can take into account the comprehensive influence between basic data during prediction calculations, thereby greatly improving the reliability of the data predicted by the system. Step S5 includes: Based on the carbon emission budget threshold, when the carbon emission forecast value is less than the carbon emission budget threshold, an emission safety report is generated; when the carbon emission forecast value is greater than or equal to the carbon emission budget threshold, an emission hazard report is generated; The emission safety report includes a statement that the predicted carbon emission values are normal and that workers are requested to proceed with construction according to the preset work schedule; The emission risk report includes an explanation of abnormal predicted carbon emission values, and requests that staff reduce the terrain difficulty data by optimizing transportation routes, reduce the construction process data by increasing the use of prefabrication and assembly processes, and reduce the material weight data by reducing the use of high-carbon materials; Package emission safety reports and emission hazard reports to obtain emission early warning reports; The core of this implementation method is to classify the carbon emission forecast values by the carbon emission budget threshold, so that the system can identify whether the carbon emission forecast in the future time period exceeds the carbon emission budget. When the carbon emission budget is exceeded, the corresponding treatment measures can be generated, so that the system can further improve the intuitiveness of the auxiliary staff and provide the staff with intuitive and reliable treatment measures. Specifically, this implementation method is to classify the carbon emission forecast values by the carbon emission budget threshold, and give corresponding response measures according to the different classification results, so that the system can not only effectively enhance the ability to convert data into decision-making, but also effectively reduce the staff's passive processing decision-making time when facing insufficient carbon emission budget, thereby greatly improving the practicality of the system's auxiliary role; Step S6 includes: The contribution of the sub-data items in the fixed emission dataset and the dynamic emission dataset is calculated based on the carbon emission prediction value. The specific calculation formula for the contribution is: ; Get the first Contribution of each sub-data item ,in, is the partial derivative of the carbon emission prediction value, For the The partial derivative of the sub-data items, It is the collection of all sub-data items in the fixed emission dataset and the dynamic emission dataset; Output the corresponding data optimization report based on the comparison results of the contribution value and the corresponding data contribution threshold. The specific comparison method is: when Greater than When Data optimization report, which includes: For the The contribution threshold of each sub-data item; What needs to be explained is that The data optimization report includes, for example, generating a terrain data optimization report when the sub-data item is the terrain difficulty coefficient. The terrain data optimization report includes suggesting that the staff re-plan the transportation route to avoid mountains; Pack Data optimization report to obtain accurate emission reduction recommendation report; The core of this implementation method is to calculate the contribution of sub-data items in the fixed emission data set and the dynamic emission data set respectively by combining the carbon emission prediction value, compare the contribution value of the sub-data item with the contribution threshold of the corresponding data item, and generate a corresponding data optimization report based on the comparison result. This enables the system to quickly identify the construction link with the lowest positioning cost, avoid ineffective emission reduction resource investment, and thus effectively reduce the emission reduction cost in the process of carbon emission reduction; Step S7 includes: Based on the target optimization algorithm, the carbon emission prediction value, construction cost data and target construction period data are calculated. The specific expression of the target optimization algorithm is: ; in, is the decision vector, is the standard carbon emission data, is the construction cost data, is the standard construction cost data, Target duration data, is the standard construction period data, 、 and To optimize the weight factor, and satisfy ; It should be explained that the decision vector includes material weight data, terrain difficulty data, construction process data, and construction complexity data; Output low-carbon solutions by minimizing carbon emission predictions; Output economic plan by minimizing construction cost data; Output quick-fix solutions by minimizing target duration data; Package low-carbon solutions, economic solutions, and quick-fix solutions to obtain a construction optimization combination report; The core of this implementation method is to minimize the carbon emission forecast value, construction cost data and target construction period data based on the target optimization algorithm, and obtain optimization schemes with different target values. This enables the system to perform global optimization on multi-target data, thereby achieving the purpose of quantifying carbon emissions, costs and construction period, and providing data support for staff to make targeted adjustment decisions. Step S8 includes: The final carbon cost simulation is calculated based on the carbon emission forecast value combined with the construction environment changes. And steel prices increased %, the specific formula for simulation calculation is: ; Get the final carbon cost value ,in, The tax rate increase, is the ratio of steel to material cost; Packaged carbon tax rate increases , steel prices increase % and the final carbon cost value to obtain the carbon cost impact report; The core of this implementation method is that by calculating carbon emission forecasts in conjunction with changes in the construction environment, the system can accurately quantify policy and market risks, thereby enhancing the risk resistance of construction projects and providing advance data support for staff to formulate cost-related strategies in advance; Step S9 includes: Key data sets include fixed emission data sets, dynamic emission data sets, feature vectors, carbon emission prediction values, emission early warning reports, precise emission reduction recommendation reports, construction optimization combination reports, and carbon cost impact reports; Methods for outputting precise emission reduction recommendation reports and construction optimization combination reports include: Send the precise emission reduction recommendation report and construction optimization combination report to the staff's mailbox via email; The core of this implementation method is that by storing key data sets, the system can form data sedimentation, providing data reference for subsequent project construction. The emission warning report and construction optimization combination report can be displayed through the visual panel, which can convey the system processing results in real time. In order to enable the system to transmit the data converted by the system to the physical world through the visual panel, by outputting the precise emission reduction recommendation report and construction optimization combination report, the core data processed by the system can be transmitted to the personal receiving terminal of the staff, and multiple core data backups can be performed; The present embodiment has the beneficial effects of collecting fixed emission data sets based on the sensor network, collecting dynamic emission data sets based on the sensor network, performing data preprocessing on the fixed emission data sets and the dynamic emission data sets to obtain characteristic vectors for carbon emission prediction, analyzing and calculating the characteristic vectors to obtain carbon emission prediction values, processing the carbon emission prediction values, obtaining an emission warning report based on the processing results, identifying the parameters with the largest carbon emission values based on the carbon emission prediction values, obtaining an accurate emission reduction recommendation report, combining the carbon emission prediction values, construction cost data and target construction period data for analysis to obtain a construction optimization combination report, simulating construction environment changes based on the carbon emission prediction values to obtain a carbon cost impact report, storing key data sets in a database, and providing emission warnings through a visualization panel. The report and the construction optimization combination report are displayed, the precise emission reduction recommendation report and the construction optimization combination report are processed, and output according to the processing results, so that the system can accurately predict the carbon emission value in the future time period, and provide core data support for the cost accounting and advance response of the staff. In addition, the present invention also provides the staff with emission warning, precise emission reduction, construction direction optimization and carbon cost impact based on the carbon emission prediction value, thereby effectively improving the system's auxiliary function for decision-making measures in the physical world. At the same time, through the storage and transmission of important data, the security and usability of the data can be effectively guaranteed. Generally speaking, the present invention has the significant advantages of high carbon emission prediction accuracy, large practical auxiliary role and good data storage security.

[0011] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0012] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0013] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.

[0014] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A basic carbon emission calculation method for overhead transmission line construction, characterized in that: The method comprises: S1: Based on the sensor network, collect fixed emission data sets; S2: Based on the sensor network, dynamic emission data sets are collected; S3: Preprocess the fixed emission dataset and the dynamic emission dataset to obtain feature vectors for carbon emission prediction; S4: Analyze and calculate the characteristic vector to obtain the carbon emission prediction value; S5: Process the carbon emission prediction value and obtain an emission warning report based on the processing result; S6: Identify the parameter with the largest carbon emission value based on the carbon emission prediction value and obtain an accurate emission reduction recommendation report; S7: Analyze the carbon emission forecast, construction cost data, and target construction period data to obtain a construction optimization combination report; S8: Simulate construction environment changes based on carbon emission predictions to obtain a carbon cost impact report; S9: Store key data sets in the database, display the emission warning report and construction optimization combination report through the visualization panel, and output the precise emission reduction recommendation report and construction optimization combination report.

2. The method for calculating basic carbon emissions from overhead power line construction according to claim 1, characterized in that: Said S1 comprises: Collect fixed emission data sets based on sensor networks; The vehicle-mounted laser rangefinder is used to collect the total length of the planned path in the specified area to obtain the route length data; Through drone aerial photography, the number of towers that need to be built in the designated area is collected to obtain tower quantity data; Through intelligent floor scales and RFID tag tools, the weight values and material type data of material transport vehicles in the designated area are collected to obtain material weight data; Through the BIM database, the structural types of the towers required to be built in the specified area are collected, and the structural types are assigned values to obtain the tower type data.

3. The method for calculating basic carbon emissions from overhead power line construction according to claim 1, wherein: The S2 includes: Collect dynamic emission data sets based on sensor networks; Using drone laser radar, terrain data within a specified area is collected to generate a digital elevation model, and the average slope is automatically calculated. Based on the average slope, a value is assigned to obtain terrain difficulty data. Through the construction machinery control sensor, the construction signals of the construction equipment in the designated area are collected and assigned values to obtain the construction process data; By using UWB positioning work badges in conjunction with construction management tools, the distribution of personnel in a designated area is collected and assigned values to obtain construction complexity data.

4. The method for calculating basic carbon emissions from overhead power line construction according to claim 1, wherein: The S3 includes: S3.1: Eliminate line length data using a data normalization formula and material weight data dimensional influence of S3.2: By applying terrain difficulty data Multiply by construction process data , get the coupling characteristic data ; S3.3: Composite feature extraction is performed on the material weight data and construction complexity data. The specific calculation formula for composite feature extraction is: ； Get composite feature data ,in, is the construction complexity data; S3.4: Quantify the comprehensive load of the towers using the tower quantity data, tower type data, and terrain difficulty data. The specific calculation formula for quantification is: ； Get comprehensive feature data ,in, is the number of towers, is the tower type data, It is the terrain difficulty data; S3.5: By squaring the terrain difficulty data and performing logarithmic calculation on the construction complexity data, nonlinear terrain difficulty data and nonlinear construction complexity data are obtained. The expression of nonlinear construction complexity data is: ; S3.6: Package the coupled feature data, the composite feature data, the comprehensive feature data, the standardized line length data, the standardized material weight data, the nonlinear terrain difficulty data, and the nonlinear construction complexity data to obtain a feature vector.

5. The method for calculating basic carbon emissions from overhead power line construction according to claim 1, wherein: The S4 includes: A carbon emission prediction algorithm is constructed based on the eigenvector. The eigenvector is input and the output is a carbon emission prediction value that reflects the carbon emission value in the next time period. The specific formula of the carbon emission prediction algorithm is: ； Get carbon emission prediction value ,in, is the standard carbon emission constant, For the The weight factor of the eigenvector sub-vector, For the subvectors of the eigenvectors, is the feature weight factor, It is the interaction term between coupled feature data and comprehensive feature data.

6. The method for calculating basic carbon emissions from overhead power line construction according to claim 1, wherein: The S5 includes: Based on the carbon emission budget threshold, when the carbon emission forecast value is less than the carbon emission budget threshold, an emission safety report is generated; when the carbon emission forecast value is greater than or equal to the carbon emission budget threshold, an emission hazard report is generated; The emission safety report includes a statement that the predicted carbon emission values are normal and that workers are requested to proceed with construction according to the preset work schedule; The emission risk report includes an explanation of abnormal predicted carbon emission values, and requests that staff reduce the terrain difficulty data by optimizing transportation routes, reduce the construction process data by increasing the use of prefabrication and assembly processes, and reduce the material weight data by reducing the use of high-carbon materials; Package emission safety reports and emission hazard reports to obtain emission early warning reports.

7. The method for calculating basic carbon emissions from overhead power line construction according to claim 1, wherein: The S6 includes: The contribution of the sub-data items in the fixed emission dataset and the dynamic emission dataset is calculated based on the carbon emission prediction value. The specific calculation formula for the contribution is: ； Get the first Contribution of each sub-data item ,in, is the partial derivative of the carbon emission prediction value, For the The partial derivative of the sub-data items, It is the collection of all sub-data items in the fixed emission dataset and the dynamic emission dataset; Output the corresponding data optimization report based on the comparison results of the contribution value and the corresponding data contribution threshold. The specific comparison method is: when Greater than When Data optimization report, which includes: For the The contribution threshold of each sub-data item; Pack Data optimization report to obtain accurate emission reduction recommendation report.

8. The method for calculating basic carbon emissions from overhead power line construction according to claim 1, wherein: The S7 includes: Based on the target optimization algorithm, the carbon emission prediction value, construction cost data and target construction period data are calculated. The specific expression of the target optimization algorithm is: ； in, is the decision vector, is the standard carbon emission data, is the construction cost data, is the standard construction cost data, Target duration data, is the standard construction period data, 、 and To optimize the weight factor, and satisfy ; Output low-carbon solutions by minimizing carbon emission predictions; Output economic plan by minimizing construction cost data; Output quick-fix solutions by minimizing target duration data; Package low-carbon solutions, economic solutions, and quick-fix solutions to obtain a construction optimization combination report.

9. The method for calculating basic carbon emissions from overhead power line construction according to claim 1, wherein: The S8 includes: The final carbon cost simulation is calculated based on the carbon emission forecast value combined with the construction environment changes. And steel prices increased %, the specific formula for simulation calculation is: ； Get the final carbon cost value ,in, The tax rate increase, is the ratio of steel to material cost; Packaged carbon tax rate increases , steel prices increase % and the final carbon cost value to obtain the carbon cost impact report.

10. The method for calculating basic carbon emissions from overhead power line construction according to claim 1, wherein: The S9 includes: Key data sets include fixed emission data sets, dynamic emission data sets, feature vectors, carbon emission prediction values, emission early warning reports, precise emission reduction recommendation reports, construction optimization combination reports, and carbon cost impact reports; Methods for outputting precise emission reduction recommendation reports and construction optimization combination reports include: The precise emission reduction recommendation report and construction optimization combination report will be sent to the staff's email address via email.

Images