Highway carbon emission estimation method based on engineering budget

Abstract

The invention relates to the technical field of carbon emission estimation, in particular to a highway carbon emission estimation method based on engineering budget, comprising: a real-time monitoring module for monitoring the type and quantity of materials used in the construction process and power data; the energy conversion module is used for converting various consumptions in road construction into corresponding carbon dioxide emissions according to specific carbon emission factors; the visualization module is used for putting the calculation result of the road construction carbon emission to a visual graphical interface; the real-time monitoring module comprises an unmanned aerial vehicle module, the unmanned aerial vehicle module comprises a slope estimation module, a carrier carbon emission estimation module and an information collection module, the data collection efficiency and precision are improved, the unmanned aerial vehicle can rapidly obtain spatial data such as the road slope and the temporary engineering state through the camera module and the distance data measurement module, and the data collection efficiency is improved. Errors and low efficiency of manual measurement are avoided, and more accurate basic parameters are provided for carrier carbon emission calculation.

Description

Technical Field

[0001] This invention relates to the field of carbon emission estimation technology, and in particular to a method for estimating carbon emissions from highways based on engineering budgets. Background Technology

[0002] Carbon emission estimates for highway construction projects need to be calculated by comprehensively considering the energy consumption of construction machinery fuel, the production and transportation of building materials (such as cement and steel), and the construction and dismantling of temporary facilities, and quantified by combining the amount of work and the unit emission factor.

[0003] Current technologies for carbon emission estimation require manual data collection, such as spatial data on road slope, prefabricated materials, and temporary engineering conditions. However, manual measurement is prone to errors and is inefficient. Furthermore, the management of prefabricated materials is often chaotic, with manual input and location of material information leading to errors and inconsistencies. Therefore, a carbon emission estimation method for highways based on engineering budgets is proposed. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by proposing a method for estimating highway carbon emissions based on engineering budgets.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a method for estimating carbon emissions from highways based on engineering budgets, comprising:

[0006] Real-time monitoring module: Monitors the type and quantity of materials used during construction, as well as power data;

[0007] Energy Conversion Module: Converts various consumption quantities during road construction into corresponding carbon dioxide emissions based on specific carbon emission factors;

[0008] Visualization module: Displays the calculated results of carbon emissions from road construction onto an intuitive graphical interface;

[0009] The real-time monitoring module includes a drone module, which further includes an inclination estimation module, a vehicle carbon emission estimation module, and an information collection module, wherein:

[0010] The information collection module measures the height data of the UAV reaching the construction road surface, and then the slope estimation module obtains the road slope. The vehicle carbon emission estimation module estimates the carbon emission value of the vehicle based on the road slope.

[0011] Preferably, the information collection module includes a distance data measurement module, which comprises a lidar module, a single-point ranging module, a scanning frequency control module, and a point data generation module. The lidar module assists the single-point ranging module in measuring the distance between the arrival points of the UAV. The scanning frequency control module controls the measurement frequency of the single-point ranging module. The point data generation module generates discrete time-point data with coordinates, distance, and timestamps from the distance data according to a time series. The data from the point data generation module is sent to the slope estimation module.

[0012] Preferably, the slope estimation module obtains the three-dimensional coordinates of three points that are not on the same straight line in the target area through the distance data measurement module, and uses spatial geometric relationships to calculate the ratio of the elevation difference between each point to the horizontal distance to determine the slope of the target area.

[0013] Preferably, the information collection module includes a camera module, and the drone module further includes a prefabricated material input module and a material carbon emission estimation module. The camera module is used to scan building materials to assist the prefabricated material input module in inputting the type and quantity of prefabricated materials, and then the carbon emission estimation module estimates the carbon emission values.

[0014] Preferably, the drone module includes a temporary engineering carbon emission estimation module, which converts the various types of energy consumed during the construction, use, and dismantling of temporary access roads, bridges, and stations during construction into carbon dioxide emissions based on carbon emission factors and performs quantitative estimation.

[0015] Preferably, the energy conversion module includes:

[0016] Model optimization module: Improves the accuracy of carbon emission prediction and the applicability of the model by iteratively improving algorithm parameters, integrating multi-source dynamic data and optimizing calculation logic;

[0017] Error analysis module: By quantifying the deviation between model predictions and actual emission data, it identifies data anomalies, algorithm defects, and errors in the weighting of influencing factors, providing accurate basis for model calibration and emission reduction strategy optimization;

[0018] Monitoring data comparison module: It compares measured emission data from different road sections and time periods horizontally and tracks the dynamic changes of the same object vertically.

[0019] Data verification module: By cross-verifying the consistency, accuracy and completeness of multi-source monitoring data, outliers and noise interference are eliminated to ensure the reliability of basic data.

[0020] Preferably, the energy conversion module includes an environmental correction module, which corrects the basic carbon emission calculation results based on the temperature conditions at which the road construction takes place. The information collection module includes a temperature sensor, which measures the temperature and sends it to the energy conversion module through a real-time monitoring module.

[0021] Preferably, the visualization module includes a dynamic demonstration of the construction phase, a carbon emission heat map, and data reports.

[0022] Preferably, the real-time monitoring module includes:

[0023] Power monitoring module: used to collect and statistically analyze the power consumption data of electrical equipment during construction in real time and to provide a basis for carbon emission conversion;

[0024] Material monitoring module: Used for real-time collection and statistical analysis of the usage data of non-prefabricated construction materials.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. This invention improves the efficiency and accuracy of data acquisition. Through the camera module and distance data measurement module, the UAV can quickly acquire spatial data such as road slope and temporary engineering status, avoiding the errors and inefficiencies of manual measurement, and providing more accurate basic parameters for vehicle carbon emission calculation.

[0027] 2. This invention enables dynamic real-time monitoring and real-time tracking of carbon emissions from temporary projects, overcoming the lag of traditional manual inspections and facilitating timely adjustments to construction plans to reduce carbon emissions.

[0028] 3. This invention optimizes the management of prefabricated materials, assists in the rapid input and location of material information, enables accurate traceability of carbon emissions throughout the entire life cycle of materials, and improves the intelligence and digitalization level of construction management;

[0029] 4. This invention reduces monitoring costs and safety risks, effectively reduces the amount of manual on-site work, reduces monitoring safety hazards in high-altitude or complex environments, and reduces manpower and time costs through automated data processing. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of a method for estimating highway carbon emissions based on engineering budgeting according to the present invention.

[0031] Figure 2 This is a schematic diagram of the information collection module of a highway carbon emission estimation method based on engineering budget according to the present invention;

[0032] Figure 3 This is a schematic diagram of the energy conversion module of a highway carbon emission estimation method based on engineering budget according to the present invention;

[0033] Figure 4 This is a schematic diagram of a visualization module for a highway carbon emission estimation method based on engineering budget according to the present invention. Detailed Implementation

[0034] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0035] like Figures 1-4 The method shown is a highway carbon emission estimation method based on engineering budget, including:

[0036] Real-time monitoring module: Monitors the types and quantities of materials used during construction, as well as power data. With the help of high-precision sensors and intelligent recognition technology, it collects the batches and usage of building materials such as cement and asphalt in real time, and records the power consumption data of construction machinery and temporary facilities, providing accurate data support for subsequent carbon emission calculations.

[0037] Energy Conversion Module: Converts various consumptions during road construction into corresponding carbon dioxide emissions based on specific carbon emission factors. By constructing an emission factor database covering building material production, energy consumption, and other aspects, it accurately calculates carbon emissions at each stage based on different construction processes and equipment types, ensuring that the calculation results are scientific and reliable.

[0038] Visualization module: The calculation results of carbon emissions from road construction are displayed on an intuitive graphical interface. Using big data visualization technology, the distribution and trend of carbon emissions in each construction area and stage are displayed in a visual way through dynamic charts, heat maps and other forms, helping managers to quickly identify high-carbon links and formulate targeted emission reduction strategies.

[0039] The real-time monitoring module includes a drone module, which further comprises an inclination estimation module, a vehicle carbon emission estimation module, and an information collection module.

[0040] The information collection module measures the height of the drone reaching the construction road surface, then the slope estimation module obtains the road slope, and the vehicle carbon emission estimation module estimates the vehicle's carbon emission value based on the road slope.

[0041] The information collection module includes a distance data measurement module, which comprises a lidar module, a single-point ranging module, a scanning frequency control module, and a point data generation module. The lidar module assists the single-point ranging module in measuring the distance between the UAV's arrival points. The scanning frequency control module controls the measurement frequency of the single-point ranging module. The point data generation module generates discrete time-point data with coordinates, distance, and timestamps from the distance data according to a time series. The data from the point data generation module is then sent to the slope estimation module.

[0042] The slope estimation module obtains the three-dimensional coordinates of three points that are not on the same straight line in the target area through the distance data measurement module, and uses spatial geometric relationships to calculate the ratio of the elevation difference between each point to the horizontal distance to determine the slope of the target area.

[0043] Suppose that the three points obtained in the target area are not on the same straight line as... ,in These are elevation coordinates.

[0044] Calculate the elevation difference and horizontal distance between point A and point B.

[0045] Elevation difference:

[0046] Horizontal distance (two-dimensional plane projection) ;

[0047] Calculate the elevation difference and horizontal distance between point A and point C.

[0048] Elevation difference:

[0049] Horizontal distance (two-dimensional plane projection) ;

[0050] Next, the slope of the target area is calculated.

[0051] If AB and AC are taken as reference vectors, the plane normal vector can be calculated by the cross product of the vectors, and then the magnitude of the slope can be determined.

[0052] Slope vector =

[0053] Average slope =

[0054] This formula converts three-dimensional coordinates into slope parameters through spatial geometric relationships, which can be directly connected to the slope estimation module to provide terrain slope data for vehicle carbon emission calculation.

[0055] The information collection module includes a camera module, while the drone module includes a prefabricated material input module and a material carbon emission estimation module. The camera module scans building materials to assist the prefabricated material input module in recording the type and quantity of prefabricated materials. The carbon emission estimation module then estimates the carbon emissions. By using the camera module to scan and identify building materials and linking it with the drone module's prefabricated material input module to accurately record the type and quantity, and then using the carbon emission estimation module to quickly calculate the emission values, a closed-loop process is achieved for construction materials, from visual identification and digital input to automated carbon emission estimation. This significantly improves the efficiency and accuracy of carbon emission estimation in highway construction.

[0056] The drone module includes a temporary works carbon emission estimation module. This module converts various types of energy consumed during the construction, use, and dismantling of temporary access roads, bridges, and facilities into carbon dioxide emissions based on carbon emission factors and performs quantitative estimation. This enables accurate measurement and dynamic monitoring of temporary works carbon emissions, filling the gap in traditional estimation methods where carbon emissions from temporary facilities are easily overlooked.

[0057] The energy conversion module includes:

[0058] Model optimization module: By iteratively improving algorithm parameters, integrating multi-source dynamic data, and optimizing calculation logic, it enhances the accuracy of carbon emission prediction and the applicability of the model; it also enhances the dynamic evolution capability of the estimation system, enabling it to flexibly respond to changes in actual scenarios such as changes in construction technology and material updates.

[0059] Error Analysis Module: By quantifying the deviation between model predictions and actual emission data, it identifies data anomalies, algorithm defects, and errors in the weighting of influencing factors, providing accurate basis for model calibration and emission reduction strategy optimization, and helping to accurately trace the root causes of carbon emission estimation deviations from a technical perspective;

[0060] Monitoring data comparison module: By comparing measured emission data of different road sections and time periods horizontally and tracking the dynamic changes of the same object vertically, it provides decision support for differentiated emission reduction schemes and can intuitively present the emission difference characteristics of different construction stages or geographical environments.

[0061] Data verification module: By cross-validating the consistency, accuracy and completeness of multi-source monitoring data, outliers and noise interference are eliminated, ensuring the quality of basic data from the source, consolidating the reliable foundation of carbon emission estimation, and effectively avoiding the problem of estimation results deviation caused by data distortion.

[0062] The energy conversion module includes an environmental correction module, which adjusts the basic carbon emission calculation results based on the temperature conditions during road construction. The information collection module includes a temperature sensor that measures the temperature and sends it to the energy conversion module via a real-time monitoring module. By using the temperature sensor to collect real-time ambient temperature data and linking it with the energy conversion module to correct the basic carbon emission calculation results, the environmental correction module achieves dynamic compensation for the impact of temperature variables on energy consumption and emissions, improving the environmental adaptability and accuracy of carbon emission estimation for road construction under complex climatic conditions.

[0063] The visualization module includes dynamic demonstrations of the construction phase, carbon emission heat maps, and data reports.

[0064] The real-time monitoring module includes:

[0065] Power monitoring module: used to collect and statistically analyze the power consumption data of electrical equipment during construction in real time and to provide a basis for carbon emission conversion;

[0066] Material monitoring module: Used for real-time collection and statistical analysis of the usage data of non-prefabricated construction materials.

[0067] Using this method, data acquisition efficiency and accuracy are improved. Through the camera module and distance data measurement module, the UAV can quickly acquire spatial data such as road slope and temporary engineering status, avoiding the errors and inefficiencies of manual measurement, and providing more accurate basic parameters for vehicle carbon emission calculation.

[0068] Dynamic real-time monitoring is implemented during construction to track carbon emissions from temporary projects in real time, overcoming the lag of traditional manual inspections and facilitating timely adjustments to construction plans to reduce carbon emissions.

[0069] Optimizing the management of prefabricated materials and assisting in the rapid entry and location of material information can enable accurate traceability of carbon emissions throughout the entire life cycle of materials, thereby improving the intelligence and digitalization of construction management.

[0070] It reduces monitoring costs and safety risks, effectively reduces the amount of manual on-site work, reduces monitoring safety hazards in high-altitude or complex environments, and reduces manpower and time costs through automated data processing.

[0071] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for estimating carbon emissions from highways based on engineering budgets, characterized in that: include: Real-time monitoring module: Monitors the type and quantity of materials used during construction, as well as power data; Energy Conversion Module: Converts various consumption quantities during road construction into corresponding carbon dioxide emissions based on specific carbon emission factors; Visualization module: Displays the calculated results of carbon emissions from road construction onto an intuitive graphical interface; The real-time monitoring module includes a drone module, which further includes an inclination estimation module, a vehicle carbon emission estimation module, and an information collection module, wherein: The information collection module measures the height data of the UAV reaching the construction road surface, and then the slope estimation module obtains the road slope. The vehicle carbon emission estimation module estimates the carbon emission value of the vehicle based on the road slope.

2. The method for estimating highway carbon emissions based on engineering budgets according to claim 1, characterized in that: The information collection module includes a distance data measurement module, which comprises a lidar module, a single-point ranging module, a scanning frequency control module, and a point data generation module. The lidar module assists the single-point ranging module in measuring the distance between the UAV's arrival points. The scanning frequency control module controls the measurement frequency of the single-point ranging module. The point data generation module generates discrete time-point data with coordinates, distance, and timestamps from the distance data according to a time series. The data from the point data generation module is sent to the slope estimation module.

3. The method for estimating highway carbon emissions based on engineering budgets according to claim 2, characterized in that: The slope estimation module obtains the three-dimensional coordinates of three points that are not on the same straight line in the target area through the distance data measurement module, and uses spatial geometric relationships to calculate the ratio of the elevation difference between each point to the horizontal distance to determine the slope of the target area.

4. The method for estimating highway carbon emissions based on engineering budget as described in claim 1, characterized in that: The information collection module includes a camera module, and the drone module also includes a prefabricated material input module and a material carbon emission estimation module. The camera module is used to scan building materials to assist the prefabricated material input module in inputting the type and quantity of prefabricated materials, and then the carbon emission estimation module estimates the carbon emission values.

5. The method for estimating highway carbon emissions based on engineering budgets according to claim 1, characterized in that: The drone module includes a temporary engineering carbon emission estimation module, which converts the various types of energy consumed during the construction, use, and dismantling of temporary access roads, bridges, and stations into carbon dioxide emissions based on carbon emission factors and performs quantitative estimation.

6. The method for estimating highway carbon emissions based on engineering budgets according to claim 1, characterized in that: The energy conversion module includes: Model optimization module: Improves the accuracy of carbon emission prediction and the applicability of the model by iteratively improving algorithm parameters, integrating multi-source dynamic data and optimizing calculation logic; Error analysis module: By quantifying the deviation between model predictions and actual emission data, it identifies data anomalies, algorithm defects, and errors in the weighting of influencing factors, providing accurate basis for model calibration and emission reduction strategy optimization; Monitoring data comparison module: It compares measured emission data from different road sections and time periods horizontally and tracks the dynamic changes of the same object vertically. Data verification module: By cross-verifying the consistency, accuracy and completeness of multi-source monitoring data, outliers and noise interference are eliminated to ensure the reliability of basic data.

7. The method for estimating highway carbon emissions based on engineering budget according to claim 1, characterized in that: The energy conversion module includes an environmental correction module, which corrects the basic carbon emission calculation results based on the temperature conditions at which the road construction takes place. The information collection module includes a temperature sensor, which measures the temperature and sends it to the energy conversion module through a real-time monitoring module.

8. The method for estimating highway carbon emissions based on engineering budget as described in claim 1, characterized in that: The visualization module includes dynamic demonstrations of the construction phase, carbon emission heat maps, and data reports.

9. The method for estimating highway carbon emissions based on engineering budget as described in claim 1, characterized in that: The real-time monitoring module includes: Power monitoring module: used to collect and statistically analyze the power consumption data of electrical equipment during construction in real time and to provide a basis for carbon emission conversion; Material monitoring module: Used for real-time collection and statistical analysis of the usage data of non-prefabricated construction materials.

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