A green and low-carbon building materials evaluation method

Abstract

The present invention relates to a green and low-carbon building material evaluation method, which belongs to the technical field of building material evaluation methods. Specifically, the method comprises obtaining the engineering usage and initial unit carbon emission factor of each material in a project to determine the material's total carbon emission weight; obtaining a historical carbon emission factor dataset for the material to determine the material's data source volatility; and dynamically adjusting the data management priority index based on the material's total carbon emission weight and data source volatility, in response to the data update request frequency and data verification level. By comparing the data management priority index with a preset priority index threshold, the present invention can intelligently generate differentiated data update request frequencies and data verification levels for materials of different risk levels.

Description

Technical Field

[0001] The present invention relates to the technical field of building material evaluation methods, and in particular to a green and low-carbon building material evaluation method. Background Art

[0002] In the field of green and low-carbon building materials evaluation, traditional carbon data management methods for building materials rely primarily on a one-size-fits-all, static management strategy. This approach applies uniform monitoring and data update standards to all building materials, lacking differentiation in the characteristics and data risks of different materials. This situation leads to a conflict between insufficient monitoring and excessive management during the evaluation process, making it difficult to achieve accurate and efficient management of building materials carbon emissions data.

[0003] The current status and shortcomings of this static model are mainly due to the limitations of its management mechanism. Traditional methods are unable to dynamically evaluate two key indicators: one is the importance of different materials to the total carbon emissions of the project, that is, it is impossible to identify which materials are the key materials that have an overall impact; the other is the stability of the material carbon emission factor data, that is, it is impossible to distinguish whether the data comes from mature and stable national standards or from frequently changing innovative products. As a result, for key materials with significant impacts on carbon emissions and unstable data, the evaluation results may be distorted due to untimely data updates; while for auxiliary materials with minimal impact and stable data, unnecessary frequent data requests may be made, resulting in a waste of computing and human resources. This management method, which lacks risk quantification assessment and relies solely on empirical judgment, cannot focus limited monitoring resources on the risk points that really need attention, affecting the long-term accuracy and efficiency of the entire green building life cycle assessment.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a green and low-carbon building material evaluation method to solve the problems raised in the above background technology.

[0006] The technical solution of the present invention is: a green and low-carbon building material evaluation method, which specifically comprises the following steps:

[0007] Step 1: Obtain the engineering usage and initial unit carbon emission factor of each material in the project, and determine the total carbon emission weight of the material based on the engineering usage and the initial unit carbon emission factor; obtain the historical carbon emission factor dataset of the material, and determine the data source volatility of the material based on the historical carbon emission factor dataset;

[0008] Step 2: Build a data management priority index for the material based on the total carbon emission weight and data source volatility determined in Step 1. Evaluate the data management priority index based on a preset priority index threshold to generate a data update request frequency and data verification level for the material.

[0009] Step 3. In response to the data update request frequency and data verification level generated by step 2, obtain the original carbon emission factor from the external authoritative database and verify the original carbon emission factor to obtain a verified carbon emission factor; and update the verified carbon emission factor to the historical carbon emission factor dataset to trigger the redetermination of the volatility of the data source, thereby realizing dynamic adjustment of the data management priority index.

[0010] In this embodiment, the step 1 further includes:

[0011] S11. For each material, calculate the product of its engineering usage and the initial unit carbon emission factor to determine the carbon emissions of each material;

[0012] S12. Sum up the individual carbon emissions of all materials to determine the total carbon emissions of the project;

[0013] S13. Divide the carbon emissions of the single material by the total carbon emissions of the project to determine the total carbon emission weight of the material;

[0014] S14. For the historical carbon emission factor dataset, calculate the standard deviation and arithmetic mean of all carbon emission factors in the dataset;

[0015] S15. Divide the standard deviation by the arithmetic mean to determine the data source volatility.

[0016] In this embodiment, the second step includes:

[0017] S21. Multiplying the total carbon emission weight of the material by a preset weight influence coefficient to determine a weight component; and multiplying the data source volatility by a preset volatility influence coefficient to determine a volatility component;

[0018] S22. Sum the weight component and the volatility component to construct the data management priority index;

[0019] S23, comparing the data management priority index with a preset high priority index threshold and a preset low priority index threshold;

[0020] S24, and according to the comparison result, perform the following operations:

[0021] When the data management priority index is higher than the high priority index threshold, setting the data update request frequency to high frequency and setting the data verification level to deep verification;

[0022] When the data management priority index is between the low priority index threshold and the high priority index threshold, setting the data update request frequency to medium frequency and setting the data verification level to standard verification;

[0023] When the data management priority index is lower than the low priority index threshold, the data update request frequency is set to low frequency, and the data verification level is set to automatic verification.

[0024] In this embodiment, step three further includes:

[0025] S31. Combining the verified carbon emission factor with the current timestamp to form a new historical data point;

[0026] S32. Appending the new historical data point to the historical carbon emission factor dataset to form an updated historical carbon emission factor dataset;

[0027] S33. Based on the updated historical carbon emission factor data set, trigger the redetermination of the data source volatility as the updated data source volatility for dynamic adjustment of the data management priority index.

[0028] In this embodiment, in step 1, when the historical carbon emission factor dataset is empty, the method further includes:

[0029] S101. Initiate a request to an external authoritative database to obtain all historical version data of the target material within a preset retrospective period;

[0030] S102, using the acquired historical version data as the initial composition of the historical carbon emission factor data set;

[0031] S103: When the acquisition of data from the external authoritative database fails, the data source volatility is set to a preset default volatility value.

[0032] In this embodiment, the initial unit carbon emission factor is obtained according to the following preset priority order:

[0033] S1a, obtaining the value recorded in the specific environmental product declaration report specified by the user;

[0034] S1b. When the values ​​recorded in the environmental product declaration report are not available, the industry average values ​​extracted from a third-party life cycle assessment commercial database are obtained;

[0035] S1c. When the industry average value cannot be obtained, the technical value published in the building materials carbon emission factor standard issued by the country or region shall be obtained.

[0036] A green and low-carbon building material evaluation system, comprising:

[0037] The data processing module is used to obtain the engineering usage and initial unit carbon emission factor of each material in the project to determine the total carbon emission weight of the material; and obtain the historical carbon emission factor dataset of the material to determine the data source volatility of the material;

[0038] a decision generation module, connected to the data processing module, for constructing a data management priority index for the material based on the total carbon emission weight of the material and the data source volatility determined by the data processing module; and evaluating the data management priority index based on a preset priority index threshold to generate a data update request frequency and a data verification level for the material;

[0039] a dynamic update module, connected to the decision generation module, for obtaining and verifying the original carbon emission factor from an external authoritative database in response to the data update request frequency and data verification level generated by the decision generation module to obtain a verified carbon emission factor;

[0040] The verified carbon emission factor is updated to the historical carbon emission factor dataset to trigger redetermination of the data source volatility, thereby achieving dynamic adjustment of the data management priority index.

[0041] The present invention provides a green and low-carbon building material evaluation method through improvement, which has the following improvements and advantages compared with the existing technology:

[0042] (1) The present invention achieves differentiated management by constructing a data management priority index. The index is the result of quantitative assessment combining two key dimensions: first, identifying the influence of materials on the overall carbon emissions of the project by calculating the total carbon emission weight of the materials; second, evaluating the stability of the historical data of the carbon emission factor of the materials by calculating the volatility of the data source. The existing technology relies solely on empirical judgment and cannot quantify risks. The present invention compares the data management priority index with the preset priority index threshold and can intelligently generate differentiated data update request frequencies and data verification levels for materials with different risk levels, such as high-frequency / deep verification, medium-frequency / standard verification, and low-frequency / automatic verification. This allows high-risk, high-weight key materials to be monitored at high frequency and depth, ensuring the timeliness and accuracy of key data; while low-risk, low-weight auxiliary materials use low-frequency, automated verification to avoid redundant requests to stable data sources, significantly saving computing power and human resources.

[0043] (2) Aiming at two key scenarios, the present invention designs a rigorous data acquisition strategy:

[0044] Cold start scenario: When the historical carbon emission factor dataset for a new material is empty, the system will proactively initiate a request to an external authoritative database to backfill historical data for a preliminary volatility assessment; if the request fails, a preset default high volatility value will be set to force high-priority monitoring.

[0045] Initial data acquisition: When determining the initial unit carbon emission factor, a preset priority order is adopted: first, the values ​​in the specific Environmental Product Declaration report (EPD) specified by the user are obtained, followed by the industry average value extracted from the third-party life cycle assessment commercial database, and finally the technical values ​​in the building material carbon emission factor standards issued by the country or region.

[0046] (3) The present invention designs a feedback loop for continuous optimization. After the system obtains the original carbon emission factor from the external authoritative database and verifies it, it will attach a timestamp to this verified carbon emission factor and update it to the historical carbon emission factor data set. This update action will trigger the redetermination of the volatility of the data source, thereby realizing the dynamic adjustment of the data management priority index. Traditional methods are static, and the evaluation model is rarely updated once it is established. The closed-loop mechanism of the present invention enables the evaluation system to have learning capabilities. Every time new data is acquired and verified, it will be integrated into the historical data set, and the calculation results of the data source volatility will be updated in real time, thereby dynamically adjusting the monitoring priority of the material. This design enables the evaluation system to self-learn and iterate, and its risk identification and resource allocation strategies will become more accurate as data accumulates, ensuring the long-term accuracy and efficiency of the entire green building life cycle evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention will be further explained below in conjunction with the accompanying drawings and Examples:

[0048] Figure 1 It is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0050] Example 1:

[0051] See also Figure 1 The present invention provides a technical solution for a green and low-carbon building material evaluation method: a green and low-carbon building material evaluation method, the specific steps of which include:

[0052] Step 1: Obtain the engineering usage and initial unit carbon emission factor of each material in the project, and determine the total carbon emission weight of the material based on the engineering usage and the initial unit carbon emission factor; obtain the historical carbon emission factor dataset of the material, and determine the data source volatility of the material based on the historical carbon emission factor dataset;

[0053] Step 2: Build a data management priority index for the material based on the total carbon emission weight and data source volatility determined in Step 1. Evaluate the data management priority index based on a preset priority index threshold to generate a data update request frequency and data verification level for the material.

[0054] Step 3. In response to the data update request frequency and data verification level generated by step 2, obtain the original carbon emission factor from the external authoritative database and verify the original carbon emission factor to obtain a verified carbon emission factor; and update the verified carbon emission factor to the historical carbon emission factor dataset to trigger the redetermination of the volatility of the data source, thereby realizing dynamic adjustment of the data management priority index.

[0055] In this embodiment, the core purpose of the green and low-carbon building material evaluation method is to resolve the contradiction between insufficient monitoring and excessive management caused by the one-size-fits-all strategy for building material carbon data management in the existing technology. The traditional static management model cannot distinguish the importance and data stability of different materials, while this method realizes risk adaptive management through dynamic evaluation.

[0056] This method first uses the total carbon emission weights of the materials in step one to identify key materials that contribute significantly to total carbon emissions, such as C30 concrete, from the perspective of the project's overall impact. At the same time, using data source volatility to identify high-risk data sources with frequently changing carbon emission factor values ​​from the perspective of data stability, this method achieves a quantitative risk assessment compared to existing technologies that rely solely on empirical judgment.

[0057] In step two, this method combines the quantitative results of these two dimensions into a comprehensive data management priority index, and based on this, intelligently generates differentiated data update request frequencies and data verification levels; this enables high-risk, high-weight key materials, such as the innovative low-carbon concrete in the early stages of EPD, to receive high-frequency, in-depth monitoring, while low-risk, low-weight auxiliary materials (such as national standard fasteners) use low-frequency, automated verification. This not only avoids evaluation distortion caused by untimely updates of key data, but also eliminates redundant requests for stable data sources, significantly saving computing power and human resources.

[0058] Step three established a closed-loop feedback mechanism for continuous optimization. Each time a newly acquired verified carbon emission factor is integrated into the historical carbon emission factor dataset, it triggers a real-time recalculation of the data source volatility, and then dynamically adjusts the data management priority index. This design enables the evaluation system to self-learn and iterate, and its risk identification and resource allocation strategies will become more accurate as data accumulates, ensuring the long-term accuracy and efficiency of the entire green building life cycle assessment.

[0059] Example 2

[0060] This embodiment aims to explain in detail how the core indicators in steps 1 and 2 are accurately calculated. In this embodiment, step 1 also includes:

[0061] S11. For each material, calculate the product of its engineering usage and the initial unit carbon emission factor to determine the carbon emissions of each material;

[0062] S12. Sum up the individual carbon emissions of all materials to determine the total carbon emissions of the project;

[0063] S13. Divide the carbon emissions of the single material by the total carbon emissions of the project to determine the total carbon emission weight of the material;

[0064] S14. For the historical carbon emission factor dataset, calculate the standard deviation and arithmetic mean of all carbon emission factors in the dataset;

[0065] S15. Divide the standard deviation by the arithmetic mean to determine the data source volatility.

[0066] In this embodiment, the calculation of the total carbon emission weight of the material is intended to objectively quantify the contribution of a single material to the overall carbon emissions of the project. The calculation process is achieved through the following formula:

[0067]

[0068] in, Indicates the Total carbon emission weight of the material; Indicates the The total engineering usage of the materials; Indicates the The initial unit life cycle carbon emission factor of the material; Indicates the total number of building materials in the project; It is the traversal index in the summation formula, representing the number from 1 to of each material.

[0069] In this embodiment, the second step includes:

[0070] S21. Multiplying the total carbon emission weight of the material by a preset weight influence coefficient to determine a weight component; and multiplying the data source volatility by a preset volatility influence coefficient to determine a volatility component;

[0071] S22. Sum the weight component and the volatility component to construct the data management priority index;

[0072] S23, comparing the data management priority index with a preset high priority index threshold and a preset low priority index threshold;

[0073] In a preferred embodiment of the present invention, the high priority index threshold can be set to 0.75, and the low priority index threshold can be set to 0.3. The weight impact coefficient (kw) and volatility impact coefficient (kv) can be set to 0.5 and 0.5 based on experience to indicate equal importance; or, in scenarios where the overall carbon impact of a project is emphasized, kw = 0.6 and kv = 0.4. The preset lookback period can be set to 24 months to cover the annual variation patterns of most data.

[0074] S24, and according to the comparison result, perform the following operations:

[0075] When the data management priority index is higher than the high priority index threshold, setting the data update request frequency to high frequency and setting the data verification level to deep verification;

[0076] When the data management priority index is between the low priority index threshold and the high priority index threshold, setting the data update request frequency to medium frequency and setting the data verification level to standard verification;

[0077] When the data management priority index is lower than the low priority index threshold, the data update request frequency is set to low frequency, and the data verification level is set to automatic verification.

[0078] The construction of the data management priority index is to weight the quantitative results of the above two dimensions to form the final decision basis. Its calculation formula is:

[0079]

[0080] in, Indicates the Materials at time point Data management priority index; is the static total carbon emission weight calculated above; The volatility of the dynamic data source calculated above; and They are the preset weight influence coefficient and volatility influence coefficient respectively, and their values ​​can be calibrated by the project administrator according to decision models such as hierarchical analysis method.

[0081] Through the above series of precise quantitative calculations, this method can assign a scientific and dynamic priority score to each material, replacing the fuzzy and static management model in traditional methods, providing a solid data foundation for the precise allocation of resources and effective control of risks, and its evaluation results are reliable and interpretable.

[0082] In this embodiment of the present invention, step S24 is the core decision-making and execution framework for the entire adaptive risk management system. It converts the quantified and continuous data management priority index constructed in step S22 into discrete, executable management policy instructions. Compared to the one-size-fits-all static management model used in existing technologies, this step establishes a three-level gradient mapping logic to achieve differentiated and refined management of materials at different risk levels, thereby ensuring the accuracy of key data while maximizing resource utilization efficiency.

[0083] The specific execution process of the mapping logic is as follows:

[0084] Key monitoring strategies for high-risk materials:

[0085] Trigger condition: When the data management priority index is higher than the preset high priority index threshold.

[0086] This typically occurs when a key material significantly impacts the project's total carbon emissions, with a high weight for the material's total carbon emissions and highly unstable carbon emission factor data (i.e., high data source volatility). A typical example is a newly launched, innovative low-carbon concrete whose Environmental Product Declaration is still in its early stages.

[0087] The system automatically sets a high frequency of data update requests and a deep verification level for this type of material. Deep verification typically requires intervention from domain experts or senior analysts to trace the original carbon emission factors, such as verifying the latest EPD report, validating the calculation boundaries and methodologies, and ultimately performing manual confirmation.

[0088] This strategy ensures that the variables with the greatest impact and the highest risk on the overall project evaluation results are subject to the strictest monitoring, and their data changes can be captured in a timely manner, eliminating distortion of evaluation results caused by lagging or errors in key data.

[0089] Efficient automation strategies for low-risk materials:

[0090] Trigger condition: When the data management priority index falls below the preset low priority index threshold.

[0091] This typically applies to auxiliary or standardized materials that have a minimal impact on the project's overall carbon emissions (i.e., a low material weight) and whose data source is extremely stable and reliable (i.e., low data source volatility). A typical example is a general-purpose fastener produced in bulk by a large manufacturer and meeting national standards.

[0092] The system will then set a low data update request frequency (e.g., quarterly or even semi-annual) and a data verification level for automatic verification. Automatic verification means that after obtaining the raw carbon emission factors, the system will directly adopt and update them to the database with a high degree of confidence in their source, without any human intervention.

[0093] This strategy greatly saves unnecessary API call costs and human review resources, avoids excessive management of stable, low-impact data sources, and focuses valuable resources on risk points that truly require attention.

[0094] Balanced management strategy for medium risk materials:

[0095] Trigger condition: When the data management priority index is between the low priority index threshold and the high priority index threshold.

[0096] This middle ground, a key embodiment of the refined management philosophy of this invention, covers a wide range of common building materials, such as ready-mixed concrete or ordinary steel, which are technically mature and supplied by multiple suppliers. These materials have a moderate impact on the project (medium weight), but their data sources are relatively stable (medium volatility).

[0097] Action: The system will set the frequency of data update requests and the data verification level for standard verification. Standard verification is a semi-automated process where the system automatically obtains data but requires a quick review and confirmation by a junior analyst to ensure basic data rationality.

[0098] This balanced strategy perfectly fills the gaps in the traditional high / low two-tier management model. It avoids the waste of resources associated with excessive monitoring of medium-risk materials (e.g., in-depth verification), while also guarding against potential errors that could be introduced by laissez-faire approaches (e.g., automated verification), achieving an optimal balance between monitoring intensity and cost-effectiveness.

[0099] In summary, step S24 establishes a gradient response mechanism that completely covers all risk intervals of high, medium and low, transforms the abstract risk index into specific and differentiated management instructions, and thus builds a closed-loop system that can intelligently allocate monitoring resources, with high management precision, resource utilization efficiency and reliability of the final evaluation results.

[0100] Example 3

[0101] This embodiment aims to illustrate the closed-loop learning mechanism and cold start initialization process of the system.

[0102] In this embodiment, step three further includes:

[0103] S31. Combining the verified carbon emission factor with the current timestamp to form a new historical data point;

[0104] S32. Appending the new historical data point to the historical carbon emission factor dataset to form an updated historical carbon emission factor dataset;

[0105] S33. Based on the updated historical carbon emission factor data set, trigger the redetermination of the data source volatility as the updated data source volatility for dynamic adjustment of the data management priority index.

[0106] In this embodiment, in step 1, when the historical carbon emission factor dataset is empty, the method further includes:

[0107] S101. Initiate a request to an external authoritative database to obtain all historical version data of the target material within a preset retrospective period;

[0108] S102, using the acquired historical version data as the initial composition of the historical carbon emission factor data set;

[0109] S103: When the acquisition of data from the external authoritative database fails, the data source volatility is set to a preset default volatility value.

[0110] In this embodiment, the intelligence and adaptability of the method are mainly reflected in the full life cycle of its data processing, which includes two stages: cold start initialization and continuous closed-loop collection.

[0111] First, for cold start scenarios—that is, when a new material is first entered into the system and its historical carbon emission factor dataset is empty—the system automatically performs data backfilling. For example, in S101, the system initiates a request to a pre-configured external authoritative database (such as Ecoinvent or GaBi) via an API interface to obtain all historical version data for the material within a preset backtracking period. The length of the backtracking period can be set based on the data release frequency or standard update cycle of the industry's authoritative database. For example, in S102, these acquired historical version data will directly constitute the material's initial historical carbon emission factor dataset. This design ensures that even for new materials, the system can immediately conduct a preliminary assessment of the volatility of its data source, rather than assuming it is risk-free. In the exception handling of S103, if data backfill fails (for example, the material is too new and there is no historical data), the system will assign it a preset default volatility value, which is usually set to a higher value to ensure that the new material is forced to obtain high-priority monitoring when the initial information is insufficient, thereby avoiding potential risks and ensuring that the material can also obtain high-priority monitoring when the information is insufficient, thereby effectively avoiding potential monitoring blind spots.

[0112] Secondly, after the system enters the continuous operation phase, it achieves self-iteration and optimization through a chained feedback mechanism. For example, in S31 and S32, each verified carbon emission factor is timestamped as a new historical data point and added to the historical carbon emission factor dataset, forming an updated historical carbon emission factor dataset. This update is a trigger that immediately triggers the re-determination of the data source volatility in S33. Since volatility is one of the core inputs for calculating the data management priority index, its changes directly lead to dynamic adjustments to the priority index.

[0113] Through the full lifecycle process design of cold-start initialization and continuous closed-loop data collection, this method builds a logically rigorous, traceable, and self-optimizing dynamic evaluation system. Compared to existing static evaluation methods, this method clearly identifies the source of all historical data (either backfill initialization or closed-loop data collection) and ensures that every data update is immediately fed back into the decision-making model, enabling the entire evaluation system to continuously learn and adapt to new situations.

[0114] Example 4

[0115] This embodiment is a further explanation of the solution of Example 1.

[0116] In this embodiment, the initial unit carbon emission factor is obtained according to the following preset priority order:

[0117] S1a, obtaining the value recorded in the specific environmental product declaration report specified by the user;

[0118] S1b. When the values ​​recorded in the environmental product declaration report are not available, the industry average values ​​extracted from a third-party life cycle assessment commercial database are obtained;

[0119] S1c. When the industry average value cannot be obtained, the technical value published in the building materials carbon emission factor standard issued by the country or region shall be obtained.

[0120] In this example, to ensure the accuracy and reliability of the baseline data for the entire assessment system, the method uses a pre-defined, hierarchical data source acquisition strategy when obtaining the initial unit carbon emission factor. This strategy is prioritized from the most specific and relevant sources to the most general and fundamental sources.

[0121] The first priority is to obtain the values ​​recorded in the specific Environmental Product Declaration (EPD) report for the material directly provided by the project user. EPDs are third-party verified reports based on the ISO14025 standard that provide information on the environmental impact of a product throughout its life cycle. Their data is the most targeted and accurate.

[0122] If the user fails to provide an EPD report, or the report lacks the required data, the system automatically shifts to the second priority. At this point, the system extracts the corresponding industry average for the material from a connected third-party commercial life cycle assessment database. These databases contain vast amounts of industry data compiled by professional organizations. While less universal than the EPD, they remain internationally recognized and reliable data sources.

[0123] If no matching data is found in commercial databases (e.g., the material is too new or the location is unique), the system activates the third priority. At this point, the system queries the official building material carbon emission factor standards or guidelines published by the country or region where the material is used, and obtains the technical values ​​published there. This data generally serves as a common benchmark within the region, and is authoritative and universally applicable.

[0124] Through this hierarchical and prioritized acquisition strategy, this methodology ensures that the initial unit carbon emission factor, the cornerstone of its assessment system, is always based on currently available quality data. Compared to existing techniques that may arbitrarily select data sources, this methodology, through a standardized data acquisition path, significantly enhances the credibility and comparability of assessment results, laying a solid foundation for the accuracy of all subsequent risk assessments and dynamic adjustments.

[0125] A green and low-carbon building material evaluation system, comprising:

[0126] The data processing module is used to obtain the engineering usage and initial unit carbon emission factor of each material in the project to determine the total carbon emission weight of the material; and obtain the historical carbon emission factor dataset of the material to determine the data source volatility of the material;

[0127] a decision generation module, connected to the data processing module, for constructing a data management priority index for the material based on the total carbon emission weight of the material and the data source volatility determined by the data processing module; and evaluating the data management priority index based on a preset priority index threshold to generate a data update request frequency and a data verification level for the material;

[0128] a dynamic update module, connected to the decision generation module, for obtaining and verifying the original carbon emission factor from an external authoritative database in response to the data update request frequency and data verification level generated by the decision generation module to obtain a verified carbon emission factor;

[0129] The verified carbon emission factor is updated to the historical carbon emission factor dataset to trigger redetermination of the data source volatility, thereby achieving dynamic adjustment of the data management priority index.

[0130] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A green and low-carbon building material evaluation method, characterized in that: The specific steps include: Step 1: Obtain the engineering usage and initial unit carbon emission factor of each material in the project, and determine the total carbon emission weight of the material based on the engineering usage and the initial unit carbon emission factor; obtain the historical carbon emission factor dataset of the material, and determine the data source volatility of the material based on the historical carbon emission factor dataset; Step 2: Build a data management priority index for the material based on the total carbon emission weight and data source volatility determined in Step 1. Evaluate the data management priority index based on a preset priority index threshold to generate a data update request frequency and data verification level for the material. Step 3: In response to the data update request frequency and data verification level generated in step 2, obtain the original carbon emission factor from the external authoritative database, verify the original carbon emission factor, and obtain a verified carbon emission factor; update the verified carbon emission factor to the historical carbon emission factor dataset to trigger the redetermination of the volatility of the data source and realize the dynamic adjustment of the data management priority index; The second step includes: S21. Multiplying the total carbon emission weight of the material by a preset weight influence coefficient to determine a weight component; and multiplying the data source volatility by a preset volatility influence coefficient to determine a volatility component; S22. Sum the weight component and the volatility component to construct the data management priority index; S23, comparing the data management priority index with a preset high priority index threshold and a preset low priority index threshold; S24, and according to the comparison result, perform the following operations: When the data management priority index is higher than the high priority index threshold, setting the data update request frequency to high frequency and setting the data verification level to deep verification; When the data management priority index is between the low priority index threshold and the high priority index threshold, setting the data update request frequency to medium frequency and setting the data verification level to standard verification; When the data management priority index is lower than the low priority index threshold, setting the data update request frequency to low frequency and setting the data verification level to automatic verification; The step three also includes: S31. Combining the verified carbon emission factor with the current timestamp to form a new historical data point; S32. Appending the new historical data point to the historical carbon emission factor dataset to form an updated historical carbon emission factor dataset; S33. Based on the updated historical carbon emission factor data set, trigger the redetermination of the data source volatility as the updated data source volatility for dynamic adjustment of the data management priority index.

2. A green and low-carbon building material evaluation method according to claim 1, characterized in that: The step one further comprises: S11. For each material, calculate the product of its engineering usage and the initial unit carbon emission factor to determine the carbon emissions of each material; S12. Sum up the individual carbon emissions of all materials to determine the total carbon emissions of the project; S13. Divide the carbon emissions of the single material by the total carbon emissions of the project to determine the total carbon emission weight of the material; S14. For the historical carbon emission factor dataset, calculate the standard deviation and arithmetic mean of all carbon emission factors in the dataset; S15. Divide the standard deviation by the arithmetic mean to determine the data source volatility.

3. A green and low-carbon building material evaluation method according to claim 1, characterized in that: In step 1, when the historical carbon emission factor dataset is empty, the method further includes: S101. Initiate a request to an external authoritative database to obtain all historical version data of the target material within a preset retrospective period; S102, using the acquired historical version data as the initial composition of the historical carbon emission factor data set; S103: When the acquisition of data from the external authoritative database fails, the data source volatility is set to a preset default volatility value.

4. A green and low-carbon building material evaluation method according to claim 2, characterized in that: The initial unit carbon emission factors are obtained in the following preset priority order: S1a, obtaining the value recorded in the specific environmental product declaration report specified by the user; S1b. When the values ​​recorded in the environmental product declaration report are not available, the industry average values ​​extracted from a third-party life cycle assessment commercial database are obtained; S1c. When the industry average value cannot be obtained, the technical value published in the building materials carbon emission factor standard issued by the country or region shall be obtained.

5. A green and low-carbon building material evaluation system, applied to the green and low-carbon building material evaluation method according to any one of claims 1 to 4, characterized in that: include: The data processing module is used to obtain the engineering usage and initial unit carbon emission factor of each material in the project to determine the total carbon emission weight of the material; and obtain historical carbon emission factor datasets for materials to determine the volatility of the material’s data source; a decision generation module, connected to the data processing module, for constructing a data management priority index for the material based on the total carbon emission weight of the material and the data source volatility determined by the data processing module; and evaluating the data management priority index based on a preset priority index threshold to generate a data update request frequency and a data verification level for the material; a dynamic update module, connected to the decision generation module, for obtaining and verifying the original carbon emission factor from an external authoritative database in response to the data update request frequency and data verification level generated by the decision generation module to obtain a verified carbon emission factor; The verified carbon emission factor is updated to the historical carbon emission factor dataset to trigger redetermination of the data source volatility, thereby achieving dynamic adjustment of the data management priority index.

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