A method for quantifying and optimizing uncertainty of road engineering carbon emission accounting results

Abstract

This invention relates to the field of carbon emission data analysis technology, and particularly to a method for quantifying and optimizing the uncertainty of carbon emission accounting results in road engineering projects. The method includes the following steps: acquiring full-process data for carbon emission accounting in road engineering projects; identifying carbon emission uncertainties based on the full-process data to obtain carbon emission uncertainty factor data; performing various types of uncertainty quantification analysis on the carbon emission uncertainty factor data to generate carbon emission accounting uncertainty quantification data; designing carbon emission accounting optimization strategies based on the carbon emission accounting uncertainty quantification data; evaluating the effectiveness of the optimization strategies to obtain carbon emission accounting optimization strategy evaluation; and intelligently adjusting the carbon emission accounting optimization strategy data using the carbon emission accounting optimization strategy evaluation data. This invention achieves intelligent management and control of the quantification and optimization of the uncertainty of carbon emission accounting results in road engineering projects.

Description

Technical Field

[0001] This invention relates to the field of carbon emission data analysis technology, and particularly to a method for quantifying and optimizing the uncertainty of carbon emission accounting results in road engineering. Background Technology

[0002] In the current global context of actively promoting low-carbon development, road engineering, as a significant source of carbon emissions, plays a crucial role in accurately calculating its carbon emissions for scientifically assessing the environmental impact of projects and effectively formulating energy conservation and emission reduction strategies. However, in actual calculation processes, numerous factors lead to significant uncertainties in the calculation results. For example, errors in data collection result in biased data; the calculation model structure is uncertain, making it difficult to adapt to the complex needs of different construction scenarios, and differences in different model structures directly affect the final calculation results; carbon emission parameters are uncertain, such as energy consumption parameters, emission factors, and construction procedure parameters, which may fluctuate due to different environmental conditions or data sources, further increasing the uncertainty of the calculation results. However, existing methods for quantifying and optimizing the uncertainty of carbon emission calculation results in road engineering remain at a single level of uncertainty handling, such as only correcting data collection errors, lacking systematic and correlational analysis between data errors, model structure uncertainties, and parameter uncertainties, and generally lacking optimization strategy design and dynamic adjustment for the quantified uncertainty results, failing to achieve closed-loop optimization of the entire calculation process. Summary of the Invention

[0003] Based on this, the present invention provides a method for quantifying and optimizing the uncertainty of carbon emission accounting results in road engineering, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for quantifying and optimizing the uncertainty of carbon emission accounting results in road engineering includes the following steps:

[0005] Step S1: Obtain the full process data of carbon emission accounting for road engineering; identify carbon emission uncertainties based on the full process data of carbon emission accounting for road engineering to obtain carbon emission uncertainty data, wherein the carbon emission uncertainty data includes carbon emission acquisition error factor data, carbon emission accounting model structure uncertainty factor data, and carbon emission parameter uncertainty factor data.

[0006] Step S2: Perform quantitative analysis of various types of uncertainties in carbon emission accounting on the data of uncertain factors in carbon emission accounting, and generate quantitative data of uncertainties in carbon emission accounting;

[0007] Step S3: Design an optimization strategy for carbon emission accounting based on the quantification data of uncertainty in carbon emission accounting;

[0008] Step S4: Evaluate the effectiveness of the carbon emission accounting optimization strategy to obtain the carbon emission accounting optimization strategy evaluation, and perform intelligent adjustment of the carbon emission accounting optimization strategy data based on the carbon emission accounting optimization strategy evaluation data.

[0009] Furthermore, step S1 includes the following steps:

[0010] Step S11: Obtain full-process data for carbon emission accounting of road engineering projects;

[0011] Step S12: Perform carbon emission accounting process traceability analysis on the entire process data of road engineering carbon emission accounting, and generate carbon emission accounting process traceability data;

[0012] Step S13: Based on the carbon emission accounting process traceability data, perform carbon emission accounting process data classification processing on the entire process data of carbon emission accounting for road engineering, and generate carbon emission accounting process type data, wherein the carbon emission accounting process type data includes carbon emission collection type data, carbon emission accounting model structure type data, and carbon emission accounting parameter type data.

[0013] Step S14: Perform feature analysis on the subset of carbon emission accounting process types based on the carbon emission accounting process type data, and generate feature data for the subset of carbon emission accounting process types;

[0014] Step S15: Perform logical association feature analysis on the feature data of each type of accounting process type subset to generate logical association feature data of carbon emission accounting process type;

[0015] Step S16: Establish a carbon emission uncertainty factor type topology using carbon emission accounting process type data, use the characteristic data of the subset of accounting process types as topology nodes of carbon emission uncertainty factors, and use the logical association characteristic data of carbon emission accounting process types as association coefficients of topology nodes; map the topology nodes and association coefficients of carbon emission uncertainty factors to the topology structure of carbon emission uncertainty factors to establish the data storage structure of the carbon emission uncertainty factor topology network, so as to obtain the carbon emission uncertainty factor topology network, and output the carbon emission uncertainty factor data through the carbon emission uncertainty factor topology network.

[0016] Furthermore, step S2 includes the following steps:

[0017] Step S21: Based on the carbon emission collection error factor data, perform carbon emission collection uncertainty range analysis for each error factor, and generate carbon emission collection error factor uncertainty range data;

[0018] Step S22: Based on the data of uncertain factors in the carbon emission accounting model structure, perform a quantitative analysis of the uncertainty in the carbon emission accounting model structure to obtain quantitative data of the uncertainty in the carbon emission accounting model structure;

[0019] Step S23: Perform uncertainty quantification analysis based on the uncertainty factor data of carbon emission parameters to generate uncertainty quantification data of carbon emission parameters;

[0020] Step S24: Integrate the various types of uncertainty data for carbon emission accounting based on the uncertainty range data of carbon emission collection error factors, the uncertainty quantification data of carbon emission accounting model structure, and the uncertainty quantification data of carbon emission parameters, so as to obtain the uncertainty quantification data for carbon emission accounting.

[0021] Furthermore, step S21 includes the following steps:

[0022] The carbon emission collection error factors are analyzed to generate carbon emission collection numerical characteristic data.

[0023] To obtain the attribute characteristics of carbon emission collection error factors, an analysis of the carbon emission collection error factor attributes was conducted on the carbon emission collection error factor data.

[0024] Based on the characteristic data of carbon emission collection error factors, collect the corresponding energy consumption data of construction equipment and carbon emission factor data of materials;

[0025] By utilizing the energy consumption data of construction equipment and the carbon emission factor data of materials corresponding to the attribute characteristic data of carbon emission collection error factors, the carbon emission collection variation coefficient of each error factor is analyzed, and the carbon emission collection variation coefficient is generated.

[0026] Based on the coefficient of variation of carbon emission collection, the dispersion of carbon emission collection data under various error factors is analyzed to obtain the dispersion data of carbon emission collection.

[0027] By analyzing the uncertainty range of carbon emission collection for various error factors using carbon emission collection dispersion data, we can generate data on the uncertainty range of carbon emission collection error factors.

[0028] Furthermore, step S22 includes the following steps:

[0029] The carbon emission accounting model selection model is collected by collecting data on uncertain factors in the carbon emission accounting model structure, and a carbon emission accounting model library is established based on the selected carbon emission accounting model to obtain carbon emission accounting model library data.

[0030] Based on the data from the carbon emission accounting model library, we will conduct a carbon emission accounting benefit analysis of the models with different application scenarios to obtain carbon emission accounting benefit data.

[0031] Based on the model carbon emission accounting benefit data, a sensitivity characteristic analysis of the model carbon emission accounting for different application scenarios is conducted to obtain the model carbon emission accounting sensitivity characteristic data.

[0032] Based on the sensitivity characteristic data of carbon emission accounting model, a quantitative analysis of the structural uncertainty of carbon emission accounting model is conducted to obtain quantitative data of structural uncertainty of carbon emission accounting model.

[0033] Furthermore, step S23 includes the following steps:

[0034] Based on the data of uncertain factors in carbon emission parameters, we conduct a probability distribution characteristic analysis of carbon emission parameter accounting to generate carbon emission parameter accounting probability distribution characteristic data.

[0035] Based on the data of uncertain factors in carbon emission parameters, simulation analysis of carbon emission parameters is carried out to generate carbon emission parameter simulation data. Then, sampling interactive simulation analysis of carbon emission parameters is carried out through the carbon emission parameter simulation data to generate carbon emission parameter sampling interactive simulation data.

[0036] Based on the carbon emission parameter sampling interactive simulation data, the carbon emission accounting characteristics of the sampling interactive simulation parameters are analyzed to generate simulation parameter carbon emission accounting characteristic data.

[0037] Based on the carbon emission accounting characteristic data of the simulation parameters, the probability distribution characteristic of the carbon emission simulation parameters is analyzed to generate the carbon emission simulation parameter accounting probability distribution characteristic data. Then, based on the carbon emission parameter accounting probability distribution characteristic data and the carbon emission simulation parameter accounting probability distribution characteristic data, the accounting impact characteristic of the changes of each carbon emission parameter is analyzed to generate the carbon emission parameter accounting impact characteristic data.

[0038] Based on the impact characteristic data of carbon emission parameter accounting, uncertainty quantification analysis is performed on the uncertainty factor data of carbon emission parameters to generate carbon emission parameter uncertainty quantification data.

[0039] Furthermore, step S3 includes the following steps:

[0040] Step S31: Based on the uncertainty range of carbon emission collection error factors in the carbon emission accounting uncertainty quantification data, design and optimize the carbon emission collection equipment operation status monitoring engine;

[0041] Step S32: Design an optimization strategy for the carbon emission accounting model structure based on the carbon emission accounting model structure uncertainty quantification data in the carbon emission accounting uncertainty quantification data;

[0042] Step S33: Design a carbon emission parameter optimization strategy based on the carbon emission parameter uncertainty quantification data in the carbon emission accounting uncertainty quantification data;

[0043] Step S34: Integrate the optimization strategies for carbon emission accounting by optimizing the carbon emission collection equipment operation status monitoring engine, carbon emission accounting model structure optimization strategy, and carbon emission parameter optimization strategy to obtain the carbon emission accounting optimization strategy.

[0044] Furthermore, step S31 includes the following steps:

[0045] Design a carbon emission acquisition equipment operation status monitoring engine based on the uncertainty range of carbon emission acquisition error factors in the carbon emission accounting uncertainty quantification data;

[0046] Establish carbon emission collection equipment operation status maintenance rules and carbon emission collection equipment operation status multi-level screening rules. The carbon emission collection equipment operation status maintenance rules include intelligent calibration and intelligent maintenance of equipment operation status. The carbon emission collection equipment operation status multi-level screening rules include first-level rules to remove jump values, second-level rules to correct drift values, and third-level rules to mark suspicious values.

[0047] Based on the uncertainty range of carbon emission collection error factors, corresponding multi-level screening rule accuracy optimization parameters are designed. Then, the multi-level screening rule accuracy of the carbon emission collection equipment operation status is adaptively optimized using the multi-level screening rule accuracy optimization parameters to obtain the optimized multi-level screening rule for the carbon emission collection equipment operation status.

[0048] Based on the carbon emission collection equipment operation status maintenance rules and the carbon emission collection equipment operation status multi-level screening rules, the carbon emission collection equipment operation status monitoring engine is intelligently optimized to obtain an optimized carbon emission collection equipment operation status monitoring engine.

[0049] Furthermore, step S32 includes the following steps:

[0050] Based on the carbon emission accounting uncertainty quantification data and the carbon emission accounting sensitivity characteristic data of the model, carbon emission accounting model structure adaptation analysis is performed for different application scenarios to generate carbon emission accounting model structure adaptation data.

[0051] Intelligent optimization of model parameters for application scenario environmental factors is performed on carbon emission accounting model structure adaptation data to generate optimized carbon emission accounting model structure adaptation data.

[0052] Based on the data, an optimization strategy for the carbon emission accounting model structure is designed.

[0053] Furthermore, step S33 includes the following steps:

[0054] Based on the carbon emission parameter uncertainty quantification data in the carbon emission accounting uncertainty quantification data, high-sensitivity parameters of carbon emission uncertainty are extracted to obtain high-sensitivity parameters of carbon emission uncertainty.

[0055] A prior distribution analysis of the high-sensitivity parameters of uncertainty is performed on the historical data corresponding to these parameters to generate prior distribution data of the high-sensitivity parameters of uncertainty.

[0056] Based on the real-time data corresponding to the high-sensitivity parameters of carbon emission uncertainty, likelihood function analysis of the high-sensitivity parameters of uncertainty is performed to generate the likelihood function of the high-sensitivity parameters of uncertainty.

[0057] The preset Bayesian iterative algorithm is used to iteratively optimize the carbon emission uncertainty high-sensitivity parameters by processing the prior distribution data and the likelihood function of the uncertainty high-sensitivity parameters, thereby generating optimized carbon emission uncertainty high-sensitivity parameters.

[0058] A carbon emission parameter optimization strategy is designed based on the highly sensitive parameters of carbon emission uncertainty.

[0059] The beneficial effects of this application are as follows: This invention acquires and deeply analyzes the entire process data of carbon emission accounting in road engineering through a systematic approach, achieving accurate identification and structured organization of uncertain factors in carbon emissions. Process tracing analysis clarifies the source and flow path of the data, laying the foundation for subsequent classification and processing. Dividing the data into three tracing types—carbon emission collection, model structure, and parameters—further refines the dimensions of uncertainty sources, avoiding omissions or confusion in factor identification. Based on this, through subset feature analysis and logical correlation feature analysis, not only are the inherent attributes of each type of factor uncovered, but the correlations between different factors are also revealed. Finally, by constructing a topological network, these factors are structured and stored in the form of nodes and correlation coefficients, forming a clear and intuitive system of uncertainty factors. This structured processing method not only improves the comprehensiveness and accuracy of uncertainty factor identification but also provides a logically clear and well-defined data foundation for subsequent quantitative analysis, solving the problems of high analysis difficulty and low accuracy caused by scattered factors and ambiguous correlations in traditional identification methods. Targeted quantitative analysis methods are adopted for different types of carbon emission uncertainties, achieving scientific and systematic quantification of uncertainties throughout the entire process, providing a reliable basis for the design of subsequent optimization strategies. To address carbon emission collection error factors, numerical and attribute feature analysis, combined with coefficient of variation and dispersion calculations, precisely quantified the uncertainty range of collection errors, overcoming the subjectivity problem of relying solely on experience to estimate the error range in traditional methods. Regarding model structure uncertainty, by establishing a model library and analyzing the benefits and sensitivity characteristics under different scenarios, the impact of model selection on accounting results was quantified, avoiding systematic biases caused by the application of a single model. For parameter uncertainty, probability distribution analysis, simulation, and sampling interaction analysis revealed the influence of parameter fluctuations on accounting results, achieving a quantitative expression of parameter uncertainty. Finally, data integration resulted in complete quantitative data on carbon emission accounting uncertainty, comprehensively reflecting the magnitude and characteristics of uncertainty at each stage and clarifying the correlation between different types of uncertainty. This provides data support for the targeting and effectiveness of subsequent optimization strategies, overcoming the limitations of traditional local quantification methods in reflecting the uncertainty of the entire process. Based on the quantitative data on carbon emission accounting uncertainty, a targeted and systematic optimization strategy design was implemented, effectively improving the accuracy and reliability of carbon emission accounting. To address data acquisition errors, by optimizing the monitoring engine and multi-level filtering rules, combined with intelligent calibration and maintenance mechanisms, outliers in the acquired data can be accurately identified and corrected, reducing data errors at the source. To address model structure uncertainties, scenario adaptation analysis and intelligent parameter optimization ensure the applicability of the accounting model in different application scenarios, reducing systematic biases caused by improper model selection. To address parameter uncertainties, high-sensitivity parameter extraction and Bayesian iterative optimization significantly improve the accuracy of key parameters, greatly reducing the impact of parameter fluctuations on the accounting results.By integrating strategies, a full-chain optimization scheme covering data collection, model selection, and parameter setting has been formed, overcoming the limited optimization effects of traditional single-stage optimization and achieving a synergistic reduction of uncertainties in all aspects of carbon emission accounting. Benefit evaluation and intelligent tuning of optimization strategies ensure the effectiveness and dynamic adaptability of optimization measures, further improving the stability and accuracy of carbon emission accounting. Optimization strategy benefit evaluation quantifies the actual improvement effects of each strategy, clarifies the advantages and disadvantages of optimization measures, and provides a basis for strategy adjustment. Intelligent tuning based on evaluation data enables dynamic iteration of optimization strategies, continuously adjusting strategy parameters and execution methods through real-time feedback of new changes in the accounting process, ensuring that optimization measures always adapt to the needs of the actual accounting scenario. This not only avoids the problem of fixed strategies attenuating in complex and changing engineering environments but also enables the rational allocation of optimization resources according to accounting accuracy requirements and actual engineering conditions. While ensuring accounting accuracy, it improves the economy and efficiency of strategy execution, providing continuous and reliable optimization support for carbon emission accounting in road engineering.

[0060] Therefore, the uncertainty quantification and optimization method for carbon emission accounting results in road engineering of the present invention, by introducing the systematic identification and topological modeling of three types of factors—data acquisition error, model structure uncertainty, and parameter uncertainty—into the carbon emission accounting process of road engineering, not only achieves full-process coverage of multi-source uncertainties, but also clarifies the contribution and impact path of each uncertainty factor through quantitative evaluation and sensitivity ranking; further, by combining dynamic optimization strategy design and adaptive adjustment mechanism, the accounting system can achieve targeted correction and optimization under different operating conditions and constraints, thereby forming a closed-loop management process. This significantly improves the accuracy and reliability of carbon emission accounting results, solving the technical problem that carbon emission accounting only focuses on single-point correction or local adjustments, and improving the dynamic adaptability and resource allocation efficiency of the accounting system. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating the steps of a method for quantifying and optimizing the uncertainty of carbon emission accounting results in road engineering, as described in this invention.

[0062] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0063] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0064] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0065] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. Functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods. The term "and / or" as used herein includes any and all combinations of one or more of the associated items listed.

[0066] To achieve the above objectives, please refer to Figures 1 to 2 This invention provides a method for quantifying and optimizing the uncertainty of carbon emission accounting results in road engineering projects. In the embodiments of this invention, please refer to... Figure 1 The diagram shown is a flowchart illustrating the steps of a method for quantifying and optimizing the uncertainty of carbon emission accounting results in road engineering projects according to the present invention. The method includes the following steps:

[0067] To achieve the above objectives, a method for quantifying and optimizing the uncertainty of carbon emission accounting results in road engineering includes the following steps:

[0068] Step S1: Obtain the full process data of carbon emission accounting for road engineering; identify carbon emission uncertainties based on the full process data of carbon emission accounting for road engineering to obtain carbon emission uncertainty data, wherein the carbon emission uncertainty data includes carbon emission acquisition error factor data, carbon emission accounting model structure uncertainty factor data, and carbon emission parameter uncertainty factor data.

[0069] In this embodiment of the invention, during the stage of acquiring the entire process data for carbon emission accounting in road engineering and identifying uncertainties, the entire process data for carbon emission accounting in road engineering includes data acquisition procedures, model structure carbon emission accounting procedures, carbon emission parameter accounting procedures, and human verification procedures. The collected items include, but are not limited to: instantaneous fuel flow rate of construction equipment, generator set power consumption, hydraulic system power, mileage and load records of transport vehicles, weighing records of concrete and asphalt materials entering and leaving the site, carbon emission factors corresponding to material batches, construction procedure sequence logs, personnel work duration records, and environmental conditions (temperature, humidity) and geographical location information. Metering equipment includes flow meters, power meters, weighing sensors, GPS positioning terminals, and RFID tags. All collection points are equipped with equipment identification codes and calibration date records. The original observations are standardized in terms of time stamps and time zones, and standardized in terms of units of measurement and unified conversion of physical quantities. To complete the source tracing analysis of the accounting process, an event-level source table was constructed, recording the event ID, source device ID, acquisition time, original observation value, data accuracy level, and data quality score. A time series alignment algorithm was used for multi-source time synchronization, and the data confidence level of each record was calculated based on device calibration accuracy and historical error rate. Based on the source table, the process data was mapped according to classification rules: records containing device IDs and time series data and belonging to direct measurement items were classified as collected data; records originating from calculation processes and containing calculation formula version numbers were classified as model data. Subset feature analysis was performed on each type of data, calculating the mean, standard deviation, skewness, kurtosis, autocorrelation coefficient, seasonal component amplitude, and coefficient of variation. Trend and periodic features were extracted using time series decomposition methods to form subset feature vectors. Logical correlation analysis was performed on the subset feature vectors, using Pearson or Spearman correlation coefficients, mutual information measures, and Granger causality tests to assess the correlation and causal direction between variables, and partial correlation was used to control for potential confounding terms. Based on the aforementioned nodes and correlation coefficients, a topology structure for uncertainty factors is constructed: A subset of processes is used as topology nodes, with node attributes including data type, observation variance, confidence score, time stamp, and equipment calibration information; the weights between nodes are reset to normalized mutual information or partial correlation coefficients; detection and centrality analysis are performed on the topology network to identify highly coupled nodes and form a topology network data storage structure (node ​​table, edge table, attribute table, and version control table). The final output includes a list of topology nodes, an edge weight matrix, attributes for each node, and a source index for carbon emission uncertainty factor data, used for downstream uncertainty quantification analysis.

[0070] Step S2: Perform quantitative analysis of various types of uncertainties in carbon emission accounting on the data of uncertain factors in carbon emission accounting, and generate quantitative data of uncertainties in carbon emission accounting;

[0071] In this embodiment of the invention, during the stage of quantifying various types of uncertainties in carbon emission uncertainties, quantitative calculations are carried out in three parallel processes: acquisition error, model structure uncertainty, and parameter uncertainty. Finally, the three types of results are merged to form full-process uncertainty quantification data. For the uncertainty quantification of acquisition error, numerical feature analysis is first performed on each acquisition item based on the calculated subset characteristics to obtain the sample mean, sample standard deviation, interquartile range, and median absolute deviation. The coefficient of variation is calculated as an indicator of relative dispersion. The measurement error distribution is estimated based on equipment calibration accuracy and historical residuals. If the sample size is sufficient, parameter fitting (such as normal or log-normal) is used, and confidence intervals are obtained using bootstrapping; otherwise, an empirical distribution is used. Error propagation theory is used to perform a first-order Taylor expansion of the basic emission calculation formula to obtain an approximate variance propagation estimate, and the distribution pattern under data acquisition is analyzed using an empirical distribution. To quantify the uncertainty of model structure, firstly, a candidate model set is listed, and each model is evaluated on a set of predefined representative application scenarios to calculate the output bias, root mean square error, and systematic bias of each model. Sensitivity experiments are conducted on the internal structural parameters and computational paths of the models, using variance decomposition or Sobol sensitivity analysis to obtain the contribution ratio of model structure to output variance. Differences in output between models are considered as structural uncertainty, and the mean and variance of the model combination predictions are calculated using Bayesian model averaging or weighted set methods, thus obtaining the quantitative data of model structural uncertainty. To quantify parameter uncertainty, probability distribution feature fitting is performed on each key parameter, and distribution fit is tested. When the parameter distribution does not meet the parameter family assumptions, kernel density estimation or empirical distribution is used. A high-dimensional parameter sample set is generated based on stratified sampling, and sampling interaction simulation is performed. Regression and main effect analysis are conducted on the output results to obtain the first-order and total effect indices of the parameters. Second-order sensitivity analysis is performed on parameter pairs with significant interactions, and the interaction contributions are recorded. Finally, the three types of uncertainty are integrated using a hierarchical Monte Carlo or Bayesian hierarchical model: the outer layer sampling reflects the uncertainty of the model structure, the middle layer sampling reflects the uncertainty of the parameters, and the inner layer sampling reflects the measurement error. The overall emission distribution is obtained by summarizing the output. The contribution share of each type of uncertainty to the final accounting result is obtained by variance decomposition or contribution rate analysis, and carbon emission accounting uncertainty quantification data including confidence interval, sensitivity ranking and uncertainty contribution decomposition is generated.

[0072] Step S3: Design an optimization strategy for carbon emission accounting based on the quantification data of uncertainty in carbon emission accounting;

[0073] In this embodiment of the invention, during the stage of designing carbon emission accounting optimization strategies based on uncertain quantification data, three parallel and interconnected optimization paths are implemented: monitoring engine optimization, model structure optimization, and parameter optimization. For the design of the monitoring engine for the operating status of the acquisition equipment, multi-level data cleaning and anomaly handling rules are constructed. Level 1 rules are used to remove instantaneous jump values, with the judgment method being the sliding window midpoint and a Hampel filter, using window length and fault tolerance multiple as parameters. Level 2 rules are used to correct slow drift, employing a Kalman filter or exponential smoothing with a trend term to estimate the system state and drift components, and estimating the process noise and measurement noise covariance matrix using residual statistics from several past periods of the equipment. Level 3 rules are used to mark suspicious records, performing anomaly scoring based on multivariate Mahalanobis distance and historical confidence intervals; records exceeding a set threshold are entered into manual verification or a pending queue. The maintenance rules in the monitoring engine optimize parameters by minimizing the variance of the calibrated residuals while constraining the false alarm rate, using cross-validation and historical labeled events as performance acceptance criteria. The equipment operation status maintenance rules include schedule rules for calibration triggered by cumulative drift and maintenance suggestion rules triggered by error rate thresholds. Calibration priority is determined by ranking nodes according to their centrality in the topology network and their contribution to overall uncertainty. For the model structure optimization strategy, a mapping table from scene features to model performance is established. Scene features include road surface type, construction section length, material batch differences, and seasonal factors. The error distribution of the model in each scene is characterized using historical evaluation results, and then parameterized adaptation and weighted ensemble strategies are implemented for the model. Weight updates use a Bayesian update rule based on recent prediction errors or an exponentially weighted moving average method. After weight normalization, the weights are used to generate the model ensemble output. For the parameter optimization strategy, prior distributions are extracted based on identified high-sensitivity parameters, and prior construction is performed using their historical data. Subsequently, a likelihood function is constructed using real-time observation data, and the posterior distribution is updated using a Bayesian iterative algorithm. The iteration stops when the change in the posterior mean is below a preset threshold or the Gelman-Rubin convergent statistics reach a stable range. If necessary, targeted observations are obtained through directional field experiments to narrow the posterior uncertainty interval. The outputs of the three strategies are integrated in the form of a set of rule-based strategies: the monitoring engine outputs high-confidence cleaning rules and equipment maintenance plans, the model structure optimization outputs model weighting schemes and failover schemes, and the parameter optimization outputs parameter posterior distributions and calibration suggestions; the integration strategy is constrained by priority and resource constraints, and prioritizes the adjustment of the nodes that contribute the most to the overall uncertainty while saving resources.

[0074] Step S4: Evaluate the effectiveness of the carbon emission accounting optimization strategy to obtain the carbon emission accounting optimization strategy evaluation, and perform intelligent adjustment of the carbon emission accounting optimization strategy data based on the carbon emission accounting optimization strategy evaluation data.

[0075] In this embodiment of the invention, during the benefit evaluation and intelligent adjustment stage of the carbon emission accounting optimization strategy, a quantitative evaluation index system and rule-based adjustment logic are constructed. Evaluation indicators include the narrowing of uncertainty bandwidth (measured by the change in the width of the 95% confidence interval), the decrease in accounting bias (the reduction in the root mean square error relative to independent verification measurement points), and changes in sensitivity reordering (whether key contributing nodes have migrated). Benefit evaluation employs a rolling window comparative analysis: several equal-length windows are selected before and after strategy implementation, and the above indicators are calculated under the same operating conditions. Statistical significance is evaluated using paired nonparametric or parametric tests, the magnitude of the effect is calculated, and confidence intervals are provided. Intelligent adjustment rules are executed based on the evaluation results: when a strategy fails to reach a preset effect threshold within several consecutive evaluation windows, resource allocation parameters are automatically adjusted according to the node contribution rate. Resource adjustments include increasing the measurement frequency of contributing nodes, adding redundant measurement points, or increasing the calibration frequency. The triggering condition is that the node's contribution to the overall variance exceeds a predetermined proportion or the node's posterior variance growth rate exceeds a threshold. Model weights are automatically corrected using an error-driven exponential decay update formula. After the weights are updated, they are normalized, and the version number and timestamp are recorded to achieve strategy traceability. If the parameters show an upward trend after the posterior test, a targeted field test plan or sensor cross-calibration procedure is triggered to collect new observational evidence. All assessment and adjustment activities are recorded as strategy versions, assessment index values, and adjustment action logs, forming a closed-loop governance file. When the overall uncertainty fails to continue to decrease to the target range after several consecutive adjustments, a superior review process and manual review are triggered to determine whether hardware replacement or construction process improvement is needed. Through the above assessment and adjustment closed loop, an intelligent strategy iteration driven by quantitative indicators is formed. This iterative cycle continues until the uncertainty index reaches the preset compliance target or resource constraints reach the upper limit.

[0076] Furthermore, step S1 includes the following steps:

[0077] Step S11: Obtain full-process data for carbon emission accounting of road engineering projects;

[0078] In this embodiment of the invention, acquiring the entire process data for carbon emission accounting in road engineering includes comprehensive observation and metadata recording of three types of processes: carbon emission acquisition process, model-based carbon emission accounting process, and carbon emission accounting parameter analysis process. The carbon emission acquisition process includes observation items such as instantaneous fuel flow rate of construction equipment, engine speed and load, hydraulic system pressure and flow rate, generator set power output, transport vehicle mileage and load, material entry and exit weighing, material batch identification, on-site environmental temperature and humidity, GPS positioning trajectory, and sampling timestamp. Each observation point must be accompanied by an equipment identification code, sensor calibration record, and unit of measurement description. The measurement frequency is set according to the project's operating conditions as second-level, minute-level, or hour-level, and the sampling rate and sampling window information are retained in the metadata. The model-based accounting process needs to record the model identifier, model version number, model internal formula index, unit conventions for model input and output items, model assumptions and applicable boundary conditions, as well as model runtime environment information and model execution timestamp. The carbon emission parameter analysis process needs to record the parameter name, parameter source channel identifier, parameter batch number, parameter acquisition time, parameter estimation method, and uncertainty estimate. To ensure data consistency, a unified time base was implemented to standardize all timestamps, and international time standards were used to convert observations across time zones. Physical quantity conversion rules were applied to units of measurement to ensure uniformity. For each observation record, an initial estimate of measurement uncertainty was calculated and recorded. This uncertainty was calculated based on the sensor accuracy declaration, the most recent calibration residual, and historical deviation statistics according to the standard error synthesis rules. All observations and their metadata were recorded as event-level traceability entries. Each event entry included a unique event identifier, source device or document identifier, timestamp, original observation value, unit of measurement, measurement uncertainty, calibration reference, and sampling rate information for subsequent traceability analysis and uncertainty propagation calculations.

[0079] Step S12: Perform carbon emission accounting process traceability analysis on the entire process data of road engineering carbon emission accounting, and generate carbon emission accounting process traceability data;

[0080] In this embodiment of the invention, a source tracing analysis of the entire carbon emission accounting process data for road engineering is performed. An event-level genealogy tracing model is established to generate source tracing data for the carbon emission accounting process. First, time series alignment is performed on event entries. Cross-correlation functions and dynamic time warping algorithms are used to align multi-source time axes, resolving alignment issues caused by different sampling frequencies and delays. For cases with duplicate records or conflicting observations, identifier verification is performed to identify redundant and inconsistent items, and conflicting records are prioritized based on calibration dates and historical residual priority strategies. Second, a source tracing link is constructed for the model's input-output relationship. Model operation logs and formula version information are parsed to identify the specific input event identifiers upon which the model operation depends. A reverse tracing algorithm generates the input link from the final emission estimate to the most original observation. For parameter-based calculation items, parameter source identifiers and estimation methods are parsed, indicating whether the parameters are from measured values, manual values, or statistical estimates, and recording the source of parameter uncertainty and calculation methods. For each traceability link, a confidence score is calculated. The confidence score is obtained by weighting the inverse of the observation uncertainty, calibration recentity, data completeness rate, and link length. Its mathematical expression is a weighted harmonic mean, and Bayesian prior correction is applied when missing metadata is encountered. The final output traceability data includes an event-level link list, link confidence scores, node attributes on each link (device ID, calibration date, measurement uncertainty, data type), and link-level uncertainty contribution estimates, used to support subsequent type classification and topology modeling.

[0081] Step S13: Based on the carbon emission accounting process traceability data, perform carbon emission accounting process data classification processing on the entire process data of carbon emission accounting for road engineering, and generate carbon emission accounting process type data, wherein the carbon emission accounting process type data includes carbon emission collection type data, carbon emission accounting model structure type data, and carbon emission accounting parameter type data.

[0082] In this embodiment of the invention, carbon emission accounting process data for the entire road engineering carbon emission accounting process is classified based on traceability data. The classification rules are based on metadata fields and link characteristics, establishing a rule base: when event metadata contains sensor identifiers and its source field belongs to on-site measurement records, it is classified as carbon emission acquisition type data; when the event link contains model identifiers or formula version numbers and the event is the output of a calculation node, it is classified as carbon emission accounting model structure type data; when the event identifier is a parameter estimate, manual value, or material batch parameter with accompanying parameter source description, it is classified as carbon emission accounting parameter type data. Boolean rule matching is performed on each record, and the decision confidence level is calculated. The decision confidence level is synthesized from field completeness rate, field consistency verification results, and traceability link confidence level according to preset weights. To handle boundary cases, rule-based fuzzy logic inference is used to fuzzily classify low-confidence records and generate composite type labels. Simultaneously, secondary labels and original link indexes are retained for composite type records for manual review. The classification results are output as entries consisting of type code, type description, original event index, and decision confidence level. To ensure the traceability of classification results, each classification entry records the classification rule version number and execution timestamp, and records field inconsistency warnings and metadata information that needs to be supplemented in the classification table to support subsequent data completion or manual verification processes.

[0083] Step S14: Perform feature analysis on the subset of carbon emission accounting process types based on the carbon emission accounting process type data, and generate feature data for the subset of carbon emission accounting process types;

[0084] In this embodiment of the invention, subset feature analysis is performed on each type of carbon emission accounting process data to generate subset feature data for each type of accounting process. For the data collection subset, a set of numerical features is calculated, including mean, sample standard deviation, coefficient of variation, skewness, kurtosis, interquartile range, extreme value interval, and missing rate. For the time series subset, time series decomposition is performed, the amplitude and period length of periodic components are extracted, and a fast Fourier transform is used to identify spectral peaks to calibrate the dominant period. For sequences with trend abrupt changes, a change point detection algorithm (such as optimal segmentation detection) is applied to locate the abrupt change points and record the magnitude and time of the abrupt change. Frequency distributions of classification and nominal features are statistically analyzed, and information entropy and chi-square independence statistics are calculated. For the model structure subset, model performance vectors are extracted, including bias, median absolute error, root mean square error, model stability index, and model parameter dimensions under representative scenarios. For the parameter subset, distribution fitting is performed, and distribution fitting test statistics are used to record whether the parameters follow a normal, log-normal, or long-tailed distribution, and the confidence interval and bias index of the parameter estimates are calculated. Feature vectors are constructed after scaling the features of all subsets for use in cluster analysis. The cluster analysis employs a combination strategy based on hierarchical clustering and Gaussian mixture models, and evaluates the number and quality of clusters using silhouette coefficients and Bayesian information criteria. The clustering results indicate the similarity index between subsets and the representative subset samples. The final output includes a statistical feature report for each subset, a description of its time-series characteristics, distribution test results, cluster labels, and a representative sample index, serving as input for subsequent logical association analysis.

[0085] Step S15: Perform logical association feature analysis on the feature data of each type of accounting process type subset to generate logical association feature data of carbon emission accounting process type;

[0086] In this embodiment of the invention, logical association feature analysis of various types is performed on the feature data of the subset of accounting process types to generate logical association feature data of carbon emission accounting process types. First, correlation analysis is performed on continuous variable pairs, calculating the Pearson correlation coefficient and Spearman rank correlation coefficient and recording the significance level; for nonlinear associations, mutual information estimation is used and the significance threshold is obtained through permutation test. Granger causality test is performed on time-series variable pairs. First, the lag order is automatically determined according to the fitting information criterion, and then the F-statistic and corresponding significance of Granger causality are calculated. Significant causal relationships are recorded in the form of directed edges. Regression analysis is performed on relationships with categorical features and continuous outputs. The coefficients are estimated using a generalized linear model and the explanatory power and p-value are recorded; for high-dimensional parameter interaction relationships, the main effect and interaction effect decomposition method is used to calculate the first-order and second-order sensitivity values. To obtain a more refined causal structure, Bayesian network structure learning is implemented. A directed acyclic graph is generated by combining constraint search algorithm and scoring search algorithm, and the network stability is evaluated by cross-validation; during the network learning process, guided sampling is used to check the robustness of false negative constraints and the confidence distribution of edges is recorded. Association rule mining is performed on frequently co-occurring discrete features. Support, confidence, and lift are calculated to characterize rule strength, and rules with high lift are selected as logical association features. The final output includes an edge list (source node, target node, association metric, significance index, causal determination identifier), a node conditional probability table or regression coefficient matrix, and rule mining results, providing a quantitative basis for assigning edge weights to the topology network.

[0087] Step S16: Establish a carbon emission uncertainty factor type topology using carbon emission accounting process type data, use the characteristic data of the subset of accounting process types as topology nodes of carbon emission uncertainty factors, and use the logical association characteristic data of carbon emission accounting process types as association coefficients of topology nodes; map the topology nodes and association coefficients of carbon emission uncertainty factors to the topology structure of carbon emission uncertainty factors to establish the data storage structure of the carbon emission uncertainty factor topology network, so as to obtain the carbon emission uncertainty factor topology network, and output the carbon emission uncertainty factor data through the carbon emission uncertainty factor topology network.

[0088] In this embodiment of the invention, a carbon emission uncertainty factor type topology is established based on the aforementioned accounting process type data, subset feature data, and logical association feature data, and a corresponding data storage structure is constructed to obtain a carbon emission uncertainty factor topology network. Topology nodes are defined as accounting process subset nodes, and node attributes include a unique node identifier, node type label, statistical feature summary (mean, variance, coefficient of variation), time series characteristic summary (main period and trend indicators), data confidence score, last calibration time, and metadata index. Edges between nodes are weighted using the association strength value of the aforementioned logical association features. The weight calculation employs a weighted combination of normalized mutual information and standardized regression coefficients, and assigns directionality to causal indications. Based on the constructed nodes and edges, an adjacency matrix is ​​generated, and topology indicators—node degree centrality, betweenness centrality, proximity centrality, and eigenvector centrality—are calculated to identify key network nodes. The network is divided into modules, and the endogenous uncertainty contribution rate and external coupling strength of each community are recorded. The data storage structure is organized using relational tables: the node table records node metadata and statistical summaries; the edge table records source nodes, target nodes, edge weights, significance indicators, and causal symbols; the attribute table records key-value pairs of extended attributes and maintains version history; and the tracing link table records the original event indexes and tracing link identifiers associated with nodes. To achieve searchability and auditability, all topology changes are recorded with version numbers and timestamps, and change operations are logged. The topology network output is a structured network object containing a node list, edge list, attribute table, and version records, which can be used for subsequent uncertainty quantification, sensitivity ranking, and optimization strategy priority determination to obtain the carbon emission uncertainty factor topology network. Corresponding types of carbon emission uncertainty factor data are output through the carbon emission uncertainty factor topology network.

[0089] Furthermore, as an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S2 is shown below. In this embodiment, step S2 includes the following steps:

[0090] Step S21: Based on the carbon emission collection error factor data, perform carbon emission collection uncertainty range analysis for each error factor, and generate carbon emission collection error factor uncertainty range data;

[0091] In this embodiment of the invention, the uncertainty range of carbon emission collection for each error factor is analyzed based on carbon emission collection error factor data. First, an observation sequence is listed for each collection variable and a quality assessment is performed, including missing value rate calculation, outlier rate statistics, and calibration record consistency verification. Statistical characteristics are calculated for the observation sequence: arithmetic mean, sample standard deviation, coefficient of variation, skewness, and kurtosis; for time series data, autocorrelation coefficient, partial autocorrelation coefficient, and seasonal amplitude are also calculated. Using the Measurement Uncertainty Expression Principle (GUM), the sensor accuracy declaration, the last calibration residual value, and the historical residual variance are synthesized into an observation uncertainty benchmark using the sum of squares method. If multiple observation channels have redundant measurements of the same physical quantity, a weighted synthesis method is used, with weights allocated proportionally to the inverse of the observation confidence level. The synthesized observation uncertainty is recorded in the form of weighted variance. Distribution fitting is performed for each error factor, and the distribution parameters are obtained using the maximum likelihood estimation method. The Kolmogorov–Smirnov test and Anderson–Darling test are applied to evaluate the goodness of fit. For cases where the common distribution family is not met, an empirical distribution or kernel density estimation is used to construct the error distribution. Based on the error distribution and emission calculation formula, a linear approximation of variance propagation estimation is obtained using a first-order Taylor expansion of error propagation. In the nonlinear case, Monte Carlo sampling is implemented to obtain the measured confidence band of the output distribution, with at least 10,000 sampling iterations to ensure distribution stability. The acquisition errors are stratified and analyzed according to construction procedures, equipment types, and time windows, generating stratified error uncertainty ranges and corresponding confidence intervals. Meta-information is output for each error factor: observation source identifier, last calibration time, observation frequency, combined uncertainty value, and 95% confidence interval, used for subsequent uncertainty contribution calculation and priority ranking.

[0092] Step S22: Based on the data of uncertain factors in the carbon emission accounting model structure, perform a quantitative analysis of the uncertainty in the carbon emission accounting model structure to obtain quantitative data of the uncertainty in the carbon emission accounting model structure;

[0093] In this embodiment of the invention, a quantitative analysis of model structural uncertainty is performed based on data on uncertainties in the carbon emission accounting model structure. First, a candidate model set is established, and input mapping, formula version number, and applicable boundary conditions are defined for each model. The candidate models are run one by one on a representative set of application scenarios, and the mean, bias, root mean square error, and systematic bias index of the model output are recorded. Representative scenarios consist of construction type, material batch, process sequence, and environmental conditions; the scenario set must cover both common and extreme working conditions. To quantify the contribution of model structural differences to the accounting results, a global sensitivity analysis is performed: Sobol variance decomposition is performed on each model to obtain first-order and total-order sensitivity values, and variance decomposition is applied to calculate the output variance share caused by model structure for differences in output between models. To achieve uncertainty integration between models, a Bayesian model averaging method is used based on model evidence. Model weights are approximated by the marginal likelihood of the model to the validation data or converted into weights based on information criteria (such as BIC) and then normalized. The joint prediction distribution of the model set is obtained by superimposing the weighted model output distributions. To identify vulnerable structures within the model, local sensitivity experiments are performed to modify the model's topology parameters and record the output elasticity coefficients. The model structure uncertainty quantification results are output as follows: error statistics for each model, inter-model variance contribution rate, joint distribution of model weights and model ensemble predictions, and their confidence intervals. A model-scene performance mapping table is also recorded to support subsequent model structure adaptation and weight updates.

[0094] Step S23: Perform uncertainty quantification analysis based on the uncertainty factor data of carbon emission parameters to generate uncertainty quantification data of carbon emission parameters;

[0095] In this embodiment of the invention, parameter uncertainty quantification analysis is performed based on carbon emission parameter uncertainty data. First, probability distribution characteristic fitting is performed for each parameter. The fitting method uses maximum likelihood estimation supplemented by information content criteria to evaluate the suitability of the distribution family. For parameters that do not meet the parameter family assumptions, kernel density estimation or empirical distribution is used to characterize the probability density. A high-dimensional sampling scheme is constructed for the parameter set. Latin hypercube sampling or stratified sampling is used to ensure the representativeness of the samples in the high-dimensional space. The sampling sample size is set to at least 10,000 groups to ensure the convergence of subsequent statistics. Sampling interaction simulation is performed on the sampled samples: emission calculations are performed according to the accounting model under each parameter sample group, and the output values ​​are recorded. The set of output values ​​is used to estimate the statistical impact of parameter uncertainty on the accounting results. Global sensitivity analysis is performed based on the sampling output to calculate the first-order effect value and total effect value of each parameter, and to identify the individual and interaction contributions of parameters. For parameter pairs with significant interactions, second-order sensitivity decomposition is performed, and the interaction contribution share is recorded. To improve computational efficiency, surrogate models (such as Gaussian process regression or multinomial hybrid surrogates) are constructed for computationally intensive models to approximate the model mapping relationship. Cross-validation is then performed on the surrogate models to ensure that the approximation error remains within acceptable limits. The output of parameter uncertainty quantification includes a description of the parameter probability distribution, the output distribution generated by the sampling simulation, first-order and total effect indices of the parameters, the parameter interaction contribution matrix, and the posterior confidence band of the parameters.

[0096] Step S24: Integrate the various types of uncertainty data for carbon emission accounting based on the uncertainty range data of carbon emission collection error factors, the uncertainty quantification data of carbon emission accounting model structure, and the uncertainty quantification data of carbon emission parameters, so as to obtain the uncertainty quantification data for carbon emission accounting.

[0097] In this embodiment of the invention, uncertainty data of various types are integrated based on the uncertainty range data of carbon emission collection error factors, the uncertainty quantification data of carbon emission accounting model structure, and the uncertainty quantification data of carbon emission parameters to obtain carbon emission accounting uncertainty quantification data. The integration process adopts a hierarchical Monte Carlo or Bayesian hierarchical framework to achieve joint propagation of the three types of uncertainty. Under the hierarchical Monte Carlo framework, the outer loop samples or rotates the uncertainty of the model structure according to the model weights obtained in step S22. The inner layer samples the parameters of the selected model and samples the parameter uncertainty according to the distribution obtained in step S23. Under each parameter sample, the inner layer perturbs the sampling in the observation error space according to the error distribution defined in step S21, thus forming a three-layer sampling nest. The accounting output is calculated for all nested samples, and the descriptive quantities of the output distribution are statistically analyzed: mean, median, variance, 95% confidence interval, and higher-order moments. Variance decomposition is performed on all output samples to determine the variance contribution from acquisition error, model structure, and parameter uncertainty. Variance decomposition employs either static variance allocation or a global variance decomposition method based on the Sobol index, with results expressed as a percentage contribution rate. To improve interpretability, the output includes an uncertainty contribution decomposition table, a list of uncertainty factors sorted in descending order of contribution rate, key parameters and key model identifiers, and an uncertainty heatmap mapped by topological network nodes. To ensure traceability, the integrated results save version numbers, sampling seeds, model weight records, and sample counts for each layer, along with the runtime timestamp. The integrated results are delivered as uncertainty quantification data for carbon emission accounting, including joint output distribution, contribution decomposition, sensitivity ranking, and operational instructions, for subsequent optimization strategy design and performance evaluation.

[0098] Furthermore, step S21 includes the following steps:

[0099] The carbon emission collection error factors are analyzed to generate carbon emission collection numerical characteristic data.

[0100] To obtain the attribute characteristics of carbon emission collection error factors, an analysis of the carbon emission collection error factor attributes was conducted on the carbon emission collection error factor data.

[0101] Based on the characteristic data of carbon emission collection error factors, collect the corresponding energy consumption data of construction equipment and carbon emission factor data of materials;

[0102] By utilizing the energy consumption data of construction equipment and the carbon emission factor data of materials corresponding to the attribute characteristic data of carbon emission collection error factors, the carbon emission collection variation coefficient of each error factor is analyzed, and the carbon emission collection variation coefficient is generated.

[0103] Based on the coefficient of variation of carbon emission collection, the dispersion of carbon emission collection data under various error factors is analyzed to obtain the dispersion data of carbon emission collection.

[0104] By analyzing the uncertainty range of carbon emission collection for various error factors using carbon emission collection dispersion data, we can generate data on the uncertainty range of carbon emission collection error factors.

[0105] In this embodiment of the invention, numerical characteristic analysis of various error factors in carbon emission collection error data is performed. This process is based on energy consumption and emission monitoring data under different working conditions during road construction. Statistical methods are used to calculate indicators such as mean, median, skewness, and kurtosis, thereby forming the central tendency and fluctuation characteristics of the data. Simultaneously, considering the phased characteristics of the construction progress, the error factor data is divided into different subsets according to equipment type and work procedures. Time series modeling is then performed within each subset to generate numerical characteristic curves that reflect dynamic trends, ultimately forming numerical characteristic data for carbon emission collection, laying the foundation for subsequent quantitative analysis. Attribute characteristic analysis is then performed on the carbon emission collection error factor data. In this step, a classification system is established based on the source of the error factors, classifying errors into four main categories: sensor measurement errors, manual statistical errors, errors caused by fluctuations in the construction environment, and errors caused by abnormal equipment operating conditions. Attribute descriptions are then constructed for each type of error. For example, sensor measurement errors need to have their range, sensitivity, sampling frequency, and error distribution range recorded; manual statistical errors need to be analyzed in terms of recording methods, sample size, and calculation processes; construction environment fluctuation errors need to include external variable characteristics such as temperature, humidity, and wind speed; and equipment operating condition abnormality errors need to extract parameters such as operating load status and rate of change of operating conditions. Through factor decomposition and clustering methods, each error factor is projected into a multi-dimensional space to form an attribute feature dataset, thus providing a basis for coupling with energy consumption and material emission factors. Energy consumption data of construction equipment and carbon emission factor data of materials are collected based on the attribute feature data. In specific operations, the error factor attributes are first mapped to the construction stages, clarifying the equipment models and operating phases involved. Then, energy consumption detection instruments are used to obtain the fuel consumption, electricity consumption, and operating time of the equipment under different load conditions to form equipment energy consumption data. Simultaneously, combined with the construction material usage ledger, the actual consumption of various materials at different stages is extracted, and the corresponding material carbon emission factor values ​​are obtained by connecting to an authoritative carbon emission factor database. In this way, a correspondence is established between equipment energy consumption data and material carbon emission factor data and error factor attributes, ensuring that the analysis results have engineering interpretability. Based on this, carbon emission collection variation coefficient analysis was performed using the aforementioned data. The coefficient of variation was obtained by calculating the ratio of the standard deviation to the mean of the collected data for each error factor, reflecting the degree of data fluctuation. For data from the same equipment under different operating conditions, a segmented processing method was adopted to ensure that the coefficient of variation could reveal the differences between stable operation and load fluctuation states. In the analysis of material emission factors, data from different batches of materials were statistically categorized to eliminate systematic biases caused by differences in sources, thereby ensuring the representativeness of the coefficient of variation calculation results. The final generated carbon emission collection variation coefficient can be used as an input indicator for subsequent dispersion analysis. Dispersion analysis was then performed on the numerical characteristic data based on the carbon emission collection variation coefficient.This analysis combines the coefficient of variation with numerical characteristics using a weighted statistical method to generate a corrected dispersion metric. Data with a large mean but low volatility exhibits a smaller dispersion range, while data with a small mean but large volatility presents a wider range. By constructing a dispersion metric model, the fluctuation range can be quantified at different confidence levels, generating carbon emission collection dispersion data and revealing the differences in the data distribution width and fluctuation amplitude of various error factors. Uncertainty range analysis is then conducted using the carbon emission collection dispersion data. Specifically, uncertainty boundary intervals are established based on the dispersion results of different error factors, determining their upper and lower limits during actual collection. Then, confidence interval estimation methods are used to transform the fluctuation intervals into boundary values. Simultaneously, a multi-factor superposition model is constructed to jointly analyze the interaction effects between different error factors, generating a comprehensive uncertainty range result. In this process, the differences in construction stages must also be considered, calculating the error uncertainty ranges for the initial, middle, and later stages of construction separately to ensure the results better reflect actual construction conditions. The final carbon emission collection error factor uncertainty range data provides accurate boundary conditions and quantitative basis for subsequent carbon emission accounting uncertainty quantification and optimization.

[0106] Furthermore, step S22 includes the following steps:

[0107] The carbon emission accounting model selection model is collected by collecting data on uncertain factors in the carbon emission accounting model structure, and a carbon emission accounting model library is established based on the selected carbon emission accounting model to obtain carbon emission accounting model library data.

[0108] Based on the data from the carbon emission accounting model library, we will conduct a carbon emission accounting benefit analysis of the models with different application scenarios to obtain carbon emission accounting benefit data.

[0109] Based on the model carbon emission accounting benefit data, a sensitivity characteristic analysis of the model carbon emission accounting for different application scenarios is conducted to obtain the model carbon emission accounting sensitivity characteristic data.

[0110] Based on the sensitivity characteristic data of carbon emission accounting model, a quantitative analysis of the structural uncertainty of carbon emission accounting model is conducted to obtain quantitative data of structural uncertainty of carbon emission accounting model.

[0111] In this embodiment of the invention, various carbon emission accounting models involved in road engineering are collected based on the uncertainties in the carbon emission accounting model structure. These models include construction equipment energy consumption models, material consumption models, transportation energy consumption models, construction stage distribution models, and environmental condition adjustment models. Each type of model is standardized and classified according to its calculation principle, input parameter range, applicable construction stage, and calculation accuracy. A model library is established to systematically organize the models according to their structural characteristics, input parameter types, and calculation formulas. This model library not only contains model formulas and parameter calibration information but also includes statistical characteristics of the model's output results under different construction conditions, such as the mean, variance, and historical deviation records of emissions. This allows subsequent analysis to fully track the model's contribution to the overall carbon emission accounting results and the sources of uncertainty. After the model library is established, benefit analysis is performed on different carbon emission accounting models under various road engineering construction scenarios. Specifically, a parameter set for the construction scenario is first defined, including factors such as construction sequence, equipment energy consumption characteristics, material type, and transportation distance. Then, each model in the model library is called sequentially to calculate the carbon emissions under different construction scenarios. By comparing the deviations of the model output with historical reference data, standard emission benchmarks, and specific engineering conditions, carbon emission accounting benefit data of the model is generated. This quantifies the accuracy, stability, and deviation characteristics of the model under different scenarios. Simultaneously, statistical methods are used to calculate the standard deviation, maximum deviation, and fluctuation range of the model output, thereby clarifying the contribution of different model structures to the uncertainty of carbon emission accounting results in specific application scenarios, providing foundational data for subsequent sensitivity analysis. Based on the model carbon emission accounting benefit data, sensitivity characteristic analysis is performed on the model input variables, focusing on quantifying the impact of each input parameter on the accounting results. During the analysis, model input parameters such as equipment energy efficiency, material carbon emission factors, construction progress, and environmental adjustment coefficients are perturbed one by one. Sensitivity indicators, including relative change rate, elasticity coefficient, and contribution percentage, are calculated by comparing the changes in carbon emission output before and after the perturbation. These indicators represent the uncertainty impact of each input parameter change on the carbon emission results. Furthermore, interactive analysis is conducted on key parameter combinations to identify their synergistic effects on the overall emission results. The sensitivity characteristics are organized into a structured sensitivity matrix, forming model carbon emission accounting sensitivity characteristic data, providing a quantitative basis for quantifying model structure uncertainty. Based on model sensitivity characteristic data, the uncertainty of the carbon emission accounting model structure is quantitatively analyzed. By weighting and combining sensitivity indicators with model structure differences, the contribution of uncertainty in the model output is cumulatively calculated to form a quantitative index of the overall uncertainty of the model structure. In the quantification process, topological mapping is performed on the sensitivity of key nodes and parameters within the model to establish a graphical relationship network of input-output relationships, parameter interaction effects, and their impact on total carbon emissions. The quantitative value of the uncertainty of the model structure is calculated through node weights and edge weights.The final result is quantitative data on the structural uncertainty of the carbon emission accounting model. The data includes model hierarchy, contribution of key parameters, and structural uncertainty magnitude under various construction scenarios. This provides accurate and operable model-level indicators for the integration of overall carbon emission accounting uncertainty, enabling a comprehensive quantitative analysis of the model's structural uncertainty.

[0112] Furthermore, step S23 includes the following steps:

[0113] Based on the data of uncertain factors in carbon emission parameters, we conduct a probability distribution characteristic analysis of carbon emission parameter accounting to generate carbon emission parameter accounting probability distribution characteristic data.

[0114] Based on the data of uncertain factors in carbon emission parameters, simulation analysis of carbon emission parameters is carried out to generate carbon emission parameter simulation data. Then, sampling interactive simulation analysis of carbon emission parameters is carried out through the carbon emission parameter simulation data to generate carbon emission parameter sampling interactive simulation data.

[0115] Based on the carbon emission parameter sampling interactive simulation data, the carbon emission accounting characteristics of the sampling interactive simulation parameters are analyzed to generate simulation parameter carbon emission accounting characteristic data.

[0116] Based on the carbon emission accounting characteristic data of the simulation parameters, the probability distribution characteristic of the carbon emission simulation parameters is analyzed to generate the carbon emission simulation parameter accounting probability distribution characteristic data. Then, based on the carbon emission parameter accounting probability distribution characteristic data and the carbon emission simulation parameter accounting probability distribution characteristic data, the accounting impact characteristic of the changes of each carbon emission parameter is analyzed to generate the carbon emission parameter accounting impact characteristic data.

[0117] Based on the impact characteristic data of carbon emission parameter accounting, uncertainty quantification analysis is performed on the uncertainty factor data of carbon emission parameters to generate carbon emission parameter uncertainty quantification data.

[0118] In this embodiment of the invention, probability distribution characteristics of various parameters involved in road construction are analyzed based on uncertainties in carbon emission parameters. These parameters include key factors such as construction equipment power consumption, material unit carbon emission factor, construction duration, transportation distance, and construction progress. By statistically analyzing historical data, theoretical calculations, and measured deviations of each parameter, the mean, variance, skewness, kurtosis, and confidence intervals are calculated to generate probability distribution characteristic data for carbon emission parameter accounting. This data quantifies the fluctuation range and uncertainty level of each parameter under different construction conditions. Simulation analysis of carbon emission parameters is then performed. Based on the probability distribution characteristics of each parameter, a parameter stochastic perturbation model is constructed, and continuous simulation is conducted under defined construction conditions and parameter boundaries to generate carbon emission parameter simulation data. Further sampling interactive simulation analysis is performed on the simulation results. By applying multiple rounds of interactive perturbation to different parameter combinations, the cumulative impact of each parameter combination on total carbon emissions is calculated, forming carbon emission parameter sampling interactive simulation data. This reveals the interaction effects between parameters and their contribution to the carbon emission accounting results. Based on sampled interactive simulation data of carbon emission parameters, a carbon emission accounting characteristic analysis is performed on each sampled interactive simulation parameter. Specifically, this involves analyzing the emission change trend, dispersion, and extreme points under each set of simulation parameters, calculating the contribution weight of each simulation parameter to the total carbon emissions, and generating carbon emission accounting characteristic data for the simulation parameters to provide a basis for subsequent probability distribution analysis. A probability distribution characteristic analysis of the carbon emission simulation parameter accounting characteristic data is then performed. By statistically analyzing the accounting results of each simulation parameter under different simulation rounds, a parameter accounting probability distribution curve is formed. Its mean, variance, and higher-order statistical characteristics are analyzed. This distribution characteristic is then integrated with the original carbon emission parameter accounting probability distribution characteristic data. By comparing the magnitude of change of each parameter and the sensitivity of the accounting results, accounting impact characteristic data of changes in each carbon emission parameter is generated, clarifying the specific impact of parameter fluctuations on the total carbon emissions. Based on the carbon emission parameter accounting impact characteristic data, a quantitative analysis of the uncertainties of carbon emission parameters is performed. By combining the accounting impact characteristic of each parameter with its probability distribution weight, the contribution value of each parameter to the uncertainty of total carbon emissions is accumulated and a quantitative index of overall parameter uncertainty is formed. The generated quantitative data on the uncertainty of carbon emission parameters includes the contribution of each key parameter, the interaction effect between parameters, and the uncertainty of parameters at different construction stages. This provides an accurate and operable quantitative basis for the integration of uncertainty in carbon emission accounting, and enables a comprehensive quantitative analysis of the uncertainty at the level of carbon emission parameters in road engineering.

[0119] Furthermore, step S3 includes the following steps:

[0120] Step S31: Based on the uncertainty range of carbon emission collection error factors in the carbon emission accounting uncertainty quantification data, design and optimize the carbon emission collection equipment operation status monitoring engine;

[0121] In this embodiment of the invention, the carbon emission collection error factor uncertainty range data in the carbon emission accounting uncertainty quantification data are used to optimize the carbon emission collection equipment operation status monitoring engine. First, carbon emission collection equipment operation status maintenance rules are established, including intelligent calibration and intelligent maintenance of equipment operation status. Intelligent calibration corrects the collected values ​​of key collection nodes in real time to eliminate instrument measurement errors; intelligent maintenance automatically identifies potential faults and performs maintenance by monitoring equipment temperature, vibration, voltage, and sensor accuracy. Second, multi-level screening rules for carbon emission collection equipment operation status are established, including first-level rules to remove jump values, second-level rules to correct drift values, and third-level rules to mark suspicious values. Each level of rule processes different uncertainty sources layer by layer, establishing statistical thresholds by comparing historical collection data and equipment operation characteristics to determine the range of anomalies. Subsequently, based on the carbon emission collection error factor uncertainty range data, corresponding multi-level screening rule accuracy optimization parameters are designed to adjust the sensitivity and applicability of each rule. These parameters are used to adaptively optimize the multi-level screening rules, ensuring stability and accuracy when handling different error amplitudes. Finally, the carbon emission collection equipment operation status maintenance rules are integrated with multi-level screening rules to intelligently optimize the monitoring engine. This enables the collection equipment to continuously, accurately, and dynamically identify and correct abnormal data during actual operation, thereby significantly reducing the uncertainty of carbon emission collection and ensuring the reliability and continuity of accounting data.

[0122] Step S32: Design an optimization strategy for the carbon emission accounting model structure based on the carbon emission accounting model structure uncertainty quantification data in the carbon emission accounting uncertainty quantification data;

[0123] In this embodiment of the invention, an optimization strategy is designed for the carbon emission accounting model structure based on the carbon emission accounting model structure uncertainty quantification data and the model's carbon emission accounting sensitivity characteristic data from the carbon emission accounting uncertainty quantification data. First, a model structure adaptation analysis is performed for different construction scenarios and application environments to clarify the carbon emission accounting accuracy and error range of each model under specific construction procedures, material types, and operating conditions. By analyzing the sensitivity characteristic data of different model structures, the structural modules that have the greatest impact on the fluctuation of the accounting results are identified, including model assumptions, formula structure, boundary conditions, and calculation weight allocation. Subsequently, the model parameters are intelligently optimized based on the analysis results, including the redistribution of model weights, boundary condition correction, and adjustment of emission coefficients in the formula, so that the model has optimal adaptability under different construction scenarios. During the optimization strategy design process, construction environmental factors, material properties, construction equipment characteristics, and historical accounting errors are comprehensively considered. Through mathematical optimization methods and sensitivity analysis, a complete carbon emission accounting model structure optimization strategy is formed. This strategy ensures that carbon emission uncertainties caused by differences in model structure are minimized during the accounting process, while maintaining the scalability and operability of the model structure, providing a high-precision and traceable model structure foundation for subsequent accounting.

[0124] Step S33: Design a carbon emission parameter optimization strategy based on the carbon emission parameter uncertainty quantification data in the carbon emission accounting uncertainty quantification data;

[0125] In this embodiment of the invention, an optimization strategy for carbon emission accounting parameters is designed based on the uncertainty quantification data of carbon emission accounting parameters. First, highly sensitive parameters of carbon emission uncertainty are extracted from the uncertainty quantification data. These parameters mainly include the energy consumption coefficient of construction equipment, the carbon emission factor of materials, and the emission coefficient of the process. Then, prior distribution analysis is performed on the historical data of these highly sensitive parameters to establish their probability distribution models and clarify the statistical characteristics such as the parameter fluctuation range, mean, and variance. Next, a likelihood function is established based on the real-time observation data of the highly sensitive parameters to quantify the reliability of the parameter values ​​under the current construction environment. Using a Bayesian iterative algorithm, the prior distribution and the real-time likelihood function are iteratively fused to generate optimized highly sensitive parameters of carbon emission uncertainty, achieving convergence of the parameter posterior distribution. Finally, a carbon emission parameter optimization strategy is designed based on the optimized highly sensitive parameters, including the parameter control sequence, control amplitude, and constraints, to achieve closed-loop control of the fluctuations of highly sensitive parameters throughout the construction process, significantly improving the accuracy and stability of the accounting results. This optimization strategy ensures that the contribution of carbon emission parameters to the accounting results can be minimized under various construction conditions, while maintaining the traceability and quantifiability of the accounting process.

[0126] Step S34: Integrate the optimization strategies for carbon emission accounting by optimizing the carbon emission collection equipment operation status monitoring engine, carbon emission accounting model structure optimization strategy, and carbon emission parameter optimization strategy to obtain the carbon emission accounting optimization strategy.

[0127] In this embodiment of the invention, optimization measures for the carbon emission data acquisition equipment operation status monitoring engine, the carbon emission accounting model structure optimization strategy, and the carbon emission parameter optimization strategy are integrated to form a complete carbon emission accounting optimization strategy. During the integration process, the optimization rules, maintenance rules, and multi-level screening rules of the carbon emission data acquisition equipment operation status monitoring engine are first coordinated and matched with the carbon emission accounting model structure optimization strategy to ensure optimal coupling between equipment acquisition accuracy and model calculation accuracy. Subsequently, the carbon emission parameter optimization strategy and the model structure optimization strategy are applied synchronously to achieve the synergistic effect of dynamic control of highly sensitive parameters and adaptive adjustment of the model structure. During the integration process, the interactive effects of the three types of optimization strategies are analyzed, including the impact of data accuracy of the acquisition equipment on model calculation error, the impact of model structure adjustment on parameter sensitivity, and the feedback of parameter optimization on accounting accuracy. Finally, a complete carbon emission accounting optimization strategy is formed, enabling closed-loop optimization of carbon emission accounting in all aspects of equipment acquisition, model calculation, and parameter application, thereby minimizing accounting uncertainty, improving the accuracy and reliability of carbon emission management, and providing a scientific basis and operable technical solution for carbon emission control throughout the entire road engineering construction process.

[0128] Furthermore, step S31 includes the following steps:

[0129] Design a carbon emission acquisition equipment operation status monitoring engine based on the uncertainty range of carbon emission acquisition error factors in the carbon emission accounting uncertainty quantification data;

[0130] Establish carbon emission collection equipment operation status maintenance rules and carbon emission collection equipment operation status multi-level screening rules. The carbon emission collection equipment operation status maintenance rules include intelligent calibration and intelligent maintenance of equipment operation status. The carbon emission collection equipment operation status multi-level screening rules include first-level rules to remove jump values, second-level rules to correct drift values, and third-level rules to mark suspicious values.

[0131] Based on the uncertainty range of carbon emission collection error factors, corresponding multi-level screening rule accuracy optimization parameters are designed. Then, the multi-level screening rule accuracy of the carbon emission collection equipment operation status is adaptively optimized using the multi-level screening rule accuracy optimization parameters to obtain the optimized multi-level screening rule for the carbon emission collection equipment operation status.

[0132] Based on the carbon emission collection equipment operation status maintenance rules and the carbon emission collection equipment operation status multi-level screening rules, the carbon emission collection equipment operation status monitoring engine is intelligently optimized to obtain an optimized carbon emission collection equipment operation status monitoring engine.

[0133] In this embodiment of the invention, a monitoring engine for the operational status of carbon emission collection equipment at road construction sites is designed based on the uncertainty range data of carbon emission collection error factors in the uncertainty quantification data of carbon emission accounting. This design analyzes the operational stability, collection accuracy, and data fluctuation characteristics of the equipment under different construction stages and environmental conditions, clarifying the amplitude and distribution patterns of various error factors. This determines the key equipment operating parameters that the monitoring engine needs to collect, including power consumption, current and voltage fluctuations, temperature changes, and sensor response time. A real-time monitoring module is established to continuously track these parameters, ensuring the comprehensiveness and accuracy of the collected equipment operational status information. Maintenance rules for the operational status of the carbon emission collection equipment and multi-level screening rules for the operational status of the carbon emission collection equipment are established. Specifically, the equipment operational status maintenance rules include two parts: intelligent calibration and intelligent maintenance. Intelligent calibration analyzes the deviation between the equipment sensor output and the standard reference value, performing real-time correction to eliminate systematic deviations. Intelligent maintenance predicts potential equipment failure points based on historical operating data and status trends, generating maintenance suggestions to ensure long-term stable operation of the equipment. Regarding the multi-level screening rules, the first-level rule removes abrupt changes by comparing the variation amplitude of continuously sampled data to identify and remove abnormal abrupt changes; the second-level rule corrects drift values ​​by smoothing and correcting long-term offset measurements using trend analysis methods; and the third-level rule marks suspicious values ​​by labeling values ​​that do not conform to statistical characteristics or show potential anomalies for subsequent analysis and verification. Based on the uncertainty range of carbon emission collection error factors, corresponding multi-level screening rule accuracy optimization parameters are designed. These accuracy optimization parameters quantify the impact weight of each level of screening rule on data accuracy, and finely define the processing thresholds and correction amplitudes for abrupt changes, drift values, and suspicious values. By applying these accuracy optimization parameters to the multi-level screening rules, adaptive optimization of the multi-level screening rule accuracy is achieved, ensuring optimal performance under different construction scenarios and equipment operating conditions, improving the accuracy of outlier removal and correction, while maintaining the integrity of valid data. Based on the carbon emission collection equipment operating status maintenance rules and the optimized multi-level screening rules, the carbon emission collection equipment operating status monitoring engine undergoes intelligent engine optimization. The optimization process involves adjusting the parameters of the real-time data acquisition module, data analysis module, and anomaly detection module within the monitoring engine. This enables the engine to dynamically respond to changes in equipment operating status during the acquisition process, automatically correct measurement deviations, and provide intelligent predictions and anomaly alerts based on historical operating trends. Through this optimization, the monitoring engine can continuously provide high-precision, low-error carbon emission acquisition equipment operating status data throughout the entire construction process, providing a solid data foundation for the accuracy and reliability of subsequent carbon emission accounting.

[0134] Furthermore, step S32 includes the following steps:

[0135] Based on the carbon emission accounting uncertainty quantification data and the carbon emission accounting sensitivity characteristic data of the model, carbon emission accounting model structure adaptation analysis is performed for different application scenarios to generate carbon emission accounting model structure adaptation data.

[0136] Intelligent optimization of model parameters for application scenario environmental factors is performed on carbon emission accounting model structure adaptation data to generate optimized carbon emission accounting model structure adaptation data.

[0137] Based on the data, an optimization strategy for the carbon emission accounting model structure is designed.

[0138] In this embodiment of the invention, based on the carbon emission accounting model structure uncertainty quantification data and model carbon emission accounting sensitivity characteristic data from the carbon emission accounting uncertainty quantification data, an adaptation analysis is performed on the carbon emission accounting model structure under different application scenarios. This adaptation analysis identifies the structural response characteristics of the model under different construction environments, project scales, and technological conditions by constructing a coupling relationship matrix between model parameters and environmental factors. During the analysis, the sensitivity of each model parameter to the accounting results is quantified, key parameters and their weight distributions are determined, and the accuracy and stability of different model structures under various construction scenarios are evaluated, thereby generating carbon emission accounting model structure adaptation data, including the model parameter adjustment range, structural weight distribution, and accounting accuracy change characteristics. Based on the carbon emission accounting model structure adaptation data, intelligent optimization of model parameters is performed for environmental factors in the application scenarios. During the optimization process, by constructing a constraint relationship between environmental factors and model parameters, factors such as construction environmental conditions, material usage characteristics, equipment operating status, and construction cycle are used as input variables affecting accounting accuracy, and a multi-dimensional optimization method is employed to adjust the model parameters. This method analyzes the impact of parameter adjustments on the deviation of accounting results, gradually correcting uncertainties in the model structure and forming optimized carbon emission accounting model structure adaptation data. This data includes adjusted model parameter values, accounting accuracy indicators under different parameter combinations, and model stability evaluations, ensuring the reliability and accuracy of the model structure under various construction scenarios. Based on the optimized carbon emission accounting model structure adaptation data, a carbon emission accounting model structure optimization strategy is designed. This optimization strategy achieves systematic control of uncertainties in the model structure by determining the adjustment range, order, and combination of core model parameters. The strategy design includes setting the optimization priority of model structure parameters, defining model structure optimization constraints, and setting model accounting accuracy targets, ensuring that the optimized model structure maintains low uncertainty and improves the credibility of accounting results under different application scenarios. By implementing this optimization strategy, the carbon emission accounting model can dynamically respond to environmental changes throughout the entire accounting process, achieving closed-loop control of model structure uncertainties and providing a solid technical foundation for subsequent accounting optimization and decision-making.

[0139] Furthermore, step S33 includes the following steps:

[0140] Based on the carbon emission parameter uncertainty quantification data in the carbon emission accounting uncertainty quantification data, high-sensitivity parameters of carbon emission uncertainty are extracted to obtain high-sensitivity parameters of carbon emission uncertainty.

[0141] A prior distribution analysis of the high-sensitivity parameters of uncertainty is performed on the historical data corresponding to these parameters to generate prior distribution data of the high-sensitivity parameters of uncertainty.

[0142] Based on the real-time data corresponding to the high-sensitivity parameters of carbon emission uncertainty, likelihood function analysis of the high-sensitivity parameters of uncertainty is performed to generate the likelihood function of the high-sensitivity parameters of uncertainty.

[0143] The preset Bayesian iterative algorithm is used to iteratively optimize the carbon emission uncertainty high-sensitivity parameters by processing the prior distribution data and the likelihood function of the uncertainty high-sensitivity parameters, thereby generating optimized carbon emission uncertainty high-sensitivity parameters.

[0144] A carbon emission parameter optimization strategy is designed based on the highly sensitive parameters of carbon emission uncertainty.

[0145] In this embodiment of the invention, based on the uncertainty quantification data of carbon emission parameters in the uncertainty quantification data of carbon emission accounting, highly sensitive uncertain parameters in carbon emission accounting are extracted. During the extraction process, the influence of each parameter on the carbon emission accounting results is analyzed, and parameter sensitivity coefficients are calculated. Parameters that contribute significantly to the fluctuation of the accounting results are identified as highly sensitive parameters of carbon emission uncertainty. These highly sensitive parameters may involve construction equipment utilization efficiency, material consumption intensity, emission coefficients of construction procedures, and adjustment factors under environmental conditions. The highly sensitive parameters of carbon emission uncertainty generated in this step provide clear control targets for subsequent accounting optimization. Prior distribution analysis is performed on the historical data corresponding to the highly sensitive parameters of carbon emission uncertainty to obtain prior distribution data of the highly sensitive parameters. Prior distribution analysis establishes a probability distribution model, including mean, variance, and distribution type, by statistically analyzing the numerical range, fluctuation patterns, and distribution characteristics of each highly sensitive parameter in historical construction projects. Through this analysis, the stability and deviation patterns of each highly sensitive parameter under historical construction environments are clarified, providing data support and probabilistic boundary constraints for subsequent optimization and iteration. Likelihood function analysis is performed based on real-time data corresponding to highly sensitive parameters with carbon emission uncertainty to generate likelihood functions for these parameters. This analysis uses statistical modeling of real-time construction data and environmental conditions to assess the observation probability of these highly sensitive parameters under the current construction conditions, establishing a relationship model between the parameters and carbon emission accounting results. The likelihood function reflects the reliability of each highly sensitive parameter value under the current construction environment and provides real-time constraint information for Bayesian iterative optimization, enabling parameter adjustments to effectively reduce accounting uncertainty. A pre-defined Bayesian iterative algorithm is used to iteratively optimize the prior distribution data and likelihood functions of the highly sensitive parameters with uncertainty, generating optimized highly sensitive parameters for carbon emission uncertainty. The Bayesian iterative algorithm continuously updates the posterior probability distribution of the parameters, fusing the prior distribution with real-time observation data, gradually converging to parameter values ​​that minimize accounting errors and uncertainty fluctuations. During the iteration process, the influence of construction equipment operating status, material carbon emission factors, and construction environmental conditions on the highly sensitive parameters is considered, ensuring that the optimized parameters conform to historical statistical patterns and adapt to current construction conditions, thus guaranteeing the accuracy and stability of the carbon emission accounting results. A carbon emission parameter optimization strategy was designed based on the optimized highly sensitive parameters for carbon emission uncertainty. The optimization strategy includes a systematic design of the control sequence, control amplitude, and constraint conditions for the highly sensitive parameters, ensuring that the impact of fluctuations in these parameters on the accounting results is minimized throughout the entire construction process. By implementing this strategy, closed-loop control of the uncertainty of carbon emission parameters during construction can be achieved, making the accounting results more accurate and reliable, providing a scientific basis for carbon emission management and decision-making, and providing complete data support for the integration of subsequent carbon emission accounting optimization strategies.

[0146] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the application be incorporated into the invention.

[0147] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for quantifying and optimizing the uncertainty of carbon emission accounting results in road engineering projects, characterized in that, Includes the following steps: Step S1: Obtain the full-process data of carbon emission accounting for road engineering; identify carbon emission uncertainties based on the full-process data of carbon emission accounting for road engineering to obtain carbon emission uncertainty data, wherein the carbon emission uncertainty data includes carbon emission acquisition error factor data, carbon emission accounting model structure uncertainty factor data, and carbon emission parameter uncertainty factor data. Step S1 includes the following steps: Step S11: Obtain full-process data for carbon emission accounting of road engineering projects; Step S12: Perform carbon emission accounting process traceability analysis on the entire process data of road engineering carbon emission accounting, and generate carbon emission accounting process traceability data; Step S13: Based on the carbon emission accounting process traceability data, perform carbon emission accounting process data classification processing on the entire process data of carbon emission accounting for road engineering, and generate carbon emission accounting process type data, wherein the carbon emission accounting process type data includes carbon emission collection type data, carbon emission accounting model structure type data, and carbon emission accounting parameter type data. Step S14: Perform feature analysis on the subset of carbon emission accounting process types based on the carbon emission accounting process type data, and generate feature data for the subset of carbon emission accounting process types; Step S15: Perform logical association feature analysis on the feature data of each type of accounting process type subset to generate logical association feature data of carbon emission accounting process type; Step S16: Establish a carbon emission uncertainty factor type topology using carbon emission accounting process type data, use the characteristic data of the accounting process type subset as topology nodes of carbon emission uncertainty factor type, and use the logical association characteristic data of carbon emission accounting process type as association coefficients of topology nodes; map the topology nodes and association coefficients of carbon emission uncertainty factor type to the topology structure of carbon emission uncertainty factor type to establish the data storage structure of carbon emission uncertainty factor topology network, so as to obtain carbon emission uncertainty factor topology network, and output carbon emission uncertainty factor data through carbon emission uncertainty factor topology network; Step S2: Perform quantitative analysis of various types of uncertainties in carbon emission accounting on the data of uncertain factors in carbon emission accounting, and generate quantitative data of uncertainties in carbon emission accounting; Step S2 includes the following steps: Step S21: Based on the carbon emission collection error factor data, perform carbon emission collection uncertainty range analysis for each error factor, and generate carbon emission collection error factor uncertainty range data; Step S21 includes: performing carbon emission collection numerical characteristic analysis on the carbon emission collection error factor data for each error factor to generate carbon emission collection numerical characteristic data; performing carbon emission collection error factor attribute characteristic analysis on the carbon emission collection error factor data to obtain carbon emission collection error factor attribute characteristic data; collecting corresponding construction equipment energy consumption data and material carbon emission factor data based on the carbon emission collection error factor attribute characteristic data; performing carbon emission collection variation coefficient analysis on the construction equipment energy consumption data and material carbon emission factor data corresponding to the carbon emission collection error factor attribute characteristic data to generate carbon emission collection variation coefficient; performing carbon emission collection dispersion analysis on the carbon emission collection numerical characteristic data for each error factor based on the carbon emission collection variation coefficient to obtain carbon emission collection dispersion data; and performing carbon emission collection uncertainty range analysis on the carbon emission collection dispersion data for each error factor to generate carbon emission collection error factor uncertainty range data. Step S22: Based on the data of uncertain factors in the carbon emission accounting model structure, perform a quantitative analysis of the uncertainty in the carbon emission accounting model structure to obtain quantitative data of the uncertainty in the carbon emission accounting model structure; Step S23: Perform uncertainty quantification analysis based on the uncertainty factor data of carbon emission parameters to generate uncertainty quantification data of carbon emission parameters; Step S23 includes: performing probability distribution characteristic analysis of carbon emission parameters based on uncertainty factor data, generating probability distribution characteristic data of carbon emission parameters; performing simulation analysis of carbon emission parameters based on uncertainty factor data, generating simulation data of carbon emission parameters, and performing sampling interactive simulation analysis of carbon emission parameters using the simulation data of carbon emission parameters, generating sampling interactive simulation data of carbon emission parameters; performing carbon emission accounting characteristic analysis of the sampling interactive simulation data of carbon emission parameters, generating carbon emission accounting characteristic data of the simulated parameters; performing probability distribution characteristic analysis of carbon emission simulation parameters based on the carbon emission accounting characteristic data of the simulated parameters, generating probability distribution characteristic data of carbon emission simulation parameters, and performing accounting impact characteristic analysis of changes in each carbon emission parameter using the carbon emission parameter accounting probability distribution characteristic data and the carbon emission simulation parameter accounting probability distribution characteristic data, generating carbon emission parameter accounting impact characteristic data; and performing uncertainty quantification analysis of uncertainty factor data of carbon emission parameters based on the carbon emission parameter accounting impact characteristic data, generating carbon emission parameter uncertainty quantification data. Step S24: Integrate the various types of uncertainty data for carbon emission accounting based on the uncertainty range data of carbon emission collection error factors, the uncertainty quantification data of carbon emission accounting model structure, and the uncertainty quantification data of carbon emission parameters, so as to obtain the uncertainty quantification data for carbon emission accounting. Step S3: Design an optimization strategy for carbon emission accounting based on the quantification data of uncertainty in carbon emission accounting; Step S3 includes the following steps: Step S31: Design and optimize the carbon emission acquisition equipment operation status monitoring engine based on the uncertainty range of carbon emission acquisition error factors in the carbon emission accounting uncertainty quantification data; Step S31 includes: designing a carbon emission collection equipment operation status monitoring engine based on the uncertainty range data of carbon emission collection error factors in the carbon emission accounting uncertainty quantification data; establishing carbon emission collection equipment operation status maintenance rules and carbon emission collection equipment operation status multi-level screening rules, wherein the carbon emission collection equipment operation status maintenance rules include intelligent calibration and intelligent maintenance of equipment operation status, and the carbon emission collection equipment operation status multi-level screening rules include first-level rules for eliminating jump values, second-level rules for correcting drift values, and third-level rules for marking suspicious values; designing corresponding multi-level screening rule accuracy optimization parameters based on the uncertainty range data of carbon emission collection error factors, and performing multi-level screening rule accuracy adaptive optimization processing on the multi-level screening rules of carbon emission collection equipment operation status through the multi-level screening rule accuracy optimization parameters to obtain optimized carbon emission collection equipment operation status multi-level screening rules; and performing engine intelligent optimization processing on the carbon emission collection equipment operation status monitoring engine based on the carbon emission collection equipment operation status maintenance rules and carbon emission collection equipment operation status multi-level screening rules to obtain optimized carbon emission collection equipment operation status monitoring engine. Step S32: Design an optimization strategy for the carbon emission accounting model structure based on the carbon emission accounting model structure uncertainty quantification data in the carbon emission accounting uncertainty quantification data; Step S33: Design a carbon emission parameter optimization strategy based on the carbon emission parameter uncertainty quantification data in the carbon emission accounting uncertainty quantification data; Step S33 includes: extracting highly sensitive parameters of carbon emission uncertainty from the carbon emission parameter uncertainty quantification data in the carbon emission accounting uncertainty quantification data to obtain highly sensitive parameters of carbon emission uncertainty; performing prior distribution analysis on the historical data corresponding to the highly sensitive parameters of carbon emission uncertainty to generate prior distribution data of the highly sensitive parameters of uncertainty; performing likelihood function analysis on the real-time data corresponding to the highly sensitive parameters of carbon emission uncertainty to generate likelihood functions of the highly sensitive parameters of uncertainty; using a preset Bayesian iterative algorithm to iteratively optimize the highly sensitive parameters of carbon emission uncertainty using the prior distribution data and likelihood functions of the highly sensitive parameters of uncertainty to generate optimized highly sensitive parameters of carbon emission uncertainty; and designing a carbon emission parameter optimization strategy based on the optimized highly sensitive parameters of carbon emission uncertainty. Step S34: Integrate the carbon emission data collection equipment operation status monitoring engine, carbon emission accounting model structure optimization strategy, and carbon emission parameter optimization strategy into the carbon emission accounting optimization strategy to obtain the carbon emission accounting optimization strategy. Step S4: Evaluate the effectiveness of the carbon emission accounting optimization strategy to obtain the carbon emission accounting optimization strategy evaluation, and perform intelligent adjustment of the carbon emission accounting optimization strategy data based on the carbon emission accounting optimization strategy evaluation data.

2. The method for quantifying and optimizing the uncertainty of carbon emission accounting results for road engineering projects according to claim 1, characterized in that, Step S22 includes the following steps: The carbon emission accounting model selection model is collected by collecting data on uncertain factors in the carbon emission accounting model structure, and a carbon emission accounting model library is established based on the selected carbon emission accounting model to obtain carbon emission accounting model library data. Based on the data from the carbon emission accounting model library, we will conduct a carbon emission accounting benefit analysis of the models with different application scenarios to obtain carbon emission accounting benefit data. Based on the model carbon emission accounting benefit data, a sensitivity characteristic analysis of the model carbon emission accounting for different application scenarios is conducted to obtain the model carbon emission accounting sensitivity characteristic data. Based on the sensitivity characteristic data of carbon emission accounting model, a quantitative analysis of the structural uncertainty of carbon emission accounting model is conducted to obtain quantitative data of structural uncertainty of carbon emission accounting model.

3. The method for quantifying and optimizing the uncertainty of carbon emission accounting results for road engineering projects according to claim 1, characterized in that, Step S32 includes the following steps: Based on the carbon emission accounting uncertainty quantification data and the carbon emission accounting sensitivity characteristic data of the model, carbon emission accounting model structure adaptation analysis is performed for different application scenarios to generate carbon emission accounting model structure adaptation data. Intelligent optimization of model parameters for application scenario environmental factors is performed on carbon emission accounting model structure adaptation data to generate optimized carbon emission accounting model structure adaptation data. Based on the data, an optimization strategy for the carbon emission accounting model structure is designed.

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