Construction project carbon emission tracking evaluation method and system based on block chain

Abstract

The invention relates to the technical field of carbon emission tracking evaluation, in particular to a construction project carbon emission tracking evaluation method and system based on a block chain. Comprising the steps of dividing a project area into a plurality of grid areas, setting carbon emission detection point positions, arranging a carbon emission detection unmanned aerial vehicle, controlling and executing an event-driven relay mechanism, continuously collecting construction project carbon emission data, outputting the carbon emission data, uploading the carbon emission data to a block chain for evidence storage, integrating multiple groups of carbon emission data, and splicing the carbon emission data in different time periods. And constructing a regional overall carbon emission model, and evaluating the carbon emission of the construction project. According to the method, on one hand, a plurality of detection points are alternated, it is ensured that all grids are covered in the evaluation period, the same detection point is measured through a plurality of unmanned aerial vehicles, the accuracy of current carbon emission evaluation is improved, on the other hand, the unmanned aerial vehicles of the detection points are verified mutually, and more comprehensive carbon emission tracking evaluation is obtained.

Description

Technical Field

[0001] This invention relates to the field of carbon emission tracking and evaluation technology, and more specifically, to a method and system for tracking and evaluating carbon emissions from construction projects based on blockchain. Background Technology

[0002] When conducting carbon emission tracking and evaluation for construction projects, the current mainstream technologies in the field of carbon emission monitoring mainly rely on fixed monitoring networks or single remote sensing methods. However, in long-term, large-scale scenarios such as vegetation restoration and ecological carbon sinks, the following key shortcomings have been exposed: Firstly, there are inherent problems with fixed monitoring equipment: fixed sensors can only collect point data (such as weather towers and ground stations), which cannot reflect the spatial heterogeneity of carbon emissions in the region. They are also difficult to adapt to terrain undulations and vegetation cover changes, resulting in insufficient spatial coverage. Furthermore, fixed carbon emission monitoring equipment in the field is susceptible to damage from extreme weather, and it is inconvenient to disassemble and reassemble during maintenance and replacement, resulting in cost and maintenance pressure. Secondly, the application bottleneck of single remote sensing technology: When using satellite remote sensing for carbon emission detection and assessment, the time resolution is low (revisiting once every few hours to several days), which cannot capture short-term emission fluctuations during the day. Moreover, the atmospheric column concentration inversion results confuse the surface source with the upper-air background value, affecting the accuracy. Therefore, in long-term, large-scale ecological construction projects, there is an urgent need for a high spatiotemporal resolution, flexible and scalable, reliable and traceable carbon emission tracking and evaluation method and system. Summary of the Invention

[0003] The purpose of this invention is to provide a blockchain-based method and system for tracking and evaluating carbon emissions from construction projects, in order to solve the problems mentioned in the background section.

[0004] To address the aforementioned technical problems, one objective of this invention is to provide a blockchain-based method for tracking and evaluating carbon emissions from construction projects, comprising the following steps: S1. Collect raw data of the construction project area, generate a vector map based on the raw data, divide the vector map into multiple initial grid areas, identify the activity level of carbon emission activities in each initial grid area based on the raw data, and if the activity level value is higher than the activity threshold, further subdivide the initial grid area until the activity level value is no higher than the activity threshold, forming multiple grid areas, where: When identifying the activity level of carbon emissions in each initial grid area, the following indices are calculated for each initial grid area: construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index, and vegetation offsetting potential index. The construction activity density index is calculated as: (number of stationary sources in the current grid area / total number of stationary sources in the entire project area); the stationary source presence index is calculated as: (number of thermal anomalies in the current grid area / total number of thermal anomalies in the entire project area); the mobile source intensity index is calculated as: (number of moving objects in the current grid area / total number of moving objects in the entire project area); the terrain complexity index is calculated as: (terrain complexity index value in the current grid area / total terrain complexity index value in the entire project area); and the vegetation offsetting potential index is calculated as: (carbon sequestration potential value in the current grid area / total carbon sequestration potential value in the entire project area). Then, based on the weights of the construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index, and vegetation offsetting potential index, the activity level of carbon emissions in each initial grid area is calculated using a weighted average. Carbon emission monitoring points are set up in the grid area, and drone landing platforms are set up at the monitoring points. Monitoring drones are deployed on the drone landing platforms to monitor carbon emission data in the grid area. S2. Periodically control drones to execute an event-driven relay mechanism, continuously collect carbon emission data of construction projects, and output carbon emission data to be uploaded to the blockchain for evidence storage; S3. Upon reaching an evaluation cycle, multiple sets of carbon emission data are integrated through blockchain, carbon emission data from different time periods are stitched together, a regional overall carbon emission model is constructed, and mapped as a GIS heat map for evaluating the carbon emissions of construction projects.

[0005] Preferably, in step S1, raw data of the construction project area is collected, a vector map is generated based on the raw data, the vector map is divided into multiple initial grid areas, and the activity level of carbon emission activities within the initial grid areas is identified based on the raw data. If the activity level is higher than the activity threshold, the initial grid areas are further subdivided to form multiple grid areas, including the following steps: S1.1 Equip the drone with sensors to enable the drone to scan the entire construction project area, output raw data, and generate a vector map. The sensors include visible light cameras, thermal imagers, lidar, and hyperspectral imagers. The raw data includes RGB images, thermal features, 3D point cloud data, and multispectral data; RGB images were obtained by taking pictures of the construction project area with a visible light camera, thermal features were collected by a thermal imager, 3D point cloud data were obtained by emitting laser pulses from a lidar to obtain the surface of the construction project area as 3D point cloud data, and multispectral data were collected by a hyperspectral imager to obtain the vegetation growth status as multispectral data. S1.2. Use a regular grid to initially divide the vector graphic into grid regions; S1.3. Based on the original data, calculate the construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index and vegetation offset potential index for each initial grid area, respectively, and assign weights to the construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index and vegetation offset potential index respectively, and calculate the activity level of carbon emission activities by weighting. If the activity level is higher than the activity threshold, the initial grid region is subdivided into four sub-grids using a quadtree structure. The carbon emission activity level of the sub-grids is recalculated, and it is determined again whether to continue subdividing until the activity level is no higher than the activity threshold, thus forming multiple grid regions.

[0006] Preferably, the construction activity density index is calculated as follows: multiple fixed source features are extracted from the RGB image using a feature extraction algorithm. These fixed source features are then matched sequentially with the fixed source features stored in the database. If a match is found, the feature is marked as a fixed source. The fixed source features stored in the database include construction machinery features, material storage yard features, and building features under construction. First, the number of fixed sources in the RGB image of the entire project area is identified. Then, the number of fixed sources in the RGB image of the current grid area is identified. The construction activity density index is calculated as the ratio of the number of fixed sources in the current grid area to the total number of fixed sources in the entire project area: construction activity density index = number of fixed sources in the current grid area / total number of fixed sources in the entire project area. Calculate the fixed source presence index: Identify the heat emitted by multiple fixed sources in operation using a thermal imager, and use the heat value as a thermal feature. If the thermal feature exceeds the thermal threshold, it is marked as a thermal anomaly. First, calculate the number of thermal anomalies in the entire project area, and then calculate the number of thermal anomalies in the current grid area. Fixed source presence index = number of thermal anomalies in the current grid area / number of thermal anomalies in the entire project area. Calculate the moving source intensity index: continuously capture RGB images of the construction project area using a visible light camera, output a sequence of images, and calculate the number of moving objects in the entire project area and the number of moving objects in the current grid area within the same time period by detecting moving objects in the image sequence and using a multi-target tracking algorithm. Moving source intensity index = number of moving objects in the current grid area / number of moving objects in the entire project area. Calculate the terrain complexity index: Generate a digital elevation model based on 3D point cloud data, and calculate the slope and surface roughness as terrain complexity index values. First, calculate the terrain complexity index value of the entire project area and the terrain complexity index value of the current grid area. Terrain complexity index = terrain complexity index value of the current grid area / terrain complexity index value of the entire project area. Calculating the vegetation offsetting potential index: A vegetation index is constructed using key bands reflecting vegetation growth status, and a carbon sequestration model is then established. Multispectral data of the entire project area is input into the carbon sequestration model to generate the carbon sequestration potential value for the entire project area. Then, multispectral data of the current grid area is input into the carbon sequestration model to generate the carbon sequestration potential value for the current grid area. The vegetation offsetting potential index = carbon sequestration potential value of the current grid area / carbon sequestration potential value of the entire project area, where: The weight of the construction activity density index is 35%, the weight of the stationary source presence index is 25%, the weight of the mobile source intensity index is 20%, the weight of the terrain complexity index is 15%, and the weight of the vegetation offset potential index is 5%.

[0007] Preferably, in step S1, carbon emission monitoring points are set in the grid area, and a drone landing platform is set up at each monitoring point. Monitoring drones are deployed on the drone landing platform to monitor carbon emission data within the grid area, including the following steps: S1.4. Generate several candidate points in the grid area. Use the visualization degree, high adaptability, distance from fixed emission sources and carbon emission activity level of the candidate point detection as the definition indicators of environmental suitability and score them by weight. Select a set of candidate points with suitability scores higher than the detection threshold. S1.5. Equip the detection drone with multiple gas sensors, identify the detection radius of the multiple gas sensors, take minimizing the number of detection points as the objective function, and constrain each grid area to be covered by at least one detection radius, and select the detection points in the grid area that can be covered by the detection radius of the drone from the candidate point set.

[0008] Preferably, in step S2, the periodically controlled drone executes an event-driven relay mechanism to continuously collect carbon emission data from the construction project, including the following steps: S2.1 Define the global basic inspection and evaluation cycle, flexibly configure the evaluation cycle according to project requirements, and pre-plan a non-repeating optimal path sequence that includes all detection points to form a closed-loop topology. The event-driven relay mechanism is implemented: based on the order of the path sequence, the preceding drone is controlled to move from the current detection point to the next detection point, and upon arrival, the following drone is synchronously controlled to take off, until the optimal path sequence is completed, wherein: The starting point was selected based on the grid area with the lowest carbon emission activity, in order to minimize the overall error caused by the initial start-up delay. The drone triggers multiple gas sensors at each detection point to detect carbon emission data.

[0009] Preferably, the pre-planning of a non-repeating optimal path sequence containing all detection points includes the following steps: Each detection point is treated as a vertex in a graph to construct a weighted undirected graph model; The actual reachable distance is pre-calculated using path search, and an initial loop is generated using the nearest neighbor method. The calculation is iterative and the path scheme is updated. When the termination condition is met, the calculation ends and the closed-loop path of multiple detection points is output. The termination condition includes the upper limit of the number of iterations and reaching the minimum closed-loop path.

[0010] Preferably, uploading the output carbon emission data to the blockchain for evidence storage includes the following steps: Receive carbon emission data from each drone, and package it into blocks after digital signature; Deployed on trusted nodes jointly built by regulators using a consortium blockchain architecture, it supports audit interface calls, allowing regulators to verify historical data and raw data.

[0011] Preferably, in step S3, upon reaching an evaluation cycle, multiple sets of carbon emission data are integrated using blockchain, carbon emission data from different time periods are stitched together, a regional overall carbon emission model is constructed, and mapped onto a GIS heat map for evaluating the carbon emissions of construction projects, including the following steps: The system queries smart contract event logs on the blockchain, extracts all stored carbon emission records, performs batch retrieval by time window, and outputs the average of multiple carbon emission data for the same evaluation period as the carbon emission data for the current evaluation period. If the difference between two sets of carbon emission data from the same monitoring point in adjacent evaluation periods exceeds the verification threshold, an early warning signal will be output.

[0012] Preferably, S3 further includes a longitudinal point detection algorithm, which is used to divide the time period into multiple segments within the inspection and evaluation cycle, drive the drone to rise and fall longitudinally, perform carbon emission detection at different heights of the detection point, and output the average carbon emission detection value at different heights of the same detection point to the blockchain.

[0013] The second objective of this invention is to provide a blockchain-based carbon emission tracking and evaluation system for construction projects, including any one of the blockchain-based carbon emission tracking and evaluation methods for construction projects described above, comprising a drone detection and deployment module, a dynamic monitoring module, and a data integration and evaluation module; The UAV detection and deployment module is used to collect raw data of the construction project area, generate a vector map based on the raw data, divide the vector map into multiple initial grid areas, identify the activity level of carbon emission activities in each initial grid area based on the raw data, and if the activity level value is higher than the activity threshold, the initial grid area is subdivided until the activity level value is no higher than the activity threshold, forming multiple grid areas. Carbon emission monitoring points are set up in the grid area, and drone landing platforms are set up at the monitoring points. Monitoring drones are deployed on the drone landing platforms to monitor carbon emission data in the grid area. The dynamic monitoring module is used to periodically control the drone, execute an event-driven relay mechanism, continuously collect carbon emission data of the construction project, and output carbon emission data to be uploaded to the blockchain for evidence storage. The carbon emission data includes time period, coordinates of the detection point, and carbon dioxide concentration. The data integration and evaluation module is used to integrate multiple sets of carbon emission data through blockchain when an evaluation cycle is reached, splice carbon emission data from different time periods, construct an overall regional carbon emission model, and map it into a GIS heat map for evaluating the carbon emissions of construction projects.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. First, the initial grid area is divided based on the vector map. Then, the activity level of carbon emission activities is identified based on the raw data. On the one hand, the dual grid division of the construction project is realized, which is conducive to the recursive subdivision of the initial grid area with an activity level higher than the threshold until the activity level of all grid areas is no higher than the threshold. This makes the grid density match the actual intensity of carbon emission activities, which is conducive to responding to the dynamic changes of carbon emission activities in the monitoring area. On the other hand, the activity level is the basis for determining the starting point of the UAV.

[0015] 2. By periodically controlling drones and implementing an event-driven relay mechanism, carbon emission data of construction projects is continuously collected and uploaded to the blockchain for storage. On the one hand, this ensures that multiple monitoring points rotate, with monitoring gaps only occurring when carbon emission activity is at its lowest, while other monitoring points can continue monitoring uninterruptedly. This ensures that all grids are covered during the evaluation period. Measuring the same monitoring point using multiple drones avoids the limitations of drone-based carbon emission monitoring and improves the accuracy of current carbon emission assessments. On the other hand, it allows drones at different monitoring points to cross-verify each other, including verifying the accuracy of overall carbon emission data and checking for drone malfunctions, resulting in a more comprehensive tracking and evaluation of carbon emissions. Attached Figure Description

[0016] Figure 1 This is the overall flowchart of Example 1; Figure 2 This is a demonstration diagram of the drone's movement route in Example 1. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1

[0018] One of the objectives of this invention is to provide a blockchain-based method for tracking and evaluating carbon emissions from construction projects, such as... Figure 1 As shown, it includes the following steps: S1. Drone Deployment Phase: Phase 1: Collect raw data of the project area, generate a vector map based on the raw data, divide the vector map into multiple initial grid regions, and identify the activity level of carbon emissions within each initial grid region based on the raw data. If the activity level value is higher than an activity threshold, further subdivide the initial grid region until the activity level value is no higher than the activity threshold, forming multiple grid regions, where: When identifying the activity level of carbon emissions in each initial grid area, the following indices are calculated for each initial grid area: construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index, and vegetation offsetting potential index. The construction activity density index is calculated as: (number of stationary sources in the current grid area / total number of stationary sources in the entire project area); the stationary source presence index is calculated as: (number of thermal anomalies in the current grid area / total number of thermal anomalies in the entire project area); the mobile source intensity index is calculated as: (number of moving objects in the current grid area / total number of moving objects in the entire project area); the terrain complexity index is calculated as: (terrain complexity index value in the current grid area / total terrain complexity index value in the entire project area); and the vegetation offsetting potential index is calculated as: (carbon sequestration potential value in the current grid area / total carbon sequestration potential value in the entire project area). Then, based on the weights of these indices, the activity level of carbon emissions in each initial grid area is calculated using a weighted average.

[0019] Specifically, it includes the following steps: S1.1 Equip the drone with sensors to enable the drone to scan the entire construction project area, output raw data, and generate a vector map. The drone should be a quadcopter industrial-grade drone (such as DJI Matrice 350 RTK or similar products) to ensure that it can carry multiple sensors at the same time, including visible light cameras, thermal imagers, lidar and hyperspectral imagers. The raw data includes RGB images, thermal features, 3D point cloud data, and multispectral data; RGB images: captured by a visible light camera of the construction project area; thermal features: captured by a thermal imager; 3D point cloud data: obtained by lidar through laser pulse emission; multispectral data: collected by a hyperspectral imager to assess vegetation growth. The principle of generating vector graphics is as follows: all RGB images are imported using Pix4Dmapper software. Aerial triangulation is performed using the software to calculate the precise position and orientation of each RGB image. Then, a digital surface model (DSM) is generated through dense matching. Finally, the photo texture is pasted onto the DSM to generate a digital orthophoto (DOM). Vectorization graphics generation technology is used to automatically extract and vectorize the data from the digital orthophoto to form a vector graphic.

[0020] S1.2. Use a regular grid to divide the vector image into initial grid regions. The regular grid includes square / rectangular grids and unstructured triangular grids. The goal is to establish a reference frame to facilitate subsequent adaptive refinement of the initial grid regions. When identifying the level of carbon emission activity within each initial grid region: S1.3. Based on the original data, calculate the construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index, and vegetation offsetting potential index for each initial grid area. Assign weights to each of these indices, and calculate the weighted average carbon emission activity level, including: To calculate the construction activity density index, a feature extraction algorithm is used to extract multiple fixed source features from the RGB image. These fixed source features are then matched against fixed source features stored in the database. If a match is found, the feature is marked as a fixed source. The fixed source features stored in the database include construction machinery features, material storage yard features, and building features under construction. First, the number of fixed sources in the entire project area is identified. Then, the number of fixed sources in the current grid area is identified. The construction activity density index is calculated as the ratio of the number of fixed sources in the current grid area to the total number of fixed sources in the entire project area. The fixed source presence index is calculated by using a thermal imager to identify the heat emitted by multiple fixed sources in operation. The heat value is used as a thermal feature. If the thermal feature exceeds the thermal threshold (defined based on experience, the thermal threshold is 50℃), it is marked as a thermal anomaly. First, the number of thermal anomalies in the entire project area is calculated, and then the number of thermal anomalies in the current grid area is calculated. Fixed source presence index = number of thermal anomalies in the current grid area / number of thermal anomalies in the entire project area. To calculate the moving source intensity index, RGB images of the construction project area are continuously captured by a visible light camera, and a sequence of images is output. By detecting moving objects in the image sequence and using a multi-target tracking algorithm, the number of moving objects in the entire project area and the number of moving objects in the current grid area are calculated within the same time period (7 days). Moving source intensity index = number of moving objects in the current grid area / number of moving objects in the entire project area. Calculate the terrain complexity index. Generate a digital elevation model (DEM) based on 3D point cloud data, and calculate the slope and surface roughness as terrain complexity index values. First, calculate the terrain complexity index value of the entire project area and the terrain complexity index value of the current grid area. Terrain complexity index = terrain complexity index value of the current grid area / terrain complexity index value of the entire project area. The vegetation offset potential index is calculated by constructing a vegetation index using key wavelengths (such as near-infrared, red edge, and red light) that reflect vegetation growth status, and then establishing a carbon sequestration model. Carbon sequestration modeling based on vegetation indices is a mature existing technology and will not be elaborated on here. Multispectral data of the entire project area is input into the carbon sequestration model to generate the carbon sequestration potential value of the entire project area. Then, multispectral data of the current grid area is input into the carbon sequestration model to generate the carbon sequestration potential value of the current grid area. The vegetation offset potential index = carbon sequestration potential value of the current grid area / carbon sequestration potential value of the entire project area.

[0021] Then, the weights of the construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index, and vegetation offset potential index were determined by custom settings. Specifically, the weight of the construction activity density index was 35%, the weight of the stationary source presence index was 25%, the weight of the mobile source intensity index was 20%, the weight of the terrain complexity index was 15%, and the weight of the vegetation offset potential index was 5%.

[0022] The weighted calculation of the activity level of carbon emissions is expressed as follows: = 0.35 * Si + 0.25 * Ei + 0.20 * Mi + 0.15 * Ti + 0.05 * Vi, where Si is the construction activity density index, Ei is the fixed source presence index, Mi is the mobile source intensity index, Ti is the terrain complexity index, and Vi is the vegetation offset potential index.

[0023] If the activity level is higher than the activity threshold (the carbon emission activity threshold can be customized based on experience, such as 70%), the initial grid region is subdivided into four sub-grids using a quadtree structure. The carbon emission activity level of the sub-grids is recalculated, and it is determined whether to continue subdividing until the activity level is no higher than the activity threshold, forming multiple grid regions. Each grid region includes the initial grid region and sub-grids that meet the requirement that the activity level is no higher than the activity threshold. A unique ID, center coordinates, area, and level are assigned to each grid region for easy recording and retrieval later.

[0024] Phase 2: Carbon emission monitoring points are set up in the grid area. A drone landing platform is set up at the monitoring point, and monitoring drones are deployed on the drone landing platform to monitor carbon emission data in the grid area.

[0025] Specifically, it includes the following steps: S1.4. Generate several candidate points in the grid area. Use the visualization degree, high adaptability, distance from fixed emission sources and carbon emission activity level of the candidate point detection as the definition indicators of environmental suitability and assign a weighted score to select a set of candidate points whose suitability score is higher than the detection threshold (customized according to historical records or experience, indicating the critical point of environmental impact detection of carbon emissions). Candidate points include the center point, four corner points, and centroid points of an internally random or regular distribution. Each candidate point is defined. Suitability score for: ; in, For visibility (the area of ​​the candidate point that is not occluded). For height adaptability (the difference between the height of the tallest building in the grid area and the standard height of 50m, to avoid it being too low and easy to block the light, or too high and energy consumption). Distance from stationary emission sources Weighting of visibility into the level of activity in carbon emissions activities. The weight of high adaptability is 35%. The weight of distance from stationary emission sources is 30%. The weighting of carbon emission activity at 20% It is 15%. Weights are assigned based on project type, and [the following is retained] The points with the detection threshold are used as the final candidate point set. The detection threshold can be the top 30% of high-scoring points, which is beneficial to the accuracy of subsequent carbon emission data detection.

[0026] S1.5. Equip the detection drone with a multi-gas sensor, which is used to detect NDIR CO2, electrochemical CH4, and optical particulate matter; Identify the detection radius of multi-gas sensors (typically 50-100 meters, depending on atmospheric diffusion conditions and instrument sensitivity), and form a coverage set based on the candidate point set. ,in, Point To grid The shortest Euclidean distance of the boundary is used as the objective function to minimize the number of detection points, and each grid area is constrained to be covered by at least one detection radius. Detection points that can be covered by the detection radius of the drone are selected from the candidate point set, which significantly reduces the number of drones required and saves costs.

[0027] S2. Periodically control the drone to execute an event-driven relay mechanism, continuously collect carbon emission data of the construction project, and upload the carbon emission data to the blockchain for storage. The carbon emission data includes time period, detection point coordinates and carbon dioxide concentration.

[0028] Specifically, it includes the following steps: S2.1 Define a global basic inspection and evaluation cycle (e.g., complete a full area traversal every 2 hours), and pre-plan a non-repeating optimal path sequence that includes all detection points to form a closed-loop topology. The UAV control sequence at each detection point corresponds one-to-one with the optimal path sequence.

[0029] Furthermore, a non-repeating optimal path sequence containing all detection points is pre-planned, including the following steps: Each detection point is treated as a vertex in a graph to construct a weighted undirected graph model; The actual reachable distance is pre-calculated using path search, and an initial loop is generated using the nearest neighbor method. The calculation is iterative, and the path scheme is updated. When the termination condition is met, the calculation ends and the closed-loop path of multiple detection points is output. The termination condition includes the upper limit of the number of iterations and reaching the minimum closed-loop path. This realizes the construction of a spatiotemporally synchronized closed-loop inspection topology. The inspection network topology graph is a directed loop graph, where nodes represent detection points and edges represent the movement direction of the UAV.

[0030] S2.2 Execution of an event-driven relay mechanism: Based on the order of the path sequence, the mechanism controls the preceding drone to move from the current detection point to the next detection point, and synchronously controls the following drone to take off upon arrival, until the optimal path sequence is completed, wherein: The starting point was selected based on the grid area with the lowest carbon emission activity, in order to minimize the overall error caused by the initial start-up delay. The drone triggers multiple gas sensors at each detection point to detect carbon emission data.

[0031] like Figure 2As shown, assuming there are drones A1 at point a1, A2 at point a2, A3 at point a3, and A4 at point a4, their movement route is: point a1 → point a2 → point a3 → point a4. A1 is the grid corresponding to the lowest carbon emission activity level, and its location is the initial point. For example, at point a1, when the patrol cycle is reached, drone A1 at point a1 flies to point a2 according to the optimal route. Only after reaching point a2 does drone A2 at point a2 begin flying towards point a3. When drone A2 reaches point a3... When the drone reaches point a3, it begins flying towards point a4. Upon reaching point a4, drone A4 begins flying towards point a1. This ensures that multiple detection points rotate, guaranteeing that all grid areas are covered during the evaluation period. Furthermore, only the detection point with the lowest carbon emission activity level experiences carbon emission detection gaps, while other detection points can continuously monitor. When obtaining the final carbon emission data based on all the data, the detection point with the lowest activity level has the least impact on the overall accuracy of carbon emission data. This allows the drones at each detection point to cross-verify, resulting in more accurate carbon emission data.

[0032] S2.3 Receive carbon emission data output by each drone. The carbon emission data includes CO2 concentration, GPS coordinates, and timestamp. After being digitally signed, it is packaged into a block. That is, each drone is equipped with a hardware-level encryption module to form a digital fingerprint (SHA-256 hash value). Any data tampering will cause the hash value to change, which can identify the responsible party. Deploying a consortium blockchain architecture (such as Hyperledger Fabric) on trusted nodes jointly built by regulators supports audit interface calls, allowing regulators to verify historical data and raw data. This helps meet the requirements of accurate measurement, continuous tracking, and transparency in carbon emission assessment. Blockchain provides a unified and trusted ledger, breaking down information barriers.

[0033] S3. Upon reaching an evaluation cycle, multiple sets of carbon emission data are integrated through blockchain, carbon emission data from different time periods are stitched together, a regional overall carbon emission model is constructed, and mapped as a GIS heat map for evaluating the carbon emissions of construction projects.

[0034] Specifically, it includes the following steps: S3.1 Query the smart contract event log on the blockchain, extract all the carbon emission data records that have been stored, and retrieve them in batches by time window. Output the average of multiple carbon emission data for the same evaluation period as the carbon emission data for the current evaluation period. If the difference between two sets of carbon emission data from the same monitoring point in adjacent evaluation periods exceeds the verification threshold, an early warning signal will be issued. On the one hand, by using multiple drones to measure the same monitoring point, the limitations of drone carbon emission detection are avoided, which is beneficial to the accuracy of the current carbon emission assessment. On the other hand, if the difference between carbon emission data from adjacent evaluation periods exceeds the verification threshold, that is, the deviation exceeds the normal range, it may be that a drone has malfunctioned or carbon emissions have soared. An early warning should be issued to remind users to investigate, which is further beneficial to the accuracy of subsequent tracking and evaluation.

[0035] The second objective of this invention is to provide a blockchain-based carbon emission tracking and evaluation system for construction projects, including any of the above-mentioned blockchain-based carbon emission tracking and evaluation methods for construction projects, including a drone detection and deployment module, a dynamic monitoring module, and a data integration and evaluation module. The UAV detection and deployment module is used to collect raw data of the construction project area, generate a vector map based on the raw data, divide the vector map into multiple initial grid areas, identify the activity level of carbon emission activities in each initial grid area based on the raw data, and if the activity level value is higher than the activity threshold, the initial grid area is subdivided until the activity level value is no higher than the activity threshold, forming multiple grid areas. Carbon emission monitoring points are set up in the grid area, and drone landing platforms are set up at the monitoring points. Monitoring drones are deployed on the drone landing platforms to monitor carbon emission data in the grid area. The dynamic monitoring module is used to periodically control the drone, execute an event-driven relay mechanism, continuously collect carbon emission data of the construction project, and output carbon emission data to be uploaded to the blockchain for evidence storage. The carbon emission data includes time period, coordinates of the detection point, and carbon dioxide concentration. The data integration and evaluation module is used to integrate multiple sets of carbon emission data through blockchain when an evaluation cycle is reached, splice carbon emission data from different time periods, construct an overall regional carbon emission model, and map it into a GIS heat map for evaluating the carbon emissions of construction projects. Example 2

[0036] The S3 also includes a longitudinal point detection algorithm, which is used to divide the time period into multiple segments within the inspection and evaluation cycle, drive the drone to rise and fall longitudinally, conduct carbon emission detection at different heights of the detection point, and output the average carbon emission detection value at different heights of the same detection point to the blockchain, thereby further improving the accuracy of the carbon emission data of the current detection point. Furthermore, blockchain stores carbon emission data from different evaluation periods, different monitoring points, and different altitudes, which is beneficial for setting up arbitrary integration and acquisition of carbon emission data. In order to improve the diversity and comprehensiveness of carbon emission assessment, this embodiment differs from the above embodiments in that: Firstly, when obtaining carbon emission data for the required height of a construction project, the average value is calculated by extracting data from multiple monitoring points at the required height from multiple monitoring points, and then outputting the carbon emission data for the required height. Secondly, when obtaining the carbon emission data of the required monitoring points for the construction project, that is, the carbon emission data of the required grid area, is obtained by extracting all carbon emission data of the current monitoring points within different evaluation periods, calculating the average value, and outputting the carbon emission data of the current monitoring points. Third, when obtaining carbon emission data for the current evaluation period of a construction project, carbon emission data from different monitoring points within the current evaluation period are obtained. Example 3

[0037] To divide the project area into a grid of hundreds of meters and enable full-area drone patrols every two hours to capture short-term fluctuations in carbon emissions caused by construction activities, this embodiment details the following simulation implementation plan for a construction project that is a transportation hub: Step 1: Dispatch two mapping drones (equipped with LiDAR and multispectral cameras) to conduct a three-day full-area scan of the 1.5 square kilometer project area, output raw data, and generate a vector map based on the raw data, dividing it into multiple initial grid areas.

[0038] Step 2: Based on the original data, calculate the construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index, and vegetation offset potential index for each initial grid area. Determine the weights of the construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index, and vegetation offset potential index, and calculate the carbon emission activity level using a weighted average.

[0039] Step 3: Input the initial grid area (100m×100m, 150 in total); calculate the activity level of each initial grid area; if the activity level is >0.7, use a quadtree to subdivide the grid into four 50m×50m subgrids, recursively calculate the activity level of each subgrid, and if it is still greater than 0.7, continue to subdivide it into 25m×25m grids; among them, multiple small grids with adjacent activity levels <0.2 are merged, and finally 217 non-uniform grid areas are generated. Among them, the grid resolution of the core foundation pit area reaches 25m, and the grid of the edge green area is kept at 100m or merged into a larger grid.

[0040] Step 4: Pre-set 5 candidate points (center plus four corners) in each grid, calculate the suitability score (refer to Example 1), filter out the candidate point set from all candidate points with a suitability score > 0.6 (detection threshold), and set the effective detection radius of the UAV to 80 meters, so as to cover all 217 grids with the fewest points. Through a greedy algorithm, finally select 38 optimal detection points and deploy 38 UAV intelligent parking platforms (including charging piles).

[0041] Step 5: For the 38 detection points, calculate the actual flight distance of the UAV between two points (avoiding permanent buildings), and use the LKH heuristic algorithm to solve the Traveling Salesman Problem (TSP) to obtain a closed-loop inspection path sequence with a total length of about 15 kilometers. The detection point (set as P0) corresponding to the grid with the lowest CIF value (such as the green belt in the northwest corner) is set as the starting point / end point of the path.

[0042] Then, a global evaluation cycle of 2 hours is set. At time T0, the drone U0 located at point P0 takes off and goes to the next point P1. The moment U0 arrives at the P1 helipad, it triggers the drone U1 on the helipad to take off and go to P2. This process is repeated to form an "arrival-trigger" chain. When the last drone arrives at P0, one round of inspection is completed. Since the P0 point has the lowest activity level, its monitoring interval has the least impact on the overall carbon emission assessment. Other high CIF points are immediately replaced by another drone after the drone leaves, achieving near-continuous monitoring coverage. The drone hovers at each point for 5 minutes to conduct multi-point air sampling and executes a longitudinal point detection algorithm: after sampling at a height of 50 meters, it descends to 30 meters and 10 meters to sample once each, and takes the average of the three concentrations as the final concentration value of that point at that moment.

[0043] Step Six: When an evaluation cycle is reached, multiple sets of carbon emission data are integrated through blockchain, carbon emission data from different time periods are spliced ​​together, a regional overall carbon emission model is constructed, the overall carbon emission model is mapped onto the GIS platform, and the spatial distribution of carbon emissions is displayed intuitively using color gradients (green-yellow-orange-red), and the timeline scrolling playback is supported to dynamically display the spatiotemporal evolution of carbon emissions. Step 7: Set the verification threshold to 20% (determined based on historical baseline). If a drone measures a concentration difference greater than 20% at the same altitude in two adjacent cycles, a "device calibration check warning" will be triggered. If a point measures a concentration difference greater than 20% in two adjacent cycles (measured by different drones), a "local emission anomaly warning" will be triggered. The warning information, along with the relevant data hash, will be uploaded to the blockchain for auditing by the regulatory authorities.

[0044] If the detection point P15 (located downwind of the concrete mixing plant) triggers a "local emission anomaly warning", the blockchain record shows that its concentration suddenly increased from 450 ppm to 620 ppm, a difference of 37.8% > 20%. The system automatically notifies the staff, and it is found that the dust removal equipment of the mixing plant was temporarily malfunctioned. Repairs were carried out in a timely manner, and the whole process is traceable.

[0045] Therefore, the above simulation demonstrates the implementation process of this embodiment, proving that the solution is highly feasible and can provide a refined, reliable, and intelligent management tool for carbon emissions of large and complex construction projects. It effectively solves the core pain points such as insufficient spatial coverage, untraceable data, and inability to capture short-term fluctuations, and has significant application and promotion value.

[0046] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A blockchain-based method for tracking and evaluating carbon emissions from construction projects, characterized in that: Includes the following steps: S1. Collect raw data of the construction project area, generate a vector map based on the raw data, divide the vector map into multiple initial grid areas, identify the activity level of carbon emission activities in each initial grid area based on the raw data, and if the activity level value is higher than the activity threshold, further subdivide the initial grid area until the activity level value is no higher than the activity threshold, forming multiple grid areas, where: When identifying the activity level of carbon emissions in each initial grid area, the following indices are calculated for each initial grid area: construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index, and vegetation offsetting potential index. The construction activity density index is calculated as: (number of stationary sources in the current grid area / total number of stationary sources in the entire project area); the stationary source presence index is calculated as: (number of thermal anomalies in the current grid area / total number of thermal anomalies in the entire project area); the mobile source intensity index is calculated as: (number of moving objects in the current grid area / total number of moving objects in the entire project area); the terrain complexity index is calculated as: (terrain complexity index value in the current grid area / total terrain complexity index value in the entire project area); and the vegetation offsetting potential index is calculated as: (carbon sequestration potential value in the current grid area / total carbon sequestration potential value in the entire project area). Then, based on the weights of the construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index, and vegetation offsetting potential index, the activity level of carbon emissions in each initial grid area is calculated using a weighted average. Carbon emission monitoring points are set up in the grid area, and drone landing platforms are set up at the monitoring points. Monitoring drones are deployed on the drone landing platforms to monitor carbon emission data in the grid area. S2. Periodically control drones to execute an event-driven relay mechanism, continuously collect carbon emission data of construction projects, and output carbon emission data to be uploaded to the blockchain for evidence storage; S3. Upon reaching an evaluation cycle, multiple sets of carbon emission data are integrated through blockchain, carbon emission data from different time periods are stitched together, a regional overall carbon emission model is constructed, and mapped as a GIS heat map for evaluating the carbon emissions of construction projects.

2. The blockchain-based carbon emission tracking and evaluation method for construction projects according to claim 1, characterized in that: In step S1, raw data of the construction project area is collected, a vector map is generated based on the raw data, the vector map is divided into multiple initial grid areas, and the activity level of carbon emission activities within the initial grid areas is identified based on the raw data. If the activity level is higher than the activity threshold, the initial grid areas are further subdivided to form multiple grid areas, including the following steps: S1.1 Equip the drone with sensors to enable the drone to scan the entire construction project area, output raw data, and generate a vector map. The sensors include visible light cameras, thermal imagers, lidar, and hyperspectral imagers. The raw data includes: RGB images of the construction project area captured by a visible light camera, thermal features captured by a thermal imager, three-dimensional point cloud data of the surface of the construction project area obtained by lidar through the emission of laser pulses, and multispectral data of vegetation growth status collected by a hyperspectral imager. S1.

2. Use a regular grid to initially divide the vector graphic into grid regions; S1.

3. Based on the original data, calculate the construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index and vegetation offset potential index for each initial grid area, respectively, and assign weights to the construction activity density index, stationary source presence index, mobile source intensity index, terrain complexity index and vegetation offset potential index respectively, and calculate the activity level of carbon emission activities by weighting. If the activity level is higher than the activity threshold, the initial grid region is subdivided into four sub-grids using a quadtree structure. The carbon emission activity level of the sub-grids is recalculated, and it is determined again whether to continue subdividing until the activity level is no higher than the activity threshold, thus forming multiple grid regions.

3. The blockchain-based carbon emission tracking and evaluation method for construction projects according to claim 2, characterized in that: Calculate the construction activity density index: Use a feature extraction algorithm to extract multiple fixed source features from the RGB image. Then, match the multiple fixed source features with the fixed source features stored in the database. If a match is found, it is marked as a fixed source. The fixed source features stored in the database include construction machinery features, material storage yard features, and building under construction features. First, identify the number of fixed sources in the entire project area, and then identify the number of fixed sources in the current grid area. Calculate the ratio of the number of fixed sources to the total number of fixed sources in the entire project area. Calculate the fixed source presence index: Identify the heat emitted by multiple fixed sources in operation using a thermal imager, and use the heat value as a thermal feature. If the thermal feature exceeds the thermal threshold, it is marked as a thermal anomaly. First, calculate the number of thermal anomalies in the entire project area, and then calculate the number of thermal anomalies in the current grid area. Calculate the moving source intensity index: continuously capture RGB images of the construction project area using a visible light camera, output a sequence of images, and calculate the number of moving objects in the entire project area and the number of moving objects in the current grid area within the same time period by detecting moving objects in the image sequence and using a multi-target tracking algorithm. Calculate the terrain complexity index: Generate a digital elevation model based on 3D point cloud data, and calculate the slope and surface roughness as terrain complexity index values. First, calculate the terrain complexity index value of the entire project area and the terrain complexity index value of the current grid area. Calculating the vegetation offsetting potential index: A vegetation index is constructed using key bands reflecting vegetation growth status, and a carbon sequestration model is then established. Multispectral data of the entire project area is input into the carbon sequestration model to generate the carbon sequestration potential value for the entire project area. Then, multispectral data of the current grid area is input into the carbon sequestration model to generate the carbon sequestration potential value for the current grid area. Where: The weight of the construction activity density index is 35%, the weight of the stationary source presence index is 25%, the weight of the mobile source intensity index is 20%, the weight of the terrain complexity index is 15%, and the weight of the vegetation offset potential index is 5%.

4. The blockchain-based carbon emission tracking and evaluation method for construction projects according to claim 3, characterized in that: In step S1, carbon emission monitoring points are set up in the grid area. A drone landing platform is installed at each monitoring point, and a monitoring drone is deployed on the drone landing platform to monitor carbon emission data within the grid area. This includes the following steps: S1.

4. Generate several candidate points in the grid area. Use the visualization degree, high adaptability, distance from fixed emission sources and carbon emission activity level of the candidate point detection as the definition indicators of environmental suitability and score them by weight. Select a set of candidate points with suitability scores higher than the detection threshold. S1.

5. Equip the detection drone with multiple gas sensors, identify the detection radius of the multiple gas sensors, take minimizing the number of detection points as the objective function, and constrain each grid area to be covered by at least one detection radius, and select the detection points in the grid area that can be covered by the detection radius of the drone from the candidate point set.

5. The blockchain-based carbon emission tracking and evaluation method for construction projects according to claim 4, characterized in that: The S2-based periodic control drone executes an event-driven relay mechanism to continuously collect carbon emission data from the construction project, including the following steps: S2.1 Define the global basic inspection and evaluation cycle, flexibly configure the evaluation cycle according to project requirements, and pre-plan a non-repeating optimal path sequence that includes all detection points to form a closed-loop topology. The event-driven relay mechanism is implemented: based on the order of the path sequence, the preceding drone is controlled to move from the current detection point to the next detection point, and upon arrival, the following drone is simultaneously controlled to take off, until the optimal path sequence is completed, wherein: The starting point was selected based on the grid area with the lowest carbon emission activity, in order to minimize the overall error caused by the initial start-up delay. The drone triggers multiple gas sensors at each detection point to detect carbon emission data.

6. The blockchain-based carbon emission tracking and evaluation method for construction projects according to claim 5, characterized in that: The pre-planning of a non-repeating optimal path sequence containing all detection points includes the following steps: Each detection point is treated as a vertex in a graph to construct a weighted undirected graph model; The actual reachable distance is pre-calculated using path search, and an initial loop is generated using the nearest neighbor method. The calculation is iterative and the path scheme is updated. When the termination condition is met, the calculation ends and the closed-loop path of multiple detection points is output. The termination condition includes the upper limit of the number of iterations and reaching the minimum closed-loop path.

7. The blockchain-based carbon emission tracking and evaluation method for construction projects according to claim 5, characterized in that: The process of uploading the output carbon emission data to the blockchain for evidence storage includes the following steps: Receive carbon emission data from each drone, and package it into blocks after digital signature; Deployed on trusted nodes jointly built by regulators using a consortium blockchain architecture, it supports audit interface calls, allowing regulators to verify historical data and raw data.

8. The blockchain-based carbon emission tracking and evaluation method for construction projects according to claim 7, characterized in that: In step S3, upon reaching an evaluation cycle, multiple sets of carbon emission data are integrated using blockchain, carbon emission data from different time periods are stitched together, a regional overall carbon emission model is constructed, and mapped onto a GIS heat map for evaluating the carbon emissions of construction projects. This includes the following steps: The system queries smart contract event logs on the blockchain, extracts all stored carbon emission records, performs batch retrieval by time window, and outputs the average of multiple carbon emission data for the same evaluation period as the carbon emission data for the current evaluation period. If the difference between two sets of carbon emission data from the same monitoring point in adjacent evaluation periods exceeds the verification threshold, an early warning signal will be output.

9. The blockchain-based carbon emission tracking and evaluation method for construction projects according to claim 7, characterized in that: The S3 also includes a longitudinal point detection algorithm, which is used to divide the time period into multiple segments within the inspection and evaluation cycle, drive the drone to rise and fall longitudinally, perform carbon emission detection at different heights of the detection point, and output the average carbon emission detection value at different heights of the same detection point to the blockchain.

10. A blockchain-based carbon emission tracking and evaluation system for construction projects, applied to the blockchain-based carbon emission tracking and evaluation method for construction projects as described in any one of claims 1-9, characterized in that, It includes a drone detection and deployment module, a dynamic monitoring module, and a data integration and evaluation module; The UAV detection and deployment module is used to collect raw data of the construction project area, generate a vector map based on the raw data, divide the vector map into multiple initial grid areas, identify the activity level of carbon emission activities in each initial grid area based on the raw data, and if the activity level value is higher than the activity threshold, the initial grid area is subdivided until the activity level value is no higher than the activity threshold, forming multiple grid areas. Carbon emission monitoring points are set up in the grid area, and drone landing platforms are set up at the monitoring points. Monitoring drones are deployed on the drone landing platforms to monitor carbon emission data in the grid area. The dynamic monitoring module is used to periodically control the drone, execute an event-driven relay mechanism, continuously collect carbon emission data of the construction project, and output carbon emission data to be uploaded to the blockchain for evidence storage. The carbon emission data includes time period, coordinates of the detection point, and carbon dioxide concentration. The data integration and evaluation module is used to integrate multiple sets of carbon emission data through blockchain when an evaluation cycle is reached, splice carbon emission data from different time periods, construct an overall regional carbon emission model, and map it into a GIS heat map for evaluating the carbon emissions of construction projects.

Images