A building construction carbon emission real-time inspection and dynamic tracking method and system

Abstract

This invention discloses a method and system for real-time inventory and dynamic tracking of carbon emissions from building construction, relating to the field of digital low-carbon building construction. The system includes a perception layer, a digital twin engine layer, an AI analysis and prediction layer, and a visualization and decision-making layer. The method includes: collecting dynamic on-site data from multiple sources with timestamps, spatial coordinates, and equipment operating conditions; decomposing the construction BIM model into the smallest carbon accounting unit and binding it to a factor library; the AI ​​layer, through three-dimensional cross-matching of time, space, and operating conditions, automatically and accurately assigning each energy consumption data point to the smallest accounting unit, calculating real-time carbon emission data at the second level; and combining advanced simulation prediction, finally visualizing the data through a three-dimensional heat map and generating intelligent control schemes. This invention completely solves the problems of lagging traditional carbon accounting and ambiguous data attribution, achieving a technological leap from "post-event estimation" to "real-time source tracing and proactive prediction and control" of carbon emissions, effectively improving the level of refined carbon management.

Description

Technical Field

[0001] This invention relates to the field of digital low-carbon building construction, specifically to a method and system for real-time monitoring and dynamic tracking of carbon emissions from building construction. Background Technology

[0002] The statements in this section are provided only as background information in relation to this disclosure and may not constitute prior art.

[0003] As a key sector for carbon emissions, the construction industry faces the crucial need for refined carbon management during the construction phase to achieve green and low-carbon development in engineering projects. However, current carbon emission monitoring, accounting, and control systems in the construction field still suffer from numerous technical shortcomings, making it difficult to meet the demands for real-time, precise, and dynamic carbon management. Specifically: Currently, the mainstream method for assessing construction carbon emissions in the industry is the "post-construction quantity estimate method," which relies heavily on statistical data of completed construction work combined with fixed carbon emission factors for post-construction accounting. This model is extremely lagging and cannot keep track of the dynamic carbon emissions at the construction site in real time. Moreover, the traditional model can only obtain the total emission data for the entire area, and cannot accurately identify high-emission construction procedures, specific construction equipment, and spatial emission hotspots, resulting in a serious lack of accuracy in carbon emission source tracing.

[0004] At the on-site, low-level sensing level, the existing monitoring system is extremely rudimentary, relying solely on traditional electricity and fuel meters for overall energy consumption data statistics, resulting in very coarse granularity. This model cannot distinguish the independent energy consumption of different construction sites and different mechanical equipment, leading to problems such as ambiguous energy consumption data attribution and missing detailed data, making it difficult to support refined accounting.

[0005] While BIM models have been widely adopted in most projects, existing BIM models are only at the stage of static construction display and progress simulation. Few technical solutions can deeply integrate the physical BIM model with dynamic sensing data such as real-time energy consumption, equipment operation, and material transportation on the construction site. This seriously restricts project managers from timely intervention and dynamic optimization of high-carbon emission links. Summary of the Invention

[0006] The purpose of this invention is to address the problems in the current construction industry, such as the reliance on ex-post estimation for carbon emission accounting leading to poor timeliness, the extensive monitoring of underlying energy consumption resulting in ambiguous data attribution, and the lack of deep integration between existing BIM models and on-site dynamic monitoring data, resulting in a lack of closed-loop management throughout the entire process. This invention provides a method and system for real-time carbon emission monitoring and dynamic tracking in construction. Based on a multi-source sensing layer that comprehensively collects underlying equipment operating condition data carrying timestamps and spatial coordinates, and combined with a digital twin engine, the construction BIM physical model is refined into the smallest carbon accounting unit. Furthermore, an AI agent is used for three-dimensional cross-matching of time, space, and operating conditions. This enables the automatic and accurate attribution of real-time perceived energy consumption data to corresponding processes and areas, second-level carbon emission monitoring, advance prediction of carbon emission peaks, and automatic generation of targeted low-carbon intelligent control schemes.

[0007] The technical solution of the present invention is as follows: A method for real-time monitoring and dynamic tracking of carbon emissions from building construction is provided, applied to a system for real-time monitoring and dynamic tracking of carbon emissions from building construction. The system includes a multi-source sensing data acquisition layer, a carbon accounting digital twin engine layer, an AI intelligent analysis and prediction layer, and a visualization interaction and intelligent decision-making layer. The method includes the following steps: The construction site dynamic data is collected in full coverage by the multi-source sensing data acquisition layer. The construction site dynamic data includes timestamps, spatial coordinates and equipment operating data, and is connected to the carbon accounting digital twin engine layer in real time through a high-speed communication network. The carbon accounting digital twin engine layer constructs a digital twin of the construction site based on the construction BIM model. The construction BIM model is decomposed into the smallest carbon accounting unit according to the construction process and construction area, and the smallest carbon accounting unit is bound to the carbon emission factors in the built-in standardized carbon emission factor library. The AI ​​intelligent analysis and prediction layer receives the full data stream in real time, aligns the construction progress plan with the timestamp to determine the construction process being executed, matches the corresponding construction area with the spatial coordinates, and accurately determines the smallest carbon accounting unit corresponding to the energy consumption data by combining the equipment operating data, thereby realizing the automatic attribution of real-time data. The AI ​​intelligent analysis and prediction layer calls the carbon emission factor bound to the corresponding minimum carbon accounting unit to calculate the real-time carbon emission data of the corresponding minimum carbon accounting unit. Based on the real-time carbon emission data and construction progress, the AI ​​intelligent analysis and prediction layer performs dynamic simulation to predict the carbon emission distribution trend in the future period. The real-time carbon emission data is overlaid and rendered on the digital twin of the construction site in the form of a three-dimensional heat map through the visualization interaction and intelligent decision-making layer for visualization display, and carbon anomaly alarm and control scheme are executed based on the real-time carbon emission data.

[0008] Furthermore, the multi-source sensing data acquisition layer comprehensively collects dynamic data from the construction site, specifically including: An edge intelligent acquisition terminal is installed on the mobile construction equipment to collect fuel consumption data, power consumption data, start-stop status and running time of the mobile construction equipment in real time via CAN bus; the operating current data of the mobile construction equipment is collected through current transformer, and vibration sensors are used to identify the actual working conditions of the mobile construction equipment to distinguish the no-load, standby or working status of the mobile construction equipment. The three-dimensional spatial coordinates of the mobile construction equipment are collected in real time by configuring UWB positioning tags for the mobile construction equipment. The fuel consumption data, electricity consumption data, actual operating conditions, and three-dimensional spatial coordinates are combined with the corresponding acquisition time to generate dynamic data of the construction site carrying the timestamp.

[0009] Furthermore, the multi-source sensing data acquisition layer comprehensively collects dynamic data from the construction site, which also includes: Smart meters and environmental sensors are deployed in the fixed construction area to collect data on power consumption, ambient temperature and humidity, and work period in the fixed construction area in real time. Equip material transport vehicles with GPS positioning modules and connect them to the electronic waybill management system to automatically obtain data on the vehicle's starting point, destination, actual transport distance, material transport tonnage, transport time, and transport frequency. The sensed data is uploaded in real time to the carbon accounting digital twin engine layer as the full data stream via the 5G communication module.

[0010] Furthermore, the construction BIM model is broken down into the smallest carbon accounting units according to construction procedures and construction areas, including: The construction BIM model is further broken down according to the process division standard of the construction organization design, so that the granularity of the smallest carbon accounting unit is refined to a single floor, a single area and a single process. Each of the minimum carbon accounting units is linked to the corresponding construction period, construction area, and supporting construction equipment information; The standardized carbon emission factor library is established based on national and industry carbon emission accounting standards for construction. The carbon emission factors cover diesel consumption, electricity consumption, building material loss, manual labor, and material transportation. It also supports dynamic updates of corresponding factor parameters based on regional energy consumption standards and differences in construction processes.

[0011] Furthermore, the AI ​​intelligent analysis and prediction layer performs dynamic simulations based on the real-time carbon emission data and construction progress to predict future carbon emission distribution trends, specifically including: The AI ​​intelligent analysis and prediction layer is based on the real-time construction progress at the construction site, historical 7-day carbon emission pattern data, subsequent 24-hour construction plan, and equipment scheduling plan. It performs dynamic simulation in the digital twin of the construction site using a time-series prediction algorithm. Simulate the energy consumption trends and carbon emission distribution of each construction process and region in the next 24 hours, predict the time, region and corresponding process of carbon emission peaks, and identify high carbon emission risk points in advance.

[0012] Furthermore, based on the real-time carbon emission data, a carbon anomaly alarm and generation control scheme is implemented, specifically including: When the real-time carbon emission data of a certain construction area, construction process or single piece of equipment is detected to exceed the preset threshold, an alarm is triggered and the location, value and cause of the exceedance are accurately marked. Based on the on-site construction conditions, targeted carbon emission optimization and adjustment suggestions are generated intelligently. These suggestions include at least peak-shifting power scheduling suggestions, new energy machinery replacement suggestions, and construction procedure optimization suggestions.

[0013] This invention also proposes a real-time monitoring and dynamic tracking system for carbon emissions from building construction, used to implement the method described above. The system includes: The multi-source sensing data acquisition layer is used to collect dynamic data of the construction site, including timestamps, spatial coordinates and equipment operating data, and upload the full data stream in real time through a high-speed communication network. The carbon accounting digital twin engine layer is used to build a digital twin of the construction site based on the construction BIM model. The construction BIM model is broken down into the smallest carbon accounting unit according to the construction process and construction area, and the smallest carbon accounting unit is bound to the carbon emission factors in the built-in standardized carbon emission factor library. The AI-powered intelligent analysis and prediction layer receives real-time access to the full data stream, aligns the construction schedule with the timestamp to determine the ongoing construction process, matches the corresponding construction area with the spatial coordinates, and accurately determines the smallest carbon accounting unit corresponding to the energy consumption data by combining the equipment operating data, thereby achieving automatic data attribution. It is also used to call the corresponding bound carbon emission factor to calculate the real-time carbon emission data of the smallest carbon accounting unit, and to perform dynamic simulation based on the real-time carbon emission data and construction progress to predict the carbon emission distribution trend in future periods. The visualization and intelligent decision-making layer is used to overlay and render the real-time carbon emission data as a three-dimensional heat map on the digital twin of the construction site, and to execute carbon anomaly alarms and generate control schemes based on the real-time carbon emission data.

[0014] Furthermore, the multi-source sensing data acquisition layer is divided into a mobile construction equipment data acquisition module, a fixed area energy consumption data acquisition module, and a material transportation data acquisition module: The mobile construction equipment data acquisition module includes an edge intelligent acquisition terminal, a current transformer, a vibration sensor, and a UWB positioning tag; the fixed area energy consumption data acquisition module includes a smart meter and an environmental sensor; the material transportation data acquisition module includes a GPS positioning module and is connected to an electronic waybill management system.

[0015] Furthermore, the carbon accounting digital twin engine layer builds a digital twin scene that is consistent with the physical information of the construction site based on the construction BIM model of the engineering project, and deploys the standardized carbon emission factor library that supports dynamic parameter updates within it.

[0016] Furthermore, the AI ​​intelligent analysis and prediction layer is specifically used in dynamic simulations for: Based on the real-time construction progress at the construction site, historical 7-day carbon emission patterns, subsequent 24-hour construction plans, and equipment scheduling plans, the dynamic simulation is performed in the digital twin of the construction site using a time-series prediction algorithm. Simulate the energy consumption trends and carbon emission distribution of each construction process and region in the next 24 hours, predict the time, region and corresponding process of carbon emission peaks, and identify high carbon emission risk points in advance.

[0017] Furthermore, the visualization interaction and intelligent decision-making layer is also used to: trigger an alarm and accurately mark the location, value, and cause of the exceedance when the real-time carbon emission data of a certain construction area, construction process, or single piece of equipment exceeds a preset threshold; and intelligently generate targeted carbon emission optimization and adjustment suggestions based on the on-site construction conditions. The carbon emission optimization and adjustment suggestions include at least peak-shifting power scheduling suggestions, new energy machinery replacement suggestions, and construction process optimization suggestions.

[0018] Compared with existing technologies, the advantages of this invention are: 1. This invention represents a technological leap from "post-event estimation" to "real-time accurate measurement and traceability" of construction carbon emissions. It innovatively introduces a three-dimensional cross-matching algorithm combining timestamps, spatial coordinates, and actual working conditions. This ensures that every piece of dynamic energy consumption data at the underlying level can be accurately and automatically assigned to the specific smallest carbon accounting unit. This completely solves the problems of poor timeliness and ambiguous energy consumption data attribution in the traditional bill of quantities method. Carbon data can be accurately traced to specific construction procedures, individual construction equipment, and specific construction spaces, significantly improving the accuracy and reliability of carbon inventory.

[0019] 2. This invention completely breaks through the industry pain point of the disconnect between traditional construction energy monitoring and carbon accounting, achieving deep integration of software and hardware. The system architecture of this invention captures underlying dynamics through physical hardware such as edge acquisition terminals, UWB positioning, current and vibration sensors, and deeply binds them to the refined subdivision units at the single floor and single process level of the BIM model. This ensures that the carbon accounting results at the software algorithm level are completely consistent with the actual physical construction scenario, eliminating data deviations caused by invalid standby energy consumption.

[0020] 3. A closed-loop carbon management system of "real-time monitoring, dynamic accounting, advanced prediction, and intelligent control" has been constructed. Relying on digital twin and AI simulation prediction technologies (such as time-series simulation combining historical data and subsequent scheduling plans), this invention transforms carbon emission management from "passive and lagging recording" to "proactive and advanced prediction," enabling the identification of high carbon emission risk points 24 hours in advance.

[0021] 4. Significantly lowers the threshold for carbon management in projects, enabling intelligent and engineered control commands. The system can not only provide intuitive alarms for exceeding limits through 3D heat maps, but also automatically generate actionable engineering control suggestions based on physical site conditions, such as staggered power consumption, replacement of new energy machinery, and staggered work process scheduling, truly contributing to the green and low-carbon construction of engineering projects. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in the embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0023] Figure 1 This is a flowchart of a method for real-time monitoring and dynamic tracking of carbon emissions from building construction. Detailed Implementation

[0024] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0025] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0026] Example 1 This invention provides a method for real-time monitoring and dynamic tracking of carbon emissions from building construction, which is applied to a system for real-time monitoring and dynamic tracking of carbon emissions from building construction. The system is divided into four layers, from top to bottom: a visualization interaction and intelligent decision-making layer, an AI intelligent analysis and prediction layer, a carbon accounting digital twin engine layer, and a multi-source sensing data acquisition layer. Each layer operates independently and the data is interconnected, forming a complete closed loop for real-time monitoring and dynamic tracking of carbon emissions from construction.

[0027] Please see Figure 1 Specifically, the method includes the following steps: Step S1: Collect dynamic data of the construction site in full coverage through the multi-source sensing data acquisition layer. The dynamic data of the construction site includes timestamps, spatial coordinates and equipment operating data, and is connected to the carbon accounting digital twin engine layer in real time through a high-speed communication network.

[0028] In this embodiment, to achieve refined data collection across all scenarios and completely resolve the industry pain point of the disconnect between traditional energy monitoring and carbon accounting, the multi-source sensing data acquisition layer is divided into three modules for comprehensive coverage: mobile construction equipment data acquisition, fixed area energy consumption data acquisition, and material transportation data acquisition. Specifically, it includes: (1) An independent edge intelligent acquisition terminal is installed on each mobile construction equipment. The terminal is connected to the equipment control system via CAN bus to collect real-time fuel consumption data, power consumption data, start-stop status and running time of the mobile construction equipment in real time at high frequency; the working current data of the mobile construction equipment is collected through current transformer, and vibration sensor is used to identify the actual working conditions of the mobile construction equipment to distinguish the no-load, standby or working state of the mobile construction equipment; the three-dimensional spatial coordinates of the mobile construction equipment are collected in real time through UWB high-precision positioning tag configured for the mobile construction equipment; the fuel consumption data, the power consumption data, the actual working conditions, the three-dimensional spatial coordinates and the corresponding acquisition time are combined to generate the construction site dynamic data with the accurate timestamp.

[0029] In this embodiment, it should be noted that the aforementioned mobile construction equipment specifically includes core energy-consuming equipment such as tower cranes, excavators, concrete pump trucks, and loaders at the construction site. Traditional single fuel consumption or electricity consumption data collection cannot eliminate invalid idling energy consumption of the equipment. This embodiment uses a combination of "current transformer + vibration sensor" to accurately identify whether the equipment is currently in an idle, standby, or high-load operation state by combining the characteristics of current amplitude changes and the characteristics of mechanical vibration signals in a specific frequency band, thus eliminating statistical deviations in invalid energy consumption data. The UWB tag, based on the time-of-flight (TOF) or time difference of arrival (TDOA) positioning principle, achieves centimeter-level three-dimensional coordinate calculation, accurately locating the equipment's operating area.

[0030] (2) Deploy smart meters and environmental sensors in the fixed construction area to collect data on power consumption, ambient temperature and humidity and work period in the fixed construction area in real time.

[0031] In this embodiment, it should be noted that the fixed construction area specifically includes fixed energy-consuming areas such as temporary office areas, material processing sheds, steel bar processing areas, formwork processing areas, and on-site lighting systems. By deploying smart meters and environmental sensors (energy consumption acquisition modules), full coverage collection of energy consumption in fixed scenarios can be achieved.

[0032] (3) Equip material transport vehicles with GPS positioning modules and connect them to the project's electronic waybill management system to automatically obtain data on the starting point, destination, actual transport distance, material transport tonnage, transport time, and transport frequency of the material transport vehicles, providing accurate basic data for carbon emission accounting in the material transport process.

[0033] In this embodiment, it should be noted that the material transport vehicles include all vehicles transporting construction materials and construction waste upon arrival at the site.

[0034] All the aforementioned sensing data are uploaded in real time to the carbon accounting digital twin engine layer via a 5G communication module (i.e., a high-speed communication network) as a full data stream. The low latency and high bandwidth of 5G are used to ensure the real-time performance and integrity of data transmission.

[0035] Step S2: Construct a digital twin of the construction site based on the construction BIM model through the carbon accounting digital twin engine layer. Decompose the construction BIM model into the smallest carbon accounting unit according to the construction process and construction area, and bind the smallest carbon accounting unit to the carbon emission factors in the built-in standardized carbon emission factor library.

[0036] In this embodiment, the carbon accounting digital twin engine layer is the core accounting carrier of the system, and the specific implementation includes: Based on the complete BIM construction model of the project, a digital twin scene is built to restore the construction site at a 1:1 scale, replicating all physical information such as building structures, construction areas, and equipment operating ranges at the construction site.

[0037] The overall BIM model and twin scene are further broken down according to the process division standards of the construction organization design, so that the granularity of the smallest carbon accounting unit is refined to a single floor, a single area and a single process; for example, it is broken down into independent accounting units such as "concrete pouring of the main column on the first floor", "earthwork excavation in the north area" and "reinforcement binding of beams and slabs in the west area".

[0038] At the same time, each of the smallest carbon accounting units is deeply bound to the corresponding construction period, construction area, and supporting construction equipment information.

[0039] Furthermore, a standardized carbon emission factor library is built within the digital twin engine. This library strictly adheres to national and industry standards for carbon emission accounting in building construction. The carbon emission factors cover all dimensions of carbon emissions, including diesel consumption, electricity consumption, building material losses, manual labor, and material transportation. It also supports dynamic updates of corresponding factor parameters based on regional energy consumption standards and differences in construction processes. The engine automatically binds each decomposed carbon accounting unit to its corresponding carbon emission factor, providing a standardized calculation basis for subsequent real-time carbon accounting.

[0040] Step S3: Receive the full data stream in real time through the AI ​​intelligent analysis and prediction layer, align the construction progress plan with the timestamp to determine the ongoing construction process, match the corresponding construction area with the spatial coordinates, and accurately determine the smallest carbon accounting unit corresponding to the energy consumption data by combining the equipment operating data, thereby realizing the automatic attribution of real-time data.

[0041] In this embodiment, this is a core step in achieving the technological leap from "post-event estimation" to "real-time metering" of construction carbon emissions. The real-time data stream uploaded by the multi-source sensing layer carries multi-dimensional information, and the AI ​​agent initiates a data matching algorithm: The first step is to align the construction schedule (such as a Gantt chart or network diagram) with precise timestamps to determine the specific construction process currently being performed. The second step is to use the three-dimensional spatial coordinates obtained by UWB and GPS to match the corresponding physical construction area using electronic fences or spatial geometric intersection algorithms. The third step is to combine the actual operating data of the equipment to accurately determine which specific "smallest carbon accounting unit" (such as the "Northern Earthwork Excavation" unit) the current energy consumption (e.g., the fuel consumption of the excavator when it is operating in the North District) should belong to.

[0042] This step, through three-dimensional cross-verification of time, space, and operating conditions, enables automatic traceability and attribution of every piece of energy consumption and material consumption data, eliminating the problems of data mismatch, omission, and ambiguous attribution in the traditional model.

[0043] Step S4: The AI ​​intelligent analysis and prediction layer calls the carbon emission factor bound to the corresponding minimum carbon accounting unit to calculate the real-time carbon emission data of the corresponding minimum carbon accounting unit.

[0044] In this embodiment, after data matching is completed, the AI ​​agent calls the built-in carbon emission factor and combines it with the above-mentioned real-time energy consumption (oil, electricity, etc.), operation time, material tonnage and other basic parameters to perform product calculation, complete the second-level carbon emission and carbon intensity (carbon emission per unit of project) calculation of the corresponding accounting unit, and update the carbon emission data of each process and each area in real time.

[0045] In this embodiment, the method may also include an optional step, step S41: carbon emission advance prediction; In this embodiment, in order to achieve the transformation from "passive recording" to "active prediction", after calculating the real-time carbon emission data, the following steps are also included: The AI ​​intelligent analysis and prediction layer is based on the real-time construction progress at the construction site, historical 7-day carbon emission pattern data, subsequent 24-hour construction plan, and equipment scheduling plan. It performs dynamic simulation in the digital twin of the construction site using time-series prediction algorithms (such as deep learning models with time-series memory functions, such as Long Short-Term Memory Network LSTM or Temporal Convolutional Network TCN). Simulate the energy consumption trends and carbon emission distribution of each construction process and region in the next 24 hours, accurately identify and predict the time period, region and corresponding process of carbon emission peak, so as to lock in high carbon emission risk points in advance.

[0046] Step S5: The real-time carbon emission data is overlaid and rendered on the digital twin of the construction site in the form of a three-dimensional heat map through the visualization interaction and intelligent decision-making layer for visualization display, and carbon anomaly alarm and control scheme are executed based on the real-time carbon emission data.

[0047] In this embodiment, the visualization interaction and intelligent decision-making layer is used to realize carbon data visualization, anomaly alarm, and intelligent control, specifically including: The system overlays and renders real-time and predicted carbon emission data generated by AI calculations onto a digital twin construction site model in the form of a 3D heatmap. Different color gradients visually distinguish areas of low carbon emissions, normal emissions, and emissions exceeding standards, providing a visual representation of carbon emission status. Simultaneously, all data is pushed to a dedicated carbon management dashboard for the project, which updates in real time key indicators such as total carbon emissions for each process, carbon intensity per unit of work, carbon emission percentage of equipment, and carbon peak warning information.

[0048] When the real-time carbon emission data of a certain construction area, construction process or a single piece of equipment is detected to exceed the preset threshold, the system will automatically trigger an audible and visual alarm, a platform pop-up alarm, and a mobile push alarm notification, accurately marking the location, value and cause of the exceedance.

[0049] Simultaneously, the AI ​​agent, combined with on-site construction conditions, intelligently generates targeted optimal carbon emission optimization and control suggestions. These suggestions include at least: for electricity consumption exceeding limits, suggestions for staggered power scheduling and construction during off-peak hours; for high emissions from diesel machinery, suggestions for replacing with new energy machinery and managing equipment idle time; and for concentrated construction carbon peaks, suggestions for optimizing construction procedures (such as staggered work schedules and zoned construction optimization schemes). Managers can directly adjust construction plans based on system suggestions, achieving dynamic optimization and precise control of construction carbon emissions and lowering the threshold for project carbon management.

[0050] Based on the above steps, this embodiment completely solves the industry pain point of the disconnect between traditional construction energy monitoring and carbon accounting, and constructs a closed-loop carbon management system of "real-time monitoring - dynamic accounting - advanced prediction - intelligent control". Relying on digital twin and AI simulation prediction technologies, it realizes the transformation of carbon emissions from passive recording to active prediction and active control, providing core data support and technical basis for the dynamic optimization and precise carbon reduction of building construction carbon emissions.

[0051] Example 2 This invention provides a real-time monitoring and dynamic tracking system for carbon emissions from building construction. This system is based on the same inventive concept as the method in Embodiment 1 and is used to implement the real-time monitoring and dynamic tracking method for carbon emissions from building construction as described in Embodiment 1.

[0052] Specifically, the system described in this embodiment is divided into a four-layer architecture, from bottom to top including a multi-source sensing data acquisition layer, a carbon accounting digital twin engine layer, an AI intelligent analysis and prediction layer, and a visualization interaction and intelligent decision-making layer. The specific structure and functional configuration of each layer are as follows: 1. Multi-source sensing data acquisition layer This layer forms the hardware perception foundation of the system, used for comprehensive collection of dynamic construction site data, including timestamps, spatial coordinates, and equipment operating status data, and uploading the full data stream in real time via high-speed communication networks such as 5G. To achieve refined data collection across the entire construction site, this layer is subdivided into the following three hardware acquisition modules: (1) Mobile construction equipment data acquisition module: This includes an independent edge intelligent acquisition terminal, current transformer, vibration sensor, and UWB positioning tag installed on each mobile construction equipment. The edge intelligent acquisition terminal interfaces with the equipment control system via a CAN bus to extract fuel consumption data, power consumption data, start / stop status, and operating time. The current transformer and vibration sensor work together to extract operating current and mechanical vibration signals, allowing the system to identify actual operating conditions such as no-load, standby, or operation. The UWB positioning tag is used to obtain three-dimensional spatial coordinates. The data acquired by the above hardware is integrated and then stamped with a precise acquisition timestamp.

[0053] (2) Fixed area energy consumption data acquisition module: including smart meters and environmental sensors deployed in fixed energy consumption areas such as temporary office areas and processing sheds, used to collect data on power consumption, ambient temperature and humidity and working hours in fixed areas in real time.

[0054] (3) Material transportation data acquisition module: including a GPS positioning module mounted on the construction material and slag transportation vehicles, and connected to the project's electronic waybill management system, used to automatically extract data such as transportation start point, destination, actual transportation distance, tonnage and frequency.

[0055] 2. Carbon Accounting Digital Twin Engine Layer This layer serves as the core data carrier and accounting foundation of the system, and it is equipped with a twin scenario construction module and a standardized carbon emission factor library.

[0056] (1) Twin Scene Construction Module: This module is used to build a digital twin scene that is completely consistent with the physical information of the construction site based on the complete set of construction BIM models of the project. The overall construction BIM model is further broken down into the smallest carbon accounting unit (refined to a single floor, a single area, and a single process) according to the process division standard of the construction organization design. At the same time, each smallest carbon accounting unit is deeply bound to the corresponding construction period, physical construction area, and supporting construction equipment information.

[0057] (2) Standardized carbon emission factor library: As a built-in accounting basis, it covers carbon emission factors in all dimensions, including diesel consumption, electricity consumption, building material loss, manual operation and material transportation. The library supports dynamic updating of parameters according to national and industry carbon emission accounting standards for construction, regional energy consumption standards and differences in construction technology, and is bound to the smallest carbon accounting unit after dismantling.

[0058] 3. AI Intelligent Analysis and Prediction Layer This layer is the "brain" of the system, and is equipped with a data attribution matching module and a carbon emission accounting and simulation module.

[0059] (1) Data Attribution Matching Module: It is used to receive the full data stream from the underlying real-time access and execute the three-dimensional matching logic through the algorithm: First, it uses the "time stamp" to align with the construction progress plan to determine the construction process being executed; second, it uses the "spatial coordinates" to match the corresponding construction area; finally, it combines the "equipment operating data" (eliminating invalid standby energy consumption) to accurately determine the unique smallest carbon accounting unit corresponding to the current energy consumption data, thus completely solving the problem of multi-source data mismatch and ambiguity of attribution.

[0060] (2) Carbon emission accounting and simulation module: It is not only used to call the corresponding bound carbon emission factors to calculate the second-level real-time carbon emission data (including carbon emission amount and carbon intensity) of the smallest carbon accounting unit; it also has a built-in time series prediction algorithm (such as LSTM network) to perform dynamic simulation based on real-time construction progress, historical 7-day regular data and subsequent 24-hour scheduling plan, and predict the time period and high carbon emission area of ​​carbon emission peak in the next 24 hours.

[0061] 4. Visualized Interaction and Intelligent Decision-Making Layer This layer serves as the system's control terminal and user interface, and is equipped with a 3D visualization module and an alarm control engine.

[0062] (1) Three-dimensional visualization module: used to overlay and render the real-time carbon emission data and predicted carbon emission data in the form of a three-dimensional heat map on the digital twin of the construction site, and intuitively distinguish between normal and excessive areas through linkage with the dedicated carbon management dashboard.

[0063] (2) Alarm control engine: When the real-time carbon emission data of a certain construction area, construction process or single equipment exceeds the preset threshold, the platform and mobile terminal will trigger an alarm prompt, accurately mark the location and cause of the exceedance; at the same time, it will automatically generate and push targeted carbon emission optimization and adjustment suggestions in combination with the on-site construction conditions. The suggestions include at least: peak-shifting power scheduling suggestions, new energy machinery replacement suggestions and zoning process optimization suggestions.

[0064] The system provided in this embodiment, through the deep interlocking of the physical hardware and algorithm model of the aforementioned four-layer architecture, forms an industrial-grade closed-loop control system with sensing, thinking, prediction, and command capabilities. This system completely breaks the static, post-event estimation mode of traditional quantity surveying software, reducing the accuracy of carbon emission tracking to the level of single equipment and single process, and greatly improving the intelligence and precision of low-carbon management in building construction.

[0065] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

[0066] This background section is provided to generally present the context of the invention. The work of the currently named inventors, the work to the extent described in this background section, and aspects of this section that did not constitute prior art at the time of application are neither expressly nor impliedly acknowledged as prior art to the invention.

Claims

1. A method for real-time monitoring and dynamic tracking of carbon emissions from building construction, characterized in that, An application is made to a real-time monitoring and dynamic tracking system for carbon emissions from building construction. The system includes a multi-source sensing data acquisition layer, a carbon accounting digital twin engine layer, an AI intelligent analysis and prediction layer, and a visualization interaction and intelligent decision-making layer. The method includes the following steps: The construction site dynamic data is collected in full coverage by the multi-source sensing data acquisition layer. The construction site dynamic data includes timestamps, spatial coordinates and equipment operating data, and is connected to the carbon accounting digital twin engine layer in real time through a high-speed communication network. The carbon accounting digital twin engine layer constructs a digital twin of the construction site based on the construction BIM model. The construction BIM model is decomposed into the smallest carbon accounting unit according to the construction process and construction area, and the smallest carbon accounting unit is bound to the carbon emission factors in the built-in standardized carbon emission factor library. The AI ​​intelligent analysis and prediction layer receives the full data stream in real time, aligns the construction progress plan with the timestamp to determine the construction process being executed, matches the corresponding construction area with the spatial coordinates, and accurately determines the smallest carbon accounting unit corresponding to the energy consumption data by combining the equipment operating data, thereby realizing the automatic attribution of real-time data. The AI ​​intelligent analysis and prediction layer calls the carbon emission factor bound to the corresponding minimum carbon accounting unit to calculate the real-time carbon emission data of the corresponding minimum carbon accounting unit. Based on the real-time carbon emission data and construction progress, the AI ​​intelligent analysis and prediction layer performs dynamic simulation to predict the carbon emission distribution trend in the future period. The real-time carbon emission data is overlaid and rendered on the digital twin of the construction site in the form of a three-dimensional heat map through the visualization interaction and intelligent decision-making layer for visualization display, and carbon anomaly alarm and control scheme are executed based on the real-time carbon emission data.

2. The method for real-time monitoring and dynamic tracking of carbon emissions from building construction according to claim 1, characterized in that, The multi-source sensing data acquisition layer collects dynamic data from the construction site in a comprehensive manner, specifically including: An edge intelligent acquisition terminal is installed on the mobile construction equipment to collect fuel consumption data, power consumption data, start-stop status and running time of the mobile construction equipment in real time via CAN bus; the operating current data of the mobile construction equipment is collected through current transformer, and vibration sensors are used to identify the actual working conditions of the mobile construction equipment to distinguish the no-load, standby or working status of the mobile construction equipment. The three-dimensional spatial coordinates of the mobile construction equipment are collected in real time by configuring UWB positioning tags for the mobile construction equipment. The fuel consumption data, electricity consumption data, actual operating conditions, and three-dimensional spatial coordinates are combined with the corresponding acquisition time to generate dynamic data of the construction site carrying the timestamp.

3. The method for real-time monitoring and dynamic tracking of carbon emissions from building construction according to claim 1, characterized in that, The multi-source sensing data acquisition layer comprehensively collects dynamic data from the construction site, and also includes: Smart meters and environmental sensors are deployed in the fixed construction area to collect data on power consumption, ambient temperature and humidity, and work period in the fixed construction area in real time. Equip material transport vehicles with GPS positioning modules and connect them to the electronic waybill management system to automatically obtain data on the vehicle's starting point, destination, actual transport distance, material transport tonnage, transport time, and transport frequency. The sensed data is uploaded in real time to the carbon accounting digital twin engine layer as the full data stream via the 5G communication module.

4. The method for real-time monitoring and dynamic tracking of carbon emissions from building construction according to claim 1, characterized in that, The construction BIM model is broken down into the smallest carbon accounting units according to construction procedures and construction areas, including: The construction BIM model is further broken down according to the process division standard of the construction organization design, so that the granularity of the smallest carbon accounting unit is refined to a single floor, a single area and a single process. Each of the minimum carbon accounting units is linked to the corresponding construction period, construction area, and supporting construction equipment information; The standardized carbon emission factor library is established based on national and industry carbon emission accounting standards for construction. The carbon emission factors cover diesel consumption, electricity consumption, building material loss, manual labor, and material transportation. It also supports dynamic updates of corresponding factor parameters based on regional energy consumption standards and differences in construction processes.

5. The method for real-time monitoring and dynamic tracking of carbon emissions from building construction according to claim 1, characterized in that, The AI-powered intelligent analysis and prediction layer uses real-time carbon emission data and construction progress to perform dynamic simulations, predicting future carbon emission distribution trends, specifically including: The AI ​​intelligent analysis and prediction layer is based on the real-time construction progress at the construction site, historical 7-day carbon emission pattern data, subsequent 24-hour construction plan, and equipment scheduling plan. It performs dynamic simulation in the digital twin of the construction site using a time-series prediction algorithm. Simulate the energy consumption trends and carbon emission distribution of each construction process and region in the next 24 hours, predict the time, region and corresponding process of carbon emission peaks, and identify high carbon emission risk points in advance.

6. The method for real-time monitoring and dynamic tracking of carbon emissions from building construction according to claim 1, characterized in that, Based on the aforementioned real-time carbon emission data, a carbon anomaly alarm and generation control scheme is implemented, specifically including: When the real-time carbon emission data of a certain construction area, construction process or single piece of equipment is detected to exceed the preset threshold, an alarm is triggered and the location, value and cause of the exceedance are accurately marked. Based on the on-site construction conditions, targeted carbon emission optimization and adjustment suggestions are generated intelligently. These suggestions include at least peak-shifting power scheduling suggestions, new energy machinery replacement suggestions, and construction procedure optimization suggestions.

7. A real-time monitoring and dynamic tracking system for carbon emissions from building construction, characterized in that, The system for implementing the method as described in any one of claims 1-6 comprises: The multi-source sensing data acquisition layer is used to collect dynamic data of the construction site, including timestamps, spatial coordinates and equipment operating data, and upload the full data stream in real time through a high-speed communication network. The carbon accounting digital twin engine layer is used to build a digital twin of the construction site based on the construction BIM model. The construction BIM model is broken down into the smallest carbon accounting unit according to the construction process and construction area, and the smallest carbon accounting unit is bound to the carbon emission factors in the built-in standardized carbon emission factor library. The AI-powered intelligent analysis and prediction layer receives real-time access to the full data stream, aligns the construction schedule with the timestamp to determine the ongoing construction process, matches the corresponding construction area with the spatial coordinates, and accurately determines the smallest carbon accounting unit corresponding to the energy consumption data by combining the equipment operating data, thereby achieving automatic data attribution. It is also used to call the corresponding bound carbon emission factor to calculate the real-time carbon emission data of the smallest carbon accounting unit, and to perform dynamic simulation based on the real-time carbon emission data and construction progress to predict the carbon emission distribution trend in future periods. The visualization and intelligent decision-making layer is used to overlay and render the real-time carbon emission data as a three-dimensional heat map on the digital twin of the construction site, and to execute carbon anomaly alarms and generate control schemes based on the real-time carbon emission data.

8. A real-time monitoring and dynamic tracking system for carbon emissions from building construction according to claim 7, characterized in that, The multi-source sensing data acquisition layer is divided into a mobile construction equipment data acquisition module, a fixed area energy consumption data acquisition module, and a material transportation data acquisition module. The mobile construction equipment data acquisition module includes an edge intelligent acquisition terminal, a current transformer, a vibration sensor, and a UWB positioning tag; the fixed area energy consumption data acquisition module includes a smart meter and an environmental sensor; the material transportation data acquisition module includes a GPS positioning module and is connected to an electronic waybill management system. The carbon accounting digital twin engine layer builds a digital twin scene that is consistent with the physical information of the construction site based on the construction BIM model of the engineering project, and deploys the standardized carbon emission factor library that supports dynamic parameter updates within it.

9. A real-time monitoring and dynamic tracking system for carbon emissions from building construction according to claim 7, characterized in that, The AI ​​intelligent analysis and prediction layer is specifically used in dynamic simulations for: Based on the real-time construction progress at the construction site, historical 7-day carbon emission patterns, subsequent 24-hour construction plans, and equipment scheduling plans, the dynamic simulation is performed in the digital twin of the construction site using a time-series prediction algorithm. Simulate the energy consumption trends and carbon emission distribution of each construction process and region in the next 24 hours, predict the time, region and corresponding process of carbon emission peaks, and identify high carbon emission risk points in advance.

10. A real-time monitoring and dynamic tracking system for carbon emissions from building construction according to claim 6, characterized in that, The visualization interaction and intelligent decision-making layer is also used to: trigger an alarm and accurately mark the location, value, and cause of the exceedance when the real-time carbon emission data of a construction area, construction process, or single piece of equipment exceeds a preset threshold; and intelligently generate targeted carbon emission optimization and adjustment suggestions based on the on-site construction conditions. The carbon emission optimization and adjustment suggestions include at least peak-shifting power scheduling suggestions, new energy machinery replacement suggestions, and construction process optimization suggestions.

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