Method and system for monitoring carbon emissions of traffic infrastructure based on multi-source fusion

Abstract

The application provides a traffic infrastructure carbon emission ground monitoring method and system based on multi-source fusion, and relates to the technical field of traffic infrastructure carbon emission monitoring and accounting. The existing traffic infrastructure carbon emission accounting method depends on manual account books and default emission factors, can only form a total amount at the project level, cannot identify the emission contribution of specific facilities, processes, mileage sections or material categories, and is difficult to dynamically check abnormal working conditions, non-project energy consumption, repeated entry and data gaps. The application establishes a multi-source data unified fusion and hierarchical accounting mechanism, realizes the collaborative monitoring and accurate attribution of traffic infrastructure node facilities, linear operation zones and material materialization stage emissions, and combines concentration data to assist in identifying and quality grading control, thereby significantly improving the traceability, accuracy and audit compliance of the accounting results.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring and accounting technology for transportation infrastructure, and in particular to a ground-based method and system for carbon emission monitoring of transportation infrastructure based on multi-source fusion. Background Technology

[0002] Transportation infrastructure projects are characterized by long construction periods, large spatial spans, and diverse emission sources. These emission sources include relatively fixed nodal facilities such as mixing plants, prefabrication yards, and service areas, as well as linear operational sources that move continuously along roads, bridges, tunnels, or waterways. They also include implicit carbon emissions generated during the production and transportation of bulk building materials such as steel and cement. Existing accounting methods mainly rely on manual ledgers, fuel receipts, electricity meter readings, and default emission factors for statistical aggregation, which can generate project-level or monthly total carbon emissions.

[0003] However, existing accounting methods can only output a total amount, failing to break down emissions into specific facilities, equipment, processes, mileage segments, or material categories. This makes it difficult for managers to identify high-emission links and formulate targeted emission reduction measures when faced with total data. Furthermore, with the development of monitoring technology, data collected by on-site CO2 sensors, CH4 sensors, RTK positioning equipment, etc., cannot be effectively integrated with traditional ledger data due to differences in recording frequency, units, and spatial labeling methods. CO2 or CH4 concentration data are often directly used for total emission inversion, but unstable wind direction and dispersed emission sources at construction sites lead to significant deviations in the inversion results. The boundary between emissions from the material physicochemical stage and on-site energy consumption emissions is unclear, easily resulting in double counting. Moreover, all accounting results lack data quality grading standards, leading to the indiscriminate use of data of varying reliability in formal reports, further exacerbating the problems of insufficient credibility and audit compliance in the accounting results.

[0004] To address the aforementioned issues, this application provides a ground-based carbon emission monitoring method and system for transportation infrastructure based on multi-source fusion. The aim is to establish a unified monitoring and hierarchical accounting framework covering node facilities, linear work zones, and material materialization stages. By constructing a unified primary key system, multi-source data can be linked and traced within the same chain. The technical role of concentration data in auxiliary identification is clarified, rather than its direct participation in total emission inversion. Hierarchical ledgers and duplicate calculation verification mechanisms prevent boundary overlap. Simultaneously, data quality grading standards are set to control the application of results at different quality levels, thereby achieving a closed-loop process from accurate measurement to reliable attribution and effective management. Summary of the Invention

[0005] Existing methods for carbon emission accounting of transportation infrastructure often rely on manual ledgers and default emission factors, resulting only in project-level totals and failing to identify the emission contributions of specific facilities, processes, mileage sections, or material categories. Furthermore, they struggle to dynamically verify abnormal operating conditions, non-project energy consumption, duplicate entries, and data gaps. This invention first proposes a ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion, specifically including the following steps: Step S1: Establish a monitoring object list for transportation infrastructure projects. The monitoring object list includes the key system of projects, sections, facilities, equipment, energy or materials, processes, time and space units, and determines the carbon emission monitoring boundary based on the task boundary of the construction period or operation and maintenance period. Step S2: Establish a hierarchical ledger based on direct emissions, emissions from purchased energy, and emissions from the material physicochemical stage. Within the carbon emission monitoring boundary, collect ground-based multi-source monitoring data according to the key system. The ground-based multi-source monitoring data includes at least energy metering data, stationary source exhaust detection data, carbon dioxide or methane sensor network data, mobile monitoring trajectory data, equipment operating data, construction log data, and material ledger data. Step S3: Based on the primary key system, perform unified data dictionary encoding, time synchronization, spatial registration and data quality control on the ground multi-source monitoring data, so that all data are mapped to a unified time and space unit system within the monitoring boundary; Step S4: Under the unified time and space unit system, complete the emission accounting for three types of scenarios: the first type is the emission accounting for nodal facilities with relatively fixed locations, the second type is the emission accounting for linear work zones that move continuously along traffic lines, and the third type is the emission accounting for the materialization stage of building materials. Step S5: Perform hierarchical aggregation and merging and double-count verification based on the accounting results of the three scenarios, and determine the comprehensive quality level based on the quality control quality marks. Step S6: Based on the results of hierarchical aggregation and merging, duplicate calculation verification and quality level determination, output the carbon emission monitoring results at the project level, section level, facility level, mileage section level, process level and material category level. Each level of results is accompanied by a quality level label.

[0006] Furthermore, the tiered ledgers in step S2 include a direct emissions ledger, a purchased energy ledger, and a material physicochemical ledger; the direct emissions ledger includes converted emissions from fuel combustion and stationary source exhaust within the project boundary; the purchased energy ledger includes indirect emissions from purchased electricity and heat; and the material physicochemical ledger includes implicit carbon emissions from bulk building materials from production and transportation to the project use boundary.

[0007] Furthermore, in step S3, time synchronization aligns and resamples data with different sampling frequencies based on a unified time grid; spatial registration includes matching fixed monitoring data to node facilities and monitoring points, matching mobile monitoring trajectory data to mileage markers and construction grids, and matching material ledger data to projects, sections, and processes; data quality control includes anomaly removal, missing data completion, and traceable quality marking.

[0008] Furthermore, in step S4, when calculating emissions from the first type of nodal facility, upwind background points and downwind monitoring points are set according to the prevailing wind direction, and exhaust data from fuel oil, natural gas, electricity, and stationary sources are accessed. When the stationary source has organized exhaust conditions, carbon dioxide concentration and flue gas flow rate are obtained using CEMS or standard methods, and emissions are calculated using the mass flow method. The concentration difference between upwind and downwind is used for source identification and thermal zone identification. When the conditions of complete plume cross section and stable wind field are not met, the concentration data is not directly added to the total emissions of the project.

[0009] Furthermore, in step S4, when calculating emissions from the second type of linear work zone, carbon dioxide or methane concentrations are mapped to RTK trajectory points to form trajectory, concentration, and operating condition coupled data, which are used to identify hot zones in the mileage section; the total emissions from the linear work zone are based on the activity intensity of energy consumption as the main data, and the concentration data is used for spatial allocation and anomaly identification.

[0010] Furthermore, in step S4, during the emission accounting of the third category of materials in the materialization stage, the material usage is cross-verified based on the design quantity, purchase quantity, warehousing quantity, and outbound quantity, and the emission factors are matched according to the priority of supplier verification factors, regional industry factors, or default factors.

[0011] Furthermore, in step S5, the emissions are merged in layers: direct emissions are calculated based on fuel consumption and emission factors; emissions from purchased energy are calculated based on purchased electricity and regional emission factors; and emissions from the material materialization stage are calculated based on material usage and matching emission factors. Double-counting verification includes: when fuel consumption is already included in the direct emissions ledger, the ground concentration inversion results of the same combustion process are no longer superimposed into the total; when electricity metering is already included in the purchased energy ledger, the converted results of electrical equipment operation are no longer double-counted; and when the material emission factors already include the transportation boundary, transportation emissions are no longer double-counted.

[0012] Furthermore, in step S5, the quality level is determined by comprehensively scoring based on energy metering integrity rate, sensor effectiveness rate, time synchronization compliance rate, RTK positioning quality, material factor matching level, and risk of repeated calculations, and the carbon emission results are divided into four levels: A, B, C, and D. The four levels of carbon emission results correspond to formal accounting, report use, trend analysis, and data gap, respectively.

[0013] According to another aspect of the present invention, a ground-based carbon emission monitoring system for transportation infrastructure based on multi-source fusion is also proposed, applied to the above-mentioned ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion, comprising: The data acquisition layer is used to connect to energy metering equipment, CEMS or portable detection equipment, carbon dioxide or methane sensors, RTK positioning equipment, equipment operating condition interface, construction log interface, weighbridge or RFID interface and material ledger interface. The edge layer is used to perform time synchronization, protocol conversion, data caching, and resume interrupted downloads. The fusion layer is used to perform unified data dictionary management, fixed node facility monitoring fusion, mobile linear operation trajectory fusion, and material ledger cleaning. The accounting layer is used to perform hierarchical accounting, duplicate calculation verification, and quality level determination, and to establish a traceable calculation link, linking and storing raw data, processing rules, and accounting results. The application layer is used to output reports, heat maps, anomaly warnings, and emission reduction recommendations; The data acquisition layer, edge layer, fusion layer, accounting layer, and application layer are connected sequentially.

[0014] Furthermore, the fusion layer and accounting layer map data from different sources to a unified primary key system of project, section, facility, equipment, process, time and space units. The facility primary key is used to associate at least one of the following: mixing plant, prefabrication yard, service area, toll station, port terminal, lock, pumping station, tunnel electromechanical room, construction camp, road work zone, bridge work zone, tunnel work zone and waterway improvement line. The space unit primary key is used to associate node facilities, mileage station, construction grid, site boundary, upwind point, downwind point and hot spot.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: Firstly, this invention constructs a unified monitoring and hierarchical accounting framework covering nodal facilities, linear work zones, and material materialization stages. By establishing a multi-dimensional primary key system of projects, sections, facilities, equipment, processes, time, and space units, it enables multi-source data such as fuel flow, electricity metering, stationary source detection, ground concentration, RTK trajectory, equipment operating conditions, construction logs, and material ledgers to be linked and traced in the same calculation link. It can not only output the total project amount, but also finely decompose the emissions to specific facilities, equipment, processes, mileage sections, and material categories, enabling managers to accurately locate high-emission links and provide data support for formulating targeted emission reduction measures. Secondly, the technical positioning of ground-based CO2 / CH4 concentration data was clarified. This means that the concentration data is used for background subtraction, source identification, thermal zone identification, spatial allocation, anomaly warning, and model calibration, rather than being directly superimposed into the total project data when boundary conditions are incomplete. This effectively avoids the problem of unstable concentration inversion results in low-intensity, dispersed, and mobile scenarios of transportation infrastructure. At the same time, through the independent accounting and double-count verification mechanism of three-layer ledgers for direct emissions, purchased energy emissions, and material materialization stage emissions, the risk of boundary overlap and double counting is controlled from the source. This solves the problem of confusing the statistics of carbon emissions hidden in materials with on-site energy consumption emissions, and improves the consistency and auditability of the accounting results. Thirdly, a data quality level assessment mechanism is established, incorporating energy metering integrity rate, sensor effectiveness, time synchronization compliance rate, RTK positioning quality, material factor matching level, and double-counting risk into a unified quality score. Based on the score, the results are divided into four levels: A, B, C, and D, corresponding to different uses such as formal accounting, reporting, trend analysis, and data gap explanation, respectively. This prevents data of different quality levels from being used indiscriminately in formal accounting, significantly improving the credibility and audit compliance of carbon emission monitoring results. Furthermore, the system of this invention can be integrated and deployed based on existing fuel flow meters, electricity meters, CEMS, portable detectors, sensors, RTK, and material management systems, demonstrating high engineering feasibility and promotional value. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the ground-based multi-source fusion monitoring method for carbon emissions from transportation infrastructure as described in an embodiment of the present invention; Figure 2 This is an architecture diagram of the platform monitoring system for carbon emissions from transportation infrastructure as described in an embodiment of the present invention; Figure 3 This is a schematic diagram of the three types of emission stratification ledgers and data boundaries as described in the embodiments of the present invention; Figure 4 This is a schematic diagram of the ground monitoring deployment of the node-type facility according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the vehicle-mounted RTK-CO2 / CH4 continuous monitoring of linear infrastructure according to an embodiment of the present invention; Figure 6This is a flowchart illustrating the multi-source data time synchronization, spatial registration, and quality marking process described in this embodiment of the invention. Figure 7 This is a flowchart of the material physicalization stage ledger accounting and factor matching process as described in the embodiments of the present invention; Figure 8 This is a schematic diagram of carbon emission accounting, spatial attribution, and quality classification output as described in the embodiments of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0019] The specific embodiments of the present invention will be described below.

[0020] Existing carbon emission accounting methods for transportation infrastructure often rely on manual ledgers and default emission factors, resulting only in project-level totals and failing to identify the emission contributions of specific facilities, processes, mileage sections, or material categories. Furthermore, they struggle to dynamically verify abnormal operating conditions, non-project energy consumption, duplicate entries, and data gaps. This invention addresses these issues by establishing a unified, multi-source data fusion and hierarchical accounting mechanism. This mechanism enables collaborative monitoring and precise attribution of emissions from transportation infrastructure nodes, linear work zones, and material physicochemical stages. Combined with concentration data-assisted identification and quality grading control, it significantly improves the traceability, accuracy, and audit compliance of the accounting results.

[0021] Example 1 like Figure 1 As shown, this invention proposes a ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion. By establishing a unified fusion and hierarchical accounting mechanism for multi-source data, it achieves coordinated monitoring and accurate attribution of emissions from transportation infrastructure nodes, linear work zones, and material physicochemical stages. Combined with concentration data to assist in identification and quality-level control, it significantly improves the traceability, accuracy, and audit compliance of the accounting results, including: Step S1: Establish a monitoring object list for transportation infrastructure projects. The monitoring object list includes the key system of projects, sections, facilities, equipment, energy or materials, processes, time and space units, and determines the carbon emission monitoring boundary based on the task boundary of the construction period or operation and maintenance period.

[0022] Specifically, the first step is to establish a comprehensive list of monitoring objects covering all elements of the project. This list is not a single equipment list, but rather adopts a primary key system based on project, contract section, facility, equipment, energy or material, process, time, and spatial units. For projects in the construction phase, the project primary key is associated with the construction unit, contractor, contract section, and sub-item works; for projects in the operation and maintenance phase, the project primary key is associated with operation and maintenance tasks, maintenance tasks, and inspection routes. A primary key system is set up to distinguish between fixed-node facilities and mobile linear work zones. Fixed-node facilities include, but are not limited to, mixing plants, prefabrication yards, construction camps, service areas, toll stations, port terminals, locks, pumping stations, and tunnel electromechanical rooms; mobile linear work zones include road construction zones, bridge construction zones, tunnel work zones, waterway improvement lines, and maintenance work lines. Spatial units are expressed using a multi-scale approach, including stations, work points, construction grids, mileage markers, and hotspot areas. Through this primary key system, the system can uniformly associate fuel flow, meter readings, CEMS results, sensor concentrations, RTK trajectories, equipment operating conditions, construction logs, and material usage within the same time slice.

[0023] After determining the list of monitoring targets, carbon emission monitoring boundaries are established based on the task boundaries during the construction or operation and maintenance phases, including time, space, and organizational boundaries. For example, fuel consumption by construction machinery engaged in non-project operations outside the project fence is not included in the project's accounting records; electricity consumption for daily life in service areas is distinguished from electricity consumption for project operation and maintenance through sub-meters or reasonable allocation rules. By establishing a primary key system covering projects, sections, facilities, equipment, energy or materials, processes, time, and space units, a unified list of objects for carbon emission monitoring of transportation infrastructure is constructed. This list is not a single equipment list, but a multi-dimensional, interconnected primary key framework, enabling subsequent data collection such as fuel flow, electricity metering, sensor concentration, RTK trajectory, equipment operating conditions, and material ledgers to be linked and traced within the same calculation chain. This solves the problem of multi-source data being unable to be integrated due to inconsistent coding, inconsistent spatial benchmarks, and mismatched time scales from the source of data governance, providing a unified data association basis for subsequent steps, ensuring that emission accounting results can be decomposed to specific facilities, equipment, processes, and mileage segments, effectively overcoming the shortcomings of existing methods that can only output the total project amount but cannot identify the emission sources.

[0024] Step S2: Establish a tiered ledger based on direct emissions, purchased energy emissions, and material physicochemical emissions. Within the carbon emission monitoring boundary, collect ground-based multi-source monitoring data according to the primary key system. This ground-based multi-source monitoring data should include at least energy metering data, stationary source exhaust gas detection data, carbon dioxide or methane sensor network data, mobile monitoring trajectory data, equipment operating data, construction log data, and material ledger data. The tiered ledger includes a direct emissions ledger, a purchased energy ledger, and a material physicochemical ledger. The direct emissions ledger includes converted emissions from fuel combustion and stationary source exhaust gas within the project boundary; the purchased energy ledger includes indirect emissions from purchased electricity and heat; and the material physicochemical ledger includes implicit carbon emissions from bulk building materials from production and transportation to the project's usage boundary.

[0025] Continue to refer to Figure 3 The architecture diagram of the carbon emission monitoring system for transportation infrastructure shows that the carbon emission boundary of the project is determined, and a hierarchical ledger is established according to direct emissions, emissions from purchased energy, and emissions from the material physicochemical stage. The direct emissions ledger includes converted emissions from fuel oil, natural gas, construction machinery, and stationary source exhaust within the project boundary, including emission sources such as excavators, bulldozers, pavers, road rollers, cranes, generators, thermal oil heaters, mixing plant combustion equipment, and transport vehicles. The purchased energy ledger includes indirect emissions from purchased electricity, steam, heat, and cooling, including electricity used in service areas, toll stations, tunnel ventilation and lighting, and pump station operation. The material physicochemical ledger includes the implicit carbon emissions from steel, cement, asphalt, concrete, sand, gravel, precast components, and geosynthetic materials from production and transportation to the project's usage boundary. Finally, unified accounting and output are performed, paying attention to hierarchical summarization during output, avoiding double counting, and differentiating quality levels.

[0026] refer to Figure 6The multi-source data input section collects ground-based multi-source monitoring data, including energy metering data such as fuel flow rate, fuel tank level, fuel density, fuel type, natural gas consumption, electricity sub-meter readings, and transformer-side metering; stationary source data including carbon dioxide concentration, methane concentration, oxygen concentration, flue gas flow rate, flue gas temperature, pressure, and humidity collected by CEMS, as well as portable standard method detection results; sensor network data including concentration data from upwind background points, downwind monitoring points, on-site hot zones, and boundary points, as well as meteorological data such as wind speed, wind direction, temperature, humidity, and air pressure; mobile positioning monitoring trajectory data including RTK coordinates, speed, mileage marker, and positioning quality; equipment operating status including CAN or OBD operating data, equipment load, and start / stop status; construction log data recording work content, work period, and work team; and material ledger data including material name, specifications, quantity, supplier, batch, weighbridge records, RFID records, purchase records, transportation distance, inventory, and emission factor version. The collection frequency of multi-source data is set according to the data type. RTK trajectory data is collected at 5Hz to 10Hz, carbon dioxide concentration is collected at 1Hz or 1 minute average, and power and material ledgers are collected by shift, day, month or batch.

[0027] The project employs a tiered ledger system and collects multi-source ground-based monitoring data covering energy metering, stationary source exhaust gas detection, sensor networking, mobile monitoring trajectories, equipment operating conditions, construction logs, and material ledgers. The boundaries of the three emission categories are clearly distinguished at the project initiation phase, preventing boundary confusion in subsequent accounting from the outset. Simultaneously, a multi-type, multi-frequency data collection strategy ensures the complete acquisition of essential master data such as fuel oil, electricity, and materials, as well as auxiliary identification data such as concentration and trajectory. This establishes a clear tiered accounting framework, providing a structured data foundation for subsequent classification accounting and double-counting verification, enabling emissions from different sources and of different natures to be properly located within the same project boundary.

[0028] Continue to refer to Figure 6 The flowchart for multi-source data time synchronization, spatial registration, and quality labeling is as follows: Step S3: Based on the primary key system, perform unified data dictionary encoding, time synchronization, spatial registration, and data quality control on ground multi-source monitoring data, so that all data are mapped to a unified time and space unit system within the monitoring boundary. Specifically, time synchronization uses a unified time grid as a reference to align and resample data with different sampling frequencies; spatial registration includes matching fixed monitoring data to node facilities and monitoring points, matching mobile monitoring trajectory data to mileage markers and construction grids, and matching material ledger data to projects, sections, and processes; data quality control includes anomaly removal, missing data completion, and traceable quality labeling.

[0029] First, a unified data dictionary is established, standardizing equipment IDs, material codes, process codes, time formats, coordinate systems, units, emission factors, and quality indicators. After data from different sources enters the system, the edge layer or cloud platform checks field integrity, measurement range, unit consistency, and timestamp validity.

[0030] For data with different sampling frequencies, the system uses a unified time grid t. k Resampling is performed based on the baseline. The resampling formula is as follows: ; In the formula, This indicates that the nth type of data source is in the unified time grid t. k Resampling results; Indicates the local sampling time of the nth type of data source. The original observations at the location; R n Represents the resampling function for the nearest neighbor, linear interpolation, preserve sampling, or statistical aggregation corresponding to the nth type of data source; t k This represents the k-th sampling time in the unified time grid.

[0031] Spatial registration includes registration of fixed monitoring data, registration of mobile monitoring trajectories, and registration of material ledgers. Fixed monitoring data is matched to node facilities, background points, downwind points, and hotspot points. For example, upwind points, downwind points, hotspot points, and boundary points are associated with site boundaries, prevailing wind directions, and emission source locations. Mobile monitoring trajectory data is matched to mileage markers, construction grids, and work processes, mapping RTK points to corresponding work sections. Material ledger data is matched to projects, sections, work processes, material categories, and statistical periods, associating material usage with specific accounting units.

[0032] Data quality control includes physical threshold testing, dynamic threshold testing, temporal continuity testing, spatial jump testing, operating condition consistency testing, and ledger cross-checking. Negative fuel consumption, impossible speeds, concentrations exceeding the measurement range, timestamp reversal, RTK quality failure, repeated weighing of materials, and significant discrepancies between power consumption and equipment power are all marked as abnormal. For abnormal data and data that has been supplemented, the system performs traceable quality marking.

[0033] The system performs unified data dictionary encoding, time synchronization, spatial registration, and data quality control on the collected multi-source data. It aligns data of different frequencies through unified time grid resampling, maps data to unified spatial units through fixed-point registration, movement trajectory registration, and material ledger registration, and marks abnormal data using physical thresholds, dynamic thresholds, temporal continuity, and cross-validation. This solves the key technical challenge of merging multi-source heterogeneous data due to differences in sampling frequency, coordinate systems, units, and quality, ensuring that the data used in subsequent accounting is comparable in time, spatially locatable, and traceable in quality, effectively improving the reliability and verifiability of the accounting results.

[0034] Step S4: Under the unified time-space unit system, complete emission accounting for three types of scenarios: the first type is emission accounting for nodal facilities with relatively fixed locations; the second type is emission accounting for linear work zones that move continuously along transportation routes; and the third type is emission accounting for the materialization stage of building materials. Specifically, for the first type of nodal facility emission accounting, upwind background points and downwind monitoring points are set according to the prevailing wind direction, and exhaust data from fuel oil, natural gas, electricity, and stationary sources are accessed. When stationary sources have organized exhaust conditions, carbon dioxide concentration and flue gas flow rate are obtained using CEMS or standard methods, and emissions are calculated using the mass flow method. The concentration difference between upwind and downwind is used for source identification and hot zone identification. When the complete plume cross section and stable wind field conditions are not met, the concentration data is not directly added to the total project emissions. For the second type of linear work zone emission accounting, carbon dioxide or methane concentration is mapped to RTK trajectory points to form trajectory, concentration, and operating condition coupled data, which is used to identify hot zones in mileage sections. The total emissions of the linear work zone are based on the activity intensity of energy consumption as the main data, and the concentration data is used for spatial allocation and anomaly identification. When calculating emissions for Category 3 materials during the physicalization stage, the material usage is cross-verified based on the design quantity, purchase quantity, warehousing quantity, and outbound quantity, and emission factors are matched according to the priority of supplier verification factors, regional industry factors, or default factors.

[0035] Among them, reference Figure 4The schematic diagram of the ground monitoring layout for nodal facilities shows that when calculating emissions for fixed nodal facilities, upwind background points and downwind monitoring points are set according to the prevailing wind direction. Energy metering and stationary source exhaust data are connected. When the stationary source has organized exhaust conditions, CEMS or standard methods are used to obtain carbon dioxide concentration and flue gas flow rate, and emissions are calculated using the mass flow method. The upwind and downwind concentration difference is used to identify hot zones within the site. When the complete plume cross-section and stable wind field conditions are not met, the concentration data is not directly added to the total project emissions. When monitoring mobile linear work zones, carbon dioxide or methane concentrations are mapped to RTK trajectory points to form trajectory, concentration, and operating condition coupled data, which is used to identify hot zones along the mileage section. The total emissions of linear work zones are based primarily on the activity intensity of energy consumption, with concentration data used for spatial allocation and anomaly identification. For the implicit carbon emission accounting in the material physicochemical stage, the material usage is cross-verified based on design quantity, procurement quantity, warehousing quantity, and outgoing quantity, and emission factors are matched according to the priority of supplier verification factors, regional industry factors, or default factors.

[0036] Specifically, emissions accounting is performed for nodal facilities. (Continue to refer to...) Figure 4 Upwind background points, downwind monitoring points, and on-site hot zone points were set up according to the prevailing wind direction, and exhaust data from fuel oil, natural gas, electricity, and stationary sources were collected. For stationary sources with exhaust stacks or stable exhaust channels, such as mixing plant combustion equipment, boilers, thermal oil heaters, and temporary generator sets, carbon dioxide concentration and standard state flue gas flow rate were obtained using CEMS or standard methods, and stationary source emissions were calculated using the mass flow method. ; In the formula, E s C represents the carbon dioxide emissions from stationary sources. CO2,dry(t) Q represents the dry basis carbon dioxide volume fraction at time t. N (t) represents the standard dry basis flue gas flow rate at time t, M CO2 V represents the molar mass of carbon dioxide. m Σ represents the molar volume of a gas under standard conditions, Δt represents the time step, and Σ represents the molar volume of the gas under standard conditions. t This indicates the summation over the effective monitoring period, and 1000 represents the unit conversion factor from kg to ton.

[0037] In nodal facilities, on-site and boundary carbon dioxide or methane sensors are used to determine concentration increases, external interference, abnormal operating conditions, and changes in thermal zones. The concentration difference between upwind and downwind is calculated using the following formula: ; In the formula, ΔC g (t) represents the concentration increment of spatial unit g at time t, C down,g(t) represents the concentration of carbon dioxide or methane at the downwind or hot zone monitoring point within space unit g, C up (t) represents the background concentration given by the upwind background point or background model. This concentration difference is used for background subtraction, source identification, thermal zone identification, and external source interference judgment. When the wind direction change exceeds the preset threshold or the upwind background point is affected by the target source, the concentration difference data is automatically downgraded. When the complete plume cross section and stable wind field conditions are not met, the concentration data is not directly added to the total emissions of the project.

[0038] Total direct emissions are calculated using energy activity intensity and stationary source monitoring results as the primary data, and are calculated according to the following formula: ; In the formula, E1 represents direct emissions, m f,q NCV represents the mass of fuel consumed for the qth type. q EF represents the lower heating value of the fuel. C,q OF represents the carbon emission factor per unit calorific value of the q-th fuel. q This indicates the oxidation rate; 44 / 12 represents the molecular weight conversion factor from carbon to carbon dioxide; E s M represents the quantity calculated using the fixed source mass flow method. r GWP represents the mass of the r-th non-carbon dioxide greenhouse gas. r This represents the global warming potential of the r-th greenhouse gas.

[0039] Emissions from purchased energy sources are calculated using data primarily of purchased electricity and purchased heat, according to the following formula: ; In the formula, E2 represents the emissions from purchased energy, and A e,j EF represents the purchased electricity volume of the j-th power consumption unit. e,j Indicates the electricity emission factor for the corresponding region or supplier, A h,h Indicates the amount of purchased steam, heat, or cooling in category h, EF h,h This indicates the corresponding energy emission factor.

[0040] Emissions during the material physicochemical stage are calculated using the material ledger factor method, according to the following formula: ; In the formula, E3 represents the emissions during the material physicochemical stage, and Q... y EF represents the actual consumption of material type y after verification. y K represents the unit emission factor of material type y. y R represents the correction factor for transport boundaries, regional origins, or supplier boundaries. y This indicates the material loss rate.

[0041] Secondly, monitoring and spatial attribution are conducted for the moving linear work zones. For example... Figure 5 The schematic diagram of vehicle-mounted RTK-CO2 / CH4 continuous monitoring for linear infrastructure is shown. For linear objects such as roads, bridges, tunnels, waterway improvement lines, and maintenance work lines, the route is first segmented according to mileage markers, construction procedures, and work teams, forming a 10m to 50m grid in key areas. Carbon dioxide or methane sensors, RTK receivers, and edge acquisition boxes are installed on mobile machinery or survey vehicles, connecting to equipment CAN or OBD operating data. The system maps carbon dioxide or methane concentrations to RTK trajectory points, performing spatiotemporal alignment to form trajectory, concentration, and operating condition coupled data under the condition that the time deviation is no more than 1 second. It records speed, direction, mileage markers, equipment load, fuel consumption, and electricity consumption. Hot zone identification and spatial attribution are used to identify hot zones in mileage segments of roads, bridges, tunnels, waterway improvement lines, or maintenance work lines. The total emissions from linear work zones are mainly based on the activity intensity of fuel, electricity consumption, and equipment operating conditions; concentration data is used for spatial allocation and anomaly identification, not directly for total emission inversion.

[0042] Secondly, implicit carbon emissions should be calculated during the material materialization stage. For example... Figure 7 The flowchart for material materialization stage ledger accounting and factor matching shows that the system first inputs material data, including name, specifications, strength grade, quantity, unit, supplier, batch, purchase contract, weighbridge records, RFID records, transportation distance, inbound quantity, outbound quantity, return quantity, and inventory quantity. Semantic standardization is performed on material names, specifications, and units of measurement, unifying the coding of synonymous material names such as concrete, ready-mixed concrete, and C30 concrete—essentially data cleaning. Anomaly verification is performed, cross-checking design quantities, purchase quantities, inbound quantities, outbound quantities, and inventory quantities to identify rework, losses, duplicate entries, and missing data. Boundary control is also implemented to avoid duplicate calculations with fuel and electricity emissions. Emission factor matching is performed according to the priority of supplier-certified factors, regional / industry factors, or default factors, and factor boundaries and versions are recorded. Next, material materialization accounting is performed, incorporating data corrected for production, transportation, and losses. Finally, a supplementary material ledger is output. The emission calculation for the material materialization stage is as follows: After completing the stratified accounting of the three types of emissions, the system performs spatial and process attribution, allocating emissions to specific spatial and process units according to node facilities, mileage markers, construction grids, equipment, processes, material categories, and operating periods. The attribution calculation formula is as follows: ; In the formula, E o,a,g E represents the attributable emissions of object o in process a and spatial unit g. o (t) represents the amount of emissions to be allocated for object o at time t, I o(t) represents an indicator function indicating whether object o belongs to the project boundary. a (t) represents the indicator function indicating whether process a is in progress at time t, I g ω(t) represents the indicator function indicating whether the location is within a spatial cell at time t. o,a,g (t) represents the allocation weight when multiple devices, processes, or spatial units contribute together.

[0043] Different technical approaches are adopted for three different emission scenarios: For nodal facilities, total emissions are determined using energy metering and stationary source mass flow methods, with upwind and downwind concentration differences used for source identification and hotspot location; for mobile linear operation zones, total emissions are determined using the activity intensity of fuel, electricity consumption, and equipment operating conditions, with RTK trajectory mapping concentration data used for spatial attribution and hotspot identification; and for the material materialization stage, implicit carbon emissions are calculated using ledger verification and factor matching. This approach fully considers the physical characteristics of different emission scenarios in transportation infrastructure: stationary sources are suitable for mass flow methods, mobile sources rely on activity intensity methods, and material emissions cannot be directly measured on-site and require ledger factor methods. Each of the three approaches has its own specific function and does not overlap. Concentration data is used only as an auxiliary tool for identification and spatial allocation, rather than as the basis for total emission calculation, effectively avoiding the problem of unstable results caused by directly superimposing concentration data in existing technologies.

[0044] like Figure 8 The diagram illustrates the carbon emission accounting, spatial attribution, and quality grading output. Step S5 involves hierarchical aggregation and merging of the accounting results across three scenarios, along with duplicate calculation verification. Simultaneously, a comprehensive quality level is determined based on the data quality control quality markers. Specifically, hierarchical merging calculates direct emissions based on fuel consumption and emission factors, purchased energy emissions based on purchased electricity and regional emission factors, and material materialization stage emissions based on material usage and matching emission factors. Duplicate calculation verification includes: when fuel consumption is already included in the direct emission ledger, the ground-level concentration inversion results for the same combustion process are not superimposed into the total; when electricity metering is already included in the purchased energy ledger, the converted results of electrical equipment operation are not repeatedly included; and when material emission factors already include the transportation boundary, transportation emissions are not repeatedly calculated. The quality level is determined by comprehensively scoring based on energy metering integrity rate, sensor effectiveness rate, time synchronization compliance rate, RTK positioning quality, material factor matching level, and risk of double counting. The carbon emission results are divided into four levels: A, B, C, and D. The carbon emission results of the four levels correspond to formal accounting, report use, trend analysis, and data gap, respectively.

[0045] Specifically, the system summarizes E1, E2, and E3 calculated in step S4, and the total project emissions E total =E1+E2+E3.

[0046] The system performs duplicate calculation checks, the core of which is to identify whether different data sources describe the same emission process. When fuel consumption is already included in the direct emission ledger, the ground concentration inversion results of the same combustion process are no longer superimposed into the total; when electricity metering is already included in the purchased energy ledger, the converted results of electrical equipment operation are no longer counted repeatedly; in the material physicochemical ledger, when the material emission factor already includes the transport boundary, transport emissions within the same boundary are no longer calculated repeatedly; when both stationary source CEMS results and fuel accounting methods exist, the CEMS or standard method results that meet the quality requirements are given priority, and the other result is used for consistency verification.

[0047] The quality rating is determined by a comprehensive score based on factors such as energy metering integrity rate, sensor effectiveness, time synchronization compliance rate, RTK positioning quality, material factor matching level, sensor drift degree, and risk of duplicate calculations. ; In the formula, Q score R represents the overall quality score. E R represents the energy metering integrity rate. S R represents the sensor's efficiency. T R represents the time synchronization compliance rate. RTK R represents the RTK positioning quality compliance rate. F P represents the material emission factor matching level score. miss Indicates a penalty for missing items, P drift P represents the sensor drift penalty term. dup This indicates the risk penalty for repeated calculations, where α1 to α5 and β1 to β3 are preset weight parameters.

[0048] The results are categorized into grades A, B, C, and D based on their quality scores. When Q... score A score of ≥85 and with complete key master data is classified as Grade A, used for formal accounting and regulatory reporting; when 70≤Q score A value < 85 is classified as Grade B, suitable for formal reports but requiring the supplementary calculation ratio or factor level to be indicated; when 55 ≤ Q... score A score <70 is classified as Grade C, used for trend analysis and preliminary management; when Q... score A value less than 55 is classified as Level D and is only used for explaining data gaps or tracing anomalies.

[0049] E1, E2, and E3 are stratified, merged, and double-counted to ensure that the same emission process is not counted repeatedly. At the same time, a comprehensive quality score is given based on energy metering integrity rate, sensor effectiveness rate, time synchronization compliance rate, RTK positioning quality, and material factor matching level. The results are divided into four levels: A, B, C, and D, to ensure that data of different quality levels are assigned different usage permissions.

[0050] Step S6: Based on the results of hierarchical aggregation and merging, duplicate calculation verification and quality level determination, output the carbon emission monitoring results at the project level, section level, facility level, mileage section level, process level and material category level. Each level of results is accompanied by a quality level label.

[0051] The system outputs carbon emission monitoring results, including total emissions, spatial emissions, and management results. Total emissions include total emissions during the project period, daily emissions, monthly emissions, emission intensity per unit of work, and contributions from the three ledgers. Spatial results include emission intensity at key facilities, emission intensity along mileage sections, construction grid heatmaps, site heatmaps, and external interference markers. Management results include equipment emissions, process emissions, material category contributions, anomaly lists, data quality levels, data gap explanations, and emission reduction recommendations. Emission reduction recommendations are automatically generated based on the output results. If the standby emission ratio of a certain piece of equipment is too high, it will prompt optimization of scheduling, reduction of idling, or introduction of shore power or mains power substitution. If the emission intensity of a certain mileage section is abnormal, it will prompt checking of construction organization, machinery load, and transportation routes. If the contribution of a certain material category is too high, it will prompt material reduction, substitution of materials, or optimization of supplier factors. If sensor drift or RTK positioning quality is insufficient, it will prompt recalibration or supplementary sampling.

[0052] This embodiment outputs multi-dimensional carbon emission monitoring results at the project level, section level, facility level, mileage section level, process level, and material category level, and automatically generates emission reduction suggestions. It transforms the monitoring and accounting results into a direct basis for engineering management decisions, realizing a closed loop from data collection to management application.

[0053] In this embodiment, a unified monitoring and hierarchical accounting framework covering nodal facilities, linear work zones, and material materialization stages is constructed. By establishing a primary key system of projects, sections, facilities, equipment, processes, time, and spatial units, fuel flow, electricity metering, stationary source detection, ground concentration, RTK trajectory, equipment operating conditions, construction logs, and material ledgers can be linked and traced within the same calculation link. This not only outputs the total project amount but also finely divides it down to specific facilities, equipment, processes, mileage sections, and material categories, effectively solving the problem that existing methods cannot identify emission sources and spatial distribution. The technical positioning of ground carbon dioxide or methane concentration data is clarified: the concentration data is used for background subtraction, source identification, thermal zone identification, spatial allocation, anomaly early warning, and model calibration, rather than being directly superimposed into the total project amount when boundary conditions are insufficient. This avoids the problem of unstable concentration inversion results in low-intensity, dispersed, and mobile scenarios. At the same time, hierarchical ledgers are used to achieve hierarchical accounting and double-count verification of direct emissions, purchased energy emissions, and material materialization stage emissions, controlling the risk of boundary overlap and double-counting from the source, and improving the consistency and auditability of accounting results.

[0054] Example 2 like Figure 2As shown in the architecture diagram of the ground-based carbon emission monitoring system for transportation infrastructure, this invention also proposes a ground-based carbon emission monitoring system for transportation infrastructure based on multi-source fusion. It uses the ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion as described in Example 1. The system includes a data acquisition layer, an edge layer, a fusion layer, an accounting layer, and an application layer. The layers can be deployed either using a centralized cloud platform or a distributed deployment combining on-site edge gateways and a cloud-based analysis platform. Specifically, it includes the following: The data acquisition layer is used to connect to energy metering equipment, CEMS or portable detection equipment, carbon dioxide or methane sensors, RTK positioning equipment, equipment operating condition interfaces, construction log interfaces, weighbridge or RFID interfaces, and material ledger interfaces; the data acquisition layer is also used to connect to various on-site monitoring equipment and data sources. The energy metering unit includes a fuel flow meter, fuel tank level gauge, natural gas meter, electricity meter, sub-meter, and steam or heat meter; the stationary source detection unit includes a CEMS, a portable carbon dioxide or methane detector, standard sampling equipment, and a flue gas flow detection device, used to collect carbon dioxide concentration, methane concentration, oxygen concentration, flue gas flow, flue gas temperature, pressure, and humidity; carbon dioxide or methane sensors are deployed at upwind background points, downwind monitoring points, on-site hot zone points, and boundary monitoring points, and are equipped with meteorological sensors to collect wind speed, wind direction, temperature, humidity, and air pressure; the mobile monitoring unit includes a vehicle-mounted RTK receiver, carbon dioxide or methane sensors, and an edge acquisition box, used to collect coordinates, speed, trajectory, and mileage markers; the equipment operating condition interface is used to access CAN or OBD data to collect equipment load, start / stop status, fuel consumption, and power consumption; the construction log interface is used to enter work content, work period, and work team; the weighbridge or RFID interface and the material ledger interface are used to collect material name, specifications, quantity, supplier, batch, transportation distance, inbound quantity, outbound quantity, and inventory quantity.

[0055] The edge layer is used for time synchronization, protocol conversion, data caching, and breakpoint resumption. It is deployed in stations, vehicles, construction camps, service areas, pumping stations, locks, tunnel electromechanical rooms, or port management stations. The edge layer supports at least one communication method: 4G or 5G, NB-IoT, LoRa, Wi-Fi, wired network, or BeiDou short message service. When communication is interrupted, the edge layer caches raw data and processing markers, including raw values, cleaned values, corrected values, timestamps, and quality markers. After communication is restored, the data is retransmitted in chronological order, maintaining a traceable link between raw data, processing rules, calculation results, and quality levels.

[0056] The fusion layer is used to perform unified data dictionary management, fixed node facility monitoring fusion, mobile linear operation zone trajectory fusion, and material ledger cleaning. This layer maps data from different sources to a unified primary key system of project, section, facility, equipment, process, time, and spatial units. Facility primary keys are used to associate at least one of the following: mixing plant, prefabrication yard, service area, toll station, port terminal, lock, pumping station, tunnel electromechanical room, construction camp, road operation zone, bridge operation zone, tunnel operation zone, and waterway improvement line. Spatial unit primary keys are used to associate node facilities, mileage markers, construction grids, work points, site boundaries, upwind points, downwind points, hot spots, and external interference points. The fusion layer labels the relationships between different data sources for the same emission process as primary accounting data, verification data, auxiliary identification data, or anomaly alert data.

[0057] The accounting layer is used to perform hierarchical accounting, duplicate calculation verification, and quality level determination, and to establish a traceable calculation link, linking and storing raw data, processing rules, and accounting results. The accounting layer is used to perform hierarchical accounting, duplicate calculation verification, and quality level determination for direct emissions, purchased energy emissions, and emissions from the material physicochemical stage. Within the accounting layer, a traceable calculation link is established, linking and storing raw data, cleaned data, resampling data, spatial registration results, energy activity intensity results, stationary source detection results, material factor matching results, emission accounting results, spatial attribution results, duplicate calculation verification results, quality classification results, and output results. It supports querying, verification, and exporting according to project, section, facility, equipment, mileage segment, construction grid, process, material category, and accounting cycle. For the specific calculation methods performed by the accounting layer, please refer to the detailed description of each step in the method embodiment.

[0058] The application layer is used to output reports, heat maps, anomaly warnings, and emission reduction recommendations. It also outputs carbon emission monitoring results and management recommendations, including total project reports, sub-item contribution reports, heat maps, emission intensity per mileage segment, emission intensity of key facilities, equipment efficiency, material contribution, anomaly lists, data quality levels, data gap descriptions, and emission reduction recommendations. The application layer can interface with project management platforms, low-carbon construction platforms, energy management systems, material management systems, or regulatory reporting platforms to provide data support for transportation infrastructure construction, operation and maintenance, auditing, supervision, and low-carbon scheduling.

[0059] The data acquisition layer, edge layer, fusion layer, accounting layer, and application layer are connected sequentially. Among them, the fusion layer and accounting layer map data from different sources to a unified primary key system of project, section, facility, equipment, process, time, and space units. The facility primary key is used to associate at least one of the following: mixing plant, prefabrication yard, service area, toll station, port terminal, lock, pumping station, tunnel electromechanical room, construction camp, road work zone, bridge work zone, tunnel work zone, and waterway improvement line. The space unit primary key is used to associate node facilities, mileage station, construction grid, site boundary, upwind point, downwind point, and hot spot.

[0060] In practical deployments, for project sites lacking continuous CEMS capabilities, the system can employ portable standard detection methods to periodically monitor stationary sources, continuously calculating fuel activity intensity. For mobile machinery unable to read CAN or OBD data, the system estimates based on fuel consumption, equipment rated power, load rate, and operating time, lowering the quality level accordingly. In cases where material supplier factors are missing, the system uses regional industry factors or default factors, noting factor levels and uncertainties in the results. The system can dynamically adjust sensor network deployment strategies based on wind direction and site scale. For scenarios with stable wind direction and complete upwind and downwind coverage, concentration differentials are used to calibrate local emission intensity. For scenarios with unstable wind direction, significant obstruction, or severe external interference, concentration data is only used for anomaly warning and trend analysis.

[0061] This embodiment establishes a data quality grading mechanism, incorporating energy metering integrity rate, sensor effectiveness, time synchronization compliance rate, RTK positioning quality, material factor matching level, sensor drift, and risk of duplicate calculations into a unified quality score. Based on the score, the results are divided into Grade A, Grade B, Grade C, and Grade D, corresponding to different uses such as formal accounting, report use, trend analysis, and data gap explanation, respectively. This avoids the indiscriminate use of data of different quality grades in formal accounting, significantly improving the credibility and engineering applicability of carbon emission monitoring results. At the same time, the system of this invention can be integrated and deployed based on existing fuel flow meters, electricity meters, CEMS, portable detectors, sensors, RTK, and material management systems, and has high engineering feasibility and promotional value.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A ground-based monitoring method for carbon emissions from transportation infrastructure based on multi-source fusion, characterized in that, Includes the following steps: Step S1: Establish a monitoring object list for transportation infrastructure projects. The monitoring object list includes the key system of projects, sections, facilities, equipment, energy or materials, processes, time and space units, and determines the carbon emission monitoring boundary according to the task boundary of the construction period or operation and maintenance period. Step S2: Establish a hierarchical ledger according to direct emissions, purchased energy emissions, and material physicochemical stage emissions, and collect ground-based multi-source monitoring data within the carbon emission monitoring boundary based on the key system. The ground-based multi-source monitoring data includes at least energy metering data, stationary source exhaust detection data, carbon dioxide or methane sensor network data, mobile monitoring trajectory data, equipment operating data, construction log data, and material ledger data. Step S3: Perform unified data dictionary encoding, time synchronization, spatial registration, and data quality control on the ground multi-source monitoring data according to the primary key system, so that all data are mapped to the unified time and space unit system within the monitoring boundary; Step S4: Under the unified time and space unit system, complete the emission accounting for three types of scenarios: the first type is the emission accounting for nodal facilities with relatively fixed locations, the second type is the emission accounting for linear work zones that move continuously along traffic lines, and the third type is the emission accounting for the physicalization stage of building materials. Step S5: Perform hierarchical aggregation and merging and duplicate calculation verification based on the accounting results of the three types of scenarios, and at the same time determine the comprehensive quality level based on the quality marks of the data quality control. Step S6: Based on the results of the hierarchical summary and merging, duplicate calculation verification and quality level determination, output the carbon emission monitoring results at the project level, section level, facility level, mileage section level, process level and material category level, with each level of results accompanied by a quality level label.

2. The ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion according to claim 1, characterized in that, The layered ledger in step S2 includes a direct emissions ledger, a purchased energy ledger, and a material physicalization ledger; the direct emissions ledger includes converted emissions from fuel combustion and stationary source exhaust within the project boundary; the purchased energy ledger includes indirect emissions from purchased electricity and heat; and the material physicalization ledger includes implicit carbon emissions from bulk building materials from production and transportation to the project use boundary.

3. The ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion according to claim 2, characterized in that, In step S3, the time synchronization aligns and resamples data with different sampling frequencies based on a unified time grid; the spatial registration includes matching fixed monitoring data to node facilities and monitoring points, matching mobile monitoring trajectory data to mileage markers and construction grids, and matching material ledger data to projects, sections, and processes; the data quality control includes anomaly removal, missing data completion, and traceable quality marking.

4. The ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion according to claim 3, characterized in that, In step S4, when calculating emissions from the first type of nodal facility, upwind background points and downwind monitoring points are set according to the prevailing wind direction, and exhaust data from fuel oil, natural gas, electricity, and stationary sources are accessed. When the stationary source has organized exhaust conditions, carbon dioxide concentration and flue gas flow rate are obtained using CEMS or standard methods, and emissions are calculated using the mass flow method. The upwind and downwind concentration difference is used for source identification and thermal zone identification. When the complete plume cross section and stable wind field conditions are not met, the concentration data are not directly added to the total project emissions.

5. The ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion according to claim 1, characterized in that, In step S4, when calculating emissions from the second type of linear work zone, carbon dioxide or methane concentrations are mapped to RTK trajectory points to form trajectory, concentration, and operating condition coupled data, which are used to identify hot zones in the mileage section. The total emissions from the linear work zone are based on the activity intensity of energy consumption as the main data, and the concentration data is used for spatial allocation and anomaly identification.

6. The ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion according to claim 1, characterized in that, In step S4, when calculating emissions in the materialization stage of the third type of material, the material usage is cross-verified based on the design quantity, purchase quantity, warehousing quantity, and outbound quantity, and emission factors are matched according to the priority of supplier verification factors, regional industry factors, or default factors.

7. The ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion according to claim 6, characterized in that, In step S5, the stratified merging is performed as follows: direct emissions are calculated based on fuel consumption and emission factors; emissions from purchased energy are calculated based on purchased electricity and regional emission factors; and emissions from the material physicochemical stage are calculated based on material usage and matching emission factors. The double-counting verification includes: when fuel consumption is already included in the direct emissions ledger, the ground-level concentration inversion results of the same combustion process are not superimposed into the total; when electricity metering is already included in the purchased energy ledger, the converted results of electrical equipment operation are not double-counted; and when the material emission factors already include the transportation boundary, transportation emissions are not double-counted.

8. The ground-based carbon emission monitoring method for transportation infrastructure based on multi-source fusion according to claim 6, characterized in that, In step S5, the quality level determination is based on a comprehensive score of energy metering integrity rate, sensor effectiveness rate, time synchronization compliance rate, RTK positioning quality, material factor matching level, and risk of repeated calculations. The carbon emission results are divided into four levels: A, B, C, and D. The carbon emission results of the four levels correspond to formal accounting, report use, trend analysis, and data gap, respectively.

9. A ground-based carbon emission monitoring system for transportation infrastructure based on multi-source fusion, characterized in that, The system, applied to the method of any one of claims 1 to 8, comprises: The data acquisition layer is used to connect to energy metering equipment, CEMS or portable detection equipment, carbon dioxide or methane sensors, RTK positioning equipment, equipment operating condition interface, construction log interface, weighbridge or RFID interface and material ledger interface. The edge layer is used to perform time synchronization, protocol conversion, data caching, and resume interrupted downloads. The fusion layer is used to perform unified data dictionary management, fixed node facility monitoring fusion, mobile linear operation trajectory fusion, and material ledger cleaning. The accounting layer is used to perform hierarchical accounting, duplicate calculation verification, and quality level determination, and to establish a traceable calculation link, linking and storing raw data, processing rules, and accounting results. The application layer is used to output reports, heat maps, anomaly warnings, and emission reduction recommendations; The data acquisition layer, the edge layer, the fusion layer, the accounting layer, and the application layer are connected in sequence.

10. The ground-based carbon emission monitoring system for transportation infrastructure based on multi-source fusion according to claim 9, characterized in that, The fusion layer and the accounting layer map data from different sources to a unified primary key system of project, section, facility, equipment, process, time and space units. The facility primary key is used to associate at least one of the following: mixing plant, prefabrication yard, service area, toll station, port terminal, lock, pumping station, tunnel electromechanical room, construction camp, road work zone, bridge work zone, tunnel work zone and waterway improvement line. The space unit primary key is used to associate node facilities, mileage station, construction grid, site boundary, upwind point, downwind point and hot spot.

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