A method and system for tracing and controlling carbon emissions from highway construction

Abstract

This application discloses a method and system for tracing and controlling carbon emissions from highway construction. The method includes: acquiring video streams of the construction area at the highway construction site; performing target detection and continuous multi-target tracking on construction equipment to obtain equipment type, identity association results, and motion trajectory information; determining the operating status of the construction equipment; calculating additional carbon emissions for target construction equipment in a non-standard operating state, combining equipment type, non-standard operating state category, and duration; associating the additional carbon emissions with equipment identification, time information, and spatial location information to generate carbon emission tracing results; and performing graded control based on the comparison results of the additional carbon emissions and a dynamic threshold, obtaining response results to form a closed-loop control system. This application can identify non-standard operating states of construction equipment, quantify and trace additional carbon emissions, and achieve a closed-loop carbon emission control system.

Description

Technical Field

[0001] This application relates to the technical field of smart construction sites and carbon emission management, and in particular to a method and system for tracing and controlling carbon emissions from highway construction. Background Technology

[0002] With the continued advancement of the "dual carbon" goals, the highway construction sector has placed higher demands on energy conservation, emission reduction, and refined management during the construction process. Highway construction typically involves the coordinated operation of various mechanical equipment such as milling machines, pavers, and road rollers. The large number of machines, rapid changes in operational status, and dispersed work areas result in complex sources, highly dynamic nature, and discrete spatiotemporal distribution of carbon emissions at construction sites. How to achieve real-time perception, accurate analysis, and effective control of carbon emission behavior during equipment operation at construction sites has become an important research direction in smart construction site development and green management.

[0003] In related technologies, carbon emission management in highway construction largely remains at the post-project completion statistical accounting stage. It typically relies on post-construction estimations of total carbon emissions based on fuel consumption, equipment shift records, or total construction volume, making real-time monitoring and dynamic intervention during construction difficult. While some construction sites have introduced IoT sensors, video surveillance systems, or smart site management platforms, existing IoT monitoring methods primarily focus on collecting total equipment energy consumption, fuel consumption, or operating parameters. They struggle to distinguish between necessary emissions from normal construction and additional emissions caused by idling, standby, no-load operation, repetitive work, or violations. Existing video surveillance technology is mostly used for helmet detection, personnel behavior recognition, and area intrusion alarms, with limited integration of equipment type identification, operational status assessment, and carbon emission behavior analysis. This makes it difficult to accurately locate and continuously track carbon emission sources at construction sites. Furthermore, existing technologies often lack tiered early warning and automatic control mechanisms based on identification results, failing to take timely and effective intervention measures after detecting high emissions or non-standard operating behaviors. Therefore, they cannot meet the application needs of real-time carbon emission traceability and closed-loop control at highway construction sites.

[0004] Therefore, in the management of carbon emissions at highway construction sites, how to identify the operating status of construction equipment in real time, how to conduct source analysis of additional carbon emissions caused by non-standard operating behaviors, and how to implement closed-loop control based on the carbon emission source analysis results have become urgent problems to be solved. Summary of the Invention

[0005] This application provides a method and system for carbon emission traceability and control in highway construction, aiming to solve the problems in existing technologies for carbon emission management at highway construction sites, such as how to identify the operating status of construction equipment in real time, how to trace and analyze the additional carbon emissions caused by non-standard operating behaviors, and how to implement closed-loop control based on the carbon emission traceability results.

[0006] Firstly, a method for tracing and controlling carbon emissions from highway construction, the method comprising: Acquire video streams of the construction area at the highway construction site; Target detection and multi-target continuous tracking are performed on the construction equipment in the video stream of the construction area to obtain the equipment type, identity association results and motion trajectory information of each construction equipment. The operating status of each construction equipment is determined based on the video stream of the construction area, and the operating status is either normal operating status or non-standard operating status. For a target construction equipment in a non-standard operation state, a pre-established carbon emission benchmark library for construction equipment is invoked. The additional carbon emissions corresponding to the target construction equipment are calculated by combining the equipment type, non-standard operation state category, and duration of non-standard operation state of the target construction equipment. The additional carbon emissions are associated with the equipment identification, time information, and spatial location information of the target construction equipment to generate carbon emission tracing results; Based on the comparison results between the additional carbon emissions and the dynamic threshold, early warning information or equipment control commands are output according to the preset graded control strategy. The response result corresponding to the early warning information or the equipment control command is obtained and written into the control record to form a closed-loop control for the target construction equipment.

[0007] Optionally, in the above scheme, the step of performing target detection and multi-target continuous tracking on the construction equipment in the video stream of the construction area to obtain the equipment type, identity association results, and motion trajectory information of each construction device includes: Target detection is performed on the construction equipment in the video stream of the construction area to obtain the detection box and equipment type corresponding to each construction equipment. Extract the appearance, motion, and working status characteristics of each construction equipment; Based on the appearance features, motion features, and working status features, construction equipment in different video frames is associated and matched to obtain the identity association results and motion trajectory information of each construction equipment.

[0008] Optionally, in the above scheme, the extraction of the working status features from the appearance features, motion features, and working status features of each construction equipment includes: Extract the color features of the smoke exhaust area of ​​the construction equipment; Extract the first spectral feature characterizing the vibration state of the construction equipment; Extract the second spectral features corresponding to the operating noise of the construction equipment; The color feature, the first spectral feature, and the second spectral feature corresponding to the working noise are fused to obtain the working status feature of the construction equipment.

[0009] Optionally, in the above scheme, determining the operating status of each construction device based on the video stream of the construction area includes: Based on the equipment type, motion trajectory information and working status characteristics of each construction equipment, the status of each construction equipment is identified to determine whether each construction equipment is in normal working state, idling standby state, no-load operation state or illegal operation state. The idling standby state, the no-load operation state, and the violation operation state are identified as non-standard operating states.

[0010] In the above scheme, optionally, the violation operation status includes at least one of the following: If the paver's operating speed exceeds the speed allowed by the corresponding construction process and material splashing is detected, it is determined that the paver is operating at excessive speed. If the roller continues to perform repeated compaction after the target compaction area has reached the preset compaction degree requirement, it is determined to be repeated compaction by the roller. If the milling machine is found to be in a non-operational area or has not performed effective milling operations and the idling time exceeds the preset time, it is determined that the milling machine has exceeded the idling time limit.

[0011] Optionally, in the above scheme, for the target construction equipment in a non-standard operating state, the pre-established carbon emission benchmark library for construction equipment is invoked, and the additional carbon emissions corresponding to the target construction equipment are calculated by combining the equipment type, non-standard operating state category, and duration of the non-standard operating state. This includes: The carbon emission benchmark library for construction equipment stores the unit-time carbon emission parameters, standard operating state benchmark power, and non-standard operating state power parameters for different equipment types under different operating conditions. Obtain the non-standard power of the target construction equipment during the duration of the non-standard operation state; Based on the difference between the non-standard state power and the standard operating state reference power, the duration of the non-standard operating state, and the carbon emission factor of the corresponding energy, the additional carbon emissions corresponding to the target construction equipment are calculated.

[0012] Optionally, in the above scheme, calculating the additional carbon emissions corresponding to the target construction equipment based on the difference between the non-standard state power and the standard operating state reference power, the duration of the non-standard operating state, and the carbon emission factor of the corresponding energy source includes: The difference between the non-standard state power and the standard state reference power at each sampling time during the non-standard operation period of the target construction equipment is accumulated to obtain the accumulation result; The accumulated result is multiplied by the sampling interval and the carbon emission factor of the energy corresponding to the non-standard operating state to obtain the additional carbon emissions corresponding to the target construction equipment; wherein, the sampling interval is the time interval between two adjacent sampling times; the non-standard state power is the real-time power of the target construction equipment at the corresponding sampling time, or, the non-standard state power is the typical power corresponding to the non-standard operating state category of the target construction equipment in the carbon emission benchmark library of the construction equipment.

[0013] Optionally, in the above scheme, the step of associating the additional carbon emissions with the equipment identification, time information, and spatial location information of the target construction equipment to generate carbon emission tracing results includes: Obtain the spatial coordinates and timestamp of the target construction equipment at the construction site; The equipment identifier, timestamp, spatial location coordinates, and additional carbon emissions of the target construction equipment are associated to generate a spatiotemporal carbon emission event corresponding to the target construction equipment. The spatiotemporal carbon emission events are mapped onto a three-dimensional digital construction site model to obtain the carbon emission source tracing results of the target construction equipment.

[0014] Optionally, in the above scheme, the step of outputting early warning information or equipment control commands according to a preset graded control strategy based on the comparison result of the additional carbon emissions and the dynamic threshold includes: The basic threshold is adjusted based on the construction stage and environmental factors to obtain dynamic thresholds corresponding to different control levels. The dynamic thresholds include a first-level threshold, a second-level threshold, and a third-level threshold. When the additional carbon emissions reach the first-level threshold, a notification message is sent to the management terminal; When the additional carbon emissions reach the secondary threshold, a reminder message is sent to the vehicle terminal corresponding to the target construction equipment; When the additional carbon emissions reach the Level 3 threshold and no valid response is received within the preset response time, an equipment control command is sent to the equipment controller corresponding to the target construction equipment.

[0015] Secondly, a carbon emission traceability and control system for highway construction, the system comprising: The video acquisition module is used to acquire video streams of the construction area at the highway construction site; The visual analysis module is used to perform target detection and multi-target continuous tracking on the construction equipment in the video stream of the construction area, obtain the equipment type, identity association results and motion trajectory information of each construction equipment, and determine the operating status of each construction equipment based on the video stream of the construction area, wherein the operating status is a normal operating status or a non-standard operating status. The carbon emission calculation module is used to call a pre-established carbon emission benchmark library for construction equipment that is in a non-standard operation state, and calculate the additional carbon emissions corresponding to the target construction equipment by combining the equipment type, non-standard operation state category and duration of non-standard operation state of the target construction equipment. The traceability module is used to associate the additional carbon emissions with the equipment identification, time information and spatial location information of the target construction equipment to generate carbon emission traceability results; The control module is used to output early warning information or equipment control commands according to a preset hierarchical control strategy based on the comparison results of the additional carbon emissions and the dynamic threshold, and to obtain the corresponding response results. Based on the response results, it generates control records to form a closed-loop control for the target construction equipment.

[0016] Compared with the prior art, this application has at least the following beneficial effects: This application, based on further analysis and research of existing technical problems, recognizes the challenges in carbon emission management at highway construction sites, including how to identify the real-time operating status of construction equipment, how to trace the source of additional carbon emissions caused by non-standard operating behaviors, and how to implement closed-loop control based on carbon emission traceability results. By acquiring video streams of the construction area at the highway construction site and performing target detection and multi-target continuous tracking of construction equipment based on these video streams, the application obtains the equipment type, identity association results, and movement trajectory information of each piece of equipment. This enables continuous differentiation and tracking of each piece of construction equipment during parallel operations with continuously changing positions at the construction site. Furthermore, based on the video streams of the construction area, the application determines the operating status of each piece of construction equipment and classifies it into normal operating status or non-standard operating status, thereby enabling the identification of abnormal or inefficient operations of construction equipment at the construction process level. Then, for target construction equipment in a non-standard operating status, a pre-established carbon emission benchmark library for construction equipment is invoked, and the target construction equipment's equipment type, non-standard operating status category, and duration of non-standard operating status are combined to calculate the target carbon emission benchmark. The system identifies the additional carbon emissions corresponding to construction equipment, thereby transforming non-standard operating behaviors that are difficult to quantify directly into calculable additional carbon emission results. Furthermore, it associates these additional carbon emissions with the equipment identification, time information, and spatial location information of the target construction equipment to generate carbon emission traceability results. This allows for the mapping of additional carbon emissions to specific equipment, time, and location, achieving precise positioning of the carbon emission source of responsibility. Finally, based on the comparison results of the additional carbon emissions with dynamic thresholds, it outputs early warning information or equipment control commands according to a preset hierarchical control strategy, obtains the corresponding response results, and writes the response results into the control record to form a closed-loop control for the target construction equipment. This enables the system not only to detect additional carbon emissions caused by non-standard operations but also to intervene in a timely manner based on the comparison results and retain feedback results. Therefore, this application, through the above technical means, forms a complete processing chain of "construction equipment identification and tracking, operation status determination, additional carbon emission calculation, carbon emission traceability, hierarchical control, and response feedback," which can solve the technical problems in the background art of complex and variable construction equipment operation status, difficulty in real-time identification of additional carbon emission behaviors, and insufficient closed-loop carbon emission control capabilities. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for tracing and controlling carbon emissions from highway construction, provided as an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] In one embodiment, such as Figure 1 As shown, a method for tracing and controlling carbon emissions from highway construction is provided, including the following steps: Acquire video streams of the construction area at the highway construction site; Target detection and multi-target continuous tracking are performed on the construction equipment in the video stream of the construction area to obtain the equipment type, identity association results and motion trajectory information of each construction equipment. The operating status of each construction equipment is determined based on the video stream of the construction area, and the operating status is either normal operating status or non-standard operating status. For a target construction equipment in a non-standard operation state, a pre-established carbon emission benchmark library for construction equipment is invoked. The additional carbon emissions corresponding to the target construction equipment are calculated by combining the equipment type, non-standard operation state category, and duration of non-standard operation state of the target construction equipment. The additional carbon emissions are associated with the equipment identification, time information, and spatial location information of the target construction equipment to generate carbon emission tracing results; Based on the comparison results between the additional carbon emissions and the dynamic threshold, early warning information or equipment control commands are output according to the preset graded control strategy. The response result corresponding to the early warning information or the equipment control command is obtained and written into the control record to form a closed-loop control for the target construction equipment.

[0020] This embodiment provides a method for tracing and controlling carbon emissions from highway construction. The method first acquires video streams of the construction area at the highway construction site. These video streams can be acquired by multiple video acquisition devices deployed at high points and key work areas within the construction site. Preferably, a multi-view video monitoring network is set up at the construction site, with panoramic cameras deployed at high points and fixed-point cameras deployed in key work areas such as milling, paving, and compaction zones, to simultaneously obtain information on the overall equipment distribution and detailed information on local operations. In one specific embodiment, eight monitoring points can be set up at the construction site, including two panoramic cameras and six fixed-point cameras, with two fixed-point cameras set up in each key work area. This deployment method ensures the continuous visibility of construction equipment during construction, providing an image basis for subsequent target detection, continuous tracking, and status recognition.

[0021] After acquiring the video stream of the construction area, target detection and multi-target continuous tracking are performed on the construction equipment in the video stream to obtain the equipment type, identity association results, and motion trajectory information of each piece of construction equipment. Target detection is used to identify construction equipment such as milling machines, pavers, and road rollers from video frames and output the corresponding detection boxes and equipment types; multi-target continuous tracking is used to maintain the identity consistency of the same construction equipment across consecutive video frames and output the corresponding motion trajectory. Preferably, target detection is implemented using a lightweight YOLOv5s model, and multi-target continuous tracking is implemented using an improved DeepSORT algorithm. In one specific implementation, the YOLOv5s model deployed on an edge server can achieve a device recognition accuracy of 92% and an inference speed of 30 frames per second, thereby meeting the real-time detection requirements of the construction site.

[0022] After obtaining equipment type, identity association results, and movement trajectory information, the operating status of each construction device is determined based on the video stream of the construction area. The operating status is either normal operating status or non-standard operating status. Preferably, non-standard operating status includes idling standby status, no-load operation status, and violation operation status. Normal operating status refers to the construction equipment performing effective construction actions according to the construction process within the corresponding work area; idling standby status refers to the construction equipment's power system being on but not performing effective operations; no-load operation status refers to the construction equipment exhibiting movement but without corresponding effective operational output; violation operation status refers to the construction equipment performing operational behaviors that do not match the predetermined construction process, such as paver speeding, roller repeated compaction, and milling machine idling for excessive time.

[0023] For target construction equipment operating in a non-standard operating state, a pre-established carbon emission benchmark library for construction equipment is invoked. This library, combined with the equipment type, non-standard operating state category, and duration of the non-standard operating state, calculates the corresponding additional carbon emissions. The carbon emission benchmark library records unit-time carbon emission parameters, standard operating state benchmark power, and non-standard operating state power parameters for different equipment types under different operating states. Preferably, the additional carbon emissions represent the additional carbon emissions introduced by the non-standard operating state relative to the standard operating state, rather than the total carbon emissions of the target construction equipment over the entire time period.

[0024] After calculating the additional carbon emissions, these emissions are correlated with the equipment identification, time information, and spatial location information of the target construction equipment to generate carbon emission tracing results. The equipment identification can be an equipment number, asset number, or license plate number; the time information can be a timestamp, event start and end time, or shift information; and the spatial location information can be planar coordinates, geographic coordinates, or 3D coordinates mapped to a 3D digital construction site model. Through this correlation, the additional carbon emission event can be located to a specific piece of equipment, at a specific time, and at a specific location.

[0025] Then, based on the comparison between the additional carbon emissions and the dynamic threshold, early warning information or equipment control commands are output according to a preset tiered control strategy. Preferably, the dynamic threshold includes a primary threshold, a secondary threshold, and a tertiary threshold, and is dynamically adjusted according to the construction stage and environmental factors. Early warning information can be sent to a management terminal or vehicle terminal, and equipment control commands can be sent to the equipment controller through an IoT gateway to trigger speed limits, engine shutdown, or other intervention operations.

[0026] Finally, the response results corresponding to the early warning information or equipment control commands are obtained and written into the control record to form a closed-loop control for the target construction equipment. Response results may include operator manual response, equipment status recovery results, execution completion status, response time, and subsequent handling results. The control record may include information such as equipment identification, event type, additional carbon emissions, early warning level, response time, and control results, thereby providing data support for subsequent management statistics, carbon efficiency profiling, and long-term monitoring.

[0027] In one specific implementation, the system detects that milling machine No. 3 is located in a non-operational area and has been idling for 15 minutes. Based on the carbon emission parameter of 8 kg CO2 / h of the equipment under idling conditions, the system calculates that the additional carbon emission corresponding to this event is 2 kg CO2. Subsequently, the system sends a voice reminder to the intelligent terminal in the cab to "turn off the engine in time". If no effective response is received within 30 seconds, the system sends an engine shutdown command to the equipment controller through the IoT gateway and writes the equipment number, operator identification, carbon emission savings, response time and control results into the control record to form a closed-loop control.

[0028] This embodiment acquires video streams from the construction area and sequentially performs equipment detection and continuous tracking, operation status identification, additional carbon emission calculation, spatiotemporal tracing, and threshold-based hierarchical control and response result write-back. This embodiment can establish a real-time identification, quantitative analysis, responsibility positioning, and closed-loop intervention link for non-standard operating behaviors of construction equipment, thereby solving the problems of complex operating status of construction equipment, difficulty in real-time identification of additional carbon emission behaviors, and insufficient closed-loop carbon emission control capabilities in highway construction sites.

[0029] In this embodiment, the step of performing target detection and multi-target continuous tracking on the construction equipment in the video stream of the construction area to obtain the equipment type, identity association results, and motion trajectory information of each construction device includes: Target detection is performed on the construction equipment in the video stream of the construction area to obtain the detection box and equipment type corresponding to each construction equipment. Extract the appearance, motion, and working status characteristics of each construction equipment; Based on the appearance features, motion features, and working status features, construction equipment in different video frames is associated and matched to obtain the identity association results and motion trajectory information of each construction equipment.

[0030] In this embodiment, target detection and multi-target continuous tracking are performed on the construction equipment in the video stream of the construction area to obtain the equipment type, identity association results and motion trajectory information of each construction equipment. The implementation process may include the following.

[0031] First, target detection is performed on the construction equipment in the video stream of the construction area to obtain the corresponding bounding boxes and equipment types for each piece of equipment. The target detection process can be performed on each frame of the video stream or every preset number of video frames. The bounding box represents the spatial extent of the construction equipment in the current video frame, and the equipment type distinguishes equipment categories such as milling machines, pavers, and road rollers. Preferably, the target detection process is implemented using a lightweight target detection network, such as a lightweight YOLOv5s model. The target detection network can be pre-trained using sample images of construction equipment to improve its ability to identify equipment targets in complex backgrounds at the construction site.

[0032] Then, the appearance features, motion features, and working status features of each construction equipment are extracted. Appearance features can include color distribution, texture information, edge contours, local shape descriptions, etc., to characterize the external visual characteristics of the construction equipment; motion features can include center point displacement, velocity, acceleration, direction of motion, trajectory curvature, etc., to characterize the motion patterns of the construction equipment in the time dimension; working status features are used to characterize the status information related to the current working condition of the construction equipment, such as smoke exhaust status, vibration status, and working noise status.

[0033] Subsequently, based on appearance features, motion features, and operational status features, construction equipment in different video frames is associated and matched to obtain the identity association results and motion trajectory information of each piece of construction equipment. The association matching process can employ an improved DeepSORT algorithm, which determines whether a construction equipment belongs to the same entity by comprehensively comparing the appearance similarity, motion consistency, and operational status similarity between candidate targets in the current frame and historical frames. By introducing operational status features, the probability of identity switching between multiple similar construction equipment in scenarios involving proximity, cross-traffic, or partial occlusion can be reduced.

[0034] Preferably, during continuous tracking, a unique target number can be assigned to each construction equipment, and the corresponding target number can be updated in continuous video frames to form a complete time series trajectory, providing a basis for subsequent operation status determination and non-standard operation duration calculation.

[0035] This embodiment performs target detection on construction equipment and combines appearance features, motion features, and working status features for cross-frame association matching. This can obtain stable identity association results and continuous motion trajectory information in complex working environments at construction sites, thereby improving the accuracy of continuous tracking of construction equipment and providing reliable input for subsequent status recognition.

[0036] In this embodiment, the extraction of the working state features from the appearance features, motion features, and working state features of each construction equipment includes: Extract the color features of the smoke exhaust area of ​​the construction equipment; Extract the first spectral feature characterizing the vibration state of the construction equipment; Extract the second spectral features corresponding to the operating noise of the construction equipment; The color feature, the first spectral feature, and the second spectral feature corresponding to the working noise are fused to obtain the working status feature of the construction equipment.

[0037] In this embodiment, extracting the working status features from the appearance features, motion features, and working status features of each construction equipment can include the following implementation process.

[0038] First, extract the color characteristics of the smoke exhaust area of ​​the construction equipment. Preferably, the smoke exhaust area can be determined within the detection frame of the construction equipment first, and then color analysis can be performed on the smoke exhaust area. Color characteristics can include RGB color histogram, color mean, grayscale distribution, proportion of black smoke, or other parameters that can reflect changes in exhaust gas color. By analyzing the color characteristics of the smoke exhaust area, the combustion state and partial load state of the construction equipment can be characterized. For example, a high proportion of dark pixels in the smoke exhaust area can reflect incomplete combustion or abnormally high load conditions; a relatively stable smoke color can reflect a relatively stable operating condition.

[0039] Then, the first spectral feature characterizing the vibration state of the construction equipment is extracted. To avoid insufficient disclosure, the first spectral feature can be obtained in several ways: First, vibration signals can be collected by vibration sensors deployed on the construction equipment, and then the spectral feature can be obtained by frequency domain transformation of the vibration signals; second, a time series signal characterizing the vibration state can be extracted based on the sequence of small displacement changes in a local area of ​​the construction equipment in the video stream of the construction area, and the spectral feature can be obtained by fast Fourier transform; third, vibration-related parameters output from the construction equipment control bus, edge acquisition device, or other external sensing interfaces can also be received and converted to obtain the corresponding spectral feature. The first spectral feature may include the dominant frequency component, frequency band energy distribution, peak frequency, or spectral amplitude, etc.

[0040] Next, the second spectral characteristics corresponding to the operating noise of the construction equipment are extracted. Preferably, the operating noise is collected by the pickup unit built into the video acquisition device or by an independent microphone array at the construction site, and then the second spectral characteristics are obtained through short-time Fourier transform, Mel frequency analysis, or Mel frequency cepstral coefficient extraction. The second spectral characteristics can be used to distinguish the noise frequency band differences of the construction equipment under different operating conditions such as normal load, no-load operation, and abnormal vibration.

[0041] Finally, the color features, the first spectral features, and the second spectral features corresponding to the operating noise are fused to obtain the operating status features of the construction equipment. The fusion process can be achieved through feature concatenation, weighted fusion, attention fusion, or mapping to a unified feature space using a lightweight neural network. The fused operating status features can be used for association matching in continuous tracking and can also serve as input to subsequent status recognition models.

[0042] In one specific implementation, by fusing the smoke color features, the first spectral features, and the second spectral features to form the working status features, the accuracy of equipment status identification can be increased from 76% to 89%.

[0043] This embodiment extracts the color features of the smoke exhaust area, the first spectral features representing the vibration state of the construction equipment, and the second spectral features corresponding to the working noise of the construction equipment, and performs fusion processing to form a more comprehensive working state feature that reflects the differences in the working conditions of the construction equipment, thereby improving the accuracy of non-standard operation state identification and continuous tracking and matching.

[0044] In this embodiment, determining the operating status of each construction device based on the video stream of the construction area includes: Based on the equipment type, motion trajectory information and working status characteristics of each construction equipment, the status of each construction equipment is identified to determine whether each construction equipment is in normal working state, idling standby state, no-load operation state or illegal operation state. The idling standby state, the no-load operation state, and the violation operation state are identified as non-standard operating states.

[0045] In this embodiment, determining the operating status of each construction device based on the video stream of the construction area can include the following implementation process.

[0046] First, based on the equipment type, motion trajectory information, and working status characteristics of each construction device, status identification is performed. Equipment type defines the status identification rules for different construction devices; for example, milling machines, pavers, and road rollers have different motion patterns and operating behaviors under normal working conditions. Motion trajectory information is used to determine whether the equipment is within an effective working area, whether it is undergoing continuous effective movement, and whether it repeatedly passes through the same area. Working status characteristics are used to assist in determining the equipment's current load, combustion, vibration, and noise levels. Status identification can be achieved using a rule engine, a classification model, or a combination of rule and classification models.

[0047] Through the above status identification, it is determined whether each construction equipment is in normal working state, idling standby state, no-load operation state, or non-compliant operation state. Normal working state means that the equipment is performing effective work according to the predetermined process in the corresponding work area; idling standby state means that the equipment power system is turned on but no effective work action is detected within the preset time window; no-load operation state means that the equipment has movement behavior but lacks effective work output that matches the equipment type; non-compliant operation state means that the equipment behavior violates the preset construction process rules.

[0048] Subsequently, idling standby, no-load operation, and violation of operating procedures were identified as non-standard operating conditions. These three conditions were categorized as non-standard operating conditions because they all generate additional carbon emissions compared to normal operating conditions, thus serving as a unified input for carbon emission benchmark database queries and additional carbon emission calculations.

[0049] This embodiment identifies the status of construction equipment based on equipment type, motion trajectory information, and working status characteristics. It treats idling standby status, no-load operation status, and violation operation status as non-standard operation status, which can establish a unified status classification basis for carbon emission management and provide a clear basis for subsequent calculation and hierarchical control of additional carbon emissions.

[0050] In this embodiment, the violation status includes at least one of the following: If the paver's operating speed exceeds the speed allowed by the corresponding construction process and material splashing is detected, it is determined that the paver is operating at excessive speed. If the roller continues to perform repeated compaction after the target compaction area has reached the preset compaction degree requirement, it is determined to be repeated compaction by the roller. If the milling machine is found to be in a non-operational area or has not performed effective milling operations and the idling time exceeds the preset time, it is determined that the milling machine has exceeded the idling time limit.

[0051] In this embodiment, determining the operating status of each construction device based on the video stream of the construction area can include the following implementation process.

[0052] First, based on the equipment type, motion trajectory information, and working status characteristics of each construction device, status identification is performed. Equipment type defines the status identification rules for different construction devices; for example, milling machines, pavers, and road rollers have different motion patterns and operating behaviors under normal working conditions. Motion trajectory information is used to determine whether the equipment is within an effective working area, whether it is undergoing continuous effective movement, and whether it repeatedly passes through the same area. Working status characteristics are used to assist in determining the equipment's current load, combustion, vibration, and noise levels. Status identification can be achieved using a rule engine, a classification model, or a combination of rule and classification models.

[0053] Through the above status identification, it is determined whether each construction equipment is in normal working state, idling standby state, no-load operation state, or non-compliant operation state. Normal working state means that the equipment is performing effective work according to the predetermined process in the corresponding work area; idling standby state means that the equipment power system is turned on but no effective work action is detected within the preset time window; no-load operation state means that the equipment has movement behavior but lacks effective work output that matches the equipment type; non-compliant operation state means that the equipment behavior violates the preset construction process rules.

[0054] Subsequently, idling standby, no-load operation, and violation of operating procedures were identified as non-standard operating conditions. These three conditions were categorized as non-standard operating conditions because they all generate additional carbon emissions compared to normal operating conditions, thus serving as a unified input for carbon emission benchmark database queries and additional carbon emission calculations.

[0055] This embodiment identifies the status of construction equipment based on equipment type, motion trajectory information, and working status characteristics. It treats idling standby status, no-load operation status, and violation operation status as non-standard operation status, which can establish a unified status classification basis for carbon emission management and provide a clear basis for subsequent calculation and hierarchical control of additional carbon emissions.

[0056] In this embodiment, for the target construction equipment in a non-standard operating state, a pre-established carbon emission benchmark library for construction equipment is invoked. Combining the equipment type, non-standard operating state category, and duration of the non-standard operating state of the target construction equipment, the additional carbon emissions corresponding to the target construction equipment are calculated, including: The carbon emission benchmark library for construction equipment stores the unit-time carbon emission parameters, standard operating state benchmark power, and non-standard operating state power parameters for different equipment types under different operating conditions. Obtain the non-standard power of the target construction equipment during the duration of the non-standard operation state; Based on the difference between the non-standard state power and the standard operating state reference power, the duration of the non-standard operating state, and the carbon emission factor of the corresponding energy, the additional carbon emissions corresponding to the target construction equipment are calculated.

[0057] In this embodiment, for a target construction equipment in a non-standard operating state, a pre-established carbon emission benchmark library for construction equipment is invoked. The additional carbon emissions corresponding to the target construction equipment are calculated by combining the equipment type, non-standard operating state category, and duration of non-standard operating state. The implementation process may include the following.

[0058] First, the carbon emission benchmark database for construction equipment stores the unit-time carbon emission parameters, standard operating condition benchmark power, and non-standard operating condition power parameters for different equipment types under different operating conditions. The database can be indexed by equipment type, equipment model, energy type, and operating condition. The unit-time carbon emission parameter represents the carbon emissions per unit time of the equipment under a specific condition; the standard operating condition benchmark power represents the benchmark power level of the equipment under normal operating conditions; and the non-standard operating condition power parameter represents the typical power of the equipment under idling, no-load operation, or improper operation conditions.

[0059] In one specific implementation, the carbon emission parameters of the CAT milling machine under normal operating conditions are 25 kg CO2 / h and under idling conditions are 8 kg CO2 / h; the carbon emission parameters of the VOLVO paver under normal operating conditions are 18 kg CO2 / h and under idling conditions are 6 kg CO2 / h.

[0060] Then, the non-standard power of the target construction equipment during the duration of its non-standard operation is obtained. Non-standard power can be obtained either through real-time data acquisition or by retrieving typical power parameters from a carbon emission benchmark database for construction equipment. Real-time acquisition can be achieved by reading the power value of the construction equipment at the corresponding sampling time through the device bus, IoT data collector, or edge sensing unit; typical power acquisition is used when real-time power acquisition is not available on-site.

[0061] Subsequently, based on the difference between the power output under non-standard conditions and the baseline power output under standard operating conditions, the duration of the non-standard operating conditions, and the carbon emission factor of the corresponding energy source, the additional carbon emissions corresponding to the target construction equipment are calculated. The corresponding energy source can be diesel, electricity, natural gas, etc., and the carbon emission factor can be determined according to national standards, industry standards, or pre-configured parameters for the project.

[0062] This embodiment pre-stores carbon emission parameters and power parameters for different equipment types and operating conditions in a construction equipment carbon emission benchmark library, and calculates additional carbon emissions by combining the target construction equipment's status category and duration. This allows for the establishment of a quantitative mapping relationship between non-standard operating conditions and additional carbon emissions, thereby realizing the conversion from operating condition identification results to carbon emission quantification results.

[0063] In this embodiment, calculating the additional carbon emissions corresponding to the target construction equipment based on the difference between the non-standard state power and the standard operating state reference power, the duration of the non-standard operating state, and the carbon emission factor of the corresponding energy source includes: The difference between the non-standard state power and the standard state reference power at each sampling time during the non-standard operation period of the target construction equipment is accumulated to obtain the accumulation result; The accumulated result is multiplied by the sampling interval and the carbon emission factor of the energy corresponding to the non-standard operating state to obtain the additional carbon emissions corresponding to the target construction equipment; wherein, the sampling interval is the time interval between two adjacent sampling times; the non-standard state power is the real-time power of the target construction equipment at the corresponding sampling time, or, the non-standard state power is the typical power corresponding to the non-standard operating state category of the target construction equipment in the carbon emission benchmark library of the construction equipment.

[0064] In this embodiment, the additional carbon emissions corresponding to the target construction equipment are calculated based on the difference between the non-standard state power and the standard operating state reference power, the duration of the non-standard operating state, and the carbon emission factor of the corresponding energy. This can include the following implementation process.

[0065] First, the differences between the non-standard power and the baseline power of the standard operating state at each sampling time during the non-standard operating period of the target construction equipment are accumulated to obtain the accumulated result. By accumulating the differences at each sampling time, the increased power consumption of the target construction equipment relative to the normal operating state during the entire non-standard operating period can be characterized.

[0066] Then, the accumulated result is multiplied by the sampling interval and the carbon emission factor of the energy corresponding to the non-standard operating state to obtain the additional carbon emissions corresponding to the target construction equipment. The sampling interval is the time interval between two adjacent sampling moments, which can be set to 1 second, 5 seconds, 10 seconds, or other preset time lengths according to the system's real-time requirements. The non-standard state power is the real-time power of the target construction equipment at the corresponding sampling moment, or the typical power corresponding to the non-standard operating state category of the target construction equipment in the construction equipment carbon emission benchmark library.

[0067] In the carbon emission quantification and traceability steps, the formula for calculating the additional carbon emissions Eextra generated by a single piece of equipment due to non-standard operating conditions is as follows: Eextra=∑t∈T[Pabnormal(t) Pbaseline]×Δt×EF; Where T is the set of time periods during which the non-standard operating state lasts; Pabnormal(t) is the real-time power or typical power of the equipment in the non-standard state at time t; Pbaseline is the baseline power of the equipment in the standard operating state; Δt is the data sampling interval; and EF is the carbon emission factor of the energy used by the equipment.

[0068] This embodiment accumulates the difference between the non-standard state power and the baseline power at the sampling time, and calculates the additional carbon emissions by combining the sampling interval and carbon emission factor. This allows for continuous quantification of the carbon emission increment caused by non-standard operating conditions, thereby improving the correspondence between carbon emission tracing results and actual construction behavior.

[0069] In this embodiment, the step of associating the additional carbon emissions with the equipment identification, time information, and spatial location information of the target construction equipment to generate carbon emission tracing results includes: Obtain the spatial coordinates and timestamp of the target construction equipment at the construction site; The equipment identifier, timestamp, spatial location coordinates, and additional carbon emissions of the target construction equipment are associated to generate a spatiotemporal carbon emission event corresponding to the target construction equipment. The spatiotemporal carbon emission events are mapped onto a three-dimensional digital construction site model to obtain the carbon emission source tracing results of the target construction equipment.

[0070] In this embodiment, the additional carbon emissions are associated with the equipment identification, time information, and spatial location information of the target construction equipment to generate carbon emission tracing results, which may include the following implementation process.

[0071] First, obtain the spatial coordinates and timestamp of the target construction equipment at the construction site. Spatial coordinates can be obtained by fusing video coordinates with multi-view video calibration, planar coordinate mapping, UWB positioning, GNSS positioning, or other positioning methods. Timestamps can be video frame timestamps, event trigger timestamps, or system recording timestamps. Spatial coordinates can be two-dimensional planar construction coordinates or three-dimensional coordinates mapped to a three-dimensional digital construction site model.

[0072] Then, the equipment identifier, timestamp, spatial coordinates, and additional carbon emissions of the target construction equipment are associated to generate a spatiotemporal carbon emission event corresponding to the target construction equipment. The spatiotemporal carbon emission event may include fields such as event number, equipment identifier, status category, start time, end time, spatial location, additional carbon emissions, and subsequent control results.

[0073] Subsequently, spatiotemporal carbon emission events are mapped onto a 3D digital construction site model to obtain carbon emission source tracing results for the target construction equipment. The 3D digital construction site model can be a BIM model, GIS model, or a 3D visualization model overlaid with construction site elevation information. By displaying spatiotemporal carbon emission events in the 3D digital construction site model, equipment-level and regional-level carbon emission hotspot distributions can be formed, enabling spatiotemporal location of carbon emission responsibility sources.

[0074] In one specific implementation, the system associates the additional carbon emission event of 2kg CO2 generated by milling machine No. 3 idling for 15 minutes with the equipment number, the time of occurrence, and the spatial coordinates of the non-working area, and maps it into the three-dimensional digital construction site model, displaying it as a hotspot in the corresponding area.

[0075] This embodiment links additional carbon emissions with the equipment identifier, timestamp, and spatial coordinates of the target construction equipment and maps them to a three-dimensional digital construction site model. This allows the carbon emission responsibility source to be located to a specific equipment, time, and area, thereby improving the visualization of carbon emission traceability results and the ability to pinpoint responsibility.

[0076] In this embodiment, the step of outputting early warning information or equipment control commands according to a preset graded control strategy based on the comparison result of the additional carbon emissions and the dynamic threshold includes: The basic threshold is adjusted based on the construction stage and environmental factors to obtain dynamic thresholds corresponding to different control levels. The dynamic thresholds include a first-level threshold, a second-level threshold, and a third-level threshold. When the additional carbon emissions reach the first-level threshold, a notification message is sent to the management terminal; When the additional carbon emissions reach the secondary threshold, a reminder message is sent to the vehicle terminal corresponding to the target construction equipment; When the additional carbon emissions reach the Level 3 threshold and no valid response is received within the preset response time, an equipment control command is sent to the equipment controller corresponding to the target construction equipment.

[0077] In this embodiment, based on the comparison between additional carbon emissions and a dynamic threshold, early warning information or equipment control commands are output according to a preset graded control strategy, which may include the following implementation process: In the carbon emission quantification and source tracing process, the formula for calculating the additional carbon emissions (Eextra) of a single piece of equipment due to non-standard operating conditions is as follows: Eextra=∑t∈T[Pabnormal(t) Pbaseline]×Δt×EF; Where T is the set of time periods during which the non-standard operating state lasts; Pabnormal(t) is the real-time power or typical power of the equipment in the non-standard state at time t; Pbaseline is the baseline power of the equipment in the standard operating state; Δt is the data sampling interval; and EF is the carbon emission factor of the energy used by the equipment.

[0078] The dynamic threshold Th is a function of the additional carbon emissions Eextra and is related to the construction phase S and environmental factors E, specifically: Th=f(S,E). Th0, where Th0 is the basic threshold, and f(S,E) is an adjustment coefficient set according to factors such as peak construction period, night construction, and severe weather, with a value range of 0.5 to 2.0.

[0079] Then, when the additional carbon emissions reach the Level 1 threshold, a notification is sent to the management terminal. The management terminal can be a mobile phone, tablet, dispatch center console, or web management interface. The notification information may include the equipment identifier of the target construction equipment, event category, spatial location, duration, and additional carbon emissions.

[0080] When additional carbon emissions reach the secondary threshold, a reminder message is sent to the vehicle-mounted terminal corresponding to the target construction equipment. The vehicle-mounted terminal can be installed in the driver's cab of the equipment, and the reminder message can take the form of voice prompts, light reminders, text pop-ups, or a combination thereof.

[0081] In one specific implementation, the in-vehicle terminal can broadcast a voice reminder such as "Please turn off the engine in time".

[0082] When additional carbon emissions reach the Level 3 threshold and no valid response is received within the preset response time, an equipment control command is sent to the equipment controller corresponding to the target construction equipment. The equipment controller can communicate with the equipment's engine control unit, vehicle controller, or host computer control system via an IoT gateway. The equipment control command can be a speed limit command, an engine shutdown command, or other control commands used to stop non-standard operating conditions. The preset response time can be set to, for example, 30 seconds or 60 seconds; a valid response can be the operator manually stopping the non-standard operation, the equipment returning to normal operating conditions, or a confirmation response from the vehicle terminal.

[0083] In one specific implementation, for the idling event of milling machine No. 3, the system first sends a level two warning voice prompt "Please turn off the engine in time" to the intelligent terminal in the cab; if no effective response is received within 30 seconds, it is upgraded to a level three warning and a shutdown command is sent to the equipment controller through the Internet of Things gateway.

[0084] This embodiment dynamically generates first-level, second-level, and third-level thresholds based on the construction stage and environmental factors, and corresponds to management terminal notifications, vehicle terminal reminders, and equipment control commands, respectively. This can form a progressive hierarchical control mechanism that matches the intensity of construction site management, thereby enabling timely intervention in additional carbon emissions caused by non-standard operations.

[0085] In one embodiment, a carbon emission traceability and control system for highway construction is provided, comprising the following modules, wherein: The video acquisition module is used to acquire video streams of the construction area at the highway construction site; The visual analysis module is used to perform target detection and multi-target continuous tracking on the construction equipment in the video stream of the construction area, obtain the equipment type, identity association results and motion trajectory information of each construction equipment, and determine the operating status of each construction equipment based on the video stream of the construction area, wherein the operating status is a normal operating status or a non-standard operating status. The carbon emission calculation module is used to call a pre-established carbon emission benchmark library for construction equipment that is in a non-standard operation state, and calculate the additional carbon emissions corresponding to the target construction equipment by combining the equipment type, non-standard operation state category and duration of non-standard operation state of the target construction equipment. The traceability module is used to associate the additional carbon emissions with the equipment identification, time information and spatial location information of the target construction equipment to generate carbon emission traceability results; The control module is used to output early warning information or equipment control commands according to a preset hierarchical control strategy based on the comparison results of the additional carbon emissions and the dynamic threshold, and to obtain the corresponding response results. Based on the response results, it generates control records to form a closed-loop control for the target construction equipment.

[0086] The specific implementation details of each module can be found in the above description of the limitations on the carbon emission traceability and control methods for highway construction, and will not be repeated here.

[0087] In this embodiment, a carbon emission source tracing and control system for highway construction includes a video acquisition module, a visual analysis module, a carbon emission calculation module, a source tracing module, and a control module.

[0088] The video acquisition module is used to acquire video streams of the construction area at the highway construction site. Preferably, the video acquisition module consists of high-definition network cameras deployed at high points and key work areas of the construction site, as well as edge access devices. If necessary, a sound pickup unit can also be integrated to simultaneously acquire noise information from the construction equipment.

[0089] The visual analysis module is used to perform target detection and continuous multi-target tracking of construction equipment in the video stream of the construction area. It obtains the equipment type, identity association results, and motion trajectory information of each piece of equipment, and determines the operating status of each piece of equipment based on the video stream, which can be either normal operating status or non-standard operating status. Preferably, the visual analysis module may include an equipment detection submodule, a trajectory tracking submodule, an operating status feature extraction submodule, and a status recognition submodule. The equipment detection submodule can use a lightweight YOLOv5s model, and the trajectory tracking submodule can use an improved DeepSORT algorithm.

[0090] The carbon emission calculation module is used to calculate the additional carbon emissions of target construction equipment in non-standard operating conditions by calling a pre-established carbon emission benchmark library for construction equipment, and combining the equipment type, non-standard operating condition category, and duration of the non-standard operating condition. The carbon emission calculation module can have a built-in equipment carbon emission intensity database to store carbon emission parameters per unit time, benchmark power for standard operating conditions, and power parameters for non-standard operating conditions.

[0091] The source tracing module is used to associate additional carbon emissions with the equipment identification, time information, and spatial location information of the target construction equipment to generate carbon emission source tracing results. Preferably, the source tracing module includes a spatiotemporal event generation unit and a 3D positioning engine, used to map spatiotemporal carbon emission events to a 3D digital construction site model.

[0092] The control module is used to output early warning information or equipment control commands according to a preset hierarchical control strategy based on the comparison results of additional carbon emissions and dynamic thresholds, and to obtain the corresponding response results. Based on the response results, it generates control records to form a closed-loop control system for the target construction equipment. The control module supports multi-level early warning strategies and IoT device control interfaces, and can work collaboratively with mobile and web-based management platforms.

[0093] In one specific implementation, the system can also generate carbon efficiency profiles of equipment and operators based on control records to support long-term low-carbon construction management and supervision of high-frequency non-standard operating behaviors.

[0094] This embodiment divides video acquisition, visual analysis, carbon emission calculation, source tracing, and control into mutually cooperating functional modules, forming a system implementation scheme with a clear structure and well-defined interfaces, thereby supporting real-time perception, spatiotemporal source tracing, and closed-loop control of carbon emission behavior at highway construction sites.

[0095] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for tracing and controlling carbon emissions from highway construction, characterized in that, The method includes: Acquire video streams of the construction area at the highway construction site; Target detection and multi-target continuous tracking are performed on the construction equipment in the video stream of the construction area to obtain the equipment type, identity association results and motion trajectory information of each construction equipment. The operating status of each construction equipment is determined based on the video stream of the construction area, and the operating status is either normal operating status or non-standard operating status. For a target construction equipment in a non-standard operation state, a pre-established carbon emission benchmark library for construction equipment is invoked. The additional carbon emissions corresponding to the target construction equipment are calculated by combining the equipment type, non-standard operation state category, and duration of non-standard operation state of the target construction equipment. The additional carbon emissions are associated with the equipment identification, time information, and spatial location information of the target construction equipment to generate carbon emission tracing results; Based on the comparison results between the additional carbon emissions and the dynamic threshold, early warning information or equipment control commands are output according to the preset graded control strategy. The response result corresponding to the early warning information or the equipment control command is obtained and written into the control record to form a closed-loop control for the target construction equipment.

2. The method according to claim 1, characterized in that, The process involves target detection and multi-target continuous tracking of construction equipment in the video stream of the construction area to obtain the equipment type, identity association results, and motion trajectory information of each piece of construction equipment, including: Target detection is performed on the construction equipment in the video stream of the construction area to obtain the detection box and equipment type corresponding to each construction equipment. Extract the appearance, motion, and working status characteristics of each construction equipment; Based on the appearance features, motion features, and working status features, construction equipment in different video frames is associated and matched to obtain the identity association results and motion trajectory information of each construction equipment.

3. The method according to claim 2, characterized in that, The extraction of working status features from the appearance features, motion features, and working status features of each construction equipment includes: Extract the color features of the smoke exhaust area of ​​the construction equipment; Extract the first spectral feature characterizing the vibration state of the construction equipment; Extract the second spectral features corresponding to the operating noise of the construction equipment; The color feature, the first spectral feature, and the second spectral feature corresponding to the working noise are fused to obtain the working status feature of the construction equipment.

4. The method according to claim 1, characterized in that, Determining the operational status of each construction device based on the video stream of the construction area includes: Based on the equipment type, motion trajectory information and working status characteristics of each construction equipment, the status of each construction equipment is identified to determine whether each construction equipment is in normal working state, idling standby state, no-load operation state or illegal operation state. The idling standby state, the no-load operation state, and the violation operation state are identified as non-standard operating states.

5. The method according to claim 4, characterized in that, The violation status includes at least one of the following: If the paver's operating speed exceeds the permissible speed for the corresponding construction process and material splashing is detected, it is determined that the paver is operating at excessive speed. If the roller continues to perform repeated compaction after the target compaction area has reached the preset compaction degree requirement, it is determined to be repeated compaction by the roller. If the milling machine is found to be in a non-operational area or has not performed effective milling operations and the idling time exceeds the preset time, it is determined that the milling machine has exceeded the idling time limit.

6. The method according to claim 1, characterized in that, For target construction equipment in a non-standard operating state, a pre-established carbon emission benchmark library for construction equipment is invoked. Combining the equipment type, non-standard operating state category, and duration of the non-standard operating state, the additional carbon emissions corresponding to the target construction equipment are calculated, including: The carbon emission benchmark library for construction equipment stores the unit-time carbon emission parameters, standard operating state benchmark power, and non-standard operating state power parameters for different equipment types under different operating conditions. Obtain the non-standard power of the target construction equipment during the duration of the non-standard operation state; Based on the difference between the non-standard state power and the standard operating state reference power, the duration of the non-standard operating state, and the carbon emission factor of the corresponding energy, the additional carbon emissions corresponding to the target construction equipment are calculated.

7. The method according to claim 6, characterized in that, The calculation of the additional carbon emissions corresponding to the target construction equipment based on the difference between the non-standard state power and the standard operating state reference power, the duration of the non-standard operating state, and the carbon emission factor of the corresponding energy source includes: The difference between the non-standard state power and the standard state reference power at each sampling time during the non-standard operation period of the target construction equipment is accumulated to obtain the accumulation result; The accumulated result is multiplied by the sampling interval and the carbon emission factor of the energy corresponding to the non-standard operating state to obtain the additional carbon emissions corresponding to the target construction equipment; wherein, the sampling interval is the time interval between two adjacent sampling times; the non-standard state power is the real-time power of the target construction equipment at the corresponding sampling time, or, the non-standard state power is the typical power corresponding to the non-standard operating state category of the target construction equipment in the carbon emission benchmark library of the construction equipment.

8. The method according to claim 1, characterized in that, The step of associating the additional carbon emissions with the equipment identification, time information, and spatial location information of the target construction equipment to generate carbon emission tracing results includes: Obtain the spatial coordinates and timestamp of the target construction equipment at the construction site; The equipment identifier, timestamp, spatial location coordinates, and additional carbon emissions of the target construction equipment are associated to generate a spatiotemporal carbon emission event corresponding to the target construction equipment. The spatiotemporal carbon emission events are mapped onto a three-dimensional digital construction site model to obtain the carbon emission source tracing results of the target construction equipment.

9. The method according to claim 1, characterized in that, The step of outputting early warning information or equipment control commands according to a preset graded control strategy based on the comparison result of the additional carbon emissions and the dynamic threshold includes: The basic threshold is adjusted based on the construction stage and environmental factors to obtain dynamic thresholds corresponding to different control levels. The dynamic thresholds include a first-level threshold, a second-level threshold, and a third-level threshold. When the additional carbon emissions reach the first-level threshold, a notification message is sent to the management terminal; When the additional carbon emissions reach the secondary threshold, a reminder message is sent to the vehicle terminal corresponding to the target construction equipment; When the additional carbon emissions reach the Level 3 threshold and no valid response is received within the preset response time, an equipment control command is sent to the equipment controller corresponding to the target construction equipment.

10. A carbon emission traceability and control system for highway construction, characterized in that, The system includes: The video acquisition module is used to acquire video streams of the construction area at the highway construction site; The visual analysis module is used to perform target detection and multi-target continuous tracking on the construction equipment in the video stream of the construction area, obtain the equipment type, identity association results and motion trajectory information of each construction equipment, and determine the operating status of each construction equipment based on the video stream of the construction area, wherein the operating status is a normal operating status or a non-standard operating status. The carbon emission calculation module is used to call a pre-established carbon emission benchmark library for construction equipment that is in a non-standard operation state, and calculate the additional carbon emissions corresponding to the target construction equipment by combining the equipment type, non-standard operation state category and duration of non-standard operation state of the target construction equipment. The traceability module is used to associate the additional carbon emissions with the equipment identification, time information and spatial location information of the target construction equipment to generate carbon emission traceability results; The control module is used to output early warning information or equipment control commands according to a preset hierarchical control strategy based on the comparison results of the additional carbon emissions and the dynamic threshold, and to obtain the corresponding response results. Based on the response results, it generates control records to form a closed-loop control for the target construction equipment.

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