An ultra-thin asphalt pavement construction carbon emission evaluation method

Abstract

The present application relates to the technical field of road engineering carbon emission detection, specifically to a kind of ultra-thin asphalt surface construction carbon emission evaluation method, comprising: layout internet of things sensing network and energy consumption recording device, fusion material flow, mechanical operation, construction environment data constructs construction process digital image, generates primary carbon flow atlas by multi-scale feature analysis.Moving detection device collects the multi-band reflectance spectrum and near-field thermal radiation data of paving road section, combined with carbon flow atlas inversion identification construction unit carbon emission mode, form mode list.Simulate the structure performance evolution in surface layer service life, get performance attenuation trend spectrum.Through mesostructure scanning, the void distribution and aggregate arrangement image of mixture are obtained, and the durability grade of surface layer is determined.The carbon emission mode and durability grade are mapped to the preset carbon efficiency evaluation matrix, and the quantitative evaluation index is output by operation.The method realizes the coupling representation of construction carbon flow and surface layer durability, improves the accuracy and system of carbon emission evaluation.

Description

Technical Field

[0001] This invention relates to the field of carbon emission testing technology in road engineering, and in particular to a method for evaluating carbon emissions from the construction of ultra-thin asphalt pavement. Background Technology

[0002] The carbon emission accounting for conventional ultra-thin asphalt pavement construction relies on manual recording of machinery fuel consumption and material usage, combined with static calculations using fixed coefficients. Construction data collection only independently acquires energy consumption and material parameters, without collaboratively collecting and integrating data on material flow, machinery operation, and the construction environment, thus failing to form a continuous and complete dynamic data system for the construction process.

[0003] Traditional evaluation methods only calculate immediate energy consumption during the construction phase, failing to analyze multi-scale characteristics of the construction process and lacking methods for visualizing carbon flow. Surface layer quality testing relies solely on conventional compaction and thickness measurements, neglecting to collect multi-band reflectance spectra and near-field thermal radiation data, making it impossible to accurately identify the carbon emission modes of different construction units. Furthermore, detailed observation of the internal void distribution and aggregate arrangement of the asphalt mixture is lacking, making it difficult to combine the long-term service performance of the surface layer with its carbon emission status for analysis.

[0004] The carbon efficiency evaluation of construction only counts carbon emission values ​​separately, without establishing an evaluation system that links carbon emissions with surface layer durability. It is impossible to obtain quantitative evaluation results through matrix operations, and it is impossible to achieve a synergistic characterization of construction carbon emissions and the actual performance of the surface layer. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a carbon emission evaluation method for ultra-thin asphalt pavement construction.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for evaluating carbon emissions from the construction of ultra-thin asphalt pavement, comprising: Deploy IoT sensor networks and energy consumption recording devices to acquire and integrate material flow characteristic data, mechanical operation characteristic data and construction environment characteristic data, construct digital images of the construction process, perform multi-scale feature analysis on the digital images of the construction process, and generate a primary carbon flow map. A mobile detection device is dispatched along the construction route to collect multi-band reflectance spectrum and near-field thermal radiation data of the completed paved sections. Combined with the primary carbon flow map, the carbon emission status inversion process is initiated to identify the carbon emission modes of each construction unit and generate a carbon emission mode list. Based on the carbon emission mode list, the structural performance evolution path of the asphalt pavement within a set service life is simulated and calculated, and the performance degradation trend spectrum is deduced. For the compacted asphalt surface area, a microstructure scanning device is deployed to acquire images of the internal void distribution and aggregate arrangement of the mixture. Combined with the performance degradation trend spectrum, the mixture durability status identification process is initiated to determine the actual durability status level of the surface layer. The carbon emission mode list is integrated with the actual durability status level and mapped to a preset construction carbon efficiency evaluation matrix. After calculation, a quantitative carbon emission evaluation index is output.

[0007] As a further aspect of the present invention, an IoT sensor network and an energy consumption recording device are deployed to acquire and fuse material flow characteristic data, mechanical operation characteristic data, and construction environment characteristic data to construct a digital image of the construction process. Multi-scale feature analysis is then performed on the digital image to generate a primary carbon flow map, including: In the raw material mining, transportation, and storage processes, IoT sensor networks are deployed to continuously acquire the weight changes and location movement trajectories of materials, forming material flow characteristic data. Receive the material flow characteristic data, and simultaneously record the instantaneous flow rate and cumulative consumption of energy-consuming media of various construction machinery in the asphalt mixture mixing, transportation, paving and compaction stages to generate mechanical operation characteristic data; Real-time temperature, humidity, atmospheric pressure, and surface wind speed and direction at the construction site are obtained to form construction environment characteristic data; By integrating the material flow characteristic data, the mechanical operation characteristic data, and the construction environment characteristic data, a digital image of the construction process is constructed. Multi-scale feature analysis is performed on the digital image of the construction process to preliminarily estimate the carbon flow intensity distribution throughout the construction process and generate a primary carbon flow map. As a further aspect of the present invention, the deployment of an Internet of Things (IoT) sensor network during the raw material mining, transportation, and storage stages to continuously acquire the weight changes and positional movement trajectories of materials, forming material flow characteristic data, includes: Weighing sensors are installed at the discharge ports of stone quarries, asphalt storage tanks, and mineral powder silos to record the weight and timestamp of each discharge, and the data are compiled into a raw material output log. Positioning and load monitoring modules are installed on vehicles transporting aggregates, asphalt, and mineral powder to track changes in vehicle position, speed, and load status in real time, generating raw material transportation trajectory and load time sequence data. Material level monitoring sensors are installed in the aggregate cold silos, asphalt insulation tanks and mineral powder tanks of the mixing plant to continuously record the changes in the volume or height of materials in each storage container, forming a dynamic sequence of raw material inventory. By aggregating the raw material output logs, the raw material transportation trajectory and load time series data, and the raw material inventory dynamic sequence, and after time alignment and data cleaning, the material flow characteristic data is constructed with material type, quality, spatial location, and time point as dimensions.

[0008] As a further aspect of the present invention, the material flow characteristic data is received, and during the mixing, transportation, paving, and compaction stages of asphalt mixtures, the instantaneous flow rate and cumulative consumption of energy-consuming media for various construction machinery are recorded simultaneously to generate machinery operation characteristic data, including: Flow meters and smart meters are installed on the fuel supply pipelines and power input bus of the asphalt mixing plant to record the instantaneous flow rate and cumulative volume of diesel and natural gas consumed during the mixing process, as well as the real-time power and cumulative consumption of electricity, forming a mixing energy consumption curve. A fuel monitoring sensor is installed on the fuel tank of the asphalt mixture transport vehicle to record the fuel consumption throughout the entire transportation process from the mixing plant to the paving site, and the transportation energy consumption distribution is generated by combining the transportation trajectory. Monitoring equipment is installed in the engine fuel line and hydraulic system of the asphalt paver to record the fuel consumption rate and hydraulic system power consumption during paving operations, and to generate an energy consumption sequence for paving operations. A fuel metering device is installed on the diesel engine of a road roller to record the fuel consumption under different compaction passes and generate a compaction energy consumption sequence. The mixing energy consumption curve, the transportation energy consumption distribution, the paving operation energy consumption sequence, and the compaction energy consumption sequence are integrated and correlated according to the time axis of the construction process, and matched with the corresponding time period in the material flow characteristic data to generate the mechanical operation characteristic data describing the energy consumption of the entire construction chain.

[0009] As a further aspect of the present invention, real-time temperature and humidity, atmospheric pressure, and surface wind speed and direction at the construction site are obtained to constitute construction environment characteristic data, including: A network of miniature weather stations will be deployed at key nodes along asphalt mixing plants, paving sites, and material transport routes. Through the aforementioned micro weather station network, raw readings of air temperature, relative humidity, atmospheric pressure, and surface wind speed and direction are synchronously collected from each node at a set sampling frequency. The raw readings from each node are time-synchronized and outlier-removing to form a standardized environmental parameter time series. The environmental parameter time series and the mechanical operation feature data are fused and interpolated in the time dimension to ensure that there are corresponding environmental parameters for each construction action, thereby forming complete construction environmental feature data.

[0010] As a further aspect of the present invention, the material flow characteristic data, the mechanical operation characteristic data, and the construction environment characteristic data are integrated to construct a digital image of the construction process. Multi-scale feature analysis is performed on the digital image of the construction process to preliminarily estimate the carbon flow intensity distribution throughout the construction process, generating a primary carbon flow map, including: Establish a four-dimensional data fusion framework with time as the main thread, spatial location as the coordinate, and construction activities as the object; In the four-dimensional data fusion framework, the material flow characteristic data is mapped to a material flow field, the mechanical operation characteristic data is mapped to an energy consumption flow field, and the construction environment characteristic data is mapped to an environmental parameter field. The material flow field, the energy consumption flow field, and the environmental parameter field are spatiotemporally superimposed and coupled to generate a digital image of the construction process that can dynamically reflect the construction progress. A multi-resolution analysis method is used to decompose the digital images of the construction process step by step from the whole to the part and from the long time scale to the short time scale. At different scales, the carbon emission intensity in each spatiotemporal unit is dynamically calculated based on the carbon emission factors of material consumption, fuel combustion and electricity consumption, combined with the influence coefficient of environmental parameters on combustion efficiency. The carbon emission intensity of all spatiotemporal units is integrated and visualized to form the primary carbon flow map, which shows the temporal and spatial distribution of carbon emission intensity.

[0011] As a further aspect of the present invention, a mobile detection device is dispatched along the construction route to collect multi-band reflectance spectra and near-field thermal radiation data of the completed paved sections. Combined with the primary carbon flow map, a carbon emission status inversion process is initiated to identify the carbon emission modes of each construction unit and generate a carbon emission mode list, including: An unmanned inspection vehicle equipped with a multispectral imager and an infrared thermal imager is configured as the mobile inspection device; After the asphalt surface layer is laid and left to stand for a predetermined period of time, the unmanned inspection vehicle is controlled to drive at a constant speed along the lane. During driving, the multispectral imager is simultaneously triggered to collect reflectance data of the road surface in multiple specific narrow bands, and the infrared thermal imager is triggered to collect temperature distribution data of the road surface, thereby obtaining the multi-band reflectance spectrum of the surface and the near-field thermal radiation data respectively. From the primary carbon flux map, extract the estimated local carbon flux intensity corresponding to the current position and detection time of the unmanned detection vehicle; Construct a carbon emission state inversion model with the estimated local carbon flow intensity as a priori constraint; The surface multi-band reflectance spectrum and the near-field thermal radiation data are input into the carbon emission state inversion model. By solving the optimization problem, multiple implicit state variables reflecting the actual mixture temperature history, paving uniformity and compaction effectiveness are inverted. Based on the multiple implicit state variables derived from the inversion, and in accordance with the preset carbon emission mode classification rules, a specific carbon emission mode identifier is assigned to each detected road segment unit. The carbon emission mode list is formed by summarizing the identifiers of all units.

[0012] As a further aspect of the present invention, based on the aforementioned carbon emission mode list, the structural performance evolution path of the asphalt pavement within a set service life is simulated and calculated, and the performance degradation trend spectrum is deduced, including: A multiphysics coupled degradation simulation model for ultrathin asphalt pavement is established. The inputs of the multiphysics coupled degradation simulation model include material properties, structural thickness, construction quality status defined by the carbon emission mode list, and standard traffic load and environmental cycle. From the carbon emission mode list, the carbon emission mode identifier corresponding to each construction unit is parsed out, and each identifier is mapped to the initial defect parameters and material non-uniformity parameters of the corresponding construction unit in the multi-physics coupled degradation simulation model. Set a simulated service life of several decades, and load standard axial load spectrum and typical climate cycle data into the multiphysics coupled degradation simulation model; Run the multiphysics coupled degradation simulation model to calculate the dynamic changes of key performance indicators of asphalt pavement, such as deflection, tensile stress at the bottom of the layer, surface cracking index, and rutting depth, throughout the entire simulated service life. The curves of the key performance indicators of each construction unit changing over time are recorded. These curves of the key performance indicators changing over time constitute the performance degradation trend spectrum, which reflects the future performance degradation pattern.

[0013] As a further aspect of the present invention, for the compacted asphalt surface layer area, a microstructure scanning device is deployed to acquire images of the internal void distribution and aggregate arrangement of the mixture. Combined with the performance degradation trend spectrum, the mixture durability status identification process is initiated to determine the actual durability status level of the surface layer, including: A combination of penetrable three-dimensional ground-penetrating radar and laser scanner is used as the microstructure scanning device; After the road surface has been compacted and cooled to ambient temperature, the microstructure scanning device is deployed above the selected evaluation area; The evaluation area is scanned using the three-dimensional ground-penetrating radar to obtain the dielectric constant distribution of the asphalt mixture at different depths, and a three-dimensional image of the internal void distribution is reconstructed. The laser scanner is used to scan the surface of the same evaluation area to obtain the exposed outline and spatial position of the aggregate particles, and to construct the aggregate arrangement image. From the performance degradation trend spectrum, the predicted performance value of the corresponding evaluation region in the initial stage of simulated service is extracted as the a priori expected state. The three-dimensional image of the internal void distribution is fused with the image of the aggregate arrangement, and the porosity, void connectivity, number of contact points between aggregates, and aggregate orientation microscopic feature vectors are extracted. The detailed feature vector and the prior expected state are input together into a preset durability classification network. After training, the durability classification network can associate the detailed features with long-term performance potential. The durability classification network outputs a discrete level representing the actual durability potential of the current surface layer, namely the actual durability state level.

[0014] As a further aspect of the present invention, the carbon emission mode list and the actual durability status level are integrated and mapped to a preset construction carbon efficiency evaluation matrix. After calculation, a quantitative carbon emission evaluation index is output, including: A two-dimensional carbon efficiency evaluation matrix for construction is constructed. One dimension of the matrix is ​​the carbon emission mode, which is defined according to different modes in the carbon emission mode list. The other dimension of the matrix is ​​the durability status level, which is defined according to different levels of the actual durability status level. Each cell in the matrix is ​​pre-set with a basic carbon efficiency coefficient, which represents the theoretical carbon emission efficiency level under a specific combination of carbon emission mode and a specific durability state level. The distribution of the proportion of each mode obtained from the carbon emission mode inventory, together with the assessment results of the actual durability status level, are used as inputs. The input is matched and mapped with the construction carbon efficiency evaluation matrix, and the basic carbon efficiency coefficient of the corresponding cell is weighted according to the proportion of each mode. The weighted calculation result is normalized and converted into a value between zero and one hundred. This value is the carbon emission evaluation index, which is used to quantitatively characterize the overall carbon emission level of this ultra-thin asphalt pavement construction activity.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: The Internet of Things (IoT) sensor network and energy consumption recording device simultaneously acquire material flow characteristic data, mechanical operation characteristic data, and construction environment characteristic data. This multi-data fusion constructs a digital image of the construction process. The digital image undergoes multi-scale feature analysis to break down the carbon flow correlation characteristics of each construction stage, forming a preliminary carbon flow map. Multi-source data eliminates the isolation of manually collected data, allowing the digital image to fully reconstruct the dynamic construction process. Multi-scale analysis refines the carbon flow characteristics of each construction unit, and the carbon flow map visually presents the distribution and flow patterns of carbon flow throughout the entire construction process.

[0016] Mobile detection devices collect multi-band reflectance spectra and near-field thermal radiation data from the paved road surface. Combined with primary carbon flow maps, carbon emission modes for each construction unit are retrieved to form a carbon emission mode inventory. A microstructure scanning device acquires images of the internal void distribution and aggregate arrangement of the compacted layer. Combined with performance degradation trend spectra derived from structural performance evolution, the durability status level of the mixture is identified. The carbon emission mode inventory and durability status level are input into a preset construction carbon efficiency evaluation matrix, and a quantitative carbon emission evaluation index is output through matrix operations. Spectral and thermal radiation data closely reflect the physical state of the surface layer construction, while microscopic images reflect the true internal structure of the mixture. This multi-dimensional data coupling enables the quantitative correlation calculation between carbon emissions and durability. Attached Figure Description

[0017] Figure 1 This is a flowchart of a carbon emission evaluation method for ultra-thin asphalt pavement construction as described in this invention; Figure 2 A flowchart for constructing digital images of the construction process and generating primary carbon flow maps; Figure 3 A flowchart for generating mechanical operation feature data. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0020] See Figure 1 This invention provides a method for evaluating carbon emissions from the construction of ultra-thin asphalt pavement, the overall implementation of which includes: Before and during construction, an IoT sensor network and energy consumption recording devices are deployed to collect data on material flow characteristics, mechanical operation characteristics, and construction environment characteristics. These data are then fused and processed to construct a digital image of the construction process that dynamically reflects the progress. Multi-scale feature analysis of this digital image generates a primary carbon flow map reflecting the spatiotemporal distribution of carbon emission intensity. After paving, mobile detection devices are dispatched along the construction route to collect multi-band reflectance spectra and near-field thermal radiation data of the completed road sections. Combined with the primary carbon flow map, carbon emission status is inverted to identify the carbon emission modes of each construction unit, forming a carbon emission mode inventory. Based on the carbon emission mode inventory, the structural performance evolution path of the asphalt pavement within a set service life is simulated and calculated, and its future performance degradation trend spectrum is deduced. For compacted sections, microstructure scanning devices are deployed to acquire images of the internal void distribution and aggregate arrangement of the mixture. Combined with the performance degradation trend spectrum, the durability status of the mixture is identified, and the actual durability level of the pavement is determined. By integrating the carbon emission mode list with the actual durability status level, and mapping it to a preset construction carbon efficiency evaluation matrix for calculation, a quantitative carbon emission evaluation index is output, thereby completing a comprehensive evaluation of the carbon emission efficiency of the construction process.

[0021] In one embodiment of the present invention, see [reference] Figure 2 In the raw material mining, transportation, and storage stages, an IoT sensor network is deployed to continuously acquire the weight changes and location movement trajectories of materials, forming material flow characteristic data. Specifically, weighing sensors are installed at the discharge ports of quarries, asphalt storage tanks, and mineral powder silos to record the weight and timestamp of each discharge, summarizing this data into a raw material output log. Positioning and load monitoring modules are installed on vehicles transporting aggregates, asphalt, and mineral powder to track vehicle position, speed, and load status changes in real time, generating raw material transportation trajectory and load time-series data. Material level monitoring sensors are installed in the aggregate cold silos, asphalt insulation tanks, and mineral powder tanks at the mixing plant to continuously record changes in the volume or height of materials in each storage container, forming a dynamic sequence of raw material inventory. By aggregating the raw material output logs, raw material transportation trajectory and load time-series data, and raw material inventory dynamic sequence, and after time alignment and data cleaning, material flow characteristic data is constructed based on material type, quality, spatial location, and time point.

[0022] The system receives material flow characteristic data and simultaneously records the instantaneous flow rate and cumulative consumption of energy-consuming media for various construction machinery during the asphalt mixture mixing, transportation, paving, and compaction stages, generating machinery operation characteristic data. It also acquires real-time temperature, humidity, atmospheric pressure, and surface wind speed and direction at the construction site, forming construction environment characteristic data. By integrating the material flow characteristic data, machinery operation characteristic data, and construction environment characteristic data, a digital image of the construction process is constructed. Multi-scale feature analysis is performed on this digital image to preliminarily estimate the carbon flow intensity distribution throughout the construction process, generating a primary carbon flow map.

[0023] In practical implementation, an IoT sensor network is deployed during the raw material mining, transportation, and storage stages to acquire material flow characteristic data. This material flow characteristic data is then fused with mechanical operation characteristic data and construction environment characteristic data to construct a digital image of the construction process and perform multi-scale feature analysis, ultimately generating a primary carbon flow map. In some embodiments, high-precision weighing sensors are installed below the discharge belt conveyor of the quarry, at the output pump inlet of the asphalt storage tank, and at the screw conveyor outlet of the mineral powder silo. Whenever raw material is output, the weighing sensors record the net weight of the output material and add a timestamp accurate to the second. These records are automatically summarized and stored as a raw material output log, with log entries including material type, output quality, and time of occurrence. In practical implementation, trucks transporting aggregates, asphalt, and mineral powder are equipped with onboard terminals integrating GPS modules and strain gauge load cells. These terminals collect vehicle geographic coordinates, speed, and cargo load data at a fixed frequency, generating raw material transport trajectories and load time-series data including time, location, and load status. This data is transmitted in real-time to a central server via a wireless network. In some embodiments, non-contact radar level gauges are installed on the side walls of the cold aggregate bins at asphalt mixing plants, hydrostatic level sensors are installed on the top of asphalt insulation tanks, and weighted level gauges are installed inside mineral powder tanks. These level monitoring sensors continuously monitor the height or volume of materials within the containers, forming a time-indexed dynamic sequence of raw material inventory. This dynamic sequence reflects the real-time changes in material stock levels during storage. In practice, the central data platform receives raw material output logs from weighing sensors, raw material transportation trajectories and load time-series data from vehicle terminals, and raw material inventory dynamic sequences from material level monitoring sensors. The data platform timestamps all data streams and removes outliers caused by momentary sensor failures or communication interference. The cleaned data is then reorganized and aggregated according to four dimensions: material type, quality, spatial location, and time point, to construct structured material flow characteristic data. This material flow characteristic data fully depicts the dynamics of the entire chain from raw material outbound, transportation to storage.

[0024] In practice, material flow characteristic data is transmitted to the construction process analysis module, which simultaneously receives energy consumption records from mixing, transportation, paving, and compaction machinery, as well as environmental readings from a network of micro-weather stations. The construction process analysis module establishes a four-dimensional data fusion framework with an absolute time axis as the main line, a geographical coordinate grid of the construction site as the spatial reference, and specific construction activities as the descriptive objects. Within this framework, material flow characteristic data is mapped to a dynamic material flow field, where the vector at each spatiotemporal point contains the type and flux of the material; machinery operation characteristic data is mapped to an energy consumption flow field, where the scalar at each spatiotemporal point represents the fuel or electrical energy consumed by the construction activity at that location; and construction environment characteristic data is mapped to an environmental parameter field, which includes the distribution of temperature, humidity, air pressure, and wind speed. In practical implementation, the material flow field, energy consumption flow field, and environmental parameter field are spatiotemporally overlaid and coupled for analysis to generate a digital image of the construction process that continuously and dynamically reflects the entire process from raw material input to road surface compaction. This digital image is a four-dimensional data volume containing multi-dimensional attribute information. In practice, a multi-resolution analysis method is used to decompose the digital image of the construction process step-by-step from overall to local, and from long-term to short-term scales. For example, first, the daily average carbon emissions are analyzed at the scale of "entire construction project - day"; then, the carbon emission intensity during peak operating periods is analyzed at the scale of "single machine - hour"; and finally, the instantaneous carbon emission rate of a specific process is analyzed at the scale of "specific operation - minute". This can be understood as dynamically calculating the carbon emission intensity within each spatiotemporal unit based on the standard carbon emission factors corresponding to material consumption, fuel combustion, and electricity consumption, combined with the correction coefficients for the impact of real-time temperature and air pressure data on fuel combustion efficiency provided by the environmental parameter field. (Calculation of carbon emission intensity) An example is shown in the following formula:

[0025] in: Indicates the carbon emission intensity of a specific spatiotemporal unit. Indicates the first Weighting coefficients for various carbon emission sources Indicates the first Carbon emission factors of various carbon emission sources Indicates the first [unit] within this spacetime unit Activity level data of various carbon emission sources This represents the combustion efficiency correction coefficient calculated from environmental parameter field data. In practice, the carbon emission intensity calculation results of all spatiotemporal units are integrated according to time and spatial coordinates and visualized using heat maps or contour maps to form a primary carbon flow map. The primary carbon flow map visually displays the distribution of carbon emission intensity throughout the entire construction area and the entire construction period. Optionally, the primary carbon flow map can be stored as a raster data file with georeferenced information for subsequent process calls.

[0026] In one embodiment of the present invention, material flow characteristic data is received, see reference. Figure 3 In the mixing, transportation, paving, and compaction stages of asphalt mixtures, the instantaneous flow rate and cumulative consumption of energy-consuming media for various construction machinery are recorded simultaneously to generate mechanical operation characteristic data. Specific implementation includes installing flow meters and smart meters on the fuel supply pipelines and power input bus of the asphalt mixing plant to record the instantaneous flow rate and cumulative volume of diesel and natural gas consumed during mixing, as well as the real-time power and cumulative consumption of electricity, forming a mixing energy consumption curve. Fuel monitoring sensors are installed on the fuel tanks of asphalt mixture transport vehicles to record the fuel consumption throughout the transportation process from the mixing plant to the paving site, generating a transportation energy consumption distribution based on the transportation trajectory. Monitoring equipment is installed on the engine fuel pipelines and hydraulic systems of asphalt pavers to record the fuel consumption rate and hydraulic system power consumption during paving operations, generating a paving operation energy consumption sequence. Fuel metering devices are installed on the diesel engines of road rollers to record fuel consumption under different compaction passes, generating a compaction energy consumption sequence. The mixing energy consumption curve, transportation energy consumption distribution, paving operation energy consumption sequence and compaction energy consumption sequence are integrated and correlated according to the time axis of construction procedures, and matched with the corresponding time period in the material flow characteristic data to generate mechanical operation characteristic data describing the energy consumption of the entire construction chain.

[0027] Real-time temperature, humidity, atmospheric pressure, and surface wind speed and direction at the construction site are acquired to form construction environmental characteristic data. Specifically, a network of micro-weather stations is deployed at key nodes along the asphalt mixing plant, paving site, and material transport routes. Through this network, raw readings of air temperature, relative humidity, atmospheric pressure, and surface wind speed and direction are simultaneously collected at a set sampling frequency. The raw readings from each node undergo time synchronization correction and outlier removal to form a standardized environmental parameter time series. This environmental parameter time series is then fused and interpolated with the mechanical operation characteristic data along the time dimension to ensure that corresponding environmental parameters are available for each construction action, thus forming complete construction environmental characteristic data.

[0028] In specific implementation, this embodiment involves simultaneously recording the instantaneous flow rate and cumulative consumption of energy-consuming media for various construction machinery during the mixing, transportation, paving, and compaction of asphalt mixtures to generate mechanical operation characteristic data, as well as acquiring and integrating construction environment characteristic data. In some embodiments, turbine flow meters are installed on the diesel supply pipeline and natural gas main pipeline of the intermittent asphalt mixing plant, respectively, and smart meters are installed in the main power input cabinet of the mixing plant. During the mixing process, the flow meters continuously monitor the instantaneous flow rate of fuel oil or natural gas and accumulate the total consumption volume of each production batch. The smart meters record the real-time power and total power consumption of motor start-up, aggregate heating, mixing, and other stages, forming a mixing energy consumption curve indexed by the production batch time. The mixing energy consumption curve includes sub-item consumption data of fuel and electricity. In specific implementation, high-precision fuel flow sensors are installed in the fuel inlet lines of ten asphalt mixture transport vehicles. These sensors monitor and record in real time the total fuel consumption from the time the vehicle departs from the mixing plant after loading until unloading at the paving site. Simultaneously, the onboard GPS terminal records the vehicle's trajectory, speed, and idling time. The fuel consumption is correlated with the corresponding trajectory segments to generate a transportation energy consumption distribution, revealing energy consumption differences under different transport distances, road conditions, and driving behaviors. In some embodiments, a fuel metering module is installed near the high-pressure common rail system of the asphalt paver's engine. Pressure and flow sensors are installed at the main pump outlet of the paver's hydraulic system. During paving operations, the fuel metering module records the amount of diesel fuel injected into the engine per unit time, while the pressure and flow sensors simultaneously record the working pressure and flow rate of the hydraulic system. The product of these two data is used to calculate the real-time hydraulic power. Integrating the diesel consumption rate and hydraulic power data generates a paving operation energy consumption sequence correlated with the paver's forward speed and paving width. In practice, on-board fuel monitoring terminals are installed on different types of road rollers responsible for initial compaction, intermediate compaction, and final compaction. The road roller operator records the start and end time and number of passes for each compaction on the control panel. The on-board fuel monitoring terminal simultaneously records the fuel consumption within the corresponding time window, generating a compaction energy consumption sequence. The compaction energy consumption sequence clearly records the fuel consumption under different compaction process stages and different numbers of compaction passes.

[0029] In practice, construction environmental characteristic data is acquired by deploying a network of micro weather stations at intervals of 500 meters along the asphalt mixing plant area, at major intersections along the paving road, and at key intersections along the material transport routes. Each node in the micro weather station network synchronously collects and uploads raw readings of air temperature, relative humidity, atmospheric pressure, surface wind speed, and wind direction at a sampling frequency of once per minute. The central data processing system receives the raw readings from all micro weather station nodes, performs time synchronization correction on the data from different nodes, and removes outliers caused by momentary sensor obstruction or interference, forming a standardized environmental parameter time series with strictly aligned timestamps. In essence, to accurately correlate environmental parameters with mechanical operations in time, the data processing system interpolates and matches each action record point in the mechanical operation characteristic data within the environmental parameter time series, ensuring that every construction action—such as the start of heating at the mixing plant, the arrival of the transport vehicle at the paver, or the start of compaction by the roller—has corresponding environmental parameters accurate to the second, thus constituting complete construction environmental characteristic data. Optionally, time interpolation can use a linear interpolation algorithm for the timing of mechanical actions. The corresponding ambient temperature can be determined by the two most recent meteorological sampling times and temperature and The calculation method is as follows:

[0030] in: The interpolation indicates the ambient temperature at the time of the action. and These represent the ambient temperatures at adjacent meteorological sampling times. Indicates the specific moment when the mechanical action occurs. and Indicates the time of adjacent meteorological sampling.

[0031] In practice, the process of generating mechanical operation characteristic data also includes the integration and correlation of energy consumption sequences from different processes. Mixing energy consumption curves, transportation energy consumption distributions, paving operation energy consumption sequences, and compaction energy consumption sequences are imported into a unified time-series database. The database uses the natural timeline of construction processes as its main thread, arranging and splicing the energy consumption data for mixing, transportation, paving, and compaction in chronological order. In essence, each energy consumption record is matched with a corresponding time period in the material flow characteristic data. For example, fuel consumption data in the transportation energy consumption distribution is associated with the corresponding vehicle and batch of mixed material transportation records in the material flow characteristic data; paving and compaction energy consumption data are associated with the type and quantity of mixed material paved during that time period recorded in the material flow characteristic data. Through this cross-data source matching and correlation, mechanical operation characteristic data describing the energy consumption of the entire chain from raw material processing to road surface formation is generated. This mechanical operation characteristic data is a structured dataset containing energy consumption type, value, occurrence time, associated materials, and spatial location.

[0032] In one embodiment of the present invention, material flow characteristic data, mechanical operation characteristic data, and construction environment characteristic data are integrated to construct a digital image of the construction process. Multi-scale feature analysis is performed on the digital image to preliminarily estimate the carbon flow intensity distribution throughout the construction process, generating a primary carbon flow map. In implementation, a four-dimensional data fusion framework is established, with time as the main thread, spatial location as the coordinate, and construction activities as the object. Within this framework, material flow characteristic data is mapped to a material flow field, mechanical operation characteristic data to an energy consumption flow field, and construction environment characteristic data to an environmental parameter field. Spatiotemporal overlay and coupling analysis are performed on the material flow field, energy consumption flow field, and environmental parameter field to generate a digital image of the construction process that dynamically reflects the construction progress. A multi-resolution analysis method is used to decompose the digital image of the construction process step-by-step from the overall to the local, and from long-term to short-term scales. At different scales, based on the carbon emission factors of material consumption, fuel combustion, and electricity consumption, combined with the influence coefficient of environmental parameters on combustion efficiency, the carbon emission intensity within each spatiotemporal unit is dynamically calculated. By integrating and visualizing the carbon emission intensity of all spatiotemporal units, a primary carbon flow map is formed that shows the temporal and spatial distribution of carbon emission intensity.

[0033] In specific implementation, this embodiment involves the fusion processing of material flow characteristic data, mechanical operation characteristic data, and construction environment characteristic data to construct a digital image of the construction process. Multi-scale feature analysis is then performed on this digital image to generate a primary carbon flow map. In some embodiments, a four-dimensional data fusion framework is established, using Greenwich Mean Time as the unified timeline, the coordinates of the measurement control network deployed at the construction site as the spatial reference, and specific construction activities such as mixing, transportation, paving, and compaction as the descriptive objects. This four-dimensional data fusion framework is implemented in a computer system as a spatiotemporal database, where each data point includes a timestamp, three-dimensional geographic coordinates, and an activity type label. In practical implementation, within the four-dimensional data fusion framework, material flow characteristic data is mapped to a dynamic material flow field. The attributes of each grid cell in this field at each time step include the mass of aggregate, asphalt, and mineral powder flowing through that cell. Mechanical operation characteristic data is mapped to an energy consumption flow field. The attributes of each grid cell in this field at each time step include the volume of diesel fuel, natural gas, and electricity consumed by the construction machinery within that cell. Construction environment characteristic data is mapped to an environmental parameter field. The attributes of each grid cell in this field at each time step include air temperature, relative humidity, atmospheric pressure, and wind speed. In practical implementation, spatiotemporal overlay and coupling analysis are performed on the material flow field, energy consumption flow field, and environmental parameter field. This coupling analysis, based on a unified spatial grid and time step, associates and integrates attribute data from the three data fields at the same spatiotemporal coordinates, generating a digital image of the construction process that dynamically reflects the construction progress. This digital image of the construction process is a multi-dimensional data cube.

[0034] In practical implementation, a multi-resolution analysis method is used to decompose the digital images of the construction process from overall to local and from long-term to short-term scales. Multi-resolution analysis is achieved by defining different time windows and spatial unit sizes. In some embodiments, three levels of resolution scales are defined. The first level of resolution scale corresponds to the "entire project-day" scale, with a 24-hour time window and a spatial unit covering the entire construction section. At this scale, the total daily material consumption, comprehensive energy consumption, and average daily carbon emission intensity are analyzed. The second level of resolution scale corresponds to the "construction section-hour" scale, with a one-hour time window and a 100-meter-long construction section as the spatial unit. At this scale, the carbon emission intensity fluctuations of different processes in different sections are analyzed. The third level of resolution scale corresponds to the "machine-minute" scale, with a one-minute time window and a spatial unit representing the operating range of a single construction machine. At this scale, the instantaneous carbon emission rate of a specific machine during a specific operation is analyzed. For a comparison of different resolution scales, see Table 1. Table 1: Comparison of Multi-Scale Analysis Levels in Digital Images of the Construction Process Scale hierarchy Time window Spatial Unit Analysis content Overall project scale 1st The entire construction section Total daily carbon emissions, carbon emission percentage of each process Construction section dimensions 1 hour 100-meter section Hourly carbon emission intensity per unit road segment, carbon emission characteristics of process connections Mechanical operating dimensions 1 minute Operating range of a single machine Instantaneous carbon emission rate of mechanical actions, identification of carbon emissions from ineffective operations In practical implementation, at different analytical scales, combustion efficiency is corrected based on standard carbon emission factors for material consumption, fuel combustion, and electricity consumption, combined with temperature and air pressure data provided by the environmental parameter field, to dynamically calculate the carbon emission intensity within each spatiotemporal unit. Optionally, for a specific spatiotemporal unit at the "construction segment-hour" scale, its carbon emission intensity... The calculation can be expressed as:

[0035] in: This indicates the carbon emission intensity of that hour-segment spatiotemporal unit. Indicates the first [unit] within this spacetime unit The consumption of various fossil fuels. Indicates the first Carbon emission factors of fossil fuels This represents the combustion efficiency correction factor calculated from ambient temperature and air pressure. Indicates the first [unit] within this spacetime unit Power consumption of each electrical device This represents the average carbon emission factor of the regional power grid. In practice, the carbon emission intensity results calculated from all spatiotemporal units are integrated according to their time and spatial coordinates, and visualized using the heatmap rendering or contour line generation functions of geographic information system software to form a primary carbon flow map. The primary carbon flow map can be understood as visually displaying the distribution and changes of carbon emission intensity over time on the construction site plan in the form of images or layers. Optionally, the primary carbon flow map can be output as a series of raster image files arranged in chronological order, or as a multidimensional raster data file containing the time dimension.

[0036] In one embodiment of the invention, a mobile detection device is dispatched along the construction route to collect multi-band reflectance spectra and near-field thermal radiation data of the completed paved road sections. Combined with a primary carbon flow map, a carbon emission state inversion process is initiated to identify the carbon emission modes of each construction unit and generate a carbon emission mode list. In practice, an unmanned detection vehicle equipped with a multispectral imager and an infrared thermal imager is used as the mobile detection device. After the asphalt surface layer is paved and left to stand for a predetermined period, the unmanned detection vehicle travels at a constant speed along the lane. During travel, the multispectral imager is simultaneously triggered to collect reflectance data of the road surface in multiple specific narrow bands, and the infrared thermal imager is triggered to collect temperature distribution data of the road surface, obtaining surface multi-band reflectance spectra and near-field thermal radiation data respectively. From the primary carbon flow map, local carbon flow intensity estimates corresponding to the current position and detection time of the unmanned detection vehicle are extracted. A carbon emission state inversion model is constructed with the local carbon flow intensity estimates as prior constraints. Surface multi-band reflectance spectrum and near-field thermal radiation data are input into the carbon emission state inversion model. By solving an optimization problem, multiple implicit state variables reflecting the actual mixture temperature history, paving uniformity, and compaction effectiveness are inverted. Based on these inverted implicit state variables and in accordance with preset carbon emission mode classification rules, a specific carbon emission mode identifier is assigned to each tested road segment unit. The identifiers of all units are then compiled to form a carbon emission mode list.

[0037] Based on the carbon emission modal inventory, the structural performance evolution path of asphalt pavement within a set service life is simulated and calculated, and the performance degradation trend spectrum is deduced. During implementation, a multiphysics coupled degradation simulation model (MPC) for ultra-thin asphalt pavement is established. The inputs to the MPC include material properties, structural thickness, construction quality status defined in the carbon emission modal inventory, and standard traffic loads and environmental cycles. From the carbon emission modal inventory, the carbon emission mode identifier corresponding to each construction unit is parsed, and each identifier is mapped to the initial defect parameters and material inhomogeneity parameters of the corresponding construction unit in the MPC. A simulated service life of several decades is set, and standard axle load spectrum and typical climate cycle data are loaded into the MPC. The MPC simulation model is run to calculate the dynamic changes of key performance indicators of the asphalt pavement, including deflection, bottom tensile stress, surface cracking index, and rutting depth, throughout the entire simulated service life. The curves of key performance indicators changing over time for each construction unit are recorded; these curves constitute the performance degradation trend spectrum reflecting the future performance degradation pattern.

[0038] In practice, mobile detection devices are dispatched to collect road surface data to initiate a carbon emission status inversion process, generating a carbon emission mode inventory. Based on the carbon emission mode inventory, performance evolution paths are simulated and calculated to deduce the performance degradation trend spectrum. In some embodiments, an unmanned detection vehicle equipped with a hyperspectral imager and a long-wave infrared thermal imager is configured as the mobile detection device. The hyperspectral imager can collect reflectance data in 128 narrow bands within the wavelength range of 400 nanometers to 1000 nanometers, and the long-wave infrared thermal imager has a temperature measurement range of -20 degrees Celsius to 150 degrees Celsius with a temperature resolution of 0.1 degrees Celsius. In specific implementation, after the asphalt surface layer is laid and allowed to cool for 30 minutes, the unmanned inspection vehicle travels along the centerline of the construction lane at a constant speed of 5 kilometers per hour. During the journey, the system triggers a hyperspectral imager to collect hyperspectral reflectance data of the road surface and a long-wave infrared thermal imager to collect temperature distribution images of the road surface at one-meter intervals, obtaining multi-band reflectance spectra and near-field thermal radiation data of the surface. Both multi-band reflectance spectra and near-field thermal radiation data contain accurate geographical location information. In some embodiments, from the generated primary carbon flow map, based on the location information provided by the unmanned inspection vehicle's GPS trajectory and the data acquisition timestamp, a local carbon flow intensity estimate that perfectly matches the map in time and space is extracted. This local carbon flow intensity estimate represents the prior estimate of the carbon emission level during the construction period of the current inspection road segment unit by the carbon emission state inversion model. In practice, a carbon emission state inversion model is constructed with the local carbon flow intensity estimate as a prior constraint. The carbon emission state inversion model is an optimization model based on a Bayesian inference framework. Its objective function is to maximize the fit between the inverted state variables and the observed surface multi-band reflectance spectrum and near-field thermal radiation data, and minimize the deviation from the prior estimate of the local carbon flow intensity.

[0039] In practical implementation, based on the carbon emission modal inventory, the structural performance evolution path of the asphalt pavement within a set service life is simulated and calculated, and the performance degradation trend spectrum is deduced. A multiphysics coupled degradation simulation model for ultra-thin asphalt pavement is established. The inputs of the multiphysics coupled degradation simulation model include material properties, structural thickness, construction quality status defined by the carbon emission modal inventory, and standard traffic load and environmental cycle. In some embodiments, the carbon emission mode identifier corresponding to each construction unit is parsed from the carbon emission modal inventory, and each identifier is mapped to the initial defect parameters and material non-uniformity parameters of the corresponding construction unit in the multiphysics coupled degradation simulation model. The mapping relationship is determined based on a preset lookup table. Different carbon emission modes correspond to different initial porosity distributions and aggregate-asphalt interface strength reduction coefficients. Optionally, the mapping relationship is shown in Table 2. Table 2: Mapping Table of Carbon Emission Mode Identification and Simulation Model Input Parameters Carbon emission pattern labeling describe Initial dynamic modulus (MPa) Initial fatigue crack resistance coefficient Initial permanent deformation resistance coefficient CM-A Low carbon, high efficiency and uniformity 8500 1.00 1.00 CM-B Partial segregation of medium carbon 8000 0.85 0.90 CM-C Insufficient high-carbon compaction 7500 0.70 0.75 In practice, a simulated service life of up to twenty years is set. A standard axle load spectrum and typical climate cycle data are loaded into the multiphysics coupled degradation simulation model. The standard axle load spectrum is determined based on the road design documents, and the typical climate cycle data uses annual temperature and precipitation cycles generated from local historical meteorological data. The multiphysics coupled degradation simulation model is run to calculate the dynamic changes of key performance indicators of the asphalt pavement, including deflection, tensile stress at the bottom of the layer, surface cracking index, and rutting depth, over the entire simulated service life. The simulation calculation is performed in one-month time steps. Essentially, recording the curves of key performance indicators changing over time for each construction unit constitutes a performance degradation trend spectrum reflecting future performance degradation patterns. This performance degradation trend spectrum is stored as a dataset containing predicted performance indicator values ​​for each road segment unit at various time points during the simulation period.

[0040] In one embodiment of the present invention, a microstructure scanning device is deployed in the compacted asphalt surface area to acquire images of the internal void distribution and aggregate arrangement of the mixture. Combined with a performance degradation trend spectrum, the durability status identification process of the mixture is initiated to determine the actual durability status level of the surface layer. In practice, a combination of a penetrable 3D ground-penetrating radar and a laser scanner is used as the microstructure scanning device. After the pavement compaction is completed and cooled to ambient temperature, the microstructure scanning device is deployed above the selected evaluation area. The 3D ground-penetrating radar scans the evaluation area to acquire the dielectric constant distribution at different depths within the asphalt mixture, reconstructing a 3D image of the internal void distribution. The laser scanner scans the surface of the same evaluation area to acquire the exposed contours and spatial positions of the aggregate particles, constructing an aggregate arrangement image. From the performance degradation trend spectrum, the predicted performance value of the corresponding evaluation area in the initial simulated service period is extracted as the prior expected state. The 3D image of the internal void distribution and the aggregate arrangement image are fused, and the porosity, void connectivity, number of contact points between aggregates, and aggregate orientation microstructure feature vectors are extracted. The detailed feature vectors and prior expected states are input into a pre-defined durability classification network. After training, the durability classification network is able to correlate detailed features with long-term performance potential. The durability classification network outputs a discrete level representing the actual durability potential of the current surface layer, i.e., the actual durability state level.

[0041] The carbon emission modality list and actual durability status level are integrated and mapped to a pre-defined construction carbon efficiency evaluation matrix. The resulting calculation outputs a quantitative carbon emission evaluation index. During implementation, a two-dimensional construction carbon efficiency evaluation matrix is ​​constructed. One dimension represents the carbon emission modality, defined according to different modes in the carbon emission modality list; the other dimension represents the durability status level, defined according to different levels of the actual durability status level. Each cell in the matrix has a pre-set basic carbon efficiency coefficient, representing the theoretical carbon emission efficiency level under a specific combination of carbon emission modality and durability status level. The distribution of the proportion of each mode obtained from the carbon emission modality list and the evaluation results of the actual durability status level are used as inputs. The inputs are matched and mapped to the construction carbon efficiency evaluation matrix, and the basic carbon efficiency coefficients of the corresponding cells are weighted according to the proportion of each mode. The weighted calculation result is normalized and converted into a value between zero and one hundred, which is the carbon emission evaluation index, used to quantitatively characterize the overall carbon emission level of this ultra-thin asphalt pavement construction activity.

[0042] In specific implementation, a microstructure scanning device is deployed in the compacted asphalt surface area to obtain images of the internal structure of the mixture. The actual durability status level is determined by combining this with a performance degradation trend spectrum. Furthermore, the carbon emission mode list is integrated with the actual durability status level and mapped to a construction carbon efficiency evaluation matrix to output a carbon emission evaluation index. In some embodiments, a combination of a three-dimensional ground-penetrating radar equipped with a high-frequency antenna array and a high-precision line laser scanner is used as the microstructure scanning device. The three-dimensional ground-penetrating radar has a center frequency of 2 GHz and a depth resolution of up to 1 mm. The line laser scanner has a longitudinal sampling interval of 0.2 mm and a lateral resolution of 0.5 mm. In specific implementation, after the pavement compaction is completed and it has naturally cooled to a temperature difference of no more than 3 degrees Celsius from the ambient temperature, a mobile detection frame equipped with a three-dimensional ground-penetrating radar and a line laser scanner is deployed above a selected 3m x 3m evaluation area. The mobile detection frame automatically moves along a preset grid path. During the process, the 3D ground-penetrating radar emits electromagnetic waves at 10-millimeter steps and receives reflected signals from within the surface layer. It processes the full matrix data using an inversion algorithm to obtain the dielectric constant distribution within the asphalt mixture at different depths, thereby reconstructing a 3D image of the internal void distribution. This 3D image is expressed in voxel form, with each voxel value representing the void probability inferred from the dielectric constant at that location. Similarly, a line laser scanner simultaneously scans the surface of the same evaluation area, acquiring elevation point cloud data of the road surface. Through point cloud segmentation and feature recognition algorithms, it extracts the exposed contours, spatial positions, and normal vectors of the aggregate particles, constructing an aggregate arrangement image. This image records the center coordinates, equivalent diameter, and major axis orientation of each aggregate particle.

[0043] In practical implementation, based on the precise geographical location of the evaluation area and the performance degradation trend spectrum, the average deflection value and predicted rut depth of the area during the first year of simulated service are extracted as the prior expected state. In practical implementation, the three-dimensional image of the internal void distribution and the aggregate arrangement image are spatially registered and fused. From the fused mesoscopic structural model, four mesoscopic feature vectors are extracted: porosity, void connectivity index, number of aggregate-aggregate contact points, and average aggregate orientation angle. Optionally, the void connectivity index... The calculation is performed by analyzing the topological structure of the void phase in the 3D image, and the formula is as follows:

[0044] in: Represents the dimensionless interstitial connectivity index. This indicates the number of through-hole channels identified in a 3D image. This represents the average length of these through-hole channels. This represents the average cross-sectional area of ​​the analyzed image volume data on a section perpendicular to the main direction of the channel. In specific implementation, the extracted mesoscopic feature vectors and the prior expected state (i.e., the initial performance prediction value) obtained from the performance degradation trend spectrum are input into a preset durability classification network. The durability classification network is a multilayer perceptron model trained with a large number of historical samples. The training samples come from the correlation between core drilling laboratory test results and long-term performance observation data of the same type of road surface. The durability classification network can learn the complex nonlinear relationship between mesoscopic features and long-term performance potential. It can be understood that the durability classification network performs forward propagation calculation on the current input and outputs a discrete level representing the actual durability potential of the current surface layer, i.e., the actual durability state level. This level can be labeled as DS-1 (excellent), DS-2 (good), DS-3 (medium), and DS-4 (poor).

[0045] In practical implementation, the carbon emission mode list and the actual durability status level are integrated and mapped to a preset construction carbon efficiency evaluation matrix. After calculation, a quantitative carbon emission evaluation index is output. A two-dimensional construction carbon efficiency evaluation matrix is ​​constructed. The row dimension of the construction carbon efficiency evaluation matrix is ​​defined according to the different carbon emission mode identifiers defined in the carbon emission mode list, such as CM-A, CM-B, and CM-C. The column dimension of the construction carbon efficiency evaluation matrix is ​​defined according to the different levels of the actual durability status, such as DS-1, DS-2, DS-3, and DS-4. In some embodiments, each cell in the construction carbon efficiency evaluation matrix is ​​preset with a basic carbon efficiency coefficient. The basic carbon efficiency coefficient represents the theoretical carbon emission efficiency level under a specific combination of carbon emission mode and a specific durability status level. The basic carbon efficiency coefficient ranges from zero to one. The higher the value, the higher the durability benefit obtained per unit of carbon emission under that combination state. The coefficient value is calibrated based on life cycle assessment theory and historical benchmark data. In practical implementation, the distribution of the proportion of each mode obtained from the carbon emission modal inventory, together with the actual durability status level assessment result, are used as inputs. Optionally, the carbon emission modal inventory statistics for a construction activity show that the CM-A mode accounts for 70%, the CM-B mode for 20%, and the CM-C mode for 10%; the actual durability status level assessment result is DS-2. In practical implementation, the input is matched and mapped with the construction carbon efficiency evaluation matrix. The basic carbon efficiency coefficient of the corresponding cell is weighted according to the proportion of each mode. After normalization, the weighted calculation result is converted into a value between zero and one hundred, which is the carbon emission evaluation index. It can be understood that calculating the carbon emission evaluation index... The formula is expressed as:

[0046] in: Indicates the carbon emission assessment index. This indicates the total number of categories of carbon emission patterns. Indicates the first The proportion of carbon emission modes in this construction project. This indicates the actual assessed durability condition level. This indicates the corresponding number in the construction carbon efficiency evaluation matrix. Carbon emission patterns and levels The basic carbon efficiency coefficient This represents the maximum value of all basic carbon efficiency coefficients in the construction carbon efficiency evaluation matrix.

[0047] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for evaluating carbon emissions from the construction of ultra-thin asphalt pavement, characterized in that, include: Deploy IoT sensor networks and energy consumption recording devices to acquire and integrate material flow characteristic data, mechanical operation characteristic data and construction environment characteristic data, construct digital images of the construction process, perform multi-scale feature analysis on the digital images of the construction process, and generate a primary carbon flow map. A mobile detection device is dispatched along the construction route to collect multi-band reflectance spectrum and near-field thermal radiation data of the completed paved sections. Combined with the primary carbon flow map, the carbon emission status inversion process is initiated to identify the carbon emission modes of each construction unit and generate a carbon emission mode list. Based on the carbon emission mode list, the structural performance evolution path of the asphalt pavement within a set service life is simulated and calculated, and the performance degradation trend spectrum is deduced. For the compacted asphalt surface area, a microstructure scanning device is deployed to acquire images of the internal void distribution and aggregate arrangement of the mixture. Combined with the performance degradation trend spectrum, the mixture durability status identification process is initiated to determine the actual durability status level of the surface layer. The carbon emission mode list is integrated with the actual durability status level and mapped to a preset construction carbon efficiency evaluation matrix. After calculation, a quantitative carbon emission evaluation index is output.

2. The carbon emission assessment method for ultra-thin asphalt pavement construction according to claim 1, characterized in that, Deploy IoT sensor networks and energy consumption recording devices to acquire and integrate material flow characteristic data, mechanical operation characteristic data, and construction environment characteristic data; construct digital images of the construction process; perform multi-scale feature analysis on the digital images of the construction process; and generate primary carbon flow maps, including: In the raw material mining, transportation, and storage processes, IoT sensor networks are deployed to continuously acquire the weight changes and location movement trajectories of materials, forming material flow characteristic data. Receive the material flow characteristic data, and simultaneously record the instantaneous flow rate and cumulative consumption of energy-consuming media of various construction machinery in the asphalt mixture mixing, transportation, paving and compaction stages to generate mechanical operation characteristic data; Real-time temperature, humidity, atmospheric pressure, and surface wind speed and direction at the construction site are obtained to form construction environment characteristic data; By integrating the material flow characteristic data, the mechanical operation characteristic data, and the construction environment characteristic data, a digital image of the construction process is constructed. Multi-scale feature analysis is performed on the digital image of the construction process to preliminarily estimate the carbon flow intensity distribution throughout the construction process and generate a primary carbon flow map.

3. The carbon emission evaluation method for ultra-thin asphalt pavement construction according to claim 2, characterized in that, In the raw material mining, transportation, and storage stages, an IoT sensor network is deployed to continuously acquire the weight changes and positional movement trajectories of materials, forming material flow characteristic data, including: Weighing sensors are installed at the discharge ports of stone quarries, asphalt storage tanks, and mineral powder silos to record the weight and timestamp of each discharge, and the data is compiled into a raw material output log. Positioning and load monitoring modules are installed on vehicles transporting aggregates, asphalt, and mineral powder to track changes in vehicle position, speed, and load status in real time, generating raw material transportation trajectory and load time sequence data. Material level monitoring sensors are installed in the aggregate cold silos, asphalt insulation tanks and mineral powder tanks of the mixing plant to continuously record the changes in the volume or height of materials in each storage container, forming a dynamic sequence of raw material inventory. By aggregating the raw material output logs, the raw material transportation trajectory and load time series data, and the raw material inventory dynamic sequence, and after time alignment and data cleaning, the material flow characteristic data is constructed with material type, quality, spatial location, and time point as dimensions.

4. The carbon emission evaluation method for ultra-thin asphalt pavement construction according to claim 2, characterized in that, Upon receiving the material flow characteristic data, during the asphalt mixture mixing, transportation, paving, and compaction stages, the instantaneous flow rate and cumulative consumption of energy-consuming media for various construction machinery are simultaneously recorded to generate machinery operation characteristic data, including: Flow meters and smart meters are installed on the fuel supply pipelines and power input bus of the asphalt mixing plant to record the instantaneous flow rate and cumulative volume of diesel and natural gas consumed during the mixing process, as well as the real-time power and cumulative consumption of electricity, forming a mixing energy consumption curve. A fuel monitoring sensor is installed on the fuel tank of the asphalt mixture transport vehicle to record the fuel consumption throughout the entire transportation process from the mixing plant to the paving site, and the transportation energy consumption distribution is generated by combining the transportation trajectory. Monitoring equipment is installed in the engine fuel line and hydraulic system of the asphalt paver to record the fuel consumption rate and hydraulic system power consumption during paving operations, and to generate an energy consumption sequence for paving operations. A fuel metering device is installed on the diesel engine of a road roller to record the fuel consumption under different compaction passes and generate a compaction energy consumption sequence. The mixing energy consumption curve, the transportation energy consumption distribution, the paving operation energy consumption sequence, and the compaction energy consumption sequence are integrated and correlated according to the time axis of the construction process, and matched with the corresponding time period in the material flow characteristic data to generate the mechanical operation characteristic data describing the energy consumption of the entire construction chain.

5. The carbon emission evaluation method for ultra-thin asphalt pavement construction according to claim 2, characterized in that, Real-time temperature, humidity, atmospheric pressure, and surface wind speed and direction at the construction site are acquired to form construction environment characteristic data, including: A network of miniature weather stations will be deployed at key nodes along asphalt mixing plants, paving sites, and material transport routes. Through the aforementioned micro weather station network, raw readings of air temperature, relative humidity, atmospheric pressure, and surface wind speed and direction are synchronously collected from each node at a set sampling frequency. The raw readings from each node are time-synchronized and outlier-removing to form a standardized environmental parameter time series. The environmental parameter time series and the mechanical operation feature data are fused and interpolated in the time dimension to ensure that there are corresponding environmental parameters for each construction action, thereby forming complete construction environmental feature data.

6. The carbon emission assessment method for ultra-thin asphalt pavement construction according to claim 2, characterized in that, By integrating the material flow characteristic data, the mechanical operation characteristic data, and the construction environment characteristic data, a digital image of the construction process is constructed. Multi-scale feature analysis is performed on the digital image to preliminarily estimate the carbon flow intensity distribution throughout the construction process, generating a primary carbon flow map, including: Establish a four-dimensional data fusion framework with time as the main thread, spatial location as the coordinate, and construction activities as the object; In the four-dimensional data fusion framework, the material flow characteristic data is mapped to a material flow field, the mechanical operation characteristic data is mapped to an energy consumption flow field, and the construction environment characteristic data is mapped to an environmental parameter field. The material flow field, the energy consumption flow field, and the environmental parameter field are spatiotemporally superimposed and coupled to generate a digital image of the construction process that can dynamically reflect the construction progress. A multi-resolution analysis method is used to decompose the digital images of the construction process step by step from the whole to the part and from the long time scale to the short time scale. At different scales, the carbon emission intensity in each spatiotemporal unit is dynamically calculated based on the carbon emission factors of material consumption, fuel combustion and electricity consumption, combined with the influence coefficient of environmental parameters on combustion efficiency. The carbon emission intensity of all spatiotemporal units is integrated and visualized to form the primary carbon flow map, which shows the temporal and spatial distribution of carbon emission intensity.

7. The carbon emission evaluation method for ultra-thin asphalt pavement construction according to claim 1, characterized in that, A mobile detection device is dispatched along the construction route to collect multi-band reflectance spectra and near-field thermal radiation data of the completed paved sections. Combined with the primary carbon flow map, a carbon emission status inversion process is initiated to identify the carbon emission modes of each construction unit and generate a carbon emission mode list, including: An unmanned inspection vehicle equipped with a multispectral imager and an infrared thermal imager is configured as the mobile inspection device; After the asphalt surface layer is laid and left to stand for a predetermined period of time, the unmanned inspection vehicle is controlled to drive at a constant speed along the lane. During driving, the multispectral imager is simultaneously triggered to collect reflectance data of the road surface in multiple specific narrow bands, and the infrared thermal imager is triggered to collect temperature distribution data of the road surface, thereby obtaining the multi-band reflectance spectrum of the surface and the near-field thermal radiation data respectively. From the primary carbon flux map, extract the estimated local carbon flux intensity corresponding to the current position and detection time of the unmanned detection vehicle; Construct a carbon emission state inversion model with the estimated local carbon flow intensity as a priori constraint; The surface multi-band reflectance spectrum and the near-field thermal radiation data are input into the carbon emission state inversion model. By solving the optimization problem, multiple implicit state variables reflecting the actual mixture temperature history, paving uniformity and compaction effectiveness are inverted. Based on the multiple implicit state variables derived from the inversion, and in accordance with the preset carbon emission mode classification rules, a specific carbon emission mode identifier is assigned to each detected road segment unit. The carbon emission mode list is formed by summarizing the identifiers of all units.

8. The carbon emission evaluation method for ultra-thin asphalt pavement construction according to claim 7, characterized in that, Based on the aforementioned carbon emission mode inventory, the structural performance evolution path of the asphalt pavement within a set service life is simulated and calculated, and the performance degradation trend spectrum is deduced, including: A multiphysics coupled degradation simulation model for ultrathin asphalt pavement is established. The inputs of the multiphysics coupled degradation simulation model include material properties, structural thickness, construction quality status defined by the carbon emission mode list, and standard traffic load and environmental cycle. From the carbon emission mode list, the carbon emission mode identifier corresponding to each construction unit is parsed out, and each identifier is mapped to the initial defect parameters and material non-uniformity parameters of the corresponding construction unit in the multi-physics coupled degradation simulation model. Set a simulated service life of several decades, and load standard axial load spectrum and typical climate cycle data into the multiphysics coupled degradation simulation model; Run the multiphysics coupled degradation simulation model to calculate the dynamic changes of key performance indicators of asphalt pavement, such as deflection, tensile stress at the bottom of the layer, surface cracking index, and rutting depth, throughout the entire simulated service life. The curves of the key performance indicators of each construction unit changing over time are recorded. These curves of the key performance indicators changing over time constitute the performance degradation trend spectrum, which reflects the future performance degradation pattern.

9. The carbon emission evaluation method for ultra-thin asphalt pavement construction according to claim 1, characterized in that, For the compacted asphalt surface layer area, a microstructure scanning device is deployed to acquire images of the internal void distribution and aggregate arrangement of the mixture. Combined with the performance degradation trend spectrum, the mixture durability status identification process is initiated to determine the actual durability status level of the surface layer, including: A combination of penetrable three-dimensional ground-penetrating radar and laser scanner is used as the microstructure scanning device; After the road surface has been compacted and cooled to ambient temperature, the microstructure scanning device is deployed above the selected evaluation area; The evaluation area is scanned using the three-dimensional ground-penetrating radar to obtain the dielectric constant distribution of the asphalt mixture at different depths, and a three-dimensional image of the internal void distribution is reconstructed. The laser scanner is used to scan the surface of the same evaluation area to obtain the exposed outline and spatial position of the aggregate particles, and to construct the aggregate arrangement image. From the performance degradation trend spectrum, the predicted performance value of the corresponding evaluation region in the initial stage of simulated service is extracted as the a priori expected state. The three-dimensional image of the internal void distribution is fused with the image of the aggregate arrangement, and the porosity, void connectivity, number of contact points between aggregates, and aggregate orientation microscopic feature vectors are extracted. The detailed feature vector and the prior expected state are input together into a preset durability classification network. After training, the durability classification network can associate the detailed features with long-term performance potential. The durability classification network outputs a discrete level representing the actual durability potential of the current surface layer, namely the actual durability state level.

10. The carbon emission assessment method for ultra-thin asphalt pavement construction according to claim 1, characterized in that, The carbon emission mode list is integrated with the actual durability status level and mapped to a preset construction carbon efficiency evaluation matrix. After calculation, a quantitative carbon emission evaluation index is output, including: A two-dimensional carbon efficiency evaluation matrix for construction is constructed. One dimension of the matrix is ​​the carbon emission mode, which is defined according to different modes in the carbon emission mode list. The other dimension of the matrix is ​​the durability status level, which is defined according to different levels of the actual durability status level. Each cell in the matrix is ​​pre-set with a basic carbon efficiency coefficient, which represents the theoretical carbon emission efficiency level under a specific combination of carbon emission mode and a specific durability state level. The distribution of the proportion of each mode obtained from the carbon emission mode inventory, together with the assessment results of the actual durability status level, are used as inputs. The input is matched and mapped with the construction carbon efficiency evaluation matrix, and the basic carbon efficiency coefficient of the corresponding cell is weighted according to the proportion of each mode. The weighted calculation result is normalized and converted into a value between zero and one hundred. This value is the carbon emission evaluation index, which is used to quantitatively characterize the overall carbon emission level of this ultra-thin asphalt pavement construction activity.

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