Building raw material traceability tracking management system based on internet of things

Abstract

The application discloses a building raw material traceability tracking management system based on Internet of Things, and belongs to the technical field of quality management. The system comprises a blockchain generation module, a data binding module, a verification triggering module, a strategy adjustment module, a delayed settlement module, a judgment result generation module and a report generation module. The blockchain generation module is used for generating a unique blockchain ID; the data binding module is used for generating traceability data and storing the data in a chain for evidence; the verification triggering module is used for triggering and starting a transport anomaly verification mechanism; the strategy adjustment module is used for adjusting an execution strategy; the delayed settlement module is used for submitting a quality objection request; the judgment result generation module is used for generating a responsibility judgment result; and the report generation module is used for generating a dynamic traceability report. The application ensures the authenticity of terminal data by establishing a time lock delayed settlement mechanism and a multi-party supervision system, and realizes intelligent verification of abnormal conditions in combination with a blockchain oracle, so that the effects of full-chain credible traceability and intelligent flexible control are achieved, and the problems of material switching, acceptance fraud and intelligent contract execution rigidity caused by the lack of an effective verification mechanism in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of quality management technology, and in particular to an Internet of Things-based traceability and management system for building raw materials. Background Technology

[0002] With the continuous expansion of infrastructure construction, the requirements for quality and safety management in building projects are becoming increasingly stringent. The quality of building materials directly affects the structural safety of engineering projects, and quality control of raw materials during transportation is particularly critical. Traditional management of building material transportation mainly relies on manual records and paper-based document transfer, which suffers from problems such as lack of transparency, easy loss of records, and difficulty in tracing problems. Although some projects have attempted to use digital means such as GPS positioning and electronic locks for management, the lack of a comprehensive collaborative mechanism makes it difficult to achieve reliable traceability from production to construction.

[0003] The above-disclosed technical solutions have at least the following technical problems: Traditional methods, lacking an effective verification mechanism linking the physical and digital worlds, can lead to situations where materials are swapped during loading while digital identification remains intact, resulting in genuine labels on counterfeit materials and rendering the traceability system meaningless. Furthermore, the lack of multi-party oversight during acceptance testing allows unqualified personnel to release substandard materials due to vested interests, granting them legitimacy. Additionally, the rigidity of smart contract execution rules makes it difficult to differentiate between force majeure events and human error during transportation, leading to numerous disputes in practice and reducing the willingness of all parties to use the system. Summary of the Invention

[0004] This application provides an IoT-based building raw material traceability and management system, which solves the technical problems of material substitution, acceptance fraud, and rigid smart contract execution caused by the lack of effective verification mechanisms in the prior art, and realizes full-chain reliable traceability and intelligent flexible control from source to end.

[0005] This application provides an Internet of Things-based building raw material traceability and management system, including: a blockchain generation module: used to collect raw material production data and generate a unique blockchain ID for each batch of raw materials; Data binding module: used to bind the raw material production data with the blockchain ID, and generate raw material production traceability data for on-chain storage; Verification trigger module: Used to monitor and collect transportation status data on the blockchain in real time. When the transportation status data deviates from the preset threshold, it triggers and starts the transportation anomaly verification mechanism through the blockchain oracle. Strategy Adjustment Module: Used to acquire verification data, generate verification results, and dynamically adjust the execution strategy of smart contracts based on the verification results; Delayed Settlement Module: Used to initiate the time-locked delayed settlement mechanism, allowing multiple parties to submit quality objection requests during the delayed settlement period; Judgment Result Generation Module: Used to acquire raw material quality inspection data, and when an objection is received, to perform responsibility determination by fusion of multi-source data and generate responsibility determination results; Report generation module: Used to construct spatiotemporal data maps and generate dynamic source tracing reports that include anomaly handling processes and responsibility division.

[0006] Furthermore, the step of generating a unique blockchain ID for each batch of raw materials includes: Batch number of raw materials collected And production parameters, and perform hash operations on the production parameters to obtain ; Get the current system timestamp And generate a random number ; Will , , , The data is chained together, and a blockchain ID for this batch of materials is generated. : ; in, This represents a string concatenation operation, the... This serves as the unique identifier for this batch of raw materials throughout the entire traceability chain.

[0007] Furthermore, the steps to initiate a transport anomaly verification mechanism via a blockchain oracle include: When the device detects an anomaly, it sends a verification request containing the anomaly type and spatiotemporal information to the blockchain oracle. Blockchain oracles acquire external data such as real-time traffic conditions and weather information; The overall credibility of transportation anomalies is obtained through the formula for calculating the overall credibility of transportation anomalies. : ; In the formula, To assess the overall credibility of transportation anomalies, The number of external data sources obtained. For the first The weight of each data source, For time matching functions, The number of location sampling points. The shortest spherical distance between the GPS location of the transportation vehicle and the area of ​​impact of the event as recorded by authoritative data. The number of blockchain nodes that reach a consensus on this anomaly. The total number of nodes participating in the consensus; When the overall credibility of a transportation anomaly is not less than the preset credibility threshold, the transportation anomaly is determined to be caused by a real external event, and the smart contract automatically extends the delivery time.

[0008] Furthermore, the steps for dynamically adjusting the execution strategy of the smart contract based on the verification results include: Obtain the verification results of transportation anomalies generated by the blockchain oracle, including the overall credibility of the transportation anomalies. Values ​​and their corresponding exception types; when If the value is not less than the preset first confidence threshold, it is determined to be an anomaly caused by force majeure, and the smart contract automatically executes the first adjustment strategy: extending the estimated delivery time. ; in For time adjustment factor, As the reference value for maximum credibility, Based on the basic transportation time; when If the value is less than the first confidence threshold and not less than the second confidence threshold, it is determined to be an anomaly caused by mixed factors, and the smart contract executes the second adjustment strategy: proportionally exempting the transporter from liability for breach of contract, with the exemption ratio being... ; in For liability exemption coefficient, This is the minimum credibility threshold; when If the value is less than the second confidence threshold, it is determined to be an anomaly caused by the subjective factors of the transporter, and the smart contract maintains the original liability for breach of contract clauses; The adjusted smart contract execution strategy is broadcast to all relevant nodes through the blockchain network, the contract status is updated, and the adjustment log is recorded.

[0009] Furthermore, the steps to initiate the time-locked delay settlement mechanism include: Calculate the time lock period based on the transportation distance and total value of the raw materials for this batch. : ; In the formula, This refers to the actual delayed settlement time. Based on the delay time, This is the actual distance of this transport. This is a reference value for the maximum transport distance recorded. This represents the total value of the batch of raw materials. This is a reference value for the highest single batch of materials recorded. , These are adjustment coefficients for distance and value, respectively; After the raw materials are delivered and confirmed, the payment is automatically locked via a smart contract for a specified period. ; exist During the period, digital signatures can be made and quality objections can be initiated. Once the objection request is recorded on the blockchain, it will trigger a quality review process and temporarily freeze the settlement.

[0010] Furthermore, the steps for determining responsibility in multi-source data fusion include: The liability determination coefficient is obtained through the formula for calculating the liability determination coefficient. : ; In the formula, For quantifying the responsibility determination coefficient, This refers to the number and types of sensors monitored during transportation. For the first Standardized abnormal readings of each sensor For the first The responsibility of each sensor affects the weighting. The number of external environmental parameters that affect material quality. For the first oracle obtained from the oracle Quantified values ​​of environmental parameters For the first The weights of each environmental parameter, This refers to the number of abnormal operations recorded during transportation. This represents the total number of operations during the transportation process. For operational influencing factors, weighting coefficients and impact factors Determined by principal component analysis; The obtained responsibility determination coefficient is compared with the preset responsibility threshold. If the responsibility determination coefficient is not less than the preset production party responsibility threshold, the responsibility is determined to belong to the production party. If the liability determination coefficient is less than the liability threshold of the transporter, then the liability is determined to belong to the transporter. If the liability determination coefficient is not less than the liability threshold of the transporter and is less than the liability threshold of the producer, then it is considered joint liability.

[0011] Furthermore, the steps for generating a dynamic source tracing report that includes the exception handling process and responsibility allocation include: Using blockchain ID as the primary key, a spatiotemporal relational graph data structure is constructed; Retrieve all on-chain records associated with the blockchain ID, highlight the time points where abnormal events occurred, and attach the oracle verification results at the time, the triggered smart contract strategy change records, and the state change history during the time lock period.

[0012] Furthermore, the time matching function is: ; In the formula, The timestamp for IoT devices reporting anomalies. The timestamp of the event recorded by the authoritative data source. For time tolerance parameters, It is a natural constant.

[0013] Furthermore, the standardized abnormal readings of the sensor are calculated as follows: ; In the formula, For sensors This reading, This represents the average historical readings of the sensor under similar transportation conditions. This represents the standard deviation of the corresponding historical readings. This is an out-of-bounds indication function, used when the sensor reading... Exceeding the specified safety threshold range for the raw material When the condition is met, the function takes the value 2; otherwise, it takes the value 1.

[0014] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: By establishing a time-locked delayed settlement mechanism and setting a reasonable waiting period after material acceptance, the problem of substandard materials slipping through due to unilateral rapid confirmation is effectively prevented, creating a time window for subsequent quality supervision. Furthermore, based on this waiting period, a multi-party digital signature confirmation mechanism is introduced, creating a mutually constraining acceptance environment for supervision, construction, and other parties. By deeply coupling blockchain oracles with smart contracts, the system can automatically identify the true causes of transportation anomalies, providing a reliable basis for subsequent liability determination. Furthermore, based on the credibility assessment results, intelligent adjustments to contract execution strategies are made, avoiding disputes that may arise from rigid rules. By constructing a full-chain traceability data system, data from each stage is organically linked, providing a data foundation for rapid location of quality problems and liability determination. Furthermore, the liability determination method through multi-source data fusion, effectively combined with the time-lock mechanism, ensures that liability determination can be completed quickly within the delayed settlement period. Attached Figure Description

[0015] Figure 1This is a schematic diagram of a building raw material traceability and tracking management system based on the Internet of Things, provided as an embodiment of this application. Detailed Implementation

[0016] This application provides an IoT-based traceability and management system for building raw materials, which solves the technical problems in the prior art such as the inability to ensure the real binding of physical entities and digital identities, the lack of effective supervision in the acceptance process, and the rigid handling mechanism for transportation anomalies. By establishing a time-locked delayed settlement and multi-party supervision mechanism to ensure the authenticity of terminal data, and introducing a blockchain oracle to realize intelligent verification and flexible handling of anomalies, it achieves full-chain trusted traceability and intelligent flexible management and control.

[0017] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0018] like Figure 1 As shown, this application embodiment provides an Internet of Things-based building raw material traceability and management system, including: a blockchain generation module: used to collect raw material production data containing batch number, strength grade and durability parameters at the raw material production end, and generate a unique blockchain ID for each batch of raw materials that integrates timestamp, batch number, production parameter hash and random number; Data binding module: used to bind the raw material production data with the blockchain ID, generate raw material production traceability data and store it on the blockchain, which can ensure the initial data binding; Verification trigger module: Used to monitor and collect transportation status data in real time through IoT devices at the transportation end and upload it to the blockchain. When the transportation status data is detected to deviate from the preset threshold, the transportation anomaly verification mechanism through the blockchain oracle is triggered and started. Internet of Things (IoT) devices include: GPS, temperature and humidity sensors, smart electronic locks, vibration sensors, and radio frequency identification (RFID) tags.

[0019] When the transportation status data (including location coordinates, temperature readings, humidity levels, vibration intensity, and speed indicators) deviates from the preset threshold, an alarm will be triggered and the event will be identified as an abnormal event.

[0020] Strategy Adjustment Module: Used to obtain verification data from authoritative off-chain data sources, generate verification results, and dynamically adjust the execution strategy of smart contracts based on the verification results; Authoritative external data sources include: traffic management department APIs for obtaining road traffic control and accident information; meteorological bureau APIs for obtaining real-time weather conditions and severe weather warnings; map service provider APIs for obtaining real-time road conditions and congestion information; and emergency management department APIs for obtaining natural disaster warning information.

[0021] The steps to obtain verification data and generate verification results are as follows: query data from authoritative off-chain data sources through oracles (such as calling weather services through the meteorological bureau API), the queried data is aggregated and verified by multiple independent nodes to ensure consistency; finally, the verification results are generated through a consensus mechanism (such as majority voting) and broadcast to the blockchain through a smart contract.

[0022] Delayed settlement module: When raw materials are received at the construction end, a time-locked delayed settlement mechanism is initiated, which is determined by the transportation distance and the value of the materials. During the delayed settlement period, multiple parties (such as contractors, customers, and supervisors) are allowed to submit quality objection requests. Judgment Result Generation Module: Used to acquire raw material quality inspection data. When an objection is received, it integrates the raw material production traceability data, transportation status data, and quality inspection data to perform multi-source data fusion responsibility determination and generate responsibility determination result. Report generation module: Based on raw material production traceability data, transportation status data, verification results and responsibility determination results, it constructs a spatiotemporal data map, generates a dynamic traceability report that includes the anomaly handling process and responsibility division, and provides blockchain-based penetrating query and hash verification.

[0023] Furthermore, the step of generating a unique blockchain ID for each batch of raw materials includes: The batch number of raw materials is automatically collected using RFID readers or barcode scanners. And production parameters, and perform hash operations on the production parameters to obtain ; Get the current system timestamp And generate a random number ; Will , , , The data is chained together, and a blockchain ID for this batch of materials is generated using the SHA-256 hash algorithm. : ; in, This represents a string concatenation operation, the... This serves as the unique identifier for this batch of raw materials throughout the entire traceability chain.

[0024] Furthermore, the steps to initiate a transport anomaly verification mechanism via a blockchain oracle include: When the IoT device at the transportation end detects a data anomaly, it sends a verification request containing the anomaly type and spatiotemporal information to the blockchain oracle. Blockchain oracles obtain real-time traffic conditions and weather information from authoritative off-chain data sources. The overall credibility of transportation anomalies is obtained through the formula for calculating the overall credibility of transportation anomalies. : ; In the formula, To assess the overall credibility of transportation anomalies, The number of external data sources obtained. For the first The weights of each data source are determined using the Analytic Hierarchy Process (AHP). This is a time-matching function used to measure the consistency between the time of an anomaly report and the time of the authoritative event. The number of location sampling points. The shortest spherical distance between the GPS location of the transportation vehicle and the area of ​​impact of the event as recorded by authoritative data. The number of blockchain nodes that reach a consensus on this anomaly. The total number of nodes participating in the consensus; Determining weights using the Analytic Hierarchy Process (AHP) involves the following steps: First, construct a hierarchical structure to decompose the problem into objectives, criteria, and alternatives; second, perform pairwise comparisons, using a 1-9 scale matrix to assess relative importance, then derive eigenvectors to obtain weights, and check the consistency ratio (less than 0.1 is considered acceptable); finally, obtain the final priority by aggregating the weights.

[0025] Authoritative events refer to verifiable external disruptions in the supply chain, such as traffic congestion or weather changes. The authority of these events is determined by the credibility of their source: if they originate from a government agency or a certified API, they are authoritative; otherwise, they are not, confirmed through cross-validation of multiple data sources.

[0026] When the overall credibility of a transportation anomaly is not less than the preset credibility threshold, the transportation anomaly is determined to be caused by a real external event, and the smart contract automatically extends the delivery time.

[0027] Furthermore, the steps for dynamically adjusting the execution strategy of the smart contract based on the verification results include: Obtain the verification results of transportation anomalies generated by the blockchain oracle, including the overall credibility of the transportation anomalies. Values ​​and their corresponding exception types; when If the value is not less than the preset first confidence threshold, it is determined to be an anomaly caused by force majeure, and the smart contract automatically executes the first adjustment strategy: extending the estimated delivery time. ; in For time adjustment factor, As the reference value for maximum credibility, Based on the basic transportation time; when If the value is less than the first confidence threshold and not less than the second confidence threshold, it is determined to be an anomaly caused by mixed factors, and the smart contract executes the second adjustment strategy: proportionally exempting the transporter from liability for breach of contract, with the exemption ratio being... ; in For liability exemption coefficient, This is the minimum credibility threshold; when If the value is less than the second confidence threshold, it is determined to be an anomaly caused by the subjective factors of the transporter, and the smart contract maintains the original liability for breach of contract clauses; The adjusted smart contract execution strategy is broadcast to all relevant nodes through the blockchain network, the contract status is updated, and the adjustment log is recorded.

[0028] Furthermore, the steps to initiate the time-locked delay settlement mechanism include: Calculate the time lock period based on the transportation distance and total value of the raw materials for this batch. : ; In the formula, This refers to the actual delayed settlement time. The base delay time is preset to 24 hours. This is the actual distance of this transport. The maximum transport distance reference value in the records is obtained by querying the logistics database. This represents the total value of the batch of raw materials. The reference value for the highest single batch of materials recorded was obtained by querying the logistics database. , The adjustment coefficients for distance and value were determined through multiple linear regression analysis. By conducting multiple linear regression analysis on historical dispute data, the impact of different transportation distances and cargo values ​​on potential risks was determined, and thus dynamically optimized. After the raw materials are confirmed to have been delivered, the smart contract will automatically lock the payment for a period of time. ; exist During the period, the authorized representative of the supervisor or the construction party can use their private key to digitally sign and initiate a quality objection. Once the objection request is recorded on the blockchain, it will trigger a quality review process and temporarily freeze the settlement.

[0029] Furthermore, the steps for determining responsibility in multi-source data fusion include: The liability determination coefficient is obtained through the formula for calculating the liability determination coefficient. : ; In the formula, For quantifying the responsibility determination coefficient, This refers to the number and types of sensors monitored during transportation. For the first Standardized abnormal readings of each sensor For the first The responsibility of each sensor affects the weighting. The number of external environmental parameters that affect material quality. For the first oracle obtained from the oracle Quantified values ​​of environmental parameters For the first The weights of each environmental parameter, This refers to the number of abnormal operations (such as emergency braking, abnormal opening of the container) recorded during transportation. This represents the total number of operations during the transportation process. For operational influencing factors, weighting coefficients and impact factors The historical quality problem data was analyzed using principal component analysis (PCA) to extract the influencing factors that led to the quality changes and their contribution. The obtained responsibility determination coefficient is compared with the preset responsibility threshold obtained from the historical database. If the responsibility determination coefficient is not less than the preset production party responsibility threshold, the responsibility is determined to belong to the production party. If the liability determination coefficient is less than the liability threshold of the transporter, then the liability is determined to belong to the transporter. If the liability determination coefficient is not less than the liability threshold of the transporter and is less than the liability threshold of the producer, then it is considered joint liability.

[0030] Furthermore, the steps for generating a dynamic source tracing report that includes the exception handling process and responsibility allocation include: Using blockchain ID as the primary key, a spatiotemporal relational graph data structure containing data from the production, transportation, and acceptance stages is constructed. The steps for constructing a spatiotemporal relational graph data structure include: creating graph nodes with blockchain IDs as the primary key to represent production, transportation, and acceptance stages; then connecting nodes with edges to label spatiotemporal attributes (such as timestamps and locations); storing immutable records on the blockchain; and finally analyzing the associations using graph algorithms (such as path optimization).

[0031] When generating a dynamic traceability report, all on-chain records associated with the blockchain ID are retrieved, and the time nodes where abnormal events occurred are highlighted, along with the oracle verification results at the time, the triggered smart contract strategy change records, and the state change history during the time lock period. The report provides an immutable QR code that users can scan to be redirected to a public blockchain explorer, where they can enter the transaction hash to publicly verify the authenticity and integrity of the data.

[0032] Furthermore, the time matching function is: ; In the formula, The timestamp for IoT devices reporting anomalies. The timestamp of the event recorded by the authoritative data source. This is a time tolerance parameter, dynamically adjusted through a Gaussian process regression. Its value is dynamically adjusted based on the total transportation distance; the longer the transportation distance, the better. The larger the value, the wider the allowable range of time deviation, in order to accommodate delays in information transmission during long-distance transportation. It is a natural constant; The time matching function is used to calculate the first time obtained by the oracle. The time when each data source reported an anomaly The actual time of the event as recorded by authoritative data The degree of matching of time deviations between them.

[0033] Furthermore, the standardized abnormal readings of the sensor are calculated as follows: ; In the formula, For sensors This reading, This represents the average historical readings of the sensor under similar transportation conditions. This represents the standard deviation of the corresponding historical readings. This is an out-of-bounds indication function, used when the sensor reading... Exceeding the specified safety threshold range for the raw material When the condition is met, the function takes the value 2; otherwise, it takes the value 1. The formula is used to quantify the severity of sensor readings deviating from their normal statistical range and to double-penalize readings that exceed the absolute safety threshold.

[0034] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0035] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0036] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0037] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0038] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0039] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A traceability and management system for building raw materials based on the Internet of Things, characterized in that, include: Blockchain generation module: used to collect raw material production data and generate a unique blockchain ID for each batch of raw materials; Data binding module: used to bind the raw material production data with the blockchain ID, and generate raw material production traceability data for on-chain storage; Verification trigger module: Used to monitor and collect transportation status data on the blockchain in real time. When the transportation status data deviates from the preset threshold, it triggers and starts the transportation anomaly verification mechanism through the blockchain oracle. Strategy Adjustment Module: Used to acquire verification data, generate verification results, and dynamically adjust the execution strategy of smart contracts based on the verification results; Delayed Settlement Module: Used to initiate the time-locked delayed settlement mechanism, allowing multiple parties to submit quality objection requests during the delayed settlement period; Judgment Result Generation Module: Used to acquire raw material quality inspection data, and when an objection is received, to perform responsibility determination by fusion of multi-source data and generate responsibility determination results; Report generation module: Used to construct spatiotemporal data maps and generate dynamic source tracing reports that include anomaly handling processes and responsibility division.

2. The IoT-based building material traceability and management system as described in claim 1, characterized in that, The steps for generating a unique blockchain ID for each batch of raw materials include: Batch number of raw materials collected And production parameters, and perform hash operations on the production parameters to obtain ; Get the current system timestamp And generate a random number ; Will , , , The data is chained together, and a blockchain ID for this batch of materials is generated. : ； in, This represents a string concatenation operation, the... This serves as the unique identifier for this batch of raw materials throughout the entire traceability chain.

3. The IoT-based building material traceability and management system as described in claim 1, characterized in that, The steps to initiate a transit anomaly verification mechanism via a blockchain oracle include: When the device detects an anomaly, it sends a verification request containing the anomaly type and spatiotemporal information to the blockchain oracle. Blockchain oracles acquire external data such as real-time traffic conditions and weather information; The overall credibility of transportation anomalies is obtained through the formula for calculating the overall credibility of transportation anomalies. : ； In the formula, To assess the overall credibility of transportation anomalies, The number of external data sources obtained. For the first The weight of each data source, For time matching functions, The number of location sampling points. The shortest spherical distance between the GPS location of the transportation vehicle and the area of ​​impact of the event as recorded by authoritative data. The number of blockchain nodes that reach a consensus on this anomaly. The total number of nodes participating in the consensus; When the overall credibility of a transportation anomaly is not less than the preset credibility threshold, the transportation anomaly is determined to be caused by a real external event, and the smart contract automatically extends the delivery time.

4. The IoT-based building material traceability and management system as described in claim 1, characterized in that, The steps for dynamically adjusting the execution strategy of a smart contract based on the verification results include: Obtain the verification results of transportation anomalies generated by the blockchain oracle, including the overall credibility of the transportation anomalies. Values ​​and their corresponding exception types; when If the value is not less than the preset first confidence threshold, it is determined to be an anomaly caused by force majeure, and the smart contract automatically executes the first adjustment strategy: extending the estimated delivery time. ; in For time adjustment factor, As the reference value for maximum credibility, Basic transportation time; when If the value is less than the first confidence threshold and not less than the second confidence threshold, it is determined to be an anomaly caused by mixed factors, and the smart contract executes the second adjustment strategy: proportionally exempting the transporter from liability for breach of contract, with the exemption ratio being... ; in For liability exemption coefficient, This is the minimum credibility threshold; when If the value is less than the second confidence threshold, it is determined to be an anomaly caused by the subjective factors of the transporter, and the smart contract maintains the original liability for breach of contract clauses; The adjusted smart contract execution strategy is broadcast to all relevant nodes through the blockchain network, the contract status is updated, and the adjustment log is recorded.

5. The IoT-based building material traceability and management system as described in claim 1, characterized in that, The steps to initiate the time-locked delay settlement mechanism include: Calculate the time lock period based on the transportation distance and total value of the raw materials for this batch. : ； In the formula, This refers to the actual delayed settlement time. Based on the delay time, This is the actual distance of this transport. This is a reference value for the maximum transport distance recorded. This represents the total value of the batch of raw materials. This is a reference value for the highest single batch of materials recorded. , These are adjustment coefficients for distance and value, respectively; After the raw materials are delivered and confirmed, the payment is automatically locked via a smart contract for a specified period. ; exist During the period, digital signatures can be made and quality objections can be initiated. Once the objection request is recorded on the blockchain, it will trigger a quality review process and temporarily freeze the settlement.

6. The IoT-based building material traceability and management system as described in claim 1, characterized in that, The steps for determining responsibility in multi-source data fusion include: The liability determination coefficient is obtained through the formula for calculating the liability determination coefficient. : ； In the formula, For quantifying the responsibility determination coefficient, This refers to the number and types of sensors monitored during transportation. For the first Standardized abnormal readings of each sensor For the first The responsibility of each sensor affects the weighting. The number of external environmental parameters that affect material quality. For the first oracle obtained from the oracle Quantified values ​​of environmental parameters For the first The weights of each environmental parameter, This refers to the number of abnormal operations recorded during transportation. This represents the total number of operations during the transportation process. For operational influencing factors, weighting coefficients and impact factors Determined by principal component analysis; The obtained responsibility determination coefficient is compared with the preset responsibility threshold. If the responsibility determination coefficient is not less than the preset production party responsibility threshold, the responsibility is determined to belong to the production party. If the liability determination coefficient is less than the liability threshold of the transporter, then the liability is determined to belong to the transporter. If the liability determination coefficient is not less than the liability threshold of the transporter and is less than the liability threshold of the producer, then it is considered joint liability.

7. The IoT-based building material traceability and management system as described in claim 1, characterized in that, The steps to generate a dynamic source tracing report that includes the exception handling process and responsibility allocation include: Using blockchain ID as the primary key, a spatiotemporal relational graph data structure is constructed; Retrieve all on-chain records associated with the blockchain ID, highlight the time points where abnormal events occurred, and attach the oracle verification results at the time, the triggered smart contract strategy change records, and the state change history during the time lock period.

8. The IoT-based building material traceability and management system as described in claim 3, characterized in that, The time matching function is: ； In the formula, The timestamp for IoT devices reporting anomalies. The timestamp of the event recorded by the authoritative data source. For time tolerance parameters, It is a natural constant.

9. The IoT-based building material traceability and management system as described in claim 6, characterized in that, The standardized abnormal readings of the sensor are calculated as follows: ； In the formula, For sensors This reading, This represents the average historical readings of the sensor under similar transportation conditions. This represents the standard deviation of the corresponding historical readings. This is an out-of-bounds indication function, triggered when the sensor reading... Exceeding the specified safety threshold range for the raw material When the condition is met, the function takes the value 2; otherwise, it takes the value 1.

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