Methods and Systems for Calculating Carbon Emissions from Building Construction Projects

Abstract

This invention relates to the field of carbon emission calculation technology, specifically to a method and system for calculating carbon emissions in building construction projects. The method includes the following steps: monitoring and acquiring fuel consumption rate and load curves, collecting work radius data, generating a time-series synchronous dataset, identifying overlapping fuel consumption in parallel work processes and extracting the excess, generating an influence coefficient based on the geometric intersection of work radii, correcting fuel consumption and calculating carbon emissions, adjusting the ratio to obtain the corrected result, summarizing and weighting the results to obtain the total emissions, and decomposing and calculating the contribution to generate a carbon emission inventory. In this invention, fuel consumption rate and mechanical load curves are processed synchronously, energy consumption changes are subdivided, the superposition of energy consumption from parallel operations is extracted and the offset is corrected by combining the radius intersection, the corrected result is adjusted proportionally to match the working conditions, and the emission contribution of multiple processes is weighted, summarized, and decomposed on the time axis. The result reflects both the overall trend and highlights key differences, providing a basis for emission distribution.

Description

Technical Field

[0001] This invention relates to the field of carbon emission calculation technology, and in particular to a method and system for calculating carbon emissions from building construction projects. Background Technology

[0002] The field of carbon emission calculation technology involves the quantitative accounting of carbon dioxide and other greenhouse gases generated by energy consumption, material use and related activities. Its core aspects include the collection of energy types and usage, the determination of emission coefficients for different materials throughout their entire life cycle, the statistical analysis of carbon emission data in construction and transportation, and the compilation of emission inventories based on unified standards. This technology field as a whole covers the construction of carbon emission factor databases and the establishment of application rules in various scenarios such as construction, transportation and industrial production, and emphasizes the scientific quantification of emissions through data-driven calculation methods.

[0003] The traditional method for calculating carbon emissions from building construction projects refers to the calculation of carbon emissions generated by the use of building materials such as steel, cement, sand, and concrete, as well as fuel consumption of construction machinery during the construction process. This is done by multiplying the amount of materials used by the corresponding emission coefficient and the amount of fuel consumed by the standard emission coefficient. Alternatively, it can be done by consulting the energy usage ledger and material input list during the construction phase and combining it with the industry's emission factor manual to estimate the emissions.

[0004] Traditional calculation methods rely on the static multiplicative relationship between material usage and fuel consumption, lacking the processing of time dimension and dynamic fluctuations in mechanical load. When multiple processes are running simultaneously, it is difficult to identify the impact of energy consumption superposition or overlapping work scope, resulting in biased results. In particular, the error will be amplified in construction scenarios with frequent cross-operations. The emission inventory can only reflect the static total amount and cannot reveal the true contribution distribution of multiple links, making it difficult to meet the need for targeted optimization of emissions of key processes. Summary of the Invention

[0005] To address the shortcomings of traditional carbon emission calculation methods that rely on the static multiplicative relationship between material usage and fuel consumption, lacking consideration for the dynamic fluctuations of time and mechanical load, and failing to identify the cumulative energy consumption or overlapping work scopes during simultaneous operation of multiple processes, leading to biased results, especially amplified in construction scenarios with frequent overlapping operations, and the fact that emission inventories only reflect static totals and cannot reveal the true contribution distribution of multiple stages, thus failing to meet the technical requirements for targeted optimization of emissions from key processes, this invention provides a method for calculating carbon emissions in building construction projects, including the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a method for calculating carbon emissions from building construction projects, comprising the following steps: S1: Obtain fuel consumption rate and mechanical load change curves through on-site monitoring, collect process operation radius data, and perform synchronous alignment processing on the fuel consumption rate and load change curves based on the time axis to generate a time-series synchronous dataset. S2: Based on the time-series synchronization dataset, identify the overlapping interval of parallel processes, perform superposition operation on the fuel consumption rate of multiple processes within the overlapping interval, extract the excess part when the superposition value exceeds the upper limit of single machine load, and perform intersection operation after geometric processing of the working radius to generate process cross-influence coefficient. S3: Call the process cross-influence coefficient to correct the process fuel consumption data, input the corrected data into the emission factor method to calculate carbon emissions, perform proportional adjustment on the calculation results based on the influence coefficient, and generate corrected carbon emissions; S4: Based on the corrected carbon emissions, perform a summary operation on the time axis, input the emissions into the weighted average method to calculate the total emissions, and obtain the total carbon emissions; S5: Call the total carbon emissions and the corrected carbon emissions to perform decomposition statistics, calculate the percentage contribution of process carbon emissions, sort in descending order of emissions, and generate a carbon emission inventory for building construction.

[0006] As a further aspect of the present invention, the time-series synchronization dataset includes fuel consumption characteristics, mechanical load characteristics, and operating radius distribution; the process cross-influence coefficient includes superimposed load overload, spatial intersection range, and interaction intensity; the corrected carbon emissions include time correction value, spatial correction value, and proportional correction value; the total carbon emissions include stage emissions, cumulative emissions, and weighted average emissions; and the construction carbon emission inventory includes process contribution rate, emission level, and statistical results.

[0007] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Obtain fuel consumption rate and mechanical load change curve through on-site monitoring, and compare the data of multiple sampling points of fuel consumption rate sequence with the rate of change of mechanical load curve at the same sampling point to generate time-series paired data frames. S102: Based on the time-series paired data frame, collect the process operation radius data, and aggregate the operation radius data at multiple sampling points with the fuel consumption rate and mechanical load value data at the same sampling point in the time-series paired data frame to obtain the joint operation radius sequence; S103: Based on the joint sequence of the working radius, compare the synchronicity of multiple parameters in the process at the same sampling point, and align the fuel consumption rate and mechanical load curve along the time axis to establish a time-series synchronization dataset.

[0008] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the time-series synchronization dataset, retrieve the start and end timestamps of parallel processes, aggregate the time overlap segment index, align the process fuel consumption rate sequence with the time axis and perform numerical superposition processing to generate a fuel superposition rate sequence. S202: Call the fuel superposition rate sequence, compare the superposition rate value with the single unit load upper limit threshold one by one, extract the excess part and record the time node, index and label the node to obtain the excess load index set; S203: Based on the overload index set, locate the operation radius parameter corresponding to the parallel process, perform geometric mapping to a circular region, perform intersection operation and calculate the area size, and generate the process cross-influence coefficient.

[0009] As a further aspect of the present invention, the fuel superposition rate sequence is a sequence obtained by superimposing the fuel consumption rate sequences of multiple parallel processes on the time axis point by point. The single-machine load limit threshold is the fuel consumption rate that a single device can withstand under rated operating conditions.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the process cross-influence coefficient and process fuel consumption data, retrieve the corresponding coefficient value for each item of the consumption data, perform item-by-item multiplication and record it, perform weighted superposition on the recorded values, and generate a corrected fuel consumption sequence. S302: Call the modified fuel consumption sequence, perform product calculation on the sequence values ​​and emission factor parameters item by item, and then fit the matrix structure after accumulating the calculated values ​​to generate a set of carbon emission calculated values; S303: Based on the process cross-influence coefficient, adjust the values ​​of each item in the carbon emission calculation set proportionally, and integrate the adjusted values ​​into a unified sequence to generate the corrected carbon emission amount.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the corrected carbon emissions, aggregate the emission data at multiple time points on the time axis, call the carbon emission values ​​in the emission data frames at each time point, accumulate the values ​​in chronological order and arrange them in ascending order to generate the total emissions for the time period. S402: Call the total emissions for the time period, retrieve the weight parameter set for the emissions values ​​of different time periods, perform weighted processing on the emissions values ​​and corresponding weights and perform continuous calculations to obtain the weighted cumulative emissions value. S403: Based on the weighted cumulative emission value, call the cumulative result to perform a unified summation operation, and perform numerical compression processing on the summation value according to the normalization parameter set to obtain the total carbon emissions.

[0012] As a further aspect of the present invention, the total emissions over a time period refers to the value obtained by summing up the carbon emissions at time points within a preset time interval. The weighted cumulative emission value refers to the value obtained by multiplying the total emissions over a time period by the corresponding weight parameters and then summing them up segment by segment. The normalization adopts a linear mapping method, which performs numerical mapping processing on the weighted cumulative emission values ​​according to the normalization parameter set to generate values ​​within a unified range.

[0013] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the total carbon emissions and the corrected carbon emissions, the carbon emission data frames of the process are split into groups, the ratio of carbon emissions of a single process to total carbon emissions is converted, and the comparison results are standardized and mapped to generate a process carbon emission ratio matrix. S502: Call the process carbon emission percentage matrix, perform percentage expansion on the percentage values ​​in the matrix, map the expanded values ​​to the percentage axis range, and perform label matching between the process index and the expanded values ​​to obtain the process carbon emission percentage sequence; S503: Based on the carbon emission percentage sequence of the process, sort the percentage values ​​in descending order, and then perform aggregation processing on the sorted process index and percentage values ​​to obtain a carbon emission inventory of building construction.

[0014] A carbon emission calculation system for building construction projects includes: The data acquisition module obtains fuel consumption rate and mechanical load change curves through on-site monitoring, collects process operation radius data, and performs synchronization alignment processing on the fuel consumption rate and load change curves based on the time axis to generate a time-series synchronization dataset, which is then transmitted to the load synchronization module. The load synchronization module identifies the overlapping interval of parallel processes based on the time-series synchronization dataset, performs superposition operation on the fuel consumption rate of multiple processes within the overlapping interval, extracts the excess part when the superposition value exceeds the single machine load limit, performs intersection operation after geometric processing of the working radius, generates process cross-influence coefficient, and transmits it to the cross-correction module. The cross-correction module performs correction calculations on fuel consumption data based on the cross-influence coefficient of the process, inputs the corrected data into the emission factor method to calculate carbon emissions, performs proportional adjustment on the calculation results based on the influence coefficient, generates corrected carbon emissions, and transmits them to the emission accounting module. The emissions accounting module performs a summary calculation on the time axis based on the corrected carbon emissions, inputs the corrected emissions data into the weighted average method for total calculation, performs weighted processing on the emissions data, calculates the total carbon emissions, and transmits it to the inventory statistics module. The inventory statistics module calls the total carbon emissions and the corrected carbon emissions data to perform decomposition and statistical operations, calculates the percentage contribution of each process to carbon emissions, sorts the processes in descending order based on the emission values, and calculates the proportion of each process in the total emissions to generate a carbon emission inventory for construction.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by performing time-synchronized processing on the fuel consumption rate and mechanical load curves, energy consumption changes are subdivided in a dynamic dimension. Furthermore, energy consumption superposition is extracted within the parallel operation interval, and the interaction effect is determined by combining the intersection of operation radii. This achieves the correction of abnormal energy consumption deviations. After proportional adjustment in the emission factor calculation stage, the correction results can be consistent with the actual working conditions. Finally, weighted summaries and decomposition of the emission contributions of multiple processes are completed on the time axis, so that the results not only reflect the overall trend but also highlight the differences in key links, providing a clear basis for emission distribution. Attached Figure Description

[0016] 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 of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] Please see Figure 1 This invention provides a method for calculating carbon emissions from building construction projects, comprising the following steps: S1: Obtain fuel consumption rate and mechanical load change curves through on-site monitoring, collect process operation radius data, and perform synchronous alignment processing on the fuel consumption rate and load change curves based on the time axis to generate a time-series synchronous dataset. S2: Identify the overlapping intervals of parallel processes based on the time-series synchronous dataset, perform superposition operation on the fuel consumption rates of multiple processes within the overlapping interval, extract the excess part when the superposition value exceeds the single machine load limit, and perform intersection operation after geometric processing of the working radius to generate the process cross-influence coefficient. S3: Call the process cross-influence coefficient to correct the process fuel consumption data, input the corrected data into the emission factor method to calculate carbon emissions, perform proportional adjustment on the calculation results based on the influence coefficient, and generate the corrected carbon emissions. S4: Perform a summary operation on the time axis based on the corrected carbon emissions, input the emissions into the weighted average method to calculate the total emissions, and obtain the total carbon emissions; S5: Call the total carbon emissions and the corrected carbon emissions to perform decomposition statistics, perform percentage calculation on the carbon emission contribution of each process, sort in descending order of emissions, and generate a carbon emission inventory for construction.

[0024] The time-series synchronous dataset includes fuel consumption characteristics, mechanical load characteristics, and operating radius distribution. The process cross-influence coefficient includes superimposed load overload, spatial intersection range, and interaction intensity. The corrected carbon emissions include time correction values, spatial correction values, and proportional correction values. The total carbon emissions include stage emissions, cumulative emissions, and weighted average emissions. The construction carbon emission inventory includes process contribution rate, emission level, and statistical results.

[0025] Please see Figure 2 The specific steps of S1 are as follows: S101: Obtain fuel consumption rate and mechanical load change curve through on-site monitoring, and compare the data of multiple sampling points of fuel consumption rate sequence with the rate of change of mechanical load curve at the same sampling point to generate time-series paired data frames. Based on the fuel consumption rate sequence data and mechanical load change curves obtained through on-site monitoring, the fuel flow meter installed on the Caterpillar 320 excavator was first timestamped with the engine control unit (ECU) to ensure that the data acquisition starting point was consistent, and the total monitoring time was set to [missing information]. seconds, data sampling frequency is Hertz, that is, every [time] Data is collected once per second, thereby obtaining data containing... The original sequence of fuel consumption rates for data points, for example, at the _th_ data point Second, the Second, the Second, the The fuel consumption rates collected per second were respectively Liters / hour Liters / hour Liters / hour The system calculates the instantaneous power by multiplying engine speed and torque by the mechanical load at the corresponding moment from the ECU, and then compares this instantaneous power with the equipment's rated power. By comparing kilowatts, the percentage of mechanical load is obtained. For example, the mechanical load values ​​at corresponding times are respectively... , , , Next, for each sampling point in the fuel consumption rate sequence, a temporal correspondence and comparison is performed with the rate of change on the mechanical load curve of its adjacent sampling point. Specifically, the difference between the mechanical load value of the next sampling point and the current sampling point is calculated and used as the rate of change of mechanical load corresponding to the current sampling point. For example, in the first sampling point... The rate of change of mechanical load at the sampling point of 1 second is determined by the 1st sampling point. Mechanical load value per second With the Mechanical load value per second Perform the subtraction operation to obtain the rate of change. , and in the The rate of change of mechanical load at the initial effective sampling point of second is determined by the [missing information - likely a specific sampling point or time period]. Mechanical load value per second With oneself Subtracting them, we get Then, the fuel consumption rate value at the current sampling point is paired with the calculated mechanical load change rate to generate a data tuple, such as in the first sampling point. seconds, the generated tuple is ( , ), in the Seconds, through the first Mechanical load value per second With oneself Subtract them to get the rate of change. This generates a binary tuple ( , ), for all This difference calculation and data pairing operation is repeated for each valid sampling point, eventually forming a data set containing three dimensions: timestamp, fuel consumption rate, and mechanical load change rate. This is the time-series paired data frame.

[0026] Table 1: Time Sequence Data of Excavator Operation Monitoring 5 15.2 45 5 10 16.1 50 35 15 20.5 85 −15 20 18.9 70 5 25 19.5 75 −20 As shown in Table 1, this table lists the monitoring and calculation data of some sampling points of the excavator during continuous operation. Among them, the mechanical load change rate is one of the key parameters for subsequent data aggregation.

[0027] S102: Based on time-series paired data frames, collect process operation radius data, and aggregate the operation radius data at multiple sampling points with the fuel consumption rate and mechanical load values ​​at the same sampling points in the time-series paired data frames to obtain a joint operation radius sequence. Based on the time-paired data frames generated in the aforementioned steps, which include a data set containing timestamps, fuel consumption rates, and mechanical load change rates, the excavator's operating radius data during the process is further collected. This data is acquired by a LiDAR sensor installed at the base of the excavator's boom, using the same sensor as the aforementioned data. At second intervals, the straight-line distance from the center point of the cab to the end of the bucket teeth is continuously measured and recorded, forming a point-to-point working radius data sequence on the time axis corresponding to the time-series paired data frames. For example, at timestamp 1... Second, Second, Second, At what time, the working radius measured by the sensor is respectively rice, rice, rice, Next, the data from the operating radius sequence at multiple sampling points are aggregated with the data from the time-series paired data frames on fuel consumption rate and mechanical load change rate at the same sampling points. Specifically, this aggregation operation uses the timestamp as a unique index to merge the three independent data sequences into a new data structure. For any given sampling time point... The corresponding data points have changed from the original binary (fuel consumption rate) Mechanical load change rate Expanded into a ternary set (fuel consumption rate) Mechanical load change rate Operating radius Taking the aforementioned example data as an example, at timestamps... The extracted fuel consumption rate is seconds per second. Liters per hour, mechanical load change rate At the same time, the operating radius at that moment was obtained. Meters, these three values ​​are combined to form data points ( , , Similarly, at timestamps Data points are formed in seconds. , , This aggregation process will traverse... A valid time sampling point is used to bind the three parameters at each time point, and finally generate a joint sequence of operation radius.

[0028] S103: Based on the joint sequence of operating radii, compare the synchronicity of multiple parameters in the process at the same sampling point, and align the fuel consumption rate and mechanical load curve along the time axis to establish a time-series synchronization dataset; Based on the generated joint sequence of job radii, which includes each A complete dataset containing timestamps at second-by-second sampling intervals, fuel consumption rates, mechanical load change rates, and operating radii is used to compare the synchronicity of multiple parameters in the sequence point by point. This synchronicity comparison process employs a timestamp tolerance threshold, which is set with reference to the data transmission delay characteristics of the airborne sensor network. Specifically, the threshold is set by statistically analyzing a... During a test cycle of minutes, the average latency for uploading sensor data to the data logger was [value missing]. milliseconds, maximum latency is milliseconds, therefore the tolerance threshold Set to maximum delay rounded up to milliseconds, i.e. Milliseconds, during comparison, refer to any preset sampling time point. (For example (seconds), check the fuel consumption rate corresponding to that time point. Mechanical load and operating radius The actual timestamps of the three data points , , Calculate the absolute difference between each actual timestamp and the preset sampling time point. For example, if Second, Second, If the time interval is 1 second, then their absolute differences are respectively millisecond, milliseconds and Milliseconds, since all three differences are less than the set value. The millisecond threshold determines whether this set of data is in the millisecond range. The moments are synchronized; if at another point in time... The timestamp of its operating radius data is Seconds, compared to the preset The difference in seconds is Milliseconds, exceeding The millisecond threshold will then The entire data at any given time (including) , , After removing the data from the sequence, the data that passed the synchronization test are strictly sorted according to their timestamp order. The fuel consumption rate and the mechanical load curve (and its derived rate of change) are aligned along the time axis. This alignment operation is to organize the data structure to ensure that each row in the data table represents a unique, time-precise sampling moment that has passed the synchronization test, and there are no rows with misaligned timestamps or missing data. This establishes a time-series synchronized dataset.

[0029] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the time-series synchronization dataset, retrieve the start and end timestamps of parallel processes, aggregate the index of overlapping time segments, align the process fuel consumption rate sequence with the time axis, and perform numerical overlay processing to generate a fuel overlay rate sequence. Based on a time-series synchronized dataset containing synchronization data from two pieces of equipment on site (denoted as excavator A and bulldozer B), the parallel execution procedures of the two pieces of equipment are first retrieved from the construction logs. The start and end timestamps of the "excavation of the foundation pit" procedure performed by excavator A are determined to be the [number missing]. Seconds to the The start and end timestamps for bulldozer B performing the "earthwork backfilling" operation are the [number]th [number]. Seconds to the Seconds, then compare these two time intervals, calculate their intersection, and obtain the segment where the time overlaps as the [number]. Seconds to the The time interval is calculated in seconds, and the corresponding row indices in the time-series synchronized dataset are aggregated within this time period. For example, since the sampling interval is... Seconds, this paragraph will contain information from the index. (correspond (seconds) to index (correspond seconds) total One data point, then, regarding this At each time point, the fuel consumption rate sequence of excavator A was extracted. Fuel consumption rate sequence of bulldozer B Align the two sequences on the timeline and for each identical timestamp (in from arrive The corresponding fuel consumption rate value is subjected to numerical superposition processing. Specifically, the two values ​​are added together. For example, in the first... Seconds (index) The fuel consumption rate of excavator A was found to be [value] from the dataset. Liters per hour, bulldozer B is If the rate is liters per hour, then the superimposed rate at that moment is... Liters per hour, at the Seconds (index) The speed of excavator A is Liters per hour, bulldozer B is Liters per hour, the superimposed rate is Liters per hour, apply this overlay operation to the overlapping sections. At each time point, a fuel superposition rate sequence is ultimately generated.

[0030] Table 2: Fuel Consumption Data for Parallel Processes 1800 21.2 19.5 40.7 1805 21.5 19.8 41.3 1810 22.1 20.5 42.6 1815 20.8 21.1 41.9 1820 23.0 21.8 44.8 As shown in Table 2, this table displays the fuel consumption rate of the two devices at some sampling points during the time overlap of the parallel processes, as well as the fuel superposition rate obtained after numerical superposition processing.

[0031] S202: Call the fuel superposition rate sequence, compare the superposition rate value with the single unit load upper limit threshold one by one, extract the excess part and record the time node, index and label the node to obtain the excess load index set; Call the fuel superposition rate sequence generated in the preceding steps. This sequence consists of the total fuel consumption rate of sampling points within the time overlap segment of parallel processes. For example, the sequence value is [ , , , , […] liters / hour, and set a single-machine load upper limit threshold. This threshold is set with reference to the original operating data of the main equipment on the construction site (taking the high-powered bulldozer B as an example), and statistically analyzing its operation when exceeding… Continuous operation under rated load Fuel consumption data over several minutes was used to calculate the consumption rate. The quantile is used as a threshold; specifically, the data is collected from bulldozer B at... The high-load operating condition sample data points are sorted in ascending order, and the first one is selected. The value at each position is obtained. Liters / hour, therefore the upper limit threshold for single-unit load is set as follows: Liters per hour, then, starting with the first value in the fuel stacking rate sequence, each stacking rate value is successively compared with this threshold. The numerical values ​​are compared using liters per hour. When the comparison result shows that the superposition rate value is greater than a threshold, the data point is determined to be an excess portion, and the corresponding time node is extracted. For example, for the first value of the sequence... Liters per hour, perform comparison calculations If the judgment result is true, then the corresponding time node will be... Record the seconds, for the second value Liters / hour, execute If the condition is true, record the time point. For each second, this comparison and extraction process is repeated for the data points in the sequence until the end of the sequence. Then, for the recorded time points, such as [ Second, Second, Second, Second, [seconds, ...], find the corresponding original row index number in the time-series synchronization dataset, for example [ , , , , ..., and the index numbers are grouped together to obtain the overload index set.

[0032] S203: Based on the overload index set, locate the operation radius parameter corresponding to the parallel process, perform geometric mapping to a circular area, perform intersection operation and calculate the area size, and generate the process cross-influence coefficient; Based on the overload index set, i.e. [ , , , , [, ...], first locate the first index in the index set. (corresponding timestamp) (seconds), query the index row in the time-series synchronization dataset, extract the working radius parameters corresponding to the two machines (excavator A and bulldozer B) in the parallel process, and at this moment, the working radius of excavator A. for Meters, the operating radius of bulldozer B for Meanwhile, the real-time position coordinates of the two devices are obtained from the vehicle's GPS module. The center point coordinates of excavator A are recorded as follows: The coordinates of the center point of bulldozer B are Subsequently, the two machines and their operating radii were geometrically mapped, and the operating area of ​​excavator A was mapped to points. With center and radius as The circular area of ​​rice Map the working area of ​​bulldozer B to points With center and radius as The circular area of ​​rice Next, perform an intersection operation on the two circular regions and calculate the area of ​​the intersection region, starting by calculating the distance between the centers of the two circles. meters, due to the sum of the two radii Meters greater than the distance between the centers The two circular regions intersect, and the area of ​​the intersection is meters. The result is obtained by calculation using a specific geometric formula. Finally, to convert this absolute area value into a standardized coefficient, the sum of the areas of the two circular regions is calculated. Square meters, then divide the intersection area by the total area, that is... This result will be used as the basis for... The cross-influence coefficient of the process at a given second.

[0033] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the process cross-influence coefficient and process fuel consumption data, retrieve the corresponding coefficient value for each item of the consumption data, perform item-by-item multiplication and record it, perform weighted superposition on the recorded values, and generate a corrected fuel consumption sequence. Based on the sequence of cross-influence coefficients of processes, such as in timestamps Second, Second, The second-hour coefficient values ​​are respectively , , And the original fuel consumption data of excavator A and bulldozer B during the recorded parallel operations. First, for each time point where there is cross-influence (i.e., time point where the coefficient is not zero), the fuel consumption rate of the two machines at that time is retrieved, so as to... Taking seconds as an example, the consumption rate of excavator A for Liters per hour, the consumption rate of bulldozer B for liters per hour, then perform a product operation to calculate the total fuel consumption rate at that moment. Liters per hour and the corresponding process cross-influence coefficient Multiply by the product to obtain the additional fuel consumption caused by the overlapping operations. The fuel consumption is calculated in liters per hour. This additional consumption is then weighted and allocated, with the weighting coefficients set based on the contribution of each machine to the total fuel consumption at the current moment. Specifically, the weight of excavator A is calculated as follows: It is the proportion of its fuel consumption value to the total consumption value, that is The weight of bulldozer B yes Multiply the additional fuel consumption by its corresponding weight to obtain the correction value allocated to excavator A. The correction value allocated to bulldozer B is liters per hour. The recorded correction value is then overlaid with the original consumption data to obtain the output of excavator A at [volume per hour]. The corrected fuel consumption value per second is Liters per hour, the correction value for bulldozer B is For time points with cross-influence, repeat the retrieval, product, weight calculation, and superposition process to generate a corrected fuel consumption sequence.

[0034] Table 3: Revised Fuel Consumption Calculation Table 1800 0.0155 21.529 19.802 1805 0.0182 21.879 20.174 1810 0.0201 22.535 20.919 1815 0.0195 21.211 21.505 1820 0.0233 23.535 22.310 As shown in Table 3, this table lists the corrected fuel consumption rates of some sampling points calculated based on the process cross-influence coefficient. These values ​​will serve as the basis for subsequent carbon emission calculations.

[0035] S302: Call the corrected fuel consumption sequence, perform product calculation on the sequence values ​​and emission factor parameters item by item, and then sum the calculated values ​​and fit the matrix structure to generate a set of carbon emission calculated values. Call the corrected fuel consumption sequence for excavator A and bulldozer B, for example in The corrected fuel consumption rates of the two devices per second are respectively liters / hour and The figure is calculated in liters per hour, and an emission factor parameter is introduced. This parameter is set with reference to the recommended values ​​for diesel combustion in the "Guidelines for the Compilation of Provincial Greenhouse Gas Inventories," and its value is the amount of emissions produced per liter of diesel combustion. kilograms of carbon dioxide, i.e. kgCO2 / L. Next, for each value in the corrected fuel consumption sequence, the emission factor parameter is multiplied item by item. This calculation also needs to take into account the time interval of data sampling, since the sampling frequency is... Hertz, where each data point represents a time span of . Seconds, that is Hours, therefore, in At this point in time, excavator A is... The carbon emissions calculated within a second interval are kilograms; similarly, the carbon emission calculation value for bulldozer B is [value missing]. kilograms, for the entire parallel process time period ( Instant seconds) This multiplication operation is repeated for each sampling point, and the carbon emission values ​​of the two devices calculated at each time point are summed. For example, the carbon emission values ​​of the first two time points ( Seconds and The total emissions (per second) are The calculated emissions from the devices at each time point are then organized and fitted into a matrix structure. Each row of the matrix represents a device, and each column represents a timestamp. The elements in the matrix represent the carbon emissions of the corresponding device within the corresponding time interval, thus generating a set of carbon emission calculation values.

[0036] S303: Based on the cross-influence coefficient of the process, adjust the values ​​of each item in the carbon emission calculation set proportionally, and integrate the adjusted values ​​into a unified sequence to generate the corrected carbon emission amount; Based on the sequence of cross-influence coefficients of the processes, for example [ [, and a set of carbon emission calculations, which includes the carbon emissions of excavator A and bulldozer B at each point in time, for example in seconds and hours are respectively kilograms and First, extract the carbon emission values ​​of each device from the set, and find their process cross-influence coefficients at the same timestamp. Then, perform a proportional adjustment on the extracted carbon emission values. The specific calculation process for this adjustment is to compare the original carbon emission values ​​with... Process cross-influence coefficient The product of these is used as the adjusted value. Taking excavator A at a given moment as an example, its adjusted carbon emission value is kilograms, for bulldozer B, its adjusted carbon emissions value is kilograms, in At 10:00, the initial emissions of excavator A were 100%. kilograms, with a cross-influence coefficient of 1. Then its adjusted value is The proportional adjustment operation is repeated for all values ​​in this set of carbon emission calculations, and the adjusted values ​​are then integrated into two independent and unified time series according to equipment type and time sequence. These two series are the final corrected carbon emissions for excavator A and bulldozer B.

[0037] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the corrected carbon emissions, aggregate the emission data from multiple time points on the time axis, call the carbon emission values ​​in the emission data frames at each time point, accumulate the values ​​in chronological order and sort them in ascending order to generate the total emissions for the time period. Based on the corrected carbon emission sequence of excavator A and bulldozer B, this sequence is for the time period of parallel operation ( Instant Within seconds Carbon emission data at second intervals is first aggregated by performing an aggregation operation on the emission data at each same time point on the time axis. Specifically, this involves calling... Extract the corrected carbon emission value of excavator A from the data frame at the second time point. Corrected carbon emissions per kilogram and bulldozer B kilograms, summing these two values, yields the aggregate emissions at that time point. Kilograms, and then, numerical accumulation of this aggregated emission sequence was performed in chronological order, with the initial accumulation value being the first time point ( (seconds) kilograms, then process the second time point ( (seconds), its aggregate emissions were calculated to be If the weight is kilograms, the cumulative value is updated to [value]. kilograms, then process a third time point ( (seconds), its aggregate emissions are kilograms, cumulative value updated to kilograms, applying this polymerization and accumulation process to from Instant total seconds All data at each time point, since the accumulation process is performed chronologically, naturally form an ascending sequence. After processing the last time point ( After collecting data for each second, the final cumulative value is the total emissions for that time period. This is after analyzing all... The total emissions for this parallel process time period are calculated by performing calculations on each data point. kilogram.

[0038] S402: Call the total emissions for a time period. For the emissions values ​​in different time periods, retrieve the set of weight parameters, perform weighted processing on the emissions values ​​and corresponding weights, and perform continuous calculations to obtain the weighted cumulative emissions value. The system retrieves total emissions for different time periods. For example, it divides a workday into three time periods and calculates the morning peak period (…). - The total emissions were kilograms, during the afternoon off-peak period ( - The total emissions were kilograms, during the evening low period ( - The total emissions were The data is collected in kilograms, and a preset set of weighted parameters is retrieved for these three time periods. This set of weighted parameters is set according to the "Guidelines for the Control of Construction Dust and Exhaust Gas Emissions" issued by the local environmental protection department. These guidelines set risk levels for different time periods based on atmospheric diffusion conditions and environmental sensitivity at different times of the day. Specifically, the risk level for the morning peak period is set as "high" due to the presence of a temperature inversion layer and the difficulty in pollutant dispersion, with a base score of [missing information]. Atmospheric convection is active during the afternoon off-peak period, with a risk level of "medium" and a baseline score of [missing information]. As evening approaches and traffic decreases, the risk level is "low," with a base score of [missing information]. The base scores are normalized to calculate the final weights, and the total score is... The weight of the morning session is then... The weight of the afternoon period is The weight of the evening period is Next, the emission values ​​for each time period are weighted according to their corresponding weights to calculate the weighted emission value for the morning period. kilograms were obtained by calculating the afternoon period. kilograms were obtained by calculating the weight during the evening period. Kilograms, by performing this continuous calculation over a set time period, yields the weighted cumulative emission value.

[0039] Table 4: Weighted Emission Calculation Table for Construction Days by Time Period 08:00−11:00 45.5 high 0.455 20.70 13:00−16:00 38.2 middle 0.303 11.57 16:00−18:00 15.8 Low 0.242 3.82 As shown in Table 4, this table displays the weighting coefficients set according to the environmental risk level of different construction periods, and the process and results of using the coefficients to calculate the total original carbon emissions for each period.

[0040] S403: Based on the weighted cumulative emission value, call the cumulative result to perform a unified summation operation, and perform numerical compression processing on the summation value according to the normalization parameter set to obtain the total carbon emissions; Based on the weighted cumulative emissions, i.e., the set of weighted emissions values ​​for a given time period [ , , First, the accumulated result is called to perform a unified summation operation, adding the values ​​in the set together. The calculation process is as follows: The total weighted emissions for the workday are calculated in kilograms. Then, this summation is numerically compressed using a normalized parameter set. The normalization here employs a linear mapping method. The normalized parameter set is set according to the daily carbon emission limit determined in the project's environmental impact assessment report. This report stipulates that, for earthwork projects of the current scale, the daily carbon dioxide emission limit is set at [amount missing]. kilograms, while the ideal minimum emission state, i.e., no construction work, has an emission level of [missing information]. kilograms, therefore, the summation value is... When kilograms are substituted into the linear mapping calculation, the specific operation is to divide the difference between the current value and the minimum value by the difference between the maximum value and the minimum value, that is... This calculation converts absolute emission values ​​into a value located at... arrive The relative values ​​within the intervals are used to ultimately obtain the total carbon emissions.

[0041] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the total carbon emissions and the corrected carbon emissions, the carbon emission data frames of the process are grouped and split, the ratio of the carbon emissions of a single process to the total carbon emissions is converted, and the comparison results are standardized and mapped to generate a process carbon emission ratio matrix. Its normalized value is based on total carbon emissions. The corresponding unnormalized daily total emissions are Kilograms, and the time series of carbon emissions corrected for each process, are first grouped and split into groups based on the process carbon emission data frames for the entire project. This operation categorizes the data for the corresponding time periods in the corrected carbon emission series according to the process name and start and end times recorded in the construction log. For example, the time period for the "foundation pit excavation" process (process index 1) is identified as the [missing information]. Seconds to the Seconds, the "earthwork backfilling" process (process index 2) is the first Seconds to the Seconds, the "rebar tying" process (process index 3) is the first... Seconds to the The carbon emissions for the excavation of the foundation pit are summed up over the specified time intervals. kilograms, "earthwork backfill" is kilograms, "steel bar binding" is The total weight of kilograms and the remaining processes is 1 kilogram. Kilograms, next, the carbon emissions of a single process will be compared with the total daily carbon emissions. For the ratio conversion of kilograms, the ratio for "foundation pit excavation" is calculated as follows: For "earthwork backfilling", the ratio is For "reinforcing bar binding", the ratio is Then, the calculated ratio is standardized by mapping the process index number, process name, and corresponding carbon emission ratio into a structured data record. For example, for process index 1, the record {process index: 1, process name: "excavation", emission ratio:} is generated. Repeat this ratio calculation and mapping process for the already separated processes to generate a process carbon emission ratio matrix.

[0042] S502: Call the process carbon emission percentage matrix, perform percentage expansion on the percentage values ​​in the matrix, map the expanded values ​​to the percentage axis range, and perform annotation matching between the process index and the expanded values ​​to obtain the process carbon emission percentage sequence; The process carbon emission percentage matrix is ​​accessed. This matrix contains the process index and its corresponding carbon emission percentage. For example, the record could be: {Process Index: 1, Process Name: "Foundation Pit Excavation", Emission Percentage:} }, {Process Index: 2, Process Name: "Earthwork Backfilling", Emission Ratio:} }, {Process Index: 3, Process Name: "Rebar Binding", Emission Ratio:} First, for each scale value within the matrix, for example... To perform a percentage-based extension, the specific calculation involves multiplying the decimal value by... ,get This operation converts the original ratio value into a percentage value, and then expands this value... Mapping to the percentage axis range, that is, mapping the numerical value to the percentage sign "". "Concatenate the characters to form..." The visualization is then used to represent the process in the construction log, such as process index 1, and its corresponding extended value. Perform annotation matching to establish a one-to-one correspondence, i.e., (process index 1, ... Repeat this complete process of percentage expansion, mapping, and label matching for the emission percentage data of each process in the matrix. For example, for the percentage of process index 2... Processing to obtain And form a matching pair (process index 2, ), the proportion of process index 3 Processed Form a matching pair (process index 3), The matching pairs are arranged in the natural order of the process index to obtain the process carbon emission percentage sequence.

[0043] S503: Based on the carbon emission percentage sequence of the process, sort the percentage values ​​in descending order, and then perform aggregation processing on the sorted process index and percentage values ​​to obtain the carbon emission inventory of building construction. Based on the process carbon emission percentage sequence, this sequence includes process indices and their corresponding carbon emission percentages, for example, [(process index 1, ... (Process Index 2) (Process Index 3) (Summary of remaining processes) First, sort the percentage values ​​in this sequence in descending order. Specifically, compare the percentage values ​​of each element in the sequence, and sort the first element... With the second element Comparison, because Remove the second element from the left, and then set the current maximum value. With the third element Comparison, because The position remains unchanged, continuing with the fourth element. Comparison, because Move the fourth element to the first position and repeat this comparison and swapping process until the elements are arranged in descending order of percentage value, resulting in the sorted sequence [(summary of remaining steps, ... (Process Index 2) (Process Index 1,) (Process Index 3) Subsequently, the sorted process index (or name) and its corresponding percentage value are aggregated to integrate the information into a structured list format, which uses the ranking, process name and carbon emission contribution percentage as fields to obtain the carbon emission list of construction.

[0044] Table 5: Carbon Emission Inventory of Building Construction 1 Summary of other processes 44.84 2 earthwork backfill 22.22 3 Foundation pit excavation 19.42 4 Rebar tying 13.52 As shown in Table 5, this table clearly demonstrates the contribution of different construction processes to total carbon emissions by arranging the carbon emission percentages of each process in descending order.

[0045] Please see Figure 7 A carbon emission calculation system for building construction projects, including: The data acquisition module obtains fuel consumption rate and mechanical load change curves through on-site monitoring, collects process operation radius data, and performs synchronization alignment processing on the fuel consumption rate and load change curves based on the time axis to generate a time-series synchronization dataset, which is then transmitted to the load synchronization module. The load synchronization module identifies the overlapping intervals of parallel processes based on the time-series synchronization dataset, performs superposition calculation on the fuel consumption rates of multiple processes within the overlapping interval, extracts the excess part when the superposition value exceeds the single machine load limit, performs intersection calculation after geometric processing of the working radius, generates the process cross-influence coefficient, and passes it to the cross-correction module. The cross-correction module performs correction calculations on fuel consumption data based on the cross-influence coefficient of the process, inputs the corrected data into the emission factor method to calculate carbon emissions, performs proportional adjustments on the calculation results based on the influence coefficient, generates corrected carbon emissions, and transmits them to the emission accounting module. The emissions accounting module performs a summary calculation on the time axis based on the corrected carbon emissions, inputs the corrected emissions data into the weighted average method for total calculation, performs weighted processing on the emissions data, calculates the total carbon emissions, and transmits it to the inventory statistics module. The inventory statistics module calls the total carbon emissions and corrected carbon emissions data to perform decomposition and statistical calculations, calculates the percentage contribution of each process to carbon emissions, sorts the processes in descending order based on the emission values, calculates the proportion of each process in the total emissions, and generates a carbon emissions inventory for construction.

[0046] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for calculating carbon emissions from building construction projects, characterized in that, Includes the following steps: S1: Obtain fuel consumption rate and mechanical load change curves through on-site monitoring, collect process operation radius data, and perform synchronous alignment processing on the fuel consumption rate and load change curves based on the time axis to generate a time-series synchronous dataset. S2: Based on the time-series synchronization dataset, identify the overlapping interval of parallel processes, perform superposition operation on the fuel consumption rate of multiple processes within the overlapping interval, extract the excess part when the superposition value exceeds the upper limit of single machine load, and perform intersection operation after geometric processing of the working radius to generate process cross-influence coefficient. The specific steps of S2 are as follows: S201: Based on the time-series synchronization dataset, retrieve the start and end timestamps of parallel processes, aggregate the time overlap segment index, align the process fuel consumption rate sequence with the time axis and perform numerical superposition processing to generate a fuel superposition rate sequence. S202: Call the fuel superposition rate sequence, compare the superposition rate value with the single unit load upper limit threshold one by one, extract the excess part and record the time node, index and label the node to obtain the excess load index set; S203: Based on the overload index set, locate the operation radius parameter corresponding to the parallel process, perform geometric mapping to a circular region, perform intersection operation and calculate the area size, and generate the process cross-influence coefficient; S3: Call the process cross-influence coefficient to correct the process fuel consumption data, input the corrected data into the emission factor method to calculate carbon emissions, and perform proportional adjustment on the calculation results based on the process cross-influence coefficient to generate corrected carbon emissions. S4: Based on the corrected carbon emissions, perform aggregation calculations on emission data at multiple time points on the time axis, input the emissions into the weighted average method to calculate the total emissions, and obtain the total carbon emissions; S5: Call the total carbon emissions and the corrected carbon emissions to perform decomposition statistics on the process carbon emissions data frame, perform percentage calculation on the process carbon emissions contribution, sort in descending order of emissions, and generate a construction carbon emissions inventory.

2. The method for calculating carbon emissions from building construction projects according to claim 1, characterized in that, The time-series synchronous dataset includes fuel consumption characteristics, mechanical load characteristics, and operating radius distribution. The process cross-influence coefficient includes superimposed load overload, spatial intersection range, and interaction intensity. The corrected carbon emissions include time correction values, spatial correction values, and proportional correction values. The total carbon emissions include stage emissions, cumulative emissions, and weighted average emissions. The construction carbon emission inventory includes process contribution rate, emission level, and statistical results.

3. The method for calculating carbon emissions from building construction projects according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain fuel consumption rate and mechanical load change curves through on-site monitoring, and compare the data of multiple sampling points of the fuel consumption rate sequence with the rate of change of the mechanical load curve at the same sampling point to generate time-series paired data frames. S102: Based on the time-series paired data frame, collect the process operation radius data, and aggregate the operation radius data at multiple sampling points with the fuel consumption rate and mechanical load value data at the same sampling point in the time-series paired data frame to obtain the joint operation radius sequence; S103: Based on the joint sequence of the working radius, compare the synchronicity of multiple parameters in the process at the same sampling point, and align the fuel consumption rate and mechanical load curve along the time axis to establish a time-series synchronization dataset.

4. The method for calculating carbon emissions from building construction projects according to claim 3, characterized in that, The fuel superposition rate sequence is a sequence obtained by superimposing the fuel consumption rate sequences of multiple parallel processes point by point after aligning them on the time axis. The single-machine load limit threshold is the fuel consumption rate that a single device can withstand under rated operating conditions.

5. The method for calculating carbon emissions from building construction projects according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the process cross-influence coefficient and process fuel consumption data, retrieve the corresponding coefficient value for each item of the consumption data, perform item-by-item multiplication and record it, perform weighted superposition on the recorded values, and generate a corrected fuel consumption sequence. S302: Call the modified fuel consumption sequence, perform product calculation on the sequence values ​​and emission factor parameters item by item, and then sum the calculated values ​​and fit the matrix structure to generate a set of carbon emission calculated values. S303: Based on the process cross-influence coefficient, adjust the values ​​of each item in the carbon emission calculation set proportionally, and integrate the adjusted values ​​into a unified sequence to generate the corrected carbon emission amount.

6. The method for calculating carbon emissions from building construction projects according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Based on the corrected carbon emissions, aggregate the emission data at multiple time points on the time axis, call the carbon emission values ​​in the emission data frames at each time point, accumulate the values ​​in chronological order and arrange them in ascending order to generate the total emissions for the time period. S402: Call the total emissions for the time period, retrieve the weight parameter set for the emissions values ​​for different time periods, perform weighted processing on the emissions values ​​and corresponding weights and perform continuous calculations to obtain the weighted cumulative emissions value. S403: Based on the weighted cumulative emission value, call the cumulative result to perform a unified summation operation, and perform numerical compression processing on the summation value according to the normalization parameter set to obtain the total carbon emissions.

7. The method for calculating carbon emissions from building construction projects according to claim 6, characterized in that, The total emissions over the time period refers to the value obtained by summing up the carbon emissions at each point in time within a preset time interval. The weighted cumulative emission value refers to the value obtained by multiplying the total emissions over a time period by the corresponding weight parameters and then summing them up segment by segment. The normalization adopts a linear mapping method, which performs numerical mapping processing on the weighted cumulative emission values ​​according to the normalization parameter set to generate values ​​within a unified range.

8. The method for calculating carbon emissions from building construction projects according to claim 6, characterized in that, The specific steps of S5 are as follows: S501: Based on the total carbon emissions and the corrected carbon emissions, the carbon emission data frames of the process are split into groups, the ratio of carbon emissions of a single process to total carbon emissions is converted, and the comparison results are standardized and mapped to generate a process carbon emission ratio matrix. S502: Call the process carbon emission percentage matrix, perform percentage expansion on the percentage values ​​in the matrix, map the expanded values ​​to the percentage axis range, and perform label matching between the process index and the expanded values ​​to obtain the process carbon emission percentage sequence; S503: Based on the carbon emission percentage sequence of the process, sort the percentage values ​​in descending order, and then perform aggregation processing on the sorted process index and percentage values ​​to obtain a carbon emission inventory of building construction.

9. A carbon emission calculation system for building construction projects, characterized in that, The system is used to implement the carbon emission calculation method for building construction projects according to any one of claims 1-8, and the system includes: The data acquisition module obtains fuel consumption rate and mechanical load change curves through on-site monitoring, collects process operation radius data, and performs synchronization alignment processing on the fuel consumption rate and load change curves based on the time axis to generate a time-series synchronization dataset, which is then transmitted to the load synchronization module. The load synchronization module identifies the overlapping interval of parallel processes based on the time-series synchronization dataset, performs superposition operation on the fuel consumption rate of multiple processes within the overlapping interval, extracts the excess part when the superposition value exceeds the single machine load limit, performs intersection operation after geometric processing of the working radius, generates process cross-influence coefficient, and transmits it to the cross-correction module. The cross-correction module performs correction calculations on the process fuel consumption data based on the process cross-influence coefficient, inputs the corrected data into the emission factor method to calculate carbon emissions, performs proportional adjustments on the calculation results based on the process cross-influence coefficient, generates corrected carbon emissions, and transmits them to the emission accounting module. The emission accounting module performs aggregation calculations on emission data at multiple time points on the time axis based on the corrected carbon emission amount, inputs the corrected emission amount data into the weighted average method for total calculation, performs weighted processing on the emission data, calculates the total carbon emission amount, and transmits it to the inventory statistics module. The inventory statistics module calls the total carbon emissions and corrected carbon emissions data to perform decomposition and statistical operations on the process carbon emissions data frame, performs percentage calculations on the process carbon emissions contribution, sorts the processes in descending order based on the emissions values, and calculates the proportion of the processes in the total emissions to generate a construction carbon emissions inventory.

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