Short stress path rolling mill sliding seat quality tracing and management method based on block chain

Abstract

The invention relates to the technical field of metal pressure processing, and discloses a block chain-based short stress path rolling mill sliding seat quality tracing and management method, which comprises the following specific steps: S1, block chain architecture design, S2, network layer node deployment, S3, application layer quality tracing module and S4, implementation. The S1 block chain architecture design comprises the steps of S101, data layer multi-dimensional data acquisition and S102, data chaining; s3, an application layer quality tracing module comprises an S301 predictive maintenance module and an S302 supply chain collaboration module; and S4, implementing the steps of S401 initialization stage (T0), S402 production and processing stage (T1-T2) and S403 use and maintenance stage (T3-Tn), quality traceability rising and full-link tampering-free recording: encrypting and chaining data such as sliding seat design parameters, processing technologies (cutting speed of 150m / min, quenching temperature of 850 DEG C + / -10 DEG C), assembly torque (50Nm + / -5%) and the like, ensuring that the data cannot be tampered by utilizing distributed storage characteristics of a block chain, and recording the whole link in a tampering-free manner. And a judicial-level evidence chain is provided for quality dispute.

Description

Technical Field

[0001] This invention relates to the field of metal pressure processing technology, specifically to a blockchain-based method for quality traceability and management of short stress line rolling mill slides. Background Technology

[0002] In the field of metal pressure processing, the guide mounting slide mechanism of a short stress line rolling mill is a core component that ensures rolling accuracy and production stability. Traditional slide mechanisms employ a unidirectional locking structure (such as a combination of slide, moving lead screw, and locking triangular iron), which presents significant technical bottlenecks. Quality traceability is difficult: The entire life cycle data of the slide (design parameters, processing technology, assembly records, failure history) lacks systematic management and relies on manual records and paper documents. The data is easily lost or tampered with, and fault tracing requires cross-departmental coordination, which takes up to 2-4 hours, resulting in low production efficiency.

[0003] Delayed maintenance decisions: Relying on manual experience to judge the wear status of components lacks real-time data support. For example, when the wear of the transverse lead screw exceeds the limit, there is no early warning. Sudden failures often lead to machine shutdowns, with single losses reaching hundreds of thousands of yuan.

[0004] Inefficient supply chain collaboration: Data silos are evident among manufacturers, steel mills, and quality inspection agencies; raw material quality issues (such as substandard tensile strength of steel) are difficult to synchronize in a timely manner; process optimization cycles can be as long as several weeks; and spare parts inventory management relies on manual experience, which can easily lead to inventory backlog or shortage.

[0005] Existing technologies cannot meet the requirements of high-precision rolling for slide stability, traceability, and intelligent maintenance, and there is an urgent need to introduce new technologies to achieve full-process quality control. Summary of the Invention

[0006] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a blockchain-based method for quality traceability and management of short-stress-line rolling mill slides, which has advantages such as a significant improvement in quality traceability capabilities and solves the problem of difficult quality traceability.

[0007] (II) Technical Solution To achieve the aforementioned leap in quality traceability capabilities, this invention provides the following technical solution: a blockchain-based method for quality traceability and management of short stress line rolling mill slides, comprising the following specific steps: S1 blockchain architecture design, S2 network layer node deployment, S3 application layer quality traceability module, and S4 implementation steps. The S1 blockchain architecture design includes S101 multi-dimensional data acquisition at the data layer and S102 data uploading to the blockchain. The S3 application layer quality traceability module includes the S301 predictive maintenance module and the S302 supply chain collaboration module. The S4 implementation steps include the S401 initialization phase (T0), the S402 production and processing phase (T1-T2), and the S403 use and maintenance phase (T3-Tn).

[0008] Preferably, the S101 data layer performs multi-dimensional data acquisition: Design phase: Collect parameters of the three-dimensional model of the slide (inclination of the guide frame inclined surface, clearance of the locking block, and specifications of the screw thread), and material properties (such as hardness HRC45-50 and tensile strength ≥600MPa). Machining stage: Record machine tool number, tool parameters (such as cutting speed 150m / min, feed rate 0.2mm / r), and heat treatment process (quenching temperature 850℃±10℃, tempering temperature 550℃±15℃). Assembly stage: Store assembly personnel ID, locking bolt torque value (e.g., 50 N·m ± 5%), and slider-screw meshing clearance (0.02-0.05 mm). Usage phase: Real-time acquisition of vibration sensor data (vibration frequency ≤50Hz, amplitude ≤0.1mm), temperature sensor data (operating temperature ≤80℃), and fault alarm records (such as locking failure timestamps and loosening count statistics).

[0009] Preferably, the data in S102 is uploaded to the blockchain: The above data is encrypted to generate a hash value, packaged into blocks in timestamp order, and written into the blockchain distributed ledger through a consensus mechanism (such as PBFT).

[0010] Preferably, the S2 network layer node deployment involves establishing consortium blockchain nodes among rolling mill manufacturers, steel companies, quality inspection agencies, and other entities to achieve real-time data synchronization and verification. Smart contract trigger conditions: When the vibration frequency of the slide block is > 50Hz and lasts for 5 minutes, the warning contract will be automatically triggered to notify maintenance personnel; When the torque value of the locking bolt deviates from the standard value by ±10%, the abnormality is automatically recorded and the relevant batch of parts is locked.

[0011] Preferably, the S3 application layer quality traceability module: Full-chain query: By using the unique identifier of the slide (such as an RFID tag), query its full-process data of "design-processing-assembly-use". The slope of the inner locking block of the guide installation slide (number SLZ-20250301) is 30°, which is complementary to the slope of the crossbeam. The torque value during assembly is 48N・m, which meets the standard of 50N・m±5%. Fault tracing: If the slide fails to lock, it can be traced back to a batch of locking bolts with a heat treatment hardness of only HRC42 (lower than the standard HRC45), which can be linked to 100 bolts in the same batch.

[0012] Preferably, the S301 predictive maintenance module includes a wear prediction model: based on historical data stored on the blockchain (such as the number of slider movements and the number of screw rotations), the remaining life of the component is predicted using the LSTM algorithm. For example, after the transverse screw rotates more than 100,000 times, its thread wear rate is predicted to be > 5%, and a maintenance work order is automatically generated. Inventory Management: Based on the frequency of slide usage and lifespan prediction, the spare parts inventory is intelligently adjusted. For example, when the median remaining lifespan of a certain model of external locking block is less than 3 months, a purchase contract is triggered.

[0013] Preferably, the S302 supply chain collaboration module: Supplier Management: The quality data of raw material suppliers is stored on the blockchain. If the tensile strength of a certain batch of steel is less than 600MPa, it will be automatically marked and the supplier's performance evaluation will be deducted. Process optimization: By analyzing the on-chain machining data, it was found that the pass rate of slide locking blocks machined by machine tool (No. CNC-007) was only 85%. Tracing back, it was found that tool wear caused dimensional deviations. After timely tool replacement, the pass rate increased to 98%.

[0014] Preferably, the S401 initialization phase (T0): Each slide is assigned a unique blockchain ID and its design parameters (such as the guide frame slope of 30° and the locking block fit clearance of 0.03mm) are recorded on the blockchain. Create smart contracts in the blockchain to define data writing permissions at each stage (e.g., manufacturers can write design / processing data, and steel mills can write usage data).

[0015] Preferably, the S402 production and processing stage (T1-T2): Processing data acquisition (T1): The CNC-001 machine tool was used to machine the guide rail. The cutting speed was 150m / min, the feed rate was 0.2mm / r, and the heat treatment quenching temperature was 855℃. The data was uploaded to the blockchain after being signed by operator A. Assembly data entry (T2): Assembler B applied a torque of 49 N·m to the locking bolt using a torque wrench, transmitted the torque to the blockchain via Bluetooth, and simultaneously recorded a slider-screw engagement clearance of 0.04 mm.

[0016] Preferably, the S403 usage maintenance phase (T3-Tn): Condition monitoring (T3): During the operation of the rolling mill, the sensor collects the vibration frequency of the slide block at 45Hz and the temperature at 75℃ in real time. The data is filtered by the edge computing node and then uploaded to the chain (packed once every 10 minutes). Troubleshooting (T4): When the vibration frequency is > 50Hz for 10 consecutive minutes, the smart contract triggers an alarm. Maintenance personnel C inspected and found that the outer locking block buckle was worn. After replacement, the maintenance time and spare part number (SP-20250305) were recorded and uploaded to the blockchain. Life Prediction and Maintenance (Tn): The system predicts that the remaining life of the transverse lead screw is 2 months, automatically generates a work order, prompts the lead screw to be replaced on Tn+30 days, and pushes the spare parts requirement to the purchasing department.

[0017] (III) Beneficial Effects Compared with existing technologies, this invention provides a blockchain-based method for quality traceability and management of short-stress-line rolling mill slides, which has the following beneficial effects: 1. This blockchain-based method for quality traceability and management of short-stress line rolling mill slides significantly enhances quality traceability capabilities and ensures tamper-proof records across the entire chain: Design parameters of the slides (such as a guide frame slope of 30° and a locking block clearance of 0.03mm), processing technology (cutting speed of 150m / min and quenching temperature of 850℃±10℃), and assembly torque (50N・m±5%) are encrypted and uploaded to the blockchain. Utilizing the distributed storage characteristics of blockchain, the data is ensured to be tamper-proof, providing a judicial-grade chain of evidence for quality disputes. Minute-level fault tracing: By linking the slide's unique ID to the entire lifecycle data, when a locking failure occurs, it can quickly locate the specific batch of parts (such as a batch of locking bolts with a hardness of less than HRC45). The tracing time is reduced from the traditional 2-4 hours to minutes, reducing downtime losses by more than 60%.

[0018] 2. This blockchain-based method for quality traceability and management of short-stress-line rolling mill slides represents an intelligent upgrade in maintenance management and a predictive maintenance system. Based on blockchain-stored data such as vibration frequency (≤50Hz), temperature (≤80℃), and number of movements, a wear prediction model is constructed using the LSTM algorithm. This model provides 30-day advance warnings of component lifespan (e.g., predicting wear rate > 5% after the transverse lead screw has rotated more than 100,000 times), improving maintenance accuracy by 30% and reducing spare parts inventory costs by 25%. Smart contract automatic response: When a vibration frequency > 50Hz is detected for 5 minutes, or the torque of the tightening bolt deviates from the standard value by ±10%, the smart contract automatically triggers an alert and assigns a maintenance work order, reducing human intervention errors and improving maintenance efficiency by 50%.

[0019] 3. This blockchain-based method for quality traceability and management of short-stress line rolling mill slides optimizes supply chain collaboration efficiency and enables real-time data synchronization among multiple parties: Consortium blockchain nodes are established among manufacturers, steel mills, and quality inspection agencies, allowing for real-time sharing of raw material quality data (such as steel tensile strength ≥600MPa). When a batch of steel fails to meet standards, the smart contract automatically flags the supplier and triggers an assessment mechanism, improving response speed by 40%. Continuous process optimization: By analyzing on-chain machining data (such as CNC-007 machine tool wear leading to a pass rate of only 85%), we can quickly locate process bottlenecks and drive optimization measures such as tool replacement. As a result, the pass rate has increased from 85% to 98%, and the process optimization cycle has been shortened by 50%.

[0020] 4. This blockchain-based short-stress-line rolling mill slide quality traceability and management method comprehensively improves production efficiency and significantly enhances stability: the combination of blockchain traceability and intelligent locking structure reduces the slide locking failure rate from 5% to below 1%, increases the dimensional accuracy qualification rate of rolled products from 92% to 98%, and reduces surface quality defects by 70%. Compliance and Competitiveness: Meeting international standards such as ISO 9001 provides enterprises with verifiable quality certifications, helping them pass customer audits while reducing manual record-keeping costs by 30% and enhancing market competitiveness. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] This solution provides a technical solution, specifically a blockchain-based method for quality traceability and management of short stress line rolling mill slides; The specific methods and steps include: S1 blockchain architecture design: S101 Data Layer Multidimensional Data Acquisition: Design phase: Collect parameters of the three-dimensional model of the slide (inclination of the guide frame inclined surface, clearance of the locking block, and specifications of the screw thread), and material properties (such as hardness HRC45-50 and tensile strength ≥600MPa). Machining stage: Record machine tool number, tool parameters (such as cutting speed 150m / min, feed rate 0.2mm / r), and heat treatment process (quenching temperature 850℃±10℃, tempering temperature 550℃±15℃). Assembly stage: Store assembly personnel ID, locking bolt torque value (e.g., 50 N·m ± 5%), and slider-screw meshing clearance (0.02-0.05 mm). Usage phase: Real-time acquisition of vibration sensor data (vibration frequency ≤50Hz, amplitude ≤0.1mm), temperature sensor data (operating temperature ≤80℃), and fault alarm records (such as locking failure timestamp, loosening count statistics); S102 data on-chain: The above data is encrypted to generate a hash value, packaged into blocks in timestamp order, and written into the blockchain distributed ledger through a consensus mechanism (such as PBFT). S2 network layer node deployment: Establish consortium blockchain nodes among rolling mill manufacturers, steel companies, quality inspection agencies and other entities to achieve real-time data synchronization and verification; Smart contract trigger conditions: When the vibration frequency of the slide block is > 50Hz and lasts for 5 minutes, the warning contract will be automatically triggered to notify maintenance personnel; When the torque value of the locking bolt deviates from the standard value by ±10%, the abnormality is automatically recorded and the relevant batch of parts is locked. S3 Application Layer Quality Traceability Module: Full-chain query: By using the unique identifier of the slide (such as an RFID tag), query its full-process data of "design-processing-assembly-use". The slope of the inner locking block of the guide installation slide (number SLZ-20250301) is 30°, which is complementary to the slope of the crossbeam. The torque value during assembly is 48N・m, which meets the standard of 50N・m±5%. Fault tracing: If the slide fails to lock, it can be traced back to a batch of locking bolts with a heat treatment hardness of only HRC42 (lower than the standard HRC45), which can be linked to 100 bolts in the same batch. S301 Predictive Maintenance Module: Wear prediction model: Based on historical data stored on the blockchain (such as the number of slider movements and the number of rotations of the lead screw), the LSTM algorithm is used to predict the remaining life of the component. For example, after the transverse lead screw rotates more than 100,000 times, its thread wear rate is predicted to be >5%, and maintenance work orders are automatically generated. Inventory Management: Based on the frequency of use and life prediction of the slide block, the spare parts inventory is intelligently adjusted. For example, when the median remaining life of a certain model of external locking block is less than 3 months, a purchase contract is triggered. S302 Supply Chain Collaboration Module: Supplier Management: The quality data of raw material suppliers is stored on the blockchain. If the tensile strength of a certain batch of steel is less than 600MPa, it will be automatically marked and the supplier's performance evaluation will be deducted. Process optimization: By analyzing the machining data on the chain, it was found that the pass rate of the slide locking block machined by machine tool (No. CNC-007) was only 85%. The investigation revealed that tool wear caused dimensional deviations. After timely tool replacement, the pass rate increased to 98%. S4 Implementation Steps: S401 Initialization Phase (T0): Each slide is assigned a unique blockchain ID and its design parameters (such as the guide frame slope of 30° and the locking block fit clearance of 0.03mm) are recorded on the blockchain. Create smart contracts in the blockchain to define data writing permissions at each stage (e.g., manufacturers can write design / processing data, and steel mills can write usage data). S402 Production and Processing Stages (T1-T2): Processing data acquisition (T1): The CNC-001 machine tool was used to machine the guide rail. The cutting speed was 150m / min, the feed rate was 0.2mm / r, and the heat treatment quenching temperature was 855℃. The data was uploaded to the blockchain after being signed by operator A. Assembly data entry (T2): Assembler B applied a torque of 49 N·m to the locking bolt using a torque wrench, transmitted the torque to the blockchain via Bluetooth, and simultaneously recorded a slider-screw engagement clearance of 0.04 mm. S403 usage and maintenance phases (T3-Tn): Condition monitoring (T3): During the operation of the rolling mill, the sensors collect the vibration frequency of the slide block at 45Hz and the temperature at 75℃ in real time. The data is filtered by the edge computing node and then uploaded to the chain (packaged once every 10 minutes). Troubleshooting (T4): When the vibration frequency is > 50Hz for 10 consecutive minutes, the smart contract triggers an alarm. Maintenance personnel C inspected and found that the outer locking block buckle was worn. After replacement, the maintenance time and spare part number (SP-20250305) were recorded and uploaded to the blockchain. Life Prediction and Maintenance (Tn): The system predicts that the remaining life of the transverse lead screw is 2 months, automatically generates a work order, prompts the lead screw to be replaced in Tn+30 days, and pushes the spare parts requirement to the purchasing department at the same time. This solution has the following effects: Furthermore, the quality traceability capability has been greatly enhanced, with tamper-proof records throughout the entire chain: design parameters of the slide (such as the guide frame inclined plane slope of 30°, locking block mating clearance of 0.03mm), processing technology (cutting speed of 150m / min, quenching temperature of 850℃±10℃), assembly torque (50N・m±5%), etc. are encrypted and uploaded to the blockchain. By utilizing the distributed storage characteristics of blockchain, the immutability of data is ensured, providing a judicial-grade chain of evidence for quality disputes. Minute-level fault tracing: By linking the entire lifecycle data through the unique ID of the slide block, when a locking failure occurs, it can quickly locate the specific batch of parts (such as a batch of locking bolts with a hardness of less than HRC45). The tracing time is reduced from the traditional 2-4 hours to minutes, reducing downtime losses by more than 60%. Furthermore, the intelligent upgrade of maintenance management and the predictive maintenance system: Based on data such as vibration frequency (≤50Hz), temperature (≤80℃), and number of movements stored on the blockchain, a wear prediction model is built using the LSTM algorithm to provide early warnings of component lifespan up to 30 days in advance (e.g., predicting wear rate > 5% after the transverse lead screw has rotated more than 100,000 times), improving maintenance accuracy by 30% and reducing spare parts inventory costs by 25%. Smart contract automatic response: When a vibration frequency > 50Hz is detected for 5 minutes or the tightening bolt torque deviates from the standard value by ±10%, the smart contract automatically triggers an alert and assigns a maintenance work order, reducing human intervention errors and improving maintenance efficiency by 50%. Furthermore, supply chain collaboration efficiency is optimized, and data from multiple parties is synchronized in real time: consortium blockchain nodes are established among manufacturers, steel mills, and quality inspection agencies, and raw material quality data (such as steel tensile strength ≥600MPa) is shared in real time. When a batch of steel fails to meet the standards, the smart contract automatically marks the supplier and triggers the assessment mechanism, improving response speed by 40%. Continuous process optimization: By analyzing on-chain machining data (such as CNC-007 machine tool wear leading to a pass rate of only 85%), process bottlenecks are quickly identified, and optimization measures such as tool replacement are implemented. As a result, the pass rate has increased from 85% to 98%, and the process optimization cycle has been shortened by 50%. Furthermore, production efficiency has been comprehensively improved and stability has been significantly enhanced: the combination of blockchain traceability and intelligent locking structure has reduced the failure rate of slide locking from 5% to below 1%, increased the dimensional accuracy qualification rate of rolled products from 92% to 98%, and reduced surface quality defects by 70%. Compliance and Competitiveness: Meets international standards such as ISO 9001, providing verifiable quality certifications for enterprises, helping them pass customer audits, while reducing manual record-keeping costs by 30% and enhancing market competitiveness; In summary, this invention has constructed a slide quality control system with "data traceability, fault prediction, and collaborative control" through blockchain technology, providing an innovative solution for the efficient and stable operation of short stress line rolling mills and promoting the transformation of the metal pressure processing industry towards intelligence and digitalization.

[0023] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A blockchain-based short stress path mill shoe quality traceability and management method, comprising the following specific method steps: S1 blockchain architecture design, S2 network layer node deployment, S3 application layer quality traceability module and S4 implementation steps, characterized in that: The S1 blockchain architecture design includes S101 data layer multi-dimensional data collection and S102 data chaining; The S3 application layer quality traceability module includes S301 predictive maintenance module and S302 supply chain collaboration module; The S4 implementation steps include S401 initialization stage (T0), S402 production and processing stage (T1-T2), and S403 use and maintenance stage (T3-Tn).

2. The blockchain-based short stress path mill shoe mass traceability and management method of claim 1, wherein: The S101 data layer multi-dimensional data collection includes: Design stage: collect slide three-dimensional model parameters (guide frame slope, locking block gap, screw thread specifications), material properties (such as hardness HRC45-50, tensile strength ≥600MPa); Processing stage: record machine tool number, tool parameters (such as cutting speed 150m / min, feed rate 0.2mm / r), heat treatment process (quenching temperature 850℃±10℃, tempering temperature 550℃±15℃); Assembly stage: store assembly personnel ID, locking bolt torque value (such as 50N・m±5%), slide and screw meshing gap (0.02-0.05mm); Use stage: real-time collection of vibration sensor data (vibration frequency ≤50Hz, amplitude ≤0.1mm), temperature sensor data (working temperature ≤80℃), fault alarm record (such as locking failure timestamp, number of loosening times).

3. The blockchain-based short stress path mill shoe mass traceability and management method of claim 1, wherein: The S102 data chaining includes: After encrypting the above data, a hash value is generated, and the data is packaged into blocks in timestamp order, and written into the blockchain distributed ledger through a consensus mechanism (such as PBFT).

4. The blockchain based short stress path mill shoe mass traceability and management method of claim 1, wherein: The S2 network layer node deployment: establish alliance chain nodes among rolling mill manufacturers, steel enterprises, quality inspection agencies and other subjects to realize real-time synchronization and verification of data; Smart contract trigger condition: when the slide vibration frequency > 50Hz and lasts for 5 minutes, the early warning contract is automatically triggered, and the maintenance personnel is notified; When the locking bolt torque value deviates from the standard value ±10%, the abnormality is automatically recorded and the related batch components are locked.

5. The blockchain based short stress path mill shoe mass traceability and management method of claim 1, wherein: The S3 application layer quality traceability module includes: Full link query: through the unique identification of the slide (such as RFID tag), the "design - processing - assembly - use" full process data of the slide is queried, the inner locking block slope of the guide installation slide (number SLZ-20250301) is 30°, which is mutually complementary with the slope of the beam, and the torque value during assembly is 48N・m, which meets the standard of 50N・m±5%; Fault tracing: if the slide locking fails, it can be traced back to the heat treatment hardness of a batch of locking bolts, which is only HRC42 (lower than the standard HRC45), and 100 bolts of the same batch are associated and locked.

6. The blockchain-based short stress path mill shoe mass traceability and management method of claim 1, wherein: The S301 predictive maintenance module includes a wear prediction model: based on the historical data stored in the blockchain (such as the number of slide movements and the number of screw rotations), the remaining life of the component is predicted using the LSTM algorithm, for example, when the horizontal movement screw rotates more than 100,000 times, the thread wear rate is predicted to be > 5%, and a maintenance work order is automatically generated; Inventory management: based on the frequency and life prediction of the slide, the inventory of spare parts is intelligently adjusted, such as when the remaining life of a certain type of external locking block is less than 3 months, the procurement contract is triggered.

7. The blockchain-based short stress path mill shoe mass traceability and management method of claim 1, wherein: The S302 supply chain collaboration module: Supplier management: the quality data of raw material suppliers is stored on the chain, if the tensile strength of a batch of steel is less than 600MPa, it is automatically marked and triggers the supplier performance assessment deduction; Process optimization: by analyzing the on-chain processing data, it is found that the pass rate of the slide locking block processed by machine tool (No. CNC-007) is only 85%, and the size deviation caused by tool wear is found by tracing back, and the pass rate is improved to 98% after replacing the tool in time.

8. The blockchain-based short stress path mill shoe mass traceability and management method of claim 1, wherein: The S401 initialization stage (T0): Assign a unique blockchain ID to each slide and bind the design parameters (such as the slope of the guide bracket 30°, the locking block fitting gap 0.03mm) on the chain; Create a smart contract in the blockchain and define the data writing permissions of each link (such as manufacturers can write design / processing data, steel plants can write usage data).

9. The blockchain-based short stress path mill shoe mass traceability and management method of claim 1, wherein: The S402 production and processing stage (T1-T2): Processing data collection (T1): Machine tool CNC-001 processes guide bracket, records cutting speed 150m / min, feed amount 0.2mm / r, heat treatment quenching temperature 855℃, data is signed by operator A and uploaded to the chain; Assembly data entry (T2): Assembly worker B uses a torque wrench to apply a torque of 49N・m to the locking bolt, and transmits it to the blockchain through Bluetooth, while recording the sliding block and lead screw engagement gap 0.04mm.

10. The blockchain-based short stress path mill shoe mass traceability and management method of claim 1, wherein: The S403 use and maintenance stage (T3-Tn): State monitoring (T3): when the rolling mill is running, the sensor real-time collects the slide vibration frequency 45Hz, temperature 75℃, the data is filtered by the edge computing node and uploaded to the chain (packaged every 10 minutes); Fault handling (T4): when the vibration frequency is greater than 50Hz for 10 consecutive minutes, the smart contract triggers an alarm, maintenance personnel C checks and finds that the external locking block buckle is worn out, and after replacement, the repair time and spare part number (SP-20250305) are recorded on the chain; Life prediction and maintenance (Tn): the system predicts that the remaining life of the transverse lead screw is 2 months, automatically generates a work order, prompts to replace the lead screw in Tn+30 days, and pushes the spare part demand to the procurement department.