A low-carbon material selection optimization method for the whole decoration project

Abstract

This invention discloses a low-carbon material selection optimization method for the entire process of decoration projects, relating to the field of building information technology. By constructing a closed-loop optimization mechanism for low-carbon material selection covering the entire stages of design, construction, and completion, the method extends material selection optimization from static comparison to the entire construction process. It drives the automatic triggering of dynamic adjustment instructions for material usage and the material selection re-optimization process, effectively solving the industry pain point of unexpected carbon emissions upon completion due to frequent on-site changes and significant deviations between actual loss rates and design estimates. Simultaneously, by connecting supply chain response data and carbon factor timeliness data in real time, the method reconstructs the dynamic carbon emission values ​​of alternative materials and compares them with the original design scheme, performs comprehensive scoring, generates dynamic material selection instructions for the construction stage, and outputs an analysis of the sources of carbon emission deviations at the completion stage, achieving precise control and continuous optimization of carbon emissions throughout the entire decoration process.

Description

Technical Field

[0001] This invention relates to the field of building information technology, specifically a low-carbon material selection optimization method for the entire process of decoration projects. Background Technology

[0002] Carbon emissions from renovation projects permeate the entire chain of material production, transportation, construction, and operation and maintenance, making it a crucial area for carbon reduction in the construction sector. Early low-carbon material selection primarily relied on static accounting of the carbon footprint of individual materials. However, with the maturation of Life Cycle Assessment (LCA) theory and the development of Building Information Modeling (BIM) technology, this has gradually transitioned to comprehensive comparison and quantitative calculation during the design phase. Currently, low-carbon material selection methods covering the entire renovation project process are designed to embed environmental performance considerations into every stage, from scheme design, detailed design, procurement, on-site construction to change orders. This enables continuous tracking and dynamic control of carbon emissions, providing renovation projects with material selection decision support that balances environmental protection and economic feasibility.

[0003] However, in existing technologies, low-carbon material selection is almost entirely concentrated in the "one-time" static comparison stage in the early stages of design or procurement. This means that once a pre-set material list and fixed carbon emission factors are used for scoring and ranking, no further adjustments are made. Decoration projects inherently involve overlapping processes, frequent on-site changes, and significant discrepancies between actual material wastage rates and design estimates. In actual construction, issues such as wall layout deviations, non-standard floor dimensions, delayed supply replacements, and design refinements are commonplace. These changes can cause the actual carbon emissions of the initial material selection plan to deviate significantly from the design expectations. Due to the lack of a dynamic review and re-optimization mechanism during the construction phase for actual usage, wastage rate changes, and carbon information on replacement materials, carbon emissions at the completion stage exceeding design expectations by 15% to 30% has become a common phenomenon in the industry, significantly undermining the effectiveness of initial low-carbon design efforts during implementation.

[0004] Meanwhile, the carbon emission factor database lacks timely updates and supply chain responsiveness for the construction process. Currently, most commonly used carbon emission factor data in China are industry statistical averages from several years ago, while parameters such as actual grid emission factors, transportation fuel efficiency, and factory maintenance energy consumption fluctuate in real time depending on the season, region, and supplier production status. When material replacements or supplier changes occur during construction (which is highly probable in high-end decoration projects), existing technologies still rely on old databases for "post-event corrections," failing to dynamically calibrate based on the actual origin, actual transportation distance, and real-time production energy consumption of the current batch of materials. The direct consequence of this deficiency is that on-site managers, when faced with material change decisions, cannot obtain a true and immediate comparison of carbon emission differences between alternative solutions, and can only rely on cost or schedule experience to make judgments, missing the valuable window of opportunity to achieve carbon emission reduction compensation through reasonable material replacement during the construction phase. This deficiency is essentially an inevitable extension of the aforementioned static material selection mindset in the data dimension—because material selection is not designed as a dynamic process throughout the entire process, subsequent real-time data collection and calibration are naturally not included in the system's functional architecture. Together, these two constitute a systemic technical obstacle hindering the realization of low-carbon goals in decoration. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a low-carbon material selection optimization method for the entire decoration project process, thereby overcoming the aforementioned technical deficiencies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a low-carbon material selection optimization method for the entire decoration project process, comprising the following steps: S1: By connecting to the decoration project management system via the network, obtain the material list in the design phase, the on-site change record in the construction phase, the material supply information in the supply chain, and the updated data in the carbon emission factor database to form a design material dataset, a construction dynamic dataset, a supply chain response dataset, and a carbon factor timeliness dataset. S2: Based on the design material dataset, analyze the material design usage, preset carbon emission factor, specifications and dimensions and design estimated loss rate, and extract the actual size data of the walls, floors and ceilings by combining the geometric model of the decoration space. Combine the shearability parameters, fragility parameters and scrap utilization rate parameters in the material physical processing characteristics database to calculate the theoretical minimum loss rate of materials and generate the material geometric adaptation model and carbon budget value in the design stage. S3: Calculate the deviation rate of actual material usage and the deviation of on-site loss rate based on the construction dynamic dataset, and determine the geometric deviation type by combining the wall layout deviation and non-standard ground size records, and generate construction deviation correction factors and material usage dynamic adjustment instructions; S4: When supply delays, supplier switching or design changes occur, extract the actual place of origin of the alternative materials, actual transportation distance, energy consumption of the current batch production and real-time emission factor of the local power grid, calculate the actual carbon emission value of the transportation stage and the actual carbon emission value of the production stage respectively, and reconstruct the dynamic carbon emission value of the alternative materials. S5: Based on the carbon budget deviation threshold, material compatibility threshold, the construction deviation correction factor, and the dynamic carbon emission value of alternative materials, trigger the material selection re-optimization process, screen alternative solutions that meet geometric compatibility conditions, have better dynamic carbon emissions, and meet the timeliness requirements of the construction period, generate dynamic material selection instructions for the construction phase, and continuously iterate and execute them throughout the entire construction process, so as to output the comparative analysis results of the actual carbon emission accounting results and the carbon budget value in the design phase at the completion stage.

[0007] Preferably, in S1, the expression of the design material dataset is C1d, C2d, C3d, ..., Cmd, where C1d to Cmd are the design data of the first to the mth materials, respectively. The design data of the materials includes the material name, design quantity, preset carbon emission factor, material specifications and dimensions, and design estimated loss rate. d represents the time marker of the design stage. The expression for the construction dynamic dataset is S1p, S2p, S3p, ..., Skp, where S1p to Skp are the dynamic data of the first to the kth construction nodes, respectively. The dynamic data of the construction nodes include the measured deviation of the wall layout, the record of non-standard ground dimensions, the change order number, the actual material usage and the on-site loss rate, and p represents the time marker of the construction stage. The expression for the supply chain response dataset is G1r, G2r, G3r, ..., Gwr, where G1r to Gwr are the response data for the first to the wth supply chain events, respectively. The response data for each supply chain event includes material supply status, supplier switching records, alternative material list, actual production location information, and actual transportation distance. r represents the time identifier of the supply chain response. The expression for the carbon factor time-lapse dataset is T1e, T2e, T3e, ..., Tve, where T1e to Tve are the carbon factor update data for the first to the vth time nodes, respectively. The carbon factor update data includes the energy consumption of the current batch production, the real-time emission factor of the local power grid, the transportation fuel efficiency parameters, and the energy consumption data of factory maintenance. e represents the time identifier of the carbon factor update.

[0008] Preferably, in step S2, the process for establishing the material geometry adaptation model is as follows: S21. Extract the design data of the i-th material from the design material dataset, and label the name of the i-th material as CLI, the design usage of the i-th material as SLi, the preset carbon emission factor of the i-th material as TYi, the specification size of the i-th material as GGi, and the design estimated loss rate of the i-th material as SSi. S22. Based on the spatial geometric model of the decoration project, extract the actual size data of the walls, floors and ceilings and the characteristic parameters of the irregular space, and calculate the theoretical cutting scheme and board layout scheme of the i-th material under the current spatial layout. S23. Based on the material physical processing characteristics database, obtain the cuttable parameters, brittleness parameters and scrap utilization rate parameters of the i-th material. Combined with the theoretical cutting scheme, simulate and calculate the theoretical minimum loss rate of the i-th material under the current spatial geometry conditions. S24. Compare the theoretical minimum loss rate with the design estimated loss rate SSi. If the deviation between the two exceeds the set threshold, generate a geometric fit warning sign and mark the material as a high-loss risk material, and include it in the design scheme benchmark library for key monitoring.

[0009] Preferably, in step S2, the carbon budget value in the design phase is statistically analyzed by material category. The categories include carbon budget for wall materials, carbon budget for floor materials, carbon budget for ceiling materials, and carbon budget for auxiliary materials. The design scheme benchmark library stores the initial carbon emission value, geometric fit score, and high loss risk identifier for each material, and establishes a carbon emission correlation matrix between materials for subsequent linkage optimization analysis in the construction phase.

[0010] Preferably, in step S3, the process for generating the construction deviation correction factor is as follows: S31. Extract the dynamic data of the j-th construction node from the construction dynamic dataset, and mark the measured deviation of the wall layout of the j-th construction node as QBj, mark the non-standard record of the ground size of the j-th construction node as DBj, mark the change instruction number of the j-th construction node as BGj, mark the actual material usage of the j-th construction node as SYj, and mark the on-site loss rate of the j-th construction node as XHj. S32. Based on the material type corresponding to the j-th construction node, extract the design quantity and estimated loss rate of the material from the design scheme benchmark library, calculate the deviation rate between the actual material quantity and the design quantity, and the deviation between the on-site loss rate and the estimated loss rate. S33. Analyze the geometric characteristics of the measured deviation QBj of the wall layout and the non-standard record DBj of the ground size, determine whether the source of the deviation is spatial irregularity, construction process adjustment, or material specification mismatch, and generate the corresponding geometric deviation type identifier. S34. Based on the actual material usage deviation rate, the on-site loss rate deviation, and the geometric deviation type identifier, calculate the construction deviation correction factor for the j-th construction node. This correction factor is used to adjust the predicted material usage and loss rate for subsequent construction nodes. S35. If the j-th construction node has a change instruction number BGj, then parse the specific content of the change instruction and determine whether the change involves material type replacement, material quantity increase or decrease, or construction process change. If it involves material type replacement, then trigger the dynamic carbon emission reconfiguration process of S4.

[0011] Preferably, in step S3, the material usage dynamic adjustment command responds in stages according to the magnitude of the construction deviation correction factor. When the absolute value of the correction factor is lower than the first threshold, only the usage prediction value of subsequent construction nodes is updated, and material selection changes are not triggered. When the absolute value of the correction factor is between the first threshold and the second threshold, a material usage warning notification is generated and pushed to the construction management terminal. When the absolute value of the correction factor exceeds the second threshold, the material selection re-optimization process in step S5 is forcibly triggered.

[0012] Preferably, in step S4, the process for reconstructing the dynamic carbon emission value of the alternative material is as follows: S41. Extract the response data of the qth supply chain event from the supply chain response dataset, and mark the material supply status of the qth supply chain event as GHq, mark the supplier switching record of the qth supply chain event as QHq, mark the alternative material list of the qth supply chain event as TDq, mark the actual place of origin information of the qth supply chain event as CDq, and mark the actual transportation distance of the qth supply chain event as YSq. S42. Based on the material types in the alternative material list TDq, extract the carbon factor update data corresponding to the current time node from the carbon factor aging dataset, and obtain the production energy consumption, local power grid real-time emission factor, transportation fuel efficiency parameters and factory maintenance energy consumption data of the current batch of the material. S43. Based on the actual origin information CDq and the actual transportation distance YSq, and combined with the current transportation fuel efficiency parameters, calculate the actual carbon emission value of the alternative material during the transportation phase, which is different from the average carbon emission value of stationary transportation used in the design phase. S44. Based on the current batch production energy consumption and the local power grid real-time emission factor, calculate the actual carbon emission value of the alternative material during the production stage, which is different from the historical statistical carbon emission factor used in the design stage. S45. Based on the actual carbon emissions during the integrated transportation phase, the actual carbon emissions during the production phase, and the energy consumption data for factory maintenance, the dynamic carbon emissions of alternative materials in the q-th supply chain event are reconstructed. The differences between these values ​​and the carbon emissions of the original design materials in the design scheme benchmark library are compared and analyzed to generate a report on the increase or decrease in carbon emissions.

[0013] Preferably, in step S4, the carbon emission difference comparison analysis includes carbon emission comparison of single materials and carbon emission linkage comparison of related materials. The carbon emission comparison of single materials is used to assess the increase or decrease in carbon emissions of the alternative materials themselves, while the carbon emission linkage comparison of related materials is used to assess the chain effect of the change of alternative materials on the amount of auxiliary materials, energy consumption of construction technology and subsequent material selection. The global carbon emission impact is simulated through the carbon emission correlation matrix between materials.

[0014] Preferably, in step S5, the triggering condition judgment logic for the material selection re-optimization process is as follows: If the cumulative carbon emissions during the construction phase deviate from the carbon budget value during the design phase by more than the carbon budget deviation threshold, and there is a need for material replacement or a supply chain switching event at the current construction node, then the material selection re-optimization process will be triggered. If the construction deviation correction factor shows that the actual loss rate of a certain material continues to exceed the design estimated loss rate, and the material is marked as a high-loss risk material, then a special re-optimization process for that material will be triggered. After the material selection re-optimization process is initiated, alternative solutions that meet the criteria are screened from the supply chain response dataset, including: The specifications and dimensions of the alternative materials are compatible with the geometric deviation characteristics of the current construction nodes, and the material compatibility score is higher than the material compatibility threshold. The dynamic carbon emission value of the alternative material is lower than that of the original design material, or although it is slightly higher than that of the original design material, global carbon emission compensation can be achieved by reducing the loss rate. The delivery time of alternative materials meets the requirements of the remaining construction period and does not affect the critical path of construction progress; The selected alternatives are comprehensively evaluated based on factors including dynamic carbon emissions, geometric fit, delivery timeliness, and cost increment. The option with the highest comprehensive score is selected to generate dynamic material selection instructions for the construction phase, and the material information and carbon budget in the design baseline library are updated simultaneously.

[0015] Preferably, in step S5, the continuous iterative logic of the low-carbon material selection closed-loop optimization mechanism is as follows: During the construction phase of the renovation project, after each construction node is completed, the S3 construction deviation monitoring and the S4 supply chain response monitoring are automatically triggered. If the monitoring results meet the triggering conditions of the material selection re-optimization process, the material selection re-optimization process is executed immediately to ensure that carbon emission deviations are corrected in a timely manner during the construction process. During the completion phase of the renovation project, the actual material usage, on-site loss rate, records of alternative material usage, and dynamic carbon emission data of all construction nodes are summarized. The actual total carbon emission value of the entire renovation project is calculated and compared with the carbon budget value in the design phase to generate a carbon emission achievement rate report. The comparative analysis results detail the sources of carbon emission deviations, including the contribution of increased loss rate due to geometric deviations, the contribution of carbon factor changes due to supply chain changes, the contribution of material usage adjustments due to design changes, and the carbon emission compensation achieved through dynamic material selection optimization. This provides data feedback and experience accumulation for low-carbon material selection strategies in subsequent decoration projects.

[0016] This invention provides a low-carbon material selection optimization method for the entire process of decoration projects. It has the following beneficial effects: By constructing a closed-loop optimization mechanism for low-carbon material selection covering the entire design, construction, and completion stages, the technical limitations of traditional "one-time" static comparison and selection are fundamentally broken through. Material selection optimization is extended from the early design stage to the entire construction process, enabling dynamic factors on-site such as wall layout deviations, non-standard floor dimensions, and design change instructions to be captured in real time and transformed into construction deviation correction factors. This, in turn, drives the automatic triggering of dynamic adjustment instructions for material usage and the material selection re-optimization process. It effectively solves the industry pain point that carbon emissions upon completion exceed expectations by 15% to 30% due to overlapping processes, frequent on-site changes, and significant deviations between actual loss rates and design estimates. It ensures that the efforts of low-carbon design in the early stage are maintained continuously during implementation rather than gradually diminishing. This invention, by connecting supply chain response datasets and carbon factor timeliness datasets in real time, can extract real-time origin information (CDq), actual transportation distance (YSq), current batch production energy consumption, local power grid real-time emission factors, and transportation fuel efficiency parameters of alternative materials when material supply delays, supplier changes, or design changes occur. It reconstructs the actual dynamic carbon emission values ​​of alternative materials during the production and transportation stages, rather than retroactively correcting for industry statistical averages from several years ago. This allows on-site managers to obtain real and immediate carbon emission difference comparison data between alternative solutions when making material change decisions. Combined with geometric fit conditions, supply timeliness, and optimal carbon emission targets, a comprehensive score is calculated. This allows for capturing the critical window for carbon emission reduction compensation through reasonable material replacement during the construction phase. Finally, at the completion stage, an analysis of the sources of carbon emission deviation is output, including the contribution of increased loss rate due to geometric deviation, carbon factor changes due to supply chain changes, material usage adjustments due to design changes, and carbon emission compensation achieved through dynamic material selection optimization. This provides quantifiable data feedback and experience accumulation for low-carbon material selection strategies in subsequent decoration projects, systematically solving the technical obstacles jointly constituted by static material selection mindset and the lack of real-time data collection. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 This invention provides a low-carbon material selection optimization method for the entire process of decoration projects, including the following steps: S1: By connecting to the decoration project management system via the network, obtain the material list in the design phase, the on-site change record in the construction phase, the material supply information in the supply chain, and the updated data in the carbon emission factor database to form a design material dataset, a construction dynamic dataset, a supply chain response dataset, and a carbon factor timeliness dataset. S2: Based on the design material dataset, analyze the material design usage, preset carbon emission factor, specifications and dimensions and design estimated loss rate, and extract the actual size data of the walls, floors and ceilings by combining the geometric model of the decoration space. Combine the shearability parameters, fragility parameters and scrap utilization rate parameters in the material physical processing characteristics database to calculate the theoretical minimum loss rate of materials and generate the material geometric adaptation model and carbon budget value in the design stage. S3: Calculate the deviation rate of actual material usage and the deviation of on-site loss rate based on the construction dynamic dataset, and determine the geometric deviation type by combining the wall layout deviation and non-standard ground size records, and generate construction deviation correction factors and material usage dynamic adjustment instructions; S4: When supply delays, supplier switching or design changes occur, extract the actual place of origin of the alternative materials, actual transportation distance, energy consumption of the current batch production and real-time emission factor of the local power grid, calculate the actual carbon emission value of the transportation stage and the actual carbon emission value of the production stage respectively, and reconstruct the dynamic carbon emission value of the alternative materials. S5: Based on the carbon budget deviation threshold, material compatibility threshold, the construction deviation correction factor, and the dynamic carbon emission value of alternative materials, trigger the material selection re-optimization process, screen alternative solutions that meet geometric compatibility conditions, have better dynamic carbon emissions, and meet the timeliness requirements of the construction period, generate dynamic material selection instructions for the construction phase, and continuously iterate and execute them throughout the entire construction process, so as to output the comparative analysis results of the actual carbon emission accounting results and the carbon budget value in the design phase at the completion stage.

[0020] In S1, the expression for the design material dataset is C1d, C2d, C3d, ..., Cmd, where C1d to Cmd are the design data for the first to the mth materials, respectively. The material design data includes the material name, design quantity, preset carbon emission factor, material specifications and dimensions, and design estimated loss rate. d represents the time marker of the design stage. The expression for the construction dynamic dataset is S1p, S2p, S3p, ..., Skp, where S1p to Skp are the dynamic data of the first to the kth construction nodes, respectively. The dynamic data of the construction nodes include the measured deviation of the wall layout, the record of non-standard ground dimensions, the change order number, the actual material usage and the on-site loss rate, and p represents the time marker of the construction stage. The expression for the supply chain response dataset is G1r, G2r, G3r, ..., Gwr, where G1r to Gwr are the response data for the first to the wth supply chain events, respectively. The response data for each supply chain event includes material supply status, supplier switching records, alternative material list, actual production location information, and actual transportation distance. r represents the time identifier of the supply chain response. The expression for the carbon factor time-lapse dataset is T1e, T2e, T3e, ..., Tve, where T1e to Tve are the carbon factor update data for the first to the vth time nodes, respectively. The carbon factor update data includes the energy consumption of the current batch production, the real-time emission factor of the local power grid, the transportation fuel efficiency parameters, and the energy consumption data of factory maintenance. e represents the time identifier of the carbon factor update.

[0021] In S2, the process for establishing the material geometry adaptation model is as follows: S21. Extract the design data of the i-th material from the design material dataset, and label the name of the i-th material as CLI, the design usage of the i-th material as SLi, the preset carbon emission factor of the i-th material as TYi, the specification size of the i-th material as GGi, and the design estimated loss rate of the i-th material as SSi. S22. Based on the spatial geometric model of the decoration project, extract the actual size data of the walls, floors and ceilings and the characteristic parameters of the irregular space, and calculate the theoretical cutting scheme and board layout scheme of the i-th material under the current spatial layout. S23. Based on the material physical processing characteristics database, obtain the cuttable parameters, brittleness parameters and scrap utilization rate parameters of the i-th material. Combined with the theoretical cutting scheme, simulate and calculate the theoretical minimum loss rate of the i-th material under the current spatial geometry conditions. S24. Compare the theoretical minimum loss rate with the design estimated loss rate SSi. If the deviation between the two exceeds the set threshold, generate a geometric fit warning sign and mark the material as a high-loss risk material, and include it in the design scheme benchmark library for key monitoring.

[0022] Furthermore, for the calculation of the "theoretical minimum loss rate" in S23, the present invention adopts an implementation scheme based on a two-dimensional packing optimization algorithm.

[0023] S231. For the i-th material, let its dimensions be length L multiplied by width W, and the dimensions of the area to be paved in the decoration space be the length of the space multiplied by the width of the space. First, calculate the theoretically required number of rows and columns. The number of rows is equal to the length of the space divided by the length of the material and rounded up, and the number of columns is equal to the width of the space divided by the width of the material and rounded up.

[0024] S232. Considering the boundary cutting loss after material paving, calculate the difference between the actual used area and the theoretical coverage area. The waste area equals the theoretical total paving area minus the actual space area to be covered, where the theoretical total paving area equals the number of rows multiplied by the material length, then multiplied by the number of columns, and finally multiplied by the material width.

[0025] S233. Extract three key parameters from the materials physical processing properties database, including: The cutability parameter, ranging from zero to one, indicates how easily a material can be cut without damage. The cutability parameter for ceramic tiles is approximately 0.95, and for solid wood flooring it is approximately 0.75. A higher parameter indicates lower material loss during the cutting process.

[0026] The fragility parameter, ranging from zero to one, represents the probability of breakage during construction. The fragility parameter for glass is approximately 0.15, and for PVC flooring it is approximately 0.03. This parameter directly affects the amount of spare materials that need to be prepared during construction.

[0027] The scrap utilization rate parameter, ranging from zero to one, indicates whether the cut scraps can be used in other areas. The scrap utilization rate for marble is approximately 0.6%, and for wallpaper, it is approximately 0.2%. This parameter reflects whether small pieces of material after cutting can still be used in other construction areas.

[0028] S234. Based on the above parameters, the calculation logic for the theoretical minimum loss rate is as follows: First, calculate the basic proportion of scrap material to the total paving area. Then, subtract the product of the cutability parameter and the scrap material utilization parameter to obtain the unavoidable net loss coefficient. Multiply the basic proportion by the net loss coefficient to obtain the geometric loss portion. Finally, add the construction damage portion represented by the fragility parameter. The sum of the two is the theoretical minimum loss rate. The physical meaning of this formula is: a portion of the scrap material can be avoided through fine cutting and reuse, and the remaining unavoidable loss plus construction damage constitutes the theoretical minimum.

[0029] Furthermore, in S24, the "setting threshold" adopts a dynamic adaptive strategy; the threshold is calculated based on two factors, including: first, the average design estimated loss rate of historical projects for this material category, which is statistically derived from the design scheme benchmark library; second, the coefficient of variation of the loss rate, which reflects the volatility of the material's loss, and the coefficient of variation is equal to the standard deviation of the loss rate divided by the average loss rate.

[0030] The specific calculation logic is as follows: the larger value of 0.05, the average loss rate multiplied by 0.15, and then multiplied by the coefficient of variation is taken as the threshold. 0.05 is the minimum protection threshold, ensuring that conventional materials do not trigger false alarms. For mature materials with stable loss, such as standard ceramic tiles, the coefficient of variation is low, and the threshold is close to the minimum value; for customized materials with large loss fluctuations, such as irregularly shaped stone, the coefficient of variation is high, and the threshold is correspondingly increased to accommodate normal fluctuations.

[0031] Judgment logic: When the absolute value of the difference between the theoretical minimum loss rate and the design estimated loss rate exceeds the threshold, a geometric adaptation warning is triggered, prompting the designer to re-evaluate the material specification selection or adjust the loss rate estimate.

[0032] In S2, the carbon budget value in the design phase is statistically analyzed by material category. The categories include carbon budget for wall materials, carbon budget for floor materials, carbon budget for ceiling materials, and carbon budget for auxiliary materials. The design scheme benchmark library stores the initial carbon emission value, geometric fit score, and high loss risk identifier for each material, and establishes a carbon emission correlation matrix between materials for linkage optimization analysis in the subsequent construction phase.

[0033] In step S3, the process for generating the construction deviation correction factor is as follows: S31. Extract the dynamic data of the j-th construction node from the construction dynamic dataset, and mark the measured deviation of the wall layout of the j-th construction node as QBj, mark the non-standard record of the ground size of the j-th construction node as DBj, mark the change instruction number of the j-th construction node as BGj, mark the actual material usage of the j-th construction node as SYj, and mark the on-site loss rate of the j-th construction node as XHj. S32. Based on the material type corresponding to the j-th construction node, extract the design quantity and estimated loss rate of the material from the design scheme benchmark library, calculate the deviation rate between the actual material quantity and the design quantity, and the deviation between the on-site loss rate and the estimated loss rate. S33. Analyze the geometric characteristics of the measured deviation QBj of the wall layout and the non-standard record DBj of the ground size, determine whether the source of the deviation is spatial irregularity, construction process adjustment, or material specification mismatch, and generate the corresponding geometric deviation type identifier. S34. Based on the actual material usage deviation rate, the on-site loss rate deviation, and the geometric deviation type identifier, calculate the construction deviation correction factor for the j-th construction node. This correction factor is used to adjust the predicted material usage and loss rate for subsequent construction nodes. S35. If the j-th construction node has a change instruction number BGj, then parse the specific content of the change instruction and determine whether the change involves material type replacement, material quantity increase or decrease, or construction process change. If it involves material type replacement, then trigger the dynamic carbon emission reconfiguration process of S4.

[0034] In S3, the material usage dynamic adjustment command responds in stages according to the magnitude of the construction deviation correction factor. When the absolute value of the correction factor is lower than the first threshold, only the usage prediction value of subsequent construction nodes is updated, and material selection changes are not triggered. When the absolute value of the correction factor is between the first threshold and the second threshold, a material usage warning notification is generated and pushed to the construction management terminal. When the absolute value of the correction factor exceeds the second threshold, the material selection re-optimization process in S5 is forcibly triggered.

[0035] Calculation of actual material usage deviation rate: The material usage deviation rate is equal to the actual material usage minus the designed usage, divided by the designed usage, and then multiplied by 100%. The designed usage refers to the designed material usage for the j-th construction node, extracted from the design baseline library. A positive value indicates that the actual usage exceeds the design, while a negative value indicates savings.

[0036] Calculation of deviation of field loss rate: The deviation of the loss rate is equal to the actual measured loss rate minus the estimated design loss rate, divided by the estimated design loss rate, and then multiplied by 100%. This indicator reflects the degree of deviation between loss control and design expectations during actual construction. A positive value indicates that the loss rate is higher than expected, while a negative value indicates that loss control is better than expected.

[0037] For the "geometric deviation type identifier" in S33, a multi-factor decision tree is used for determination. The specific determination process is as follows: S331. When the measured deviation of the wall layout exceeds 30 mm, or the non-standard recorded floor dimensions exceed 50 mm, and the space has special geometric features such as arcs, slopes, or irregularly shaped columns, the deviation is determined to be caused by spatial irregularity factors and is marked as "irregular space type". The root cause of this type of deviation is the geometric complexity of the building space itself, which cannot be completely eliminated by improving the construction process.

[0038] S332. When the usage deviation rate exceeds 10% and the loss rate deviation exceeds 15%, but there is no significant geometric deviation, it is determined to be a deviation caused by construction process adjustment factors and is marked as "process adjustment type". This type of deviation usually originates from the construction team adjusting process details such as laying direction and splicing method according to site conditions.

[0039] S333. Calculate the remainder when the space dimension is divided by the material dimension. If the remainder is more than 0.7% of the material dimension, it indicates a serious mismatch between the material specifications and the space dimensions, and is marked as "specification mismatch type". For example, if the space width is 2.2 meters and the material width is 1.2 meters, the remainder is 1 meter, and the remainder ratio is 0.83%, indicating that the last row of materials needs to be cut extensively, resulting in significant waste.

[0040] Furthermore, the "construction deviation correction factor" in S34 is calculated using a weighted comprehensive method. The correction factor equals the usage deviation rate multiplied by the first weight, plus the loss rate deviation multiplied by the second weight, plus the geometric deviation type correction coefficient multiplied by the third weight.

[0041] The recommended values ​​for the three weighting coefficients are 0.4, 0.4, and 0.2, with a sum of 1. The correction coefficient for geometric deviation types is determined based on the specific type: 0.25 for irregular space types, 0.15 for process adjustment types, 0.3 for specification mismatch types, and 0.1 for other cases. The correction coefficient for specification mismatch types is the highest because this type of deviation is systematic and will continuously affect subsequent construction stages.

[0042] After the correction factor is calculated, it is used to adjust the predicted material usage for subsequent construction nodes. The predicted usage for the next construction node is equal to the designed usage for that node multiplied by the correction factor. If the correction factor is positive, it indicates that more materials need to be prepared; if it is negative, the material preparation can be appropriately reduced.

[0043] The first and second thresholds in S3 are calibrated using industry statistical data. The first threshold is set to 0.08%, corresponding to an 8% usage deviation. This threshold is based on statistical data from a large number of interior decoration projects. Deviations within 8% are within the normal range of construction fluctuations and are usually caused by uncontrollable factors such as measurement errors and material dimensional tolerances, requiring no special intervention.

[0044] The second threshold is set at 0.2%, corresponding to a 20% deviation in usage. When the deviation reaches this level, it indicates a systemic problem, such as incorrect material specification selection, a severely underestimated loss rate, or an unreasonable construction plan, requiring immediate corrective measures. This threshold is set with reference to the industry situation mentioned in the background technology, where "carbon emissions at the completion stage exceed design expectations by 15% to 30%." Intervention is triggered when the deviation reaches 20% during the construction phase, effectively controlling the final deviation within an acceptable range.

[0045] The specific judgment logic is as follows: when the absolute value of the correction factor is less than the first threshold, the system only updates the usage prediction value of subsequent nodes and does not issue an early warning notification; when the absolute value of the correction factor is between the first threshold and the second threshold, the system generates a material usage early warning notification and pushes it to the construction management terminal to remind the management personnel to pay attention but does not force intervention; when the absolute value of the correction factor exceeds the second threshold, the system forcibly triggers the material selection re-optimization process, suspends the continued use of the current material, and requires a re-evaluation of the material selection.

[0046] In step S4, the process for reconstructing the dynamic carbon emission value of alternative materials is as follows: S41. Extract the response data of the qth supply chain event from the supply chain response dataset, and mark the material supply status of the qth supply chain event as GHq, mark the supplier switching record of the qth supply chain event as QHq, mark the alternative material list of the qth supply chain event as TDq, mark the actual place of origin information of the qth supply chain event as CDq, and mark the actual transportation distance of the qth supply chain event as YSq. S42. Based on the material types in the alternative material list TDq, extract the carbon factor update data corresponding to the current time node from the carbon factor aging dataset, and obtain the production energy consumption, local power grid real-time emission factor, transportation fuel efficiency parameters and factory maintenance energy consumption data of the current batch of the material. S43. Based on the actual origin information CDq and the actual transportation distance YSq, and combined with the current transportation fuel efficiency parameters, calculate the actual carbon emission value of the alternative material during the transportation phase, which is different from the average carbon emission value of stationary transportation used in the design phase. S44. Based on the current batch production energy consumption and the local power grid real-time emission factor, calculate the actual carbon emission value of the alternative material during the production stage, which is different from the historical statistical carbon emission factor used in the design stage. S45. Based on the actual carbon emissions during the integrated transportation phase, the actual carbon emissions during the production phase, and the energy consumption data for factory maintenance, the dynamic carbon emissions of alternative materials in the q-th supply chain event are reconstructed. The differences between these values ​​and the carbon emissions of the original design materials in the design scheme benchmark library are compared and analyzed to generate a report on the increase or decrease in carbon emissions.

[0047] Regarding the "actual carbon emission value during the transportation phase" in claim 7, S43, its calculation logic is based on a comprehensive consideration of the actual transportation distance, material weight, and fuel emission factor.

[0048] The calculation formula is:

[0049] Carbon emissions during the transportation phase are calculated by multiplying the actual transportation distance by the material transportation weight, dividing by the vehicle load factor, and then multiplying by the fuel carbon emission factor. The actual transportation distance is measured in kilometers and calculated from the actual origin information and project address in the supply chain response dataset. The material transportation weight is measured in tons and calculated based on the material density and order quantity. The average vehicle load factor ranges from 0.6 to 0.8, reflecting the actual loading rate of the transport vehicles. This factor takes into account actual conditions such as empty return trips and mixed loading with other goods.

[0050] Dynamic acquisition of fuel efficiency parameters: Fuel carbon emission factors are extracted from the carbon factor time-lapse dataset, with units of carbon emissions per ton-kilometer. Specifically, the extraction method involves finding the record in the dataset closest to the current transport time and selecting the fuel emission factor from the record with the smallest time difference. The advantage of this dynamic acquisition mechanism is that it reflects the actual emission levels under different seasons, fuel standards, and vehicle technology generations, rather than using a fixed average value that remains unchanged for many years.

[0051] The difference between the design phase and the construction phase is that the design phase typically uses a fixed average carbon emission level for transportation, such as a national average of 0.12 kg of CO2 per ton-kilometer. The dynamic calculations in the construction phase, however, are based on the actual production location and transportation time, using the real emission data of the batch of materials. Actual carbon emissions may be higher than the design expectations due to factors such as longer transportation distances and increased vehicle fuel consumption.

[0052] The formula for calculating the actual carbon emissions during the production phase in S44 is as follows:

[0053] Including factory maintenance energy consumption. The unit production energy consumption for the current batch is measured in kilowatt-hours per square meter or per kilogram, obtained from supplier production data in the supply chain response dataset. This value will fluctuate between different batches due to factors such as the age of production equipment, process improvements, and production load rate.

[0054] The real-time emission factor of the power grid is obtained by matching the origin and time from the carbon factor time-sensitive dataset. Specifically, the method involves first filtering all records in the dataset whose origin location matches the actual origin of the material. Then, among these records, the record with the timestamp closest to the current transportation time is selected, and its power grid emission factor is extracted. The unit of the power grid emission factor is carbon emissions per kilowatt-hour. This value varies significantly with the regional power structure; for example, the emission factor is approximately 0.4 in the hydropower-rich southwest region and approximately 0.8 in the thermal power-dominated North China region. Furthermore, the emission factor can fluctuate in different seasons within the same region due to changes in hydropower output.

[0055] If no exact matching origin data is found in the database, the system uses data from the nearest province as a substitute and marks it with a "data estimate" label in the carbon emission increase / decrease report, indicating that the value is uncertain. For materials requiring curing, such as concrete and paint, factory curing energy consumption is accounted for separately, including air conditioning and ventilation energy consumption in constant temperature and humidity curing rooms.

[0056] The difference between the design phase and the construction phase lies in the fact that the design phase uses historically statistical carbon emission factors, which may be industry averages from several years ago. In contrast, the dynamic calculations in the construction phase use real-time data from the current batch, reflecting the supplier's recent production status, the real-time clean energy ratio of the local power grid, and other up-to-date information, significantly improving the accuracy of carbon emission accounting.

[0057] For the dynamic carbon emission value of S45, the comprehensive reconstruction logic is to add up the carbon emissions of the transportation stage, the production stage, the packaging materials, and the waste disposal stage.

[0058] Carbon emissions from packaging materials are typically estimated at 3% to 5% of production carbon emissions, taking into account the carbon emissions from the production and disposal of packaging materials such as cardboard boxes, wooden pallets, and plastic films. Carbon emissions from waste disposal are estimated at 1% to 2% of production carbon emissions, covering emissions from the transportation and disposal of scrap materials and defective products generated during the production process.

[0059] After calculating the dynamic carbon emissions of the alternative materials, the carbon emissions of the original design materials are extracted from the design baseline library. The absolute difference is obtained by subtracting the two values, expressed in kilograms of carbon dioxide. Simultaneously, the relative difference is calculated: the absolute difference divided by the carbon emissions of the original design materials, then multiplied by 100%. This percentage directly reflects the magnitude of the carbon emission change of the alternative scheme relative to the original scheme.

[0060] The report on increases and decreases includes three parts: first, the absolute value and relative percentage of the difference; second, a breakdown of the sources of the difference, listing the proportion of the total difference for both the transportation and production stages; and third, an explanation of the reasons for the difference, such as "due to the change of production location from Guangdong to Shandong, the transportation distance increased by 1,000 kilometers, resulting in a 35% increase in transportation carbon emissions." This report provides construction managers with a basis for decision-making, helping them determine whether alternative solutions are acceptable.

[0061] In S4, the carbon emission difference comparison analysis includes carbon emission comparison of single materials and carbon emission linkage comparison of related materials. The carbon emission comparison of single materials is used to assess the increase or decrease of carbon emissions of the alternative materials themselves, while the carbon emission linkage comparison of related materials is used to assess the chain effect of the change of alternative materials on the amount of auxiliary materials, energy consumption of construction technology and subsequent material selection. The global carbon emission impact is simulated through the carbon emission correlation matrix between materials.

[0062] The carbon emission correlation matrix between materials is mathematically represented as an m x m square matrix, denoted as matrix R, where m is the total number of material types. The element in the i-th row and j-th column, denoted by the subscript ij (r), ranges from zero to one, representing the influence coefficient of a change in material i on the carbon emissions of material j. The diagonal element, denoted by the subscript ii (r), is always equal to one, representing the material's complete influence on itself. Off-diagonal elements reflect the degree of carbon emission correlation between materials; larger values ​​indicate a stronger correlation.

[0063] The correlation coefficient is calculated based on a three-dimensional analysis, and the maximum value among the three dimensions is taken as the comprehensive correlation coefficient. The reason for using the maximum value is that a strong correlation in any dimension constitutes a significant impact and needs to be considered in optimization decisions; specifically, this includes: First dimension: Process dependence correlation Analyze whether the construction process of material j depends on the completed state of material i. If a strong dependency exists, meaning that the construction of material j can only begin after the construction of material i is completed, and the quality of material i directly affects the construction effect of material j, then the process dependency correlation degree is set to 0.8. For example, wall latex paint depends on the putty layer; the smoothness and curing quality of the putty directly determine the amount and effect of latex paint used.

[0064] If a weak dependency exists, meaning there is a certain order to the application of the two materials, but a change in the i-th material has a limited impact on the j-th material, then the process dependency correlation degree is 0.4. For example, in the case of a floor leveling layer and tile laying, changes in the thickness of the leveling layer will affect the amount of adhesive used for the tiles, but the impact is relatively small.

[0065] If the construction processes of the two materials are completely independent and do not affect each other, then the process dependency correlation is zero.

[0066] Second dimension: Chain linkage of auxiliary materials This analysis examines whether changes to the i-th material lead to changes in the amount of supporting auxiliary materials used, thereby affecting the selection of auxiliary materials for the j-th material. The degree of linkage between auxiliary materials is equal to the proportion of carbon emissions from shared auxiliary materials to the total carbon emissions from auxiliary materials of the j-th material.

[0067] For example, wall tiles and floor tiles may share the same tile adhesive. When the wall tiles are changed from ordinary tiles to heavy marble tiles, a strong tile adhesive is required. This change will also affect the choice of adhesive for the floor tiles. If the carbon emissions of strong tile adhesive are 50% higher than those of ordinary adhesive, and tile adhesive accounts for 40% of the total carbon emissions of auxiliary materials for floor tiles, then the interrelationship of auxiliary materials is 0.4.

[0068] Third dimension: Energy consumption transmission correlation This analysis examines whether changes to the i-th material affect construction environmental conditions, thereby altering the construction energy consumption of the j-th material. The energy consumption transmission correlation is equal to the ratio of the induced additional energy consumption to the baseline construction energy consumption of the j-th material, but an upper limit of 0.5 is set to avoid distortion of the correlation due to individual extreme cases.

[0069] In this embodiment, switching the external wall insulation material from ordinary polystyrene board to high-performance vacuum insulation board results in a more stable indoor temperature, shortens the drying time of the subsequent interior wall paint, and reduces the need for electric heaters to assist drying, thereby lowering energy consumption. If the energy saved from electric heating accounts for 12% of the baseline energy consumption for interior wall paint application, the energy consumption correlation coefficient is 0.12. Conversely, if switching to a low-performance material results in additional energy consumption, the correlation coefficient is positive.

[0070] After calculating the correlation coefficients across the three dimensions, the maximum value is taken as the subscript ij of the comprehensive correlation coefficient r between material i and material j. This value selection strategy ensures that any significant correlation path will be considered in the optimization decision, avoiding the omission of important carbon emission transmission effects.

[0071] For the simulation of global carbon emission impact, when the i-th material is replaced, it is necessary to calculate the cascading effects of this change on all other materials. The global carbon emission change equals the carbon emission change of the i-th material itself, plus the sum of the carbon emission changes of all other materials j caused by this change.

[0072] The specific calculation logic is as follows: For each material j other than i, its carbon emission change is equal to the carbon emission change of material i, multiplied by the correlation coefficient r (subscript ij), and then multiplied by the remaining usage percentage of material j in the current construction phase. The remaining usage percentage is used to ensure that only subsequent materials that are actually affected are calculated. For materials that have already been constructed, their remaining usage percentage is zero, and they do not have a cascading effect.

[0073] This embodiment assumes that during the wall tiling phase of a project, switching wall tiles from type A to type B increases carbon emissions by 1,000 kilograms of CO2. If the correlation coefficient between wall tiles and floor tiles is 0.3, and the remaining usage percentage of floor tiles is 1 before construction begins, then the increased carbon emissions from the floor tiles due to the cascading effect is 1,000 multiplied by 0.3 multiplied by 1, which equals 300 kilograms of CO2. If the ceiling paint has already been applied, its remaining usage percentage is zero, and therefore it is unaffected. Summing up the cascading effects of all materials yields the overall change in carbon emissions, providing decision-makers with a comprehensive assessment of the carbon emission impact.

[0074] In S5, the trigger condition judgment logic for the material selection re-optimization process is as follows: If the cumulative carbon emissions during the construction phase deviate from the carbon budget value during the design phase by more than the carbon budget deviation threshold, and there is a need for material replacement or a supply chain switching event at the current construction node, then the material selection re-optimization process will be triggered. If the construction deviation correction factor shows that the actual loss rate of a certain material continues to exceed the design estimated loss rate, and the material is marked as a high-loss risk material, then a special re-optimization process for that material will be triggered. After the material selection re-optimization process is initiated, alternative solutions that meet the criteria are screened from the supply chain response dataset, including: The specifications and dimensions of the alternative materials are compatible with the geometric deviation characteristics of the current construction nodes, and the material compatibility score is higher than the material compatibility threshold. The dynamic carbon emission value of the alternative material is lower than that of the original design material, or although it is slightly higher than that of the original design material, global carbon emission compensation can be achieved by reducing the loss rate. The delivery time of alternative materials meets the requirements of the remaining construction period and does not affect the critical path of construction progress; The selected alternatives are comprehensively evaluated based on factors including dynamic carbon emissions, geometric fit, delivery timeliness, and cost increment. The option with the highest comprehensive score is selected to generate dynamic material selection instructions for the construction phase, and the material information and carbon budget in the design baseline library are updated simultaneously.

[0075] In S5, the continuous iterative logic of the low-carbon material selection closed-loop optimization mechanism is as follows: During the construction phase of the renovation project, after each construction node is completed, the S3 construction deviation monitoring and the S4 supply chain response monitoring are automatically triggered. If the monitoring results meet the triggering conditions of the material selection re-optimization process, the material selection re-optimization process is executed immediately to ensure that carbon emission deviations are corrected in a timely manner during the construction process. During the completion phase of the renovation project, the actual material usage, on-site loss rate, records of alternative material usage, and dynamic carbon emission data of all construction nodes are summarized. The actual total carbon emission value of the entire renovation project is calculated and compared with the carbon budget value in the design phase to generate a carbon emission achievement rate report. The comparative analysis results detail the sources of carbon emission deviations, including the contribution of increased loss rate due to geometric deviations, the contribution of carbon factor changes due to supply chain changes, the contribution of material usage adjustments due to design changes, and the carbon emission compensation achieved through dynamic material selection optimization. This provides data feedback and experience accumulation for low-carbon material selection strategies in subsequent decoration projects.

[0076] Furthermore, a weighted multi-attribute decision model is used to obtain a comprehensive score; for the nth alternative, its comprehensive score is equal to the dynamic carbon emission score multiplied by the carbon emission weight, plus the geometric fit score multiplied by the fit weight, plus the supply timeliness score multiplied by the timeliness weight, plus the cost increment score multiplied by the cost weight.

[0077] The calculation logic for the four scoring dimensions is as follows: Dynamic carbon emission scoring: A relative difference method is used, calculating the ratio of the carbon emissions of the alternative solution to the carbon emissions of the original design materials by subtracting one from the original carbon emissions, then multiplying by 100. Lower carbon emissions result in a higher score; a 20% reduction earns a score of 20. If the carbon emissions of the alternative solution exceed those of the original design materials, the score is zero, ensuring the low-carbon optimization goal is achieved. Geometric fit rating: This rating considers both specification matching and material physical properties. The dimensional fit is calculated by subtracting the specification deviation ratio from one, then multiplied by a cutability parameter, multiplied by a minus a fragility parameter, and finally multiplied by 100. The closer the specifications are to the optimal value, the better the cutability, and the lower the fragility, the higher the rating. Delivery timeliness scoring: A piecewise linear function is used. When the delivery cycle is less than or equal to half of the remaining project time, the score is 100 points; when the delivery cycle is between half of the remaining project time and the remaining project time, the score decreases linearly to zero points; when the delivery cycle exceeds the remaining project time, the score is zero points. Cost increment scoring: Subtract the cost difference from 100, divide by the original design cost, and then multiply by 200. The score resets to zero when the cost increases by 50%, 100 points when the cost remains unchanged, and 100 points as the maximum score when the cost decreases. This design allows for a moderate increase in cost to achieve carbon emission reduction, but this must be kept within a reasonable range. Furthermore, the sum of the four weights corresponding to the four scoring dimensions equals one; the recommended benchmark configuration is: carbon emission weight 0.45, geometric fit weight 0.25, supply timeliness weight 0.2, and cost weight 0.1, reflecting the core positioning of low-carbon optimization.

[0078] Meanwhile, the weights are adjusted according to project type: green building demonstration projects increase the carbon emission weight to 0.6; commercial fast-delivery projects increase the supply timeliness weight to 0.35; affordable housing projects increase the cost weight to 0.2; and projects with irregular and complex spatial shapes increase the geometric adaptability weight to 0.35. The weights should be determined and entered into the system during the project initiation phase to ensure consistency in the scoring criteria.

[0079] The specific numerical calibration process for the threshold includes: Material compatibility threshold: set at 70 points. Industry data shows that when the score is above 70, the actual increase in the loss rate of alternative materials is controlled within 5%, which is within an acceptable range. When the score is below 70, the loss rate increases by more than 8%, which may offset the advantages of the alternative solution. Only solutions with a score of 70 or higher are retained in the comprehensive evaluation stage during the screening process.

[0080] Carbon budget deviation threshold: set at 15%. This threshold is based on the industry reality that "carbon emissions during the completion phase exceed expectations by 15% to 30%" as described in the background technology. Optimization is triggered when the cumulative deviation rate reaches 15% during the construction phase to prevent the final deviation rate from escalating to a severely excessive level. The system continuously accumulates and compares actual carbon emissions at each node with the budget. When the deviation rate reaches or exceeds 15% and there is a need for material replacement, a material selection re-optimization process is initiated.

[0081] The complete process for screening alternative solutions includes: The first step is to determine the triggering conditions: two conditions are monitored: first, the cumulative carbon emission deviation rate exceeds 15% and there is a need for material replacement; second, the loss rate of high-risk materials continues to exceed the standard. The optimization process is initiated when either condition is met.

[0082] The second step is candidate solution extraction: extract all available alternative material solutions from the supply chain response dataset, including solutions from different suppliers, with different specifications and materials, and automatically capture real-time information such as place of origin, transportation distance, production energy consumption, delivery cycle, and price.

[0083] The third step is the screening based on strict criteria: three strict criteria are applied to eliminate unqualified solutions. The first criterion is geometric fit, eliminating solutions with a score below 70; the second criterion is carbon emission directionality, the carbon emissions of alternative materials should be lower than the original design or can be compensated globally by reducing the loss rate; the third criterion is delivery timeliness, eliminating solutions with delivery cycles exceeding the remaining construction period.

[0084] The fourth step is to calculate the overall score and sort the solutions that have passed the hard screening. The solutions are then sorted from highest to lowest score using the project's preset weight configuration.

[0085] Step 5: Optimal Solution Selection and Update: Select the solution with the highest comprehensive score, generate a dynamic material selection instruction, and push it to the construction management terminal. Simultaneously update the material information and carbon budget values ​​in the design scheme benchmark library to provide updated reference standards for subsequent milestones.

[0086] Step 6: Tracking the Replacement Effect: After material replacement, continuously track actual usage, loss rate, and carbon emissions to verify the expected results. If there is a significant deviation between actual performance and prediction, generate a deviation analysis report and include it in the historical database to optimize the parameters of subsequent project scoring models, thus forming a continuous improvement mechanism.

[0087] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low-carbon material selection optimization method for the entire process of decoration projects, characterized in that, Includes the following steps: S1: By connecting to the decoration project management system via the network, obtain the material list in the design phase, the on-site change record in the construction phase, the material supply information in the supply chain, and the updated data in the carbon emission factor database to form a design material dataset, a construction dynamic dataset, a supply chain response dataset, and a carbon factor timeliness dataset. S2: Based on the design material dataset, analyze the material design usage, preset carbon emission factor, specifications and dimensions and design estimated loss rate, and extract the actual size data of the walls, floors and ceilings by combining the geometric model of the decoration space. Combine the shearability parameters, fragility parameters and scrap utilization rate parameters in the material physical processing characteristics database to calculate the theoretical minimum loss rate of materials and generate the material geometric adaptation model and carbon budget value in the design stage. S3: Calculate the deviation rate of actual material usage and the deviation of on-site loss rate based on the construction dynamic dataset, and determine the geometric deviation type by combining the wall layout deviation and non-standard ground size records, and generate construction deviation correction factors and material usage dynamic adjustment instructions; S4: When supply delays, supplier switching or design changes occur, extract the actual place of origin of the alternative materials, actual transportation distance, energy consumption of the current batch production and real-time emission factor of the local power grid, calculate the actual carbon emission value of the transportation stage and the actual carbon emission value of the production stage respectively, and reconstruct the dynamic carbon emission value of the alternative materials. S5: Based on the carbon budget deviation threshold, material compatibility threshold, the construction deviation correction factor, and the dynamic carbon emission value of alternative materials, trigger the material selection re-optimization process, screen alternative solutions that meet geometric compatibility conditions, have better dynamic carbon emissions, and meet the timeliness requirements of the construction period, generate dynamic material selection instructions for the construction phase, and continuously iterate and execute them throughout the entire construction process, so as to output the comparative analysis results of the actual carbon emission accounting results and the carbon budget value in the design phase at the completion stage.

2. The low-carbon material selection optimization method for the entire decoration project process as described in claim 1, characterized in that: In S1, the expression for the design material dataset is C1d, C2d, C3d, ..., Cmd, where C1d to Cmd are the design data for the first to the mth materials, respectively. The material design data includes the material name, design quantity, preset carbon emission factor, material specifications and dimensions, and design estimated loss rate. d represents the time marker of the design stage. The expression for the construction dynamic dataset is S1p, S2p, S3p, ..., Skp, where S1p to Skp are the dynamic data of the first to the kth construction nodes, respectively. The dynamic data of the construction nodes include the measured deviation of the wall layout, the record of non-standard ground dimensions, the change order number, the actual material usage and the on-site loss rate, and p represents the time marker of the construction stage. The expression for the supply chain response dataset is G1r, G2r, G3r, ..., Gwr, where G1r to Gwr are the response data for the first to the wth supply chain events, respectively. The response data for each supply chain event includes material supply status, supplier switching records, alternative material list, actual production location information, and actual transportation distance. r represents the time identifier of the supply chain response. The expression for the carbon factor time-lapse dataset is T1e, T2e, T3e, ..., Tve, where T1e to Tve are the carbon factor update data for the first to the vth time nodes, respectively. The carbon factor update data includes the energy consumption of the current batch production, the real-time emission factor of the local power grid, the transportation fuel efficiency parameters, and the energy consumption data of factory maintenance. e represents the time identifier of the carbon factor update.

3. The low-carbon material selection optimization method for the entire decoration project process as described in claim 2, characterized in that: In S2, the process for establishing the material geometry adaptation model is as follows: S21. Extract the design data of the i-th material from the design material dataset, and label the name of the i-th material as CLI, the design usage of the i-th material as SLi, the preset carbon emission factor of the i-th material as TYi, the specification size of the i-th material as GGi, and the design estimated loss rate of the i-th material as SSi. S22. Based on the spatial geometric model of the decoration project, extract the actual size data of the walls, floors and ceilings and the characteristic parameters of the irregular space, and calculate the theoretical cutting scheme and board layout scheme of the i-th material under the current spatial layout. S23. Based on the material physical processing characteristics database, obtain the cuttable parameters, brittleness parameters and scrap utilization rate parameters of the i-th material. Combined with the theoretical cutting scheme, simulate and calculate the theoretical minimum loss rate of the i-th material under the current spatial geometry conditions. S24. Compare the theoretical minimum loss rate with the design estimated loss rate SSi. If the deviation between the two exceeds the set threshold, generate a geometric fit warning sign and mark the material as a high-loss risk material, and include it in the design scheme benchmark library for key monitoring.

4. The low-carbon material selection optimization method for the entire decoration project process as described in claim 3, characterized in that: In S2, the carbon budget value in the design phase is statistically analyzed by material category. The categories include carbon budget for wall materials, carbon budget for floor materials, carbon budget for ceiling materials, and carbon budget for auxiliary materials. The design scheme benchmark library stores the initial carbon emission value, geometric fit score, and high loss risk identifier for each material, and establishes a carbon emission correlation matrix between materials for linkage optimization analysis in the subsequent construction phase.

5. The low-carbon material selection optimization method for the entire decoration project process as described in claim 2, characterized in that: In step S3, the process for generating the construction deviation correction factor is as follows: S31. Extract the dynamic data of the j-th construction node from the construction dynamic dataset, and mark the measured deviation of the wall layout of the j-th construction node as QBj, mark the non-standard record of the ground size of the j-th construction node as DBj, mark the change instruction number of the j-th construction node as BGj, mark the actual material usage of the j-th construction node as SYj, and mark the on-site loss rate of the j-th construction node as XHj. S32. Based on the material type corresponding to the j-th construction node, extract the design quantity and estimated loss rate of the material from the design scheme benchmark library, calculate the deviation rate between the actual material quantity and the design quantity, and the deviation between the on-site loss rate and the estimated loss rate. S33. Analyze the geometric characteristics of the measured deviation QBj of the wall layout and the non-standard record DBj of the ground size, determine whether the source of the deviation is spatial irregularity, construction process adjustment, or material specification mismatch, and generate the corresponding geometric deviation type identifier. S34. Based on the actual material usage deviation rate, the on-site loss rate deviation, and the geometric deviation type identifier, calculate the construction deviation correction factor for the j-th construction node. This correction factor is used to adjust the predicted material usage and loss rate for subsequent construction nodes. S35. If the j-th construction node has a change instruction number BGj, then parse the specific content of the change instruction and determine whether the change involves material type replacement, material quantity increase or decrease, or construction process change. If it involves material type replacement, then trigger the dynamic carbon emission reconfiguration process of S4.

6. The low-carbon material selection optimization method for the entire decoration project process as described in claim 5, characterized in that: In S3, the material usage dynamic adjustment command responds in stages according to the magnitude of the construction deviation correction factor. When the absolute value of the correction factor is lower than the first threshold, only the usage prediction value of subsequent construction nodes is updated, and material selection changes are not triggered. When the absolute value of the correction factor is between the first threshold and the second threshold, a material usage warning notification is generated and pushed to the construction management terminal. When the absolute value of the correction factor exceeds the second threshold, the material selection re-optimization process in S5 is forcibly triggered.

7. The low-carbon material selection optimization method for the entire decoration project process as described in claim 2, characterized in that: In step S4, the process for reconstructing the dynamic carbon emission value of alternative materials is as follows: S41. Extract the response data of the qth supply chain event from the supply chain response dataset, and mark the material supply status of the qth supply chain event as GHq, mark the supplier switching record of the qth supply chain event as QHq, mark the alternative material list of the qth supply chain event as TDq, mark the actual place of origin information of the qth supply chain event as CDq, and mark the actual transportation distance of the qth supply chain event as YSq. S42. Based on the material types in the alternative material list TDq, extract the carbon factor update data corresponding to the current time node from the carbon factor aging dataset, and obtain the production energy consumption, local power grid real-time emission factor, transportation fuel efficiency parameters and factory maintenance energy consumption data of the current batch of the material. S43. Based on the actual origin information CDq and the actual transportation distance YSq, and combined with the current transportation fuel efficiency parameters, calculate the actual carbon emission value of the alternative material during the transportation phase, which is different from the average carbon emission value of stationary transportation used in the design phase. S44. Based on the current batch production energy consumption and the local power grid real-time emission factor, calculate the actual carbon emission value of the alternative material during the production stage, which is different from the historical statistical carbon emission factor used in the design stage. S45. Based on the actual carbon emissions during the integrated transportation phase, the actual carbon emissions during the production phase, and the energy consumption data for factory maintenance, the dynamic carbon emissions of alternative materials in the q-th supply chain event are reconstructed. The differences between these values ​​and the carbon emissions of the original design materials in the design scheme benchmark library are compared and analyzed to generate a report on the increase or decrease in carbon emissions.

8. The low-carbon material selection optimization method for the entire decoration project process according to claim 7, characterized in that: In S4, the carbon emission difference comparison analysis includes carbon emission comparison of single materials and carbon emission linkage comparison of related materials. The carbon emission comparison of single materials is used to assess the increase or decrease of carbon emissions of the alternative materials themselves, while the carbon emission linkage comparison of related materials is used to assess the chain effect of the change of alternative materials on the amount of auxiliary materials, energy consumption of construction technology and subsequent material selection. The global carbon emission impact is simulated through the carbon emission correlation matrix between materials.

9. The low-carbon material selection optimization method for the entire decoration project process according to claim 2, characterized in that: In S5, the trigger condition judgment logic for the material selection re-optimization process is as follows: If the cumulative carbon emissions during the construction phase deviate from the carbon budget value during the design phase by more than the carbon budget deviation threshold, and there is a need for material replacement or a supply chain switching event at the current construction node, then the material selection re-optimization process will be triggered. If the construction deviation correction factor shows that the actual loss rate of a certain material continues to exceed the design estimated loss rate, and the material is marked as a high-risk material for loss, then a special re-optimization process for that material will be triggered. Once the material selection re-optimization process is initiated, alternative solutions that meet the criteria are selected from the supply chain response dataset, including: The specifications and dimensions of the alternative materials are compatible with the geometric deviation characteristics of the current construction nodes, and the material compatibility score is higher than the material compatibility threshold. The dynamic carbon emission value of the alternative material is lower than that of the original design material, or although it is slightly higher than that of the original design material, global carbon emission compensation can be achieved by reducing the loss rate. The delivery time of alternative materials meets the requirements of the remaining construction period and does not affect the critical path of construction progress; The selected alternatives are comprehensively evaluated based on factors including dynamic carbon emissions, geometric fit, delivery timeliness, and cost increment. The option with the highest comprehensive score is selected to generate dynamic material selection instructions for the construction phase, and the material information and carbon budget in the design baseline library are updated simultaneously.

10. The low-carbon material selection optimization method for the entire decoration project process according to claim 1, characterized in that: In S5, the continuous iterative logic of the low-carbon material selection closed-loop optimization mechanism is as follows: During the construction phase of the renovation project, after each construction node is completed, the S3 construction deviation monitoring and the S4 supply chain response monitoring are automatically triggered. If the monitoring results meet the triggering conditions of the material selection re-optimization process, the material selection re-optimization process is executed immediately to ensure that carbon emission deviations are corrected in a timely manner during the construction process. During the completion phase of the renovation project, the actual material usage, on-site loss rate, records of alternative material usage, and dynamic carbon emission data of all construction nodes are summarized. The actual total carbon emission value of the entire renovation project is calculated and compared with the carbon budget value in the design phase to generate a carbon emission achievement rate report. The comparative analysis results detail the sources of carbon emission deviations, including the contribution of increased loss rate due to geometric deviations, the contribution of carbon factor changes due to supply chain changes, the contribution of material usage adjustments due to design changes, and the carbon emission compensation achieved through dynamic material selection optimization. This provides data feedback and experience accumulation for low-carbon material selection strategies in subsequent decoration projects.

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