Support steel member full life cycle quality traceability system based on block chain

Abstract

The invention discloses a block chain-based support steel member full life cycle quality traceability system, and relates to the technical field of block chains, and the method comprises the steps: deploying sensors at each stage of a steel member, collecting data, and unifying the format; dynamically calculating a weight based on timeliness and confidence, and generating a comprehensive quality score; generating a block by hash encrypted data, and triggering an intelligent contract by a preset engineering rule; full life cycle data are retrieved through UID, and BIM model three-dimensional display is integrated; and analyzing a contract log to locate a responsible party, and optimizing parameters to form closed-loop management. Through a dynamic weight distribution algorithm, the system can dynamically adjust the weight of each data source according to the timeliness and confidence of the sensor data, and the objectivity and accuracy of quality evaluation are improved.

Description

Technical Field

[0001] The present invention belongs to the field of blockchain technology, and in particular relates to a blockchain-based full life cycle quality traceability system for supporting steel components. Background Art

[0002] Support steel components are used in construction projects to support surrounding rock and maintain structural stability. These components include steel arches, steel supports, anchor rods, and steel mesh. These components are the core load-bearing components of a building's initial support and main structure. Their material properties, machining precision, and installation quality directly impact the building's safety and durability. Therefore, ensuring the quality traceability of steel components throughout their lifecycle is crucial.

[0003] In existing blockchain traceability systems, steel component quality assessment often relies on fixed weights or single data source fusion methods, which are difficult to adapt to the timeliness and reliability differences of multi-source data in complex engineering environments. In construction scenarios, parameters such as material properties, installation accuracy, and environmental stress of steel components change dynamically. The static fusion mechanism cannot effectively balance the time decay effect of sensor data and equipment confidence, resulting in deviations in assessment results and affecting the accuracy of subsequent quality decisions. To address the above issues, the following solutions are proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a blockchain-based full-life cycle quality traceability system for supporting steel components. Through a dynamic weight allocation algorithm, the system can dynamically adjust the weights of each data source according to the timeliness and confidence of sensor data, thereby improving the objectivity and accuracy of quality assessment and solving the problem that the existing technology lacks the ability to adapt to dynamic environments, resulting in distortion of data confidence assessment.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a blockchain-based quality traceability system for the entire life cycle of supporting steel components, which includes a data acquisition module, an edge computing and data processing module, a blockchain storage and smart contract module, an application service and visualization module, and a responsibility tracing and optimization feedback module.

[0007] The data acquisition module, edge computing and data processing module, blockchain storage and smart contract module, application service and visualization module, and responsibility tracing and optimization feedback module are connected in sequence. The output end of the blockchain storage and smart contract module is unidirectionally connected to the input end of the responsibility tracing and optimization feedback module, and the output end of the responsibility tracing and optimization feedback module is unidirectionally connected to the input end of the edge computing and data processing module.

[0008] The traceability system workflow is as follows:

[0009] Step S1, data collection and standardization: deploy multiple types of sensors at each stage of steel structure construction to collect key parameters and unify data formats to ensure data compatibility throughout the construction project cycle;

[0010] Step S2, dynamic data fusion: dynamically assign sensor data weights and calculate comprehensive quality scores;

[0011] Step S3, blockchain and contract deployment: Encrypt data to generate blocks, design smart contracts based on engineering specifications, and automatically trigger construction process control and exception response;

[0012] Step S4, traceability and visualization: using unique identifiers to quickly retrieve full life cycle data and integrating BIM models to achieve three-dimensional visualization and trend analysis;

[0013] Step S5: Responsibility closed-loop optimization: automatically locate the responsible party and generate a report, and optimize the algorithm parameters based on historical data.

[0014] Furthermore, the data acquisition module is used to collect physical parameters and environmental data of raw materials, production, installation and operation and maintenance in real time through sensors deployed at various stages of the steel component's life cycle;

[0015] The edge computing and data processing module is used to execute a dynamic weight allocation algorithm on the local node, integrate multi-source data and generate a comprehensive quality score for steel components, thereby achieving real-time anomaly detection and preliminary alarms;

[0016] The blockchain storage and smart contract module is used to encrypt and store full life cycle data using blockchain technology and deploy smart contracts to automatically execute construction project quality rules;

[0017] The application service and visualization module is used to provide a visualization interface based on the BIM model, supporting data retrieval throughout the life cycle of steel components, quality trend analysis, and highlighting of problem locations;

[0018] The responsibility tracing and optimization feedback module is used to automatically locate the responsible party through the smart contract log, optimize the algorithm parameters and process rules, and form a closed-loop quality control system.

[0019] Furthermore, the step S1, data collection and standardization, includes the following steps:

[0020] Step S11, construction engineering field adaptation: Deploy multiple types of IoT sensors during the raw material procurement, production and processing, transportation, installation, and operation and maintenance stages of steel components, specifically:

[0021] Raw material stage: RFID tags are embedded on the surface of steel to record parameters including material batch, supplier, and chemical composition;

[0022] Production stage: Install optical fiber strain sensors on processing equipment to monitor the stress and temperature of steel components in real time during processing;

[0023] Installation phase: Deploy laser displacement sensors at the construction site to measure the installation position deviation of steel components and compare them with the BIM model coordinates;

[0024] Operation and maintenance phase: Install temperature and humidity sensors at the construction site to collect environmental parameters such as humidity and temperature;

[0025] Step S12: Data standardization: Format the multi-source heterogeneous data into a unified JSON structure with fields including: sensor type, timestamp, value, unit, and operator ID; define thresholds for key parameters of steel components based on the characteristics of the construction engineering field;

[0026] This design deploys multiple types of sensors (such as RFID and fiber optic strain sensors) to comprehensively collect key parameters of steel components from raw materials to operation and maintenance (such as material composition, stress values, installation deviations, etc.), and standardizes and formats the data to ensure the consistency and operability of subsequent analysis, providing a complete data foundation for quality traceability in the construction engineering field.

[0027] Furthermore, in step S2, the formula for calculating the comprehensive quality score in dynamic data fusion is:

[0028] Based on sensor data confidence and timeliness , dynamically calculate the weight of each data source , integrated output steel component comprehensive quality score :

[0029]

[0030] Where, are weight coefficients, is the difference between the data collection time and the current time, is the maximum allowed time difference, is the normalized raw sensor data.

[0031] Furthermore, the specific steps of step S2, dynamic data fusion are as follows:

[0032] Edge computing nodes execute algorithms in real time to generate ,when Below the preset threshold , marked as abnormal data, triggering local alarms on edge nodes;

[0033] This design dynamically calculates the weight of each sensor data (combining confidence and timeliness) and generates a comprehensive quality score for the steel structure. , detect abnormal data in real time, improve the accuracy of quality assessment, and adapt to the complex and changeable status of steel components in construction projects.

[0034] Furthermore, the step S3, blockchain and contract deployment specifically includes the following steps:

[0035] Step S31, data hash encryption and block generation: perform SHA-256 hash calculation on the merged data packet to generate a unique data fingerprint :

[0036] ;

[0037] Divide into independent blocks according to procurement, production, installation, operation and maintenance, each block contains , the hash of the previous block, timestamp and digital signature of the operator;

[0038] Step S32: Smart contract rule design: Design construction engineering field rules, specifically:

[0039] When installation deviation When the "installation correction" contract is triggered, the responsible team is automatically notified and the subsequent construction process is frozen;

[0040] When the ambient humidity If the condition lasts for more than 2 hours, the "anti-corrosion treatment" contract will be triggered and the operation and maintenance task will be pushed to the maintenance unit;

[0041] The contract code is deployed through the alliance chain, supporting simultaneous verification by multiple nodes including the construction party, the supervisor, and the owner;

[0042] This design stores the integrated data on the blockchain after hash encryption to ensure that the data cannot be tampered with; it also deploys smart contracts to automatically execute quality control rules in the construction engineering field (such as triggering a correction process when installation deviation exceeds the limit); and realizes multi-party collaboration through the alliance chain architecture to ensure data transparency and process automation.

[0043] Furthermore, the step S4, tracing and visualization, specifically includes the following steps:

[0044] Step S41, UID-based quick retrieval: After the user enters the UID of the steel component, the system extracts the full life cycle data chain from the blockchain through the hash index, including the original sensor data, fusion results , smart contract execution records;

[0045] Step S42, BIM model integration and visualization: Associating blockchain data with a BIM model (e.g., Revit) to highlight problematic component locations (e.g., installation deviation areas) in the 3D model;

[0046] Display through timeline view Change trends, combined with environmental parameters to analyze potential quality risks (such as high temperature causing material expansion);

[0047] This design uses a unique identifier (UID) to quickly retrieve full lifecycle data from the blockchain, and displays the status of steel components (such as highlighted deviation areas) through three-dimensional visualization of the BIM model. It also combines timeline analysis with the correlation between environmental parameters and quality scores to help users intuitively locate the root cause of the problem and improve decision-making efficiency in construction project management.

[0048] Furthermore, the step S5, the responsibility closed-loop optimization specifically includes the following steps:

[0049] Step S51, locating the responsible party: The system analyzes the smart contract log, automatically associates the operator of the abnormal data (such as the production batch person in charge, installation team ID), and generates a responsibility report;

[0050] Step S52: Process optimization feedback: Optimize weight coefficient based on historical contract trigger frequency Or adjust the threshold , forming quality control;

[0051] This design automatically associates the operator of abnormal data (such as the production manager, installation team) through the smart contract log to generate a responsibility report; at the same time, it dynamically optimizes the algorithm parameters (such as weight coefficient) based on historical data. ), form a quality control mechanism, and continuously improve the quality management level of steel components.

[0052] The present invention has the following beneficial effects:

[0053] 1. Through a dynamic weight allocation algorithm, the present invention enables the system to dynamically adjust the weight of each data source based on the timeliness and confidence of sensor data, taking into account the time decay effect of data and the reliability of acquisition equipment, ensuring that key parameters at different stages throughout the life cycle of steel components are scientifically reflected in the comprehensive quality score. This design improves the objectivity and accuracy of quality assessment. When there are conflicts in multi-source data or some data fails, the system can still maintain the reliability of the assessment results through dynamic adjustment, providing a solid data foundation for the triggering and decision-making of subsequent smart contracts, and avoiding the risk of misjudgment due to single data deviation.

[0054] 2. The present invention is based on the automated execution mechanism of smart contracts. When quality anomalies are detected, the system can immediately trigger predefined business processes without relying on human intervention. The contract rules are closely aligned with industry standards and steel component characteristics in the construction engineering field. For example, for typical problems such as installation deviations or environmental violations, disposal instructions are automatically generated and the relevant responsible parties are notified. At the same time, the tamper-proof nature of blockchain technology ensures the full chain of operation records. The triggering logic, execution process and responsible parties of any abnormal event can be quickly traced back through the data chain. This combination of automation and trusted traceability shortens the response cycle from problem discovery to rectification and implementation, while reducing management costs caused by human omissions or shirking of responsibility.

[0055] 3. Through the alliance chain architecture and authority hierarchical design, the present invention realizes the real-time synchronization and collaborative management of data of multiple parties such as the construction party, supervision unit, and material suppliers. Each participant can only access the data related to his or her responsibilities, which not only protects business privacy but also ensures the transparency and auditability of data modifications through the consensus mechanism of the blockchain. In the process of steel component quality traceability, it solves the problem of data silos across links. For example, the process parameters in the production stage and the monitoring data in the operation and maintenance stage can be seamlessly linked, supporting multiple parties to collaboratively analyze the root causes of problems based on the same data source. This design enhances mutual trust among project participants and provides a scalable technical framework for quality management of complex engineering projects.

[0056] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 This is a framework diagram of the blockchain-based full life cycle quality traceability system for supporting steel components of the present invention;

[0059] Figure 2 Schematic diagram of the alliance chain architecture of the blockchain-based full life cycle quality traceability system for supporting steel components of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0061] See also Figure 1-2 As shown, the present invention is a full life cycle quality traceability system for supporting steel components based on blockchain, including a data acquisition module, an edge computing and data processing module, a blockchain storage and smart contract module, an application service and visualization module, and a responsibility tracing and optimization feedback module;

[0062] The data acquisition module, edge computing and data processing module, blockchain storage and smart contract module, application service and visualization module, and responsibility tracing and optimization feedback module are connected in sequence. The output end of the blockchain storage and smart contract module is unidirectionally connected to the input end of the responsibility tracing and optimization feedback module, and the output end of the responsibility tracing and optimization feedback module is unidirectionally connected to the input end of the edge computing and data processing module.

[0063] The data acquisition module is used to collect physical parameters and environmental data of raw materials, production, installation and operation and maintenance in real time through sensors deployed at various stages of the steel structure's life cycle;

[0064] The edge computing and data processing module is used to execute a dynamic weight allocation algorithm on the local node, integrate multi-source data and generate a comprehensive quality score for steel components, thereby achieving real-time anomaly detection and preliminary alarms;

[0065] The blockchain storage and smart contract module is used to encrypt and store full life cycle data using blockchain technology and deploy smart contracts to automatically execute construction project quality rules;

[0066] The application service and visualization module is used to provide a visualization interface based on the BIM model, supporting data retrieval throughout the life cycle of steel components, quality trend analysis, and highlighting of problem locations;

[0067] The responsibility tracing and optimization feedback module is used to automatically locate the responsible party through the smart contract log, optimize the algorithm parameters and process rules, and form a closed-loop quality control system.

[0068] The traceability system workflow is as follows:

[0069] Step S1: Data collection and standardization:

[0070] Step S11, construction engineering field adaptation: Deploy multiple types of IoT sensors during the raw material procurement, production and processing, transportation, installation, and operation and maintenance stages of steel components, specifically:

[0071] Raw material stage: RFID tags are embedded on the surface of steel to record parameters including material batch, supplier, and chemical composition;

[0072] Production stage: Install optical fiber strain sensors on processing equipment to monitor the stress and temperature of steel components in real time during processing;

[0073] Installation phase: Deploy laser displacement sensors at the construction site to measure the installation position deviation of steel components and compare them with the BIM model coordinates;

[0074] Operation and maintenance phase: Install temperature and humidity sensors at the construction site to collect environmental parameters such as humidity and temperature;

[0075] Step S12: Data standardization: Format the multi-source heterogeneous data into a unified JSON structure with fields including: sensor type, timestamp, value, unit, and operator ID; define thresholds for key parameters of steel components based on the characteristics of the construction engineering field;

[0076] Throughout the lifecycle of steel structures, raw data collected by various sensors (such as material composition, processing stress, and installation deviation) must be standardized to form structured data. This standardized data provides the fundamental input for subsequent dynamic weight allocation. By incorporating parameter thresholds unique to the construction engineering field (such as safety strain values) into standardized rules, data quality is ensured to meet actual project requirements, laying the foundation for the implementation of multi-source data fusion algorithms.

[0077] Step S2: Dynamic data fusion:

[0078] Step S2: The formula for calculating the comprehensive quality score in dynamic data fusion is:

[0079] Based on sensor data confidence and timeliness , dynamically calculate the weight of each data source , integrated output steel component comprehensive quality score :

[0080]

[0081] Where, are weight coefficients, is the difference between the data collection time and the current time, is the maximum allowed time difference, is the normalized raw sensor data.

[0082] The specific steps of step S2, dynamic data fusion are:

[0083] Edge computing nodes execute algorithms in real time to generate ,when Below the preset threshold , marked as abnormal data, triggering local alarms on edge nodes;

[0084] Step S3: Blockchain and contract deployment:

[0085] Step S31, data hash encryption and block generation: perform SHA-256 hash calculation on the merged data packet to generate a unique data fingerprint :

[0086] ;

[0087] Divide into independent blocks according to procurement, production, installation, operation and maintenance, each block contains , the hash of the previous block, timestamp and digital signature of the operator;

[0088] Step S32: Smart contract rule design: Design construction engineering field rules, specifically:

[0089] When installation deviation When the "installation correction" contract is triggered, the responsible team is automatically notified and the subsequent construction process is frozen;

[0090] When the ambient humidity If the condition lasts for more than 2 hours, the "anti-corrosion treatment" contract will be triggered and the operation and maintenance task will be pushed to the maintenance unit;

[0091] The contract code is deployed through the alliance chain, supporting simultaneous verification by multiple nodes including the construction party, the supervisor, and the owner;

[0092] The dynamic weighting algorithm calculates the weight ratio of each data source in real time based on the credibility and timeliness of sensor data, generating a comprehensive quality score for the steel component. This score not only reflects the current status of the component but also identifies potential anomalies. Based on this score, the system encrypts the fused data packet to generate a unique data fingerprint, which is then stored on the blockchain in blocks divided by each link. Furthermore, the anomaly flags triggered by the quality score are directly linked to the engineering rules preset in the smart contract (such as installation deviation exceeding the limit or ambient humidity exceeding the standard), providing a decision-making basis for automated process control.

[0093] Step S4: Tracing and visualization:

[0094] Step S41, UID-based quick retrieval: After the user enters the UID of the steel component, the system extracts the full life cycle data chain from the blockchain through the hash index, including the original sensor data, fusion results , smart contract execution records;

[0095] Step S42, BIM model integration and visualization: Associating blockchain data with a BIM model (e.g., Revit) to highlight problematic component locations (e.g., installation deviation areas) in the 3D model;

[0096] Display through timeline view Change trends, combined with environmental parameters to analyze potential quality risks (such as high temperature causing material expansion);

[0097] The full lifecycle data stored in the blockchain is quickly retrieved through a unique identifier (UID). When a user queries, the system extracts the data chain of each link from the chain (including raw data, integrated scores, and contract execution records) and dynamically associates this information with the BIM model. The 3D visualization interface intuitively presents the location of problematic components and the changing trends of quality scores. Combined with environmental parameter analysis, it helps users quickly understand the engineering significance behind the data (such as high temperature causing material deformation), forming a closed-loop mapping from data storage to actual scenarios.

[0098] Step S5: Responsibility closed-loop optimization:

[0099] Step S51, locating the responsible party: The system analyzes the smart contract log, automatically associates the operator of the abnormal data (such as the production batch person in charge, installation team ID), and generates a responsibility report;

[0100] Step S52: Process optimization feedback: Optimize weight coefficient based on historical contract trigger frequency Or adjust the threshold , forming quality control;

[0101] Quality anomalies revealed by visual analysis and smart contract execution records jointly point to specific responsible parties (such as the person in charge of the production batch or the installation team); the system automatically generates responsibility reports by parsing the contract logs to identify the problem links and related parties; at the same time, the accumulation of historical data provides a basis for process optimization, such as adjusting the weight distribution strategy or revising the quality threshold, thus forming a continuous optimization mechanism from problem tracing to system improvement.

[0102] A specific application of this embodiment is:

[0103] 1. Hardware deployment and data collection:

[0104] Steel component information: Steel arch frame number is UID-TS-20231001; Material strength , installation allowable deviation , ambient humidity threshold ;

[0105] Sensor configuration and data collection:

[0106] Raw material stage: RFID tag (Impinj R2000) records steel batch as M-20230915, carbon content , the supplier is "Baosteel Group";

[0107] Production stage: Fiber optic strain sensors (Sensuron S2000) monitor processing stress ,temperature ℃;

[0108] Installation phase: Laser displacement sensor (Keyence LK-G5000) measures installation position deviation , the theoretical coordinates of the BIM model are ( )m;

[0109] Operation and maintenance phase: Temperature and humidity sensors (Sensirion SHT45) collect environmental data ℃, duration Hour;

[0110] 2. Dynamic weight allocation and data fusion calculation:

[0111] Data confidence and timeliness calculation:

[0112] Determined from the sensor calibration certificate: RFID tag confidence , fiber optic sensor confidence , laser displacement sensor confidence , temperature and humidity sensor confidence ;

[0113] The difference between data collection time and current time is: Raw material data timeliness hours, production data timeliness hours, installation data timeliness Hours, timeliness of operation and maintenance data Hour;

[0114] Dynamic weight calculation uses the formula:

[0115]

[0116] in, Hour;

[0117] RFID weight:

[0118] ;

[0119] Fiber optic sensor weight:

[0120] ;

[0121] Laser displacement sensor weight:

[0122] ;

[0123] Temperature and humidity sensor weight:

[0124] ;

[0125] Data normalization and comprehensive score calculation:

[0126] Normalization formula: (The closer to 1, the better);

[0127] Production Stress Rating: ;

[0128] Installation Deviation Score: ;

[0129] Environmental humidity rating: ;

[0130] Overall quality rating:

[0131]

[0132] Judgment result: preset threshold ,because , marked as “normal”, but the humidity data triggers the subsequent smart contract;

[0133] 3. Blockchain on-chain and smart contract execution:

[0134] Data hashing and block generation:

[0135] Packet hash calculation:

[0136] ;

[0137] Generate hash value: 3a7d8f1e...; Generate operation and maintenance phase block, including the previous block hash 0x89a3b2..., timestamp, and supervisor's digital signature;

[0138] Smart contract trigger:

[0139] Rule 1: Ambient humidity And lasts for more than 2 hours → trigger the "Anti-corrosion Treatment Contract";

[0140] Actual data: Hour;

[0141] Contract execution: Automatically push tasks to maintenance units, requiring anti-corrosion coating inspections to be completed within 12 hours;

[0142] 4. Data traceability and visualization:

[0143] Full life cycle data link query:

[0144] Enter UID-TS-20231001, and the system returns:

[0145] Raw material data: carbon content, supplier, production batch;

[0146] Production Data: Stress , weight ;

[0147] Installation data: Deviation ,score ;

[0148] Operation and maintenance data: humidity alarm records and contract execution logs;

[0149] BIM model integration:

[0150] The Revit model highlights the steel arch position and marks the installation deviation as 4.2 mm (yellow warning).

[0151] Timeline display Changes: Production phase 1.8 → Installation phase 2.1 → Operation and maintenance phase 2.49;

[0152] 5. Responsibility tracing and optimization feedback:

[0153] Responsible party location, humidity alarm associated with the operation and maintenance team (number M-EP-05), the system generates a report and notifies the responsible person;

[0154] Parameter optimization, adjust the weight coefficient according to historical data:

[0155] Production stage weight Increased to 0.65 due to multiple data confidence levels being higher than expected.

[0156] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0157] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The blockchain-based full life cycle quality traceability system for supporting steel components is characterized by: The traceability system includes a data acquisition module, an edge computing and data processing module, a blockchain storage and smart contract module, an application service and visualization module, and a responsibility tracing and optimization feedback module; The data acquisition module, edge computing and data processing module, blockchain storage and smart contract module, application service and visualization module, and responsibility tracing and optimization feedback module are connected in sequence. The output end of the blockchain storage and smart contract module is unidirectionally connected to the input end of the responsibility tracing and optimization feedback module, and the output end of the responsibility tracing and optimization feedback module is unidirectionally connected to the input end of the edge computing and data processing module. The traceability system workflow is as follows: Step S1, data collection and standardization: deploy multiple types of sensors at each stage of steel structure construction to collect key parameters and unify data formats to ensure data compatibility throughout the construction project cycle; Step S2, dynamic data fusion: dynamically assign sensor data weights and calculate comprehensive quality scores; Step S3, blockchain and contract deployment: Encrypt data to generate blocks, design smart contracts based on engineering specifications, and automatically trigger construction process control and exception response; Step S4, traceability and visualization: using unique identifiers to quickly retrieve full life cycle data and integrating BIM models to achieve three-dimensional visualization and trend analysis; Step S5: Responsibility closed-loop optimization: automatically locate the responsible party and generate a report, and optimize the algorithm parameters based on historical data.

2. The blockchain-based full life cycle quality traceability system for supporting steel components according to claim 1 is characterized in that: The data acquisition module is used to collect physical parameters and environmental data of raw materials, production, installation and operation and maintenance in real time through sensors deployed at various stages of the steel structure's life cycle; The edge computing and data processing module is used to execute a dynamic weight allocation algorithm on the local node, integrate multi-source data and generate a comprehensive quality score for steel components, thereby achieving real-time anomaly detection and preliminary alarms; The blockchain storage and smart contract module is used to encrypt and store full life cycle data using blockchain technology and deploy smart contracts to automatically execute construction project quality rules; The application service and visualization module is used to provide a visualization interface based on the BIM model, supporting data retrieval throughout the life cycle of steel components, quality trend analysis, and highlighting of problem locations; The responsibility tracing and optimization feedback module is used to automatically locate the responsible party through the smart contract log, optimize the algorithm parameters and process rules, and form a closed-loop quality control system.

3. The blockchain-based full life cycle quality traceability system for supporting steel components according to claim 1 is characterized in that: The step S1, data collection and standardization, includes the following steps: Step S11, construction engineering field adaptation: Deploy multiple types of IoT sensors during the raw material procurement, production and processing, transportation, installation, and operation and maintenance stages of steel components, specifically: Raw material stage: RFID tags are embedded on the surface of steel to record parameters including material batch, supplier, and chemical composition; Production stage: Install optical fiber strain sensors on processing equipment to monitor the stress and temperature of steel components in real time during processing; Installation phase: Deploy laser displacement sensors at the construction site to measure the installation position deviation of steel components and compare them with the BIM model coordinates; Operation and maintenance phase: Install temperature and humidity sensors at the construction site to collect environmental parameters such as humidity and temperature; Step S12, data standardization processing: uniformly format multi-source heterogeneous data into a JSON structure, with fields including: sensor type, timestamp, value, unit, operator ID; based on the characteristics of the construction engineering field, define the thresholds of key parameters of steel components.

4. The blockchain-based full life cycle quality traceability system for supporting steel components according to claim 1 is characterized in that: In step S2, the formula for calculating the comprehensive quality score in dynamic data fusion is: Based on sensor data confidence and timeliness , dynamically calculate the weight of each data source , integrated output steel component comprehensive quality score : Where, are weight coefficients, is the difference between the data collection time and the current time, is the maximum allowed time difference, is the normalized raw sensor data.

5. The blockchain-based full life cycle quality traceability system for supporting steel components according to claim 1 is characterized in that: The specific steps of step S2, dynamic data fusion are: Edge computing nodes execute algorithms in real time to generate ,when Below the preset threshold , marked as abnormal data, triggering a local alarm on the edge node.

6. The blockchain-based full life cycle quality traceability system for supporting steel components according to claim 1 is characterized in that: The step S3, blockchain and contract deployment, specifically includes the following steps: Step S31, data hash encryption and block generation: perform SHA-256 hash calculation on the merged data packet to generate a unique data fingerprint : ； Divide into independent blocks according to procurement, production, installation, operation and maintenance, each block contains , the hash of the previous block, timestamp and digital signature of the operator; Step S32: Smart contract rule design: Design construction engineering field rules, specifically: When installation deviation When the "Installation Correction" contract is triggered, the responsible team is automatically notified and the subsequent construction process is frozen; When the ambient humidity If the condition lasts for more than 2 hours, the "anti-corrosion treatment" contract will be triggered and the operation and maintenance task will be pushed to the maintenance unit. The contract code is deployed through the alliance chain, supporting simultaneous verification by multiple nodes including the construction party, the supervisor, and the owner.

7. The blockchain-based full life cycle quality traceability system for supporting steel components according to claim 1 is characterized in that: The step S4, tracing and visualization, specifically includes the following steps: Step S41, UID-based quick retrieval: After the user enters the UID of the steel component, the system extracts the full life cycle data chain from the blockchain through the hash index, including the original sensor data, fusion results , smart contract execution records; Step S42: BIM model integration and visualization: Associating blockchain data with the BIM model and highlighting the location of problematic components in the three-dimensional model; Display through timeline view Change trends and analyze potential quality risks in combination with environmental parameters.

8. The blockchain-based full life cycle quality traceability system for supporting steel components according to claim 1 is characterized in that: The step S5, the responsibility closed-loop optimization specifically includes the following steps: Step S51: Identify responsible parties: The system analyzes smart contract logs, automatically associates the operator of abnormal data, and generates a responsibility report; Step S52: Process optimization feedback: Optimize weight coefficient based on historical contract trigger frequency Or adjust the threshold , forming quality control.

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