Blockchain-enabled multi-solid waste green building material full-cycle risk traceability and low-carbon control method

Abstract

The application provides a blockchain-enabled multi-solid waste green building material full-cycle risk traceability low-carbon management method, belonging to the technical field of solid waste resource utilization and low-carbon building materials. The method comprises the following steps: collecting multi-source solid waste information and storing it in a blockchain, and constructing a holographic database; calling a synergistic utilization evaluation model to output an optimal matching scheme and storing it in a blockchain; deploying sensors to collect data in real time, and storing the processed data in a blockchain; establishing a full-life-cycle carbon emission accounting model to dynamically calculate carbon emissions; constructing comprehensive performance indicators and dynamically correcting them using an adaptive adjustment strategy, triggering risk early warning and carbon emission reduction optimization through a contract mechanism; constructing a multi-level blockchain traceability network to form an unalterable traceability chain throughout the cycle; and generating carbon credit records based on carbon emission reductions and distributing transactions through a contract mechanism. The application solves the problems of untrustworthy information, poor dynamic carbon accounting, and the disconnection between risk and carbon management in the prior art, and achieves full-cycle trusted traceability, accurate carbon accounting, and low-carbon optimization.

Description

Technical Field

[0001] This invention relates to the fields of solid waste resource utilization and low-carbon building materials technology, and in particular to a blockchain-enabled method for full-cycle risk traceability and low-carbon management of multi-solid waste green building materials. Background Technology

[0002] The co-utilization of multi-source solid waste to produce green building materials is an important way to achieve solid waste resource recovery and carbon emission reduction in the construction industry. However, solid waste from different sources (construction waste, industrial solid waste, sludge, etc.) is usually generated, transported, and treated by different entities, and there is a lack of standardized information sharing mechanisms between the various stages. The physicochemical property testing data of solid waste is stored in a scattered manner, which poses a risk of data tampering or falsification. This makes it difficult for multi-source solid waste co-utilization evaluation models to obtain real and complete input data, and thus they cannot output highly reliable optimal matching solutions, which in turn affects the quality and stability of the final building material products. The application of existing blockchain technology in construction waste management is mostly limited to a single stage of a single type of solid waste, and does not cover the entire life cycle of co-utilization of multi-source solid waste from generation to building material application.

[0003] The methods for calculating carbon emissions throughout the entire life cycle are inconsistent and lack dynamism. Most existing carbon footprint calculations for green building materials rely on static empirical formulas or simplified calculations based on fixed emission factors, failing to reflect real-time changes in carbon emissions caused by factors such as changes in solid waste sources, adjustments in transportation distances, and fluctuations in production processes. Complex carbon synergistic effects exist in the synergistic blending of multi-source solid wastes. For example, replacing cement clinker with fly ash can reduce carbon emissions, but excessive sludge content increases drying energy consumption. Existing methods lack the ability to dynamically quantify these synergistic effects, leading to significant discrepancies between carbon accounting results and actual carbon emissions, thus failing to provide a reliable basis for optimizing carbon reduction.

[0004] Risk management and low-carbon optimization are disconnected and lack a coordinated mechanism. The utilization of multi-source solid waste in building materials involves multiple dimensions, including environmental pollution risks, product performance risks, supply chain risks, compliance risks, and operational risks. Currently, risk management and carbon emission management are typically handled by different technologies, lacking a unified assessment model and a real-time coordinated control mechanism. Furthermore, end-to-end traceability is weak. From solid waste generation to green building material application and final disposal, multiple entities and long supply chains are involved. Current mainstream management systems only electronically record data for certain stages, failing to achieve tamper-proof traceability of end-to-end data. This makes it difficult to effectively verify carbon emission reductions and undermines the credibility of carbon credit records. Therefore, there is an urgent need to develop a technical solution that deeply integrates blockchain technology, multi-source solid waste collaborative utilization assessment, dynamic life-cycle carbon emission accounting, multi-dimensional risk management, and low-carbon incentive mechanisms. Summary of the Invention

[0005] The purpose of this invention is to provide a blockchain-enabled method for full-cycle risk traceability and low-carbon management of multi-source solid waste green building materials, in order to solve the technical problems of unreliable information on the collaborative utilization of multi-source solid waste and poor dynamics of carbon emission accounting in the existing technology.

[0006] To achieve the above objectives, this invention provides a blockchain-enabled method for full-cycle risk traceability and low-carbon management of multi-solid-waste green building materials, comprising the following steps: Step S1: Collect information on the generation, physicochemical properties and generation amount of multi-source solid waste, generate digital identity for each batch of solid waste, store the evidence on the blockchain, and build a multi-source solid waste holographic database. Step S2: Based on the multi-source solid waste holographic database, call the multi-source solid waste collaborative utilization evaluation model, output the optimal matching scheme and store it on the blockchain; Step S3: Deploy sensors at solid waste generation points, transport vehicles, processing workshops, and building material production lines to collect data in real time. After preprocessing and anomaly detection, upload key indicator data to the blockchain for evidence storage. Step S4: Using the life cycle assessment method, establish a carbon emission accounting model covering the generation and final disposal of solid waste. Combine the real-time data collected in step S3 to dynamically calculate the carbon emissions and total carbon footprint, and store the carbon accounting results on the blockchain. Step S5: Based on the carbon emissions in step S4 and the real-time data collected in step S3, construct a comprehensive performance index, dynamically correct it using an adaptive adjustment strategy, and trigger risk warnings and carbon emission reduction optimization through a contract mechanism. Step S6: Construct a multi-level blockchain traceability network to achieve data connectivity and form a full-cycle, tamper-proof traceability chain; Step S7: Based on the carbon emissions from Step S4 and the comprehensive efficiency indicators from Step S5, dynamically adjust the optimal matching scheme from Step S2, generate carbon credit records on the blockchain based on the actual carbon emission reduction, and execute carbon credit allocation and trading through a contract mechanism.

[0007] Therefore, the above-mentioned blockchain-enabled method for full-cycle risk traceability and low-carbon management of multi-solid waste green building materials has the following beneficial technical effects: (1) This invention writes the information on the generation, physicochemical properties and collaborative matching decision-making of multi-source solid waste into the blockchain, constructs an immutable holographic database and a five-level traceability network, solves the problems of serious information silos, easy data tampering and weak traceability capabilities of multi-source solid waste in the prior art, and provides a reliable data foundation for collaborative utilization.

[0008] (2) This invention combines real-time data collection from the Internet of Things with life cycle assessment methods to dynamically calculate carbon emissions and total carbon footprint, and introduces an adaptive adjustment strategy to correct the comprehensive performance indicators. This solves the problems of existing methods relying on static empirical formulas and failing to reflect changes in solid waste sources and process fluctuations, thereby improving the accuracy and timeliness of carbon accounting.

[0009] (3) This invention incorporates multi-dimensional risk indicators such as environmental pollution risk and product performance risk into a unified framework with carbon emission intensity indicators and environmental economic benefit indicators. Through improved reinforcement learning algorithms and contract mechanisms, dynamic correction and linkage control are achieved, solving the problems of risk control and low-carbon optimization being separated and lacking a unified evaluation model in the existing technology, and realizing the synergy between risk control and low-carbon optimization. Attached Figure Description

[0010] Figure 1 A flowchart illustrating the blockchain-enabled method for full-cycle risk traceability and low-carbon management of multi-solid-waste green building materials according to the present invention. Figure 2 Flowchart for adaptive adjustment strategy; Figure 3 To reinforce the convergence curve of learning reward values; Figure 4 This is a graph showing the change in carbon emission reduction as a function of the number of optimization iterations. Detailed Implementation

[0011] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0012] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0013] Example 1 This embodiment uses a solid waste co-processing project in Area A of a certain city as an example to illustrate the specific implementation process of the present invention. The multi-source solid waste involved includes: construction waste (waste concrete, waste bricks and tiles) from a demolition site in Area A, fly ash and desulfurization gypsum from a coal-fired power plant in Area B, and dewatered sludge from a municipal wastewater treatment plant in Area C. The project aims to co-process these four types of solid waste to produce recycled aggregates and low-carbon cementitious materials for use in green building construction in the area.

[0014] Reference Figures 1-2 A blockchain-enabled approach to risk traceability and low-carbon management of multi-solid waste green building materials throughout their entire lifecycle includes the following steps: Step S1: Collect information on the generation, physicochemical properties and production volume of multi-source solid waste, generate digital identity identifiers for each batch of solid waste, store the evidence on the blockchain, and construct a multi-source solid waste holographic database.

[0015] Step S11: The collected multi-source solid waste includes two or more combinations of construction waste, industrial solid waste, and sludge. In this embodiment, the collected construction waste consists of waste concrete and waste bricks and tiles, the industrial solid waste consists of fly ash and desulfurized gypsum, and the sludge consists of municipal dewatered sludge. The generation amount and physicochemical properties of each batch of solid waste are shown in Table 1.

[0016] Table 1 Physicochemical properties of various batches of multi-source solid waste

[0017] Step S12: Generate a unique digital identity for each batch of solid waste. The digital identity is a hash value based on the generating unit, generation time, solid waste type, and physicochemical properties of the solid waste. The digital identity is then affixed to the solid waste packaging or transport container in the form of a QR code or electronic tag. In this embodiment, a QR code tag is generated for each batch of solid waste after loading; scanning the tag reveals complete source information for the solid waste.

[0018] Step S13: The generation information, physicochemical property information, and generation quantity data are hashed using the SHA-256 or SM3 hash algorithm. The original data, hash value, and digital signature are then written to the blockchain notarization module through a consensus mechanism. The data structure of the notarization module includes: batch number, generating unit digital certificate, timestamp, solid waste type code, physicochemical property parameter vector, generation quantity, hash value, and the hash value of the previous block. In this embodiment, the data is written through the consensus nodes of the Hyperledger Fabric consortium blockchain. Each block contains the hash value of the previous block, forming a chain structure.

[0019] Blockchain-based evidence storage ensures the immutability and full lifecycle traceability of information on the sources of multi-source solid waste, effectively solving the problems of scattered, easily tampered, and low-reliability data on solid waste from different sources. This provides authentic, complete, and reliable foundational data for subsequent compatibility assessments. QR code labels bind physical packaging to digital identity, facilitating rapid identification and verification during transportation and handover.

[0020] Step S2: Based on the multi-source solid waste holographic database, call the multi-source solid waste collaborative utilization evaluation model, output the optimal matching scheme and store it on the blockchain.

[0021] Step S21: Using the compatibility of solid waste's physicochemical properties, heavy metal leaching risk, contribution to building material performance, and the economics of co-processing as constraints, and with the objective functions of maximizing solid waste resource utilization and optimizing the comprehensive performance of building materials, a multi-source solid waste co-processing evaluation model is constructed. In this embodiment, the constraints are set as follows: calcium-silicon ratio between 1.2 and 2.2, heavy metal leaching concentration below the limit of Class III water standard in GB / T 14848-2017, crushing index of recycled aggregate ≤25%, 28-day compressive strength of cementitious materials ≥32.5 MPa, and comprehensive treatment cost ≤180 yuan / ton.

[0022] Step S22: Solve the multi-source solid waste co-utilization evaluation model using a multi-objective optimization algorithm to output the optimal matching scheme. In this embodiment, the NSGA-III multi-objective optimization algorithm is used with a population size of 200 and 500 iterations to obtain the Pareto front optimal solution set. The solution with the highest total solid waste content (≥85wt%) and the best comprehensive performance score is selected as the final scheme: 40% waste concrete, 20% waste bricks and tiles, 15% fly ash, 10% desulfurized gypsum, and 15% dewatered sludge (added after drying pretreatment).

[0023] Step S23: The input parameters, intermediate variables, and output results of the optimal matching scheme are stored on the blockchain for verification. The input parameters include the matching and feeding records. In this embodiment, all running parameters of the optimization algorithm and the final matching ratio are written to the blockchain.

[0024] By employing a multi-objective optimization algorithm under multi-dimensional constraints, an optimal blending scheme with high total solid waste content and superior overall performance was generated, improving the resource utilization rate of solid waste while ensuring that the performance of building materials meets engineering requirements. The entire decision-making process was recorded and authenticated on the blockchain, making the blending optimization process completely transparent and auditable. This solved the problem of lacking reliable data support for collaborative blending decisions and provided an immutable basis for subsequent production execution.

[0025] Step S3: Deploy sensors at solid waste generation points, transport vehicles, processing workshops, and building material production lines to collect data in real time. After preprocessing and anomaly detection, the key indicator data will be uploaded to the blockchain for evidence storage.

[0026] Step S31: Deploy near-infrared spectroscopy sensors for solid waste component detection, X-ray fluorescence spectrometers for heavy metal content analysis, GPS / BeiDou positioning devices for location tracking, and temperature, humidity, and vibration sensors for process parameter monitoring at key nodes. In this embodiment, GPS positioning modules are installed on transport vehicles, temperature and vibration sensors are installed at the mixing plant, and near-infrared spectroscopy instruments are installed in the pretreatment workshop.

[0027] Step S32: Perform data cleaning, outlier removal, and missing value completion on the collected data using edge computing nodes. In this embodiment, edge computing nodes are deployed on transport vehicles and building material production lines to preprocess the data every 30 seconds and use interpolation to complete missing values.

[0028] Step S33: Use the Isolation Forest algorithm or autoencoder network to perform real-time anomaly detection on the data, and store the anomaly event information on the blockchain. In this embodiment, the anomaly threshold for the Isolation Forest algorithm is set to 0.3. During the transportation of a certain batch, if the GPS trajectory deviates from the preset route by more than 10%, the edge computing node immediately identifies it as an anomaly and stores the deviation time, location, and vehicle information on the blockchain.

[0029] By deploying multiple types of sensors, real-time perception of solid waste composition, heavy metal content, transportation trajectory, and production process parameters is achieved throughout the entire process. Edge computing nodes cleanse and detect anomalies in the data, enabling high-accuracy real-time identification of abnormal events. The latency from anomaly occurrence to on-chain evidence storage is extremely short, significantly improving risk response speed. On-chain evidence storage of key indicator data makes it possible to trace abnormal events such as transportation trajectory deviations, equipment failures, and excessive process parameters, providing real-time and reliable data support for subsequent risk warning and source tracing.

[0030] Step S4: Using the life cycle assessment method, establish a carbon emission accounting model covering the generation and final disposal of solid waste. Combine the real-time data collected in step S3 to dynamically calculate carbon emissions and total carbon footprint, and store the carbon accounting results on the blockchain.

[0031] Based on ISO 14067 and PAS 2050, the system boundary is divided into six stages: solid waste generation, collection and transportation, pretreatment, building material production, construction application, and final disposal. Carbon emission factors for each stage are obtained from a localized dynamic factor database, which is updated monthly based on factors such as grid carbon emission intensity and transportation energy structure.

[0032] The carbon emission accounting model calculates total carbon emissions over the entire lifecycle using the following formula: ; in, Total carbon emissions over the entire lifecycle, expressed in kg CO2e; Number the lifecycle stages. These represent the solid waste generation stage, collection and transportation stage, pretreatment stage, building material production stage, construction and application stage, and final disposal stage, respectively. For the first The set of activity types in a phase; For the first Phase 1 Activity data for this type of activity includes energy consumption, material consumption, and transportation turnover; The corresponding carbon emission factor is expressed in kgCO2e per unit of activity data. Carbon offset, including the emission reduction contribution of solid waste replacing virgin materials, is expressed in kgCO2e.

[0033] Combining the activity data collected in real time in step S3 (transportation fuel consumption, equipment power consumption, maintenance temperature, etc.), the carbon emissions and total carbon footprint at each stage are dynamically calculated. In this embodiment, the full-cycle carbon emission calculation results for 1 ton of recycled building materials are shown in Table 2. The baseline carbon emission for the traditional process (natural aggregate + cement clinker) is 2306.5 kg CO2e / t, while in this embodiment it is 1337.7 kg CO2e / t, resulting in carbon emission reduction. =968.8 kg CO2e / t, emission reduction rate of 42.0%. Carbon accounting results and verification evidence are stored on the blockchain.

[0034] Table 2. Full-cycle carbon emission accounting results

[0035] This step establishes a lifecycle carbon emission accounting model covering six stages. Combined with real-time data from step S3, it dynamically calculates carbon emissions, significantly improving accuracy compared to static empirical formulas. It can reflect real-time changes in carbon emissions caused by variations in solid waste sources, transportation distances, and production process fluctuations. In this embodiment, the carbon emissions from recycled building materials throughout their entire lifecycle are significantly reduced compared to traditional processes, achieving a remarkable carbon reduction effect. The carbon accounting results and their verification evidence are stored on the blockchain, providing accurate and reliable quantitative evidence for carbon reduction optimization, while also meeting international standard compliance requirements.

[0036] Step S5: Based on the carbon emissions from Step S4 and the real-time data collected in Step S3, construct a comprehensive performance index, dynamically correct it using an adaptive adjustment strategy, and trigger risk warnings and carbon emission reduction optimization through a contract mechanism.

[0037] The specific settings for the comprehensive performance indicators are as follows: The comprehensive performance indicators include carbon emission intensity, multi-dimensional risk comprehensive index, and environmental economic benefit indicators, all of which are based on the total carbon emissions throughout the entire life cycle in step S4. And the real-time data calculation in step S3; Carbon emission intensity is indicated as Calculate using the following formula: ; in, The quantity or quality of building materials per unit of function.

[0038] The multi-dimensional risk composite index is denoted as Calculate using the following formula: ; in, These respectively represent environmental pollution risk, product performance risk, supply chain risk, compliance risk, and operational risk. For the first The risk score is calculated from the real-time data in step S3 using the risk assessment model. For the first Dynamic weighting coefficients corresponding to dimensional risks.

[0039] Environmental and economic benefits are indicated as Calculate using the following formula: ; in, To save costs and by comparing the optimal matching scheme in step S2 with the baseline scheme, Based on the baseline cost, For carbon emission reduction and , As a baseline carbon emission, , These are the weighting coefficients.

[0040] The adaptive adjustment strategy specifically includes the following steps: Step S51: Based on the real-time data and blockchain-stored evidence data from step S3, an improved reinforcement learning algorithm is used to dynamically adjust the weight coefficients of the environmental and economic benefit indicators. , The improved reinforcement learning algorithm is an improved algorithm based on deep deterministic policy gradient, which introduces a priority sampling mechanism in the experience replay pool and embeds a correction error term in the reward function. Step S52: Define the adaptive adjustment coefficient Calculate using the following formula: ; in, For time variables, =0.2 is the initial coefficient. =0.5、 =0.3、 =0.4 is the adjustment coefficient. The standard deviation of solid waste composition fluctuation. This represents the historical average of solid waste composition. For the standard deviation of carbon emission fluctuations, This represents the historical average of carbon emissions. This is a comprehensive risk index for the current moment, encompassing multiple dimensions.

[0041] Step S53: Adjust according to the adaptive coefficient Dynamically update the learning rate of the reinforcement learning algorithm and correction factor Calculate using the following formula: ; ; in, =0.01 is the initial learning rate. =1000 represents the total running cycle. =0.1 is the initial correction factor.

[0042] Step S54, according to The value automatically determines the system operating condition: when When it is determined to be the first preset working condition, when When it is determined to be the second preset working condition, when The time is determined to be the third preset working condition, in which =0.3 and =0.7 is the preset threshold.

[0043] Step S55: Perform corrections based on the determined operating conditions: Under the first preset operating condition, set the correction factor... Applicable only to carbon emission intensity indicators, the corrected carbon emission intensity indicators Calculate using the following formula: ; in, The carbon emission intensity index before revision. Let the adjustment coefficient be the first operating condition factor; under the second preset operating condition, let the correction factor be... It only applies to the multi-dimensional risk composite index, the adjusted risk index. Calculate using the following formula: ; in, This is the multi-dimensional risk composite index before revision. The adjustment coefficient is set to the second operating condition; under the third preset operating condition, the correction factor is set to... It simultaneously affects both the carbon emission intensity index and the multi-dimensional risk composite index, meaning it uses both the modified carbon emission intensity index calculation formula and the modified risk index calculation formula; among which... , The output of the action network, generated through reinforcement learning, has values ​​ranging from [0, 0.3]. In this embodiment, the rainy season causes the sludge moisture content to increase from 65% to 72%. Increase When the value increases from 0.25 to 0.68, the system automatically switches to the second preset operating condition, at which point the motion network output... =0.15, A reduction of approximately 8% triggers a risk warning and necessitates adjustments to the water-reducing agent dosage. The reinforcement learning training curve is as follows: Figure 3 As shown, the reward value gradually converges with the number of iterations.

[0044] This step constructs a comprehensive performance indicator system encompassing carbon emission intensity, a multi-dimensional risk index (five dimensions), and environmental and economic benefit indicators, unifying and quantifying the three-dimensional objectives of carbon, risk, and economy. An improved DDPG reinforcement learning algorithm is introduced, adaptively adjusting the coefficients. The learning rate and correction factor are dynamically updated based on fluctuations in solid waste composition, carbon emission fluctuations, and the current risk index, enabling automatic identification and differentiated correction of system operating conditions. When solid waste composition fluctuates, the system automatically switches to the corresponding operating condition, the correction factor applies to the risk index, triggering an early warning and adjusting process parameters. This achieves coordinated management of both risk and carbon emissions, avoiding the negative effects of single-objective optimization. The reinforcement learning algorithm stabilizes after iterations, validating its effectiveness and stability.

[0045] Step S6: Construct a multi-level blockchain traceability network to achieve data connectivity and form a full-cycle, tamper-proof traceability chain.

[0046] Step S61: Construct a five-layer blockchain traceability network consisting of a solid waste source layer, a transportation chain layer, a production layer, a building materials product layer, and an application layer. In this embodiment, a consortium blockchain is built using Hyperledger Fabric, with each participating party (demolition company, power plant, sewage treatment plant, building materials factory, construction company, and regulatory agency) owning one node.

[0047] Step S62: Evidence is stored at the solid waste source layer, including the solid waste generating unit, generation time, solid waste type, physicochemical properties, and generation quantity collected in step S1; at the transport chain layer, evidence is stored at step S3, including the transport route, vehicle information, intermediate transfer records, and handover confirmation records; at the production end layer, evidence is stored at the compatibility and feeding records of step S2, the pretreatment process data of step S3, and the production process parameters of step S3; at the building material product layer, evidence is stored at the product type, product specifications, product performance indicators, carbon footprint label, and the carbon credit record generated in step S7; at the application end layer, evidence is stored at the building material product's engineering project information, construction and installation records, service monitoring data, and final demolition and disposal information. In this embodiment, the full-cycle traceability information of a certain batch of building material products (batch number GJ2025-001) is completely verifiable.

[0048] Step S63: Cross-chain interoperability is achieved between different layers through hash locking or relay chain schemes. Data changes at any layer are synchronized to the entire network through a consensus mechanism. In this embodiment, when a piece of data is updated at a certain layer, a cross-chain transaction hash is generated, and other layers confirm data consistency by verifying the hash.

[0049] This step constructs a five-tiered blockchain traceability network covering the solid waste source layer, transportation chain layer, production layer, building material product layer, and application layer, encompassing the entire lifecycle from solid waste generation to building material application and final disposal. Each layer stores key data corresponding to its respective stage, achieving tamper-proof traceability of data throughout the entire lifecycle. Through cross-chain interoperability, the independence and consistency of data at each level are ensured, and data changes at any level are synchronized to the entire network via a consensus mechanism. When a batch of building materials products has quality issues, the source of the problem and the responsible party can be quickly located along the traceability chain, significantly improving regulatory efficiency and problem handling speed.

[0050] Step S7: Based on the carbon emissions from Step S4 and the comprehensive efficiency indicators from Step S5, dynamically adjust the optimal matching scheme from Step S2, generate carbon credit records on the blockchain based on the actual carbon emission reduction, and execute carbon credit allocation and trading through a contract mechanism.

[0051] The specific process of dynamic adjustment is as follows: taking the minimization of carbon emissions throughout the entire life cycle and the minimization of comprehensive risk as joint optimization objectives, the following optimization function is constructed: ; in, The decision variable vector includes the proportions of solid waste from multiple sources. For the corresponding decision variables The total carbon emissions for the entire lifecycle are calculated in step S4. For the corresponding decision variables The multi-dimensional risk composite index calculated in step S5 is as follows. , The dynamic weighting coefficients are determined by the comprehensive performance index in step S5; after solving the optimization function, the adjusted decision variables are... The optimal matching scheme for step S2 is confirmed through a blockchain voting consensus mechanism and then issued for execution.

[0052] In this embodiment, the optimized sludge content was reduced from 15% to 12%, while fly ash was increased to 18%, further reducing carbon emissions to 1290.5 kg CO2e / t and decreasing the risk index by 8%. The change in carbon emission reduction with the number of optimization iterations is as follows: Figure 4 As shown.

[0053] The specific process of generating carbon credit records and executing allocation and trading includes the following steps: Based on carbon emission reduction Carbon credit records are generated according to the CCER methodology or international voluntary carbon standards. In this embodiment, =968.8 kg CO2e / t, which generates an equivalent amount of carbon credits after verification according to the CCER methodology.

[0054] A unique carbon footprint digital certificate is generated for each batch of green building materials. This certificate includes basic product information, a carbon footprint label, a carbon emission distribution map, and a carbon credit number. In this embodiment, scanning the QR code on the building materials product allows users to view the complete carbon footprint information and carbon credit number for that batch.

[0055] Executed via blockchain contract mechanism: If ,in To declare carbon emission reductions, If the tolerance threshold is 0.05, the certified emission reductions will be allocated to solid waste generators, disposal producers, and users according to a preset ratio. Carbon credit records will be pushed to the carbon trading market, and trading revenue will be transferred to the accounts of each participant according to the distribution rules stipulated in the contract. In this embodiment, =968.8 meets the conditions, the solid waste generator receives 30% of the revenue (approximately 58 yuan / t), the disposal producer receives 50% (approximately 96 yuan / t), and the user receives 20% (approximately 38 yuan / t).

[0056] With the joint optimization objectives of minimizing carbon emissions and risk, an optimal matching scheme is constructed for the dynamic adjustment step S2 of the optimization function, achieving a continuous increase in carbon emission reduction while simultaneously reducing the risk index. Based on the carbon emission reduction, tradable carbon credit records are generated according to the national CCER methodology, and a unique digital carbon footprint certificate is generated for each batch of products. Carbon credit allocation is automatically executed through smart contracts, with solid waste generators, disposal producers, and users receiving revenue according to agreed proportions. Carbon credit revenue covers a significant proportion of the incremental costs of solid waste treatment, forming a sustainable low-carbon incentive mechanism. This solves the problems of difficulty in effectively verifying carbon emission reductions and insufficient credibility of carbon credits, significantly enhancing the enthusiasm of all parties in the industrial chain to participate in the resource utilization of solid waste.

[0057] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0058] Therefore, the present invention adopts the above-mentioned blockchain-enabled risk traceability and low-carbon management method for green building materials with multiple solid wastes throughout the entire life cycle, realizing credible traceability of multi-source solid waste collaborative utilization, dynamic accounting of carbon emissions throughout the entire life cycle, and linkage management of risk and carbon emissions.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A blockchain-enabled method for full-cycle risk traceability and low-carbon management of multi-solid waste green building materials, characterized by: Includes the following steps: Step S1: Collect information on the generation, physicochemical properties and generation amount of multi-source solid waste, generate digital identity for each batch of solid waste, store the evidence on the blockchain, and build a multi-source solid waste holographic database. Step S2: Based on the multi-source solid waste holographic database, call the multi-source solid waste collaborative utilization evaluation model, output the optimal matching scheme and store it on the blockchain; Step S3: Deploy sensors at solid waste generation points, transport vehicles, processing workshops, and building material production lines to collect data in real time. After preprocessing and anomaly detection, upload key indicator data to the blockchain for evidence storage. Step S4: Using the life cycle assessment method, establish a carbon emission accounting model covering the generation and final disposal of solid waste. Combine the real-time data collected in step S3 to dynamically calculate the carbon emissions and total carbon footprint, and store the carbon accounting results on the blockchain. Step S5: Based on the carbon emissions in step S4 and the real-time data collected in step S3, construct a comprehensive performance index, dynamically correct it using an adaptive adjustment strategy, and trigger risk warnings and carbon emission reduction optimization through a contract mechanism. Step S6: Construct a multi-level blockchain traceability network to achieve data connectivity and form a full-cycle, tamper-proof traceability chain; Step S7: Based on the carbon emissions from Step S4 and the comprehensive efficiency indicators from Step S5, dynamically adjust the optimal matching scheme from Step S2, generate carbon credit records on the blockchain based on the actual carbon emission reduction, and execute carbon credit allocation and trading through a contract mechanism.

2. The blockchain-enabled full-cycle risk traceability and low-carbon management method for multi-solid-waste green building materials according to claim 1, characterized in that, Step S1 specifically includes the following steps: Step S11: The collected multi-source solid waste includes two or more combinations of construction waste, industrial solid waste, and sludge. The industrial solid waste is selected from one or more of fly ash, coal gangue, smelting slag, tailings, desulfurization gypsum, and red mud. The sludge includes municipal sludge or industrial sludge. Step S12: Generate a unique digital identity for each batch of solid waste. The digital identity is a hash value based on the generating unit, generation time, solid waste type and physicochemical properties of the solid waste. The digital identity is then affixed to the solid waste packaging or transport container in the form of a QR code or electronic tag. Step S13: Use SHA-256 or SM3 hash algorithm to perform hash calculation on the generation information, physicochemical property information and generation quantity data, and write the original data, hash value and digital signature into the blockchain storage module through consensus mechanism. The data structure of the storage module includes: batch number, generation unit digital certificate, timestamp, solid waste type code, physicochemical property parameter vector, generation quantity, hash value and the hash value of the previous block.

3. The blockchain-enabled full-cycle risk traceability and low-carbon management method for multi-solid-waste green building materials according to claim 1, characterized in that, Step S2 specifically includes the following steps: Step S21: With the compatibility of the physical and chemical properties of solid waste, the risk of heavy metal leaching, the contribution of building material performance and the economics of co-processing as constraints, and with the objective functions of maximizing the resource utilization rate of solid waste and optimizing the comprehensive performance of building materials, construct an evaluation model for the co-processing of multi-source solid waste. Step S22: Use a multi-objective optimization algorithm to solve the evaluation model for the collaborative utilization of multi-source solid waste and output the optimal matching scheme; Step S23: Upload the input parameters, intermediate variables and output results of the optimal matching scheme to the blockchain for verification. The input parameters include the matching and feeding records.

4. The blockchain-enabled full-cycle risk traceability and low-carbon management method for multi-solid-waste green building materials according to claim 1, characterized in that, Step S3 specifically includes the following steps: Step S31: Deploy near-infrared spectroscopy sensors for solid waste component detection, X-ray fluorescence spectrometers for heavy metal content analysis, GPS / BeiDou positioning devices for location trajectory tracking, and temperature, humidity, and vibration sensors for process parameter monitoring at key nodes. Step S32: Perform data cleaning, outlier removal, and missing value completion on the collected data through edge computing nodes; Step S33: Use the isolated forest algorithm or autoencoder network to perform real-time anomaly detection on the data, and store the anomaly event information on the blockchain.

5. The blockchain-enabled full-cycle risk traceability and low-carbon management method for multi-solid-waste green building materials according to claim 4, characterized in that, The carbon emission accounting model in step S4 calculates the total carbon emissions over the entire cycle using the following formula: ； in, Total carbon emissions over the entire lifecycle; Number the lifecycle stages. These represent the solid waste generation stage, collection and transportation stage, pretreatment stage, building material production stage, construction and application stage, and final disposal stage, respectively. For the first The set of activity types in a phase; For the first Phase 1 Activity data for this type of activity includes energy consumption, material consumption, and transportation turnover; The corresponding carbon emission factor; Carbon offsetting includes the emission reduction contribution of solid waste replacing virgin materials.

6. The blockchain-enabled full-cycle risk traceability and low-carbon management method for multi-solid-waste green building materials according to claim 5, characterized in that, The specific settings for the comprehensive performance indicators in step S5 are as follows: The comprehensive performance indicators include carbon emission intensity, multi-dimensional risk comprehensive index, and environmental economic benefit indicators, all of which are based on the total carbon emissions throughout the entire life cycle in step S4. And the real-time data calculation in step S3; Carbon emission intensity is indicated as Calculate using the following formula: ； in, The quantity or quality of building materials per unit of function; The multi-dimensional risk composite index is denoted as Calculate using the following formula: ； in, These respectively represent environmental pollution risk, product performance risk, supply chain risk, compliance risk, and operational risk. For the first The risk score is calculated from the real-time data in step S3 using the risk assessment model. For the first Dynamic weighting coefficients corresponding to dimensional risks; Environmental and economic benefits are indicated as Calculate using the following formula: ； in, To save costs and by comparing the optimal matching scheme in step S2 with the baseline scheme, Based on the baseline cost, For carbon emission reduction and , As a baseline carbon emission, , These are the weighting coefficients.

7. The blockchain-enabled full-cycle risk traceability and low-carbon management method for multi-solid-waste green building materials according to claim 6, characterized in that, The adaptive adjustment strategy in step S5 specifically includes the following steps: Step S51: Based on the real-time data and blockchain-stored evidence data from step S3, an improved reinforcement learning algorithm is used to dynamically adjust the weight coefficients of the environmental and economic benefit indicators. , The improved reinforcement learning algorithm is an improved algorithm based on deep deterministic policy gradient, which introduces a priority sampling mechanism in the experience replay pool and embeds a correction error term in the reward function. Step S52: Define the adaptive adjustment coefficient Calculate using the following formula: ； in, For time variables, These are the initial coefficients. , , For adjustment coefficients, The standard deviation of solid waste composition fluctuation. This represents the historical average of solid waste composition. For the standard deviation of carbon emission fluctuations, This represents the historical average of carbon emissions. This is a comprehensive risk index covering multiple dimensions at the current moment. Step S53: Adjust according to the adaptive coefficient Dynamically update the learning rate of the reinforcement learning algorithm and correction factor Calculate using the following formula: ； ； in, The initial learning rate, For the total operating cycle, This is the initial correction factor; Step S54, according to The value automatically determines the system operating condition: when When it is determined to be the first preset working condition, when When it is determined to be the second preset working condition, when The time is determined to be the third preset working condition, in which and The preset threshold; Step S55: Perform corrections based on the determined operating conditions: Under the first preset operating condition, set the correction factor... Applicable only to carbon emission intensity indicators, the corrected carbon emission intensity indicators Calculate using the following formula: ； in, The carbon emission intensity index before revision. Let the adjustment coefficient be the first operating condition factor; under the second preset operating condition, let the correction factor be... It only applies to the multi-dimensional risk composite index, the adjusted risk index. Calculate using the following formula: ； in, This is the multi-dimensional risk composite index before revision. The adjustment coefficient is set to the second operating condition; under the third preset operating condition, the correction factor is set to... It simultaneously affects both the carbon emission intensity index and the multi-dimensional risk composite index, meaning it uses both the modified carbon emission intensity index calculation formula and the modified risk index calculation formula; among which... , Output from the action network of reinforcement learning.

8. The blockchain-enabled full-cycle risk traceability and low-carbon management method for multi-solid-waste green building materials according to claim 7, characterized in that, Step S6 specifically includes the following steps: Step S61: Construct a blockchain traceability network with five layers: solid waste source layer, transportation chain layer, production end layer, building material product layer, and application end layer. Step S62: Record the solid waste generating unit, generation time, solid waste type, physicochemical properties, and generation quantity collected in step S1 at the solid waste source layer; record the transportation route, vehicle information, intermediate transfer records, and handover confirmation records collected in step S3 at the transportation chain layer; record the material feeding records from step S2, the pretreatment process data from step S3, and the production process parameters from step S3 at the production end layer; record the product type, product specifications, product performance indicators, carbon footprint label, and carbon credit record generated in step S7 at the building material product layer; record the engineering project information, construction and installation records, service monitoring data, and final dismantling and disposal information of the building material products at the application end layer. Step S63: Cross-chain interoperability is achieved between different levels through hash locking or relay chain schemes, and data changes at any level are synchronized to the entire network through a consensus mechanism.

9. The blockchain-enabled full-cycle risk traceability and low-carbon management method for multi-solid-waste green building materials according to claim 8, characterized in that, The specific process of dynamic adjustment in step S7 is as follows: taking the minimization of carbon emissions throughout the life cycle and the minimization of comprehensive risk as joint optimization objectives, the following optimization function is constructed: ； in, The decision variable vector includes the proportions of solid waste from multiple sources. For the corresponding decision variables The total carbon emissions for the entire lifecycle are calculated in step S4. For the corresponding decision variables The multi-dimensional risk composite index calculated in step S5 is as follows. , The dynamic weighting coefficients are determined by the comprehensive performance index in step S5; after solving the optimization function, the adjusted decision variables are... The optimal matching scheme for step S2 is confirmed through a blockchain voting consensus mechanism and then issued for execution.

10. The blockchain-enabled full-cycle risk traceability and low-carbon management method for multi-solid-waste green building materials according to claim 9, characterized in that, The specific process of generating carbon credit records and executing allocation and trading in step S7 includes the following steps: Based on carbon emission reduction Carbon credit records are generated in accordance with the national CCER methodology or international voluntary carbon standards. A unique carbon footprint digital certificate is generated for each batch of green building materials products. The carbon footprint digital certificate includes basic product information, carbon footprint label, carbon emission distribution map and carbon credit number. Executed via blockchain contract mechanism: If ,in To declare carbon emission reductions, If the tolerance threshold is set, the certified emission reductions will be allocated to solid waste generators, disposal producers, and users according to a preset ratio, and the carbon credit records will be pushed to the carbon trading market. The trading revenue will be transferred to the accounts of each participant according to the distribution rules agreed upon in the contract mechanism.

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