A method and system for tracing the quality of school equipment throughout its entire lifecycle based on the Internet of Things

Abstract

This invention discloses a method and system for quality traceability of school equipment throughout its entire lifecycle based on the Internet of Things (IoT). By deploying IoT sensing devices at various stages, including raw material procurement, intelligent production, warehousing and logistics, campus installation, and post-maintenance, key data is collected and uploaded to a cloud-based quality traceability platform in real time. Utilizing technologies such as RFID, QR codes, and blockchain, each piece of school equipment is assigned a unique digital identity, achieving seamless data flow from source to end. The platform employs big data analytics and visualization technologies to dynamically monitor, provide early warnings, and accurately trace the quality of school equipment, effectively improving the quality control level of school equipment products, ensuring student safety, and providing scientific basis and technical support for the quality management of educational equipment.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a method and system for tracing the quality of school equipment throughout its entire lifecycle based on IoT. Background Technology

[0002] In traditional school equipment manufacturing, the production process often lacks transparency, making it difficult to trace quality issues. This solution addresses the problem of "unclear raw material sources": In the traditional model, supplier information and environmental testing reports for core raw materials such as boards and steel pipes are often circulated along with paper documents, making them prone to loss or tampering. Traditional quality inspection is often done through post-production sampling, allowing substandard products to easily mix with qualified products and flow into the next process. Furthermore, school equipment products are large and have many components, making logistics and channel management a persistent industry challenge. Traditional warehousing relies on manual counting, resulting in disjointed data for individual items, boxes, and pallets, leading to inefficiency and errors. In the traditional sales model, manufacturers struggle to control the final destination of products, leading to persistent issues of distributors selling outside their designated areas. This solution automatically identifies and warns of such activities by verifying regional information upon delivery, maintaining a stable market price system. When a batch of raw materials (such as boards with excessive formaldehyde) is found to be problematic, traditional methods require days or even weeks to trace its distribution. This solution, through blockchain-based evidence storage and reverse traceability, enables second-level retrieval of affected product distribution, supports precise recalls, and reduces social risks. Summary of the Invention

[0003] A method for tracing the quality of school equipment throughout its entire lifecycle based on the Internet of Things includes the following steps; S1. Standard Construction and Source Coding: Before starting traceability, build the hardware and software foundation to support the operation of the system, establish a unique identification system of "enterprise code + category code + batch + serial number" to ensure "one item, one code", and embed anti-metal / high temperature resistant RFID tags into the core raw materials of plates and steel pipes, binding the supplier, environmental protection test report and batch information. When the core components of desktops and bed frames come off the production line, solidify the electronic tags through embedding and high-strength adhesive methods to complete the initial association between physical components and digital files, and record the initial mechanical performance parameters of the components simultaneously; S2. Production Process Transparency and Quality Lock-in: Using readers deployed on the production line, the system automatically records the operators, equipment parameters, and operation times at key assembly and edge-sealing stations, forming a process history and implementing a "quality inspection access control" mechanism. Quality inspectors input data on structural stability and formaldehyde release. If a product fails to meet the requirements, the system automatically locks it, preventing it from entering the next stage. Through miniature environmental sensors deployed in the workshop, the system automatically collects temperature, humidity, and pressing pressure environmental data during production, ensuring traceable process compliance. S3. Multi-level Packaging and Smart Logistics: Establish a four-level data association of "single item-box-pallet-warehouse location". Automatically bind box code to single item during packaging, bind pallet to warehouse location during warehousing, and automatically identify batches when forklifts / AGVs pass through the access gates, achieving seamless entry and exit with 99.9% accuracy. Bind the packing note to the logistics link, and verify the area information through handheld terminals during delivery. The system automatically identifies and warns of cross-regional delivery behavior. S4. Terminal Delivery and Proactive Maintenance: The school's logistics department uses mobile devices for batch scanning, generating asset ledgers in seconds and automatically comparing them with purchase orders, significantly shortening the acceptance cycle. Maintenance personnel can also scan tags to retrieve raw material batches and production records, quickly determining the root cause of faults (design / production / use). Based on a digital twin model combined with real-time usage data of school equipment (collected through RFID tags with integrated sensors), maintenance reminders are automatically pushed according to preset cycles (such as the start of each semester), and maintenance history such as tightening and rust prevention is recorded, realizing the transformation from "passive maintenance" to "proactive maintenance". S5. Data-driven and closed-loop decision-making: Quality reverse traceability, based on blockchain-stored full lifecycle data, combined with edge intelligence algorithms to analyze the failure rate of raw materials from different suppliers and the repair rate of production batches, accurately locate quality bottlenecks, discover environmental or safety hazards, and the system can retrieve the flow of affected products (warehouse / school) in seconds to achieve accurate recall. Full lifecycle evaluation: Based on data such as service life and maintenance frequency, it provides data support for school procurement budgets and enterprise product iteration.

[0004] Furthermore, a method for tracing the quality of school equipment throughout its entire lifecycle based on the Internet of Things, in step S1, establishes the hardware and software foundations that support the operation of the system, as detailed below; Hardware layer deployment: Deploy fixed RFID readers and antennas at key workstations on the production line (such as cutting, edge banding, drilling, and assembly), warehouse entrances and exits, and quality inspection stations; equip warehouse management personnel with handheld terminals (PDAs); and deploy RFID smart tool cabinets / shelves for the management of high-value molds and key components. Software platform setup: Establish or integrate a traceability management system. A "cloud platform + local server" architecture is recommended to ensure data security and real-time synchronization. The system should include modules for school equipment file management, Manufacturing Execution System (MES), Warehouse Management System (WMS), quality traceability, and maintenance management.

[0005] Furthermore, a method for quality traceability of school equipment throughout its entire lifecycle based on the Internet of Things (IoT) establishes a four-level data association of "single item-box-pallet-warehouse location" in step S3. The core of this method lies in establishing and recording data binding relationships at each level during packaging, palletizing, and warehousing through automated means, forming a complete and traceable data chain. The specific steps are as follows: S31. Packaging process: Establish "item-box" association: This is the foundation of the four-level association, and the goal is to bind each individual item to its corresponding packaging box; S311. Individual Product Coding: At the end of the production line, each individual product (such as a desk or a chair) is assigned a unique identification code (usually a QR code or RFID tag). This code is the individual product's "digital ID card" and is associated with its production batch, specifications, and other basic information. S312. Automatic data collection and binding: When a single item enters the automatic packing station, the vision recognition system (such as an industrial camera) deployed here will read the item's identification code at high speed; S313. Establishing a hierarchical relationship: The system simultaneously retrieves the box codes of currently used empty boxes. Subsequently, the traceability system automatically associates the identification codes of all individual items inside the box with this unique box code, forming a hierarchical data relationship where "one box code corresponds to multiple item codes." This process is fully automated, ensuring data accuracy and efficiency. S32. Palletizing process: Establishing a "box-pallet" association: After the packaging boxes come off the production line, they are neatly stacked on pallets for transportation and storage, at which point the second layer of association is established; S321. Pallet coding: Each pallet used to carry goods is also assigned a unique identification code (pallet code). S322. Whole Pallet Batch Data Acquisition: Once the packaging boxes of an entire pallet are stacked, the entire pallet of goods is scanned using a fixed reader or industrial camera, and the system will read the box codes of all the packaging boxes on the pallet at once. S323. Complete hierarchical binding: The system automatically binds these box codes to pallet codes, forming an association relationship of "one pallet code corresponding to multiple box codes"; S33. Inbound Process: Establish "pallet-location" association This is the final link in the chain, binding physical location information with cargo information to achieve digital management of the warehouse; S331. Warehouse location coding: Each storage location (warehouse location) in the warehouse has a unique identification code (warehouse location code), which is usually fixed on the shelf in the form of a barcode or RFID tag; S332. Automatic identification and association: When a forklift or automated guided vehicle (AGV) carrying a pallet passes through the access gate of the warehouse entrance, a fixed reader installed on the gate will automatically read the pallet code; S333. Binding Location Information: The forklift driver or warehouse management system (WMS) will place the pallet in the designated storage location and scan the storage location code using a handheld terminal or vehicle-mounted device. The system will then automatically bind the pallet code to the storage location code and record the storage time. Through the automated operation of the above three steps, a complete four-level data association system of "item-box-pallet-location" is successfully established. Information at any level can be quickly traced. For example, by scanning an item code, you can find out which box it is in, which pallet it is on, and the specific location of the pallet in the warehouse.

[0006] Furthermore, an IoT-based method for tracing the quality of school equipment throughout its entire lifecycle is proposed. In step S4, based on the digital twin model and combined with real-time usage status data of the school equipment: data is collected through RFID tags with integrated sensors. The digital twin model is based on the following two aspects: Precise mapping between physical and digital models: Geometric and Attribute Modeling: CAD / BIM technology is used to construct a three-dimensional geometric model of the training fixture and to assign it physical properties such as material, design load-bearing capacity, and fatigue life, forming a static digital base; Virtual-real identity binding: The unique ID of the integrated sensor RFID tag fixed on the physical training equipment is bound one-to-one with the digital model. This is equivalent to implanting "nerve endings" into the digital model, ensuring that any changes in the physical entity can be accurately mapped to the virtual space through the ID. Real-time data acquisition and synchronization mechanism: Multi-dimensional sensing and data acquisition: Integrated sensor RFID tags collect load pressure, vibration frequency, and ambient temperature and humidity data in real time, and upload them to the cloud via an IoT gateway; Two-way drive synchronization: Adopting an "event-driven + periodic synchronization" mechanism, when the sensor detects abnormal data (such as overload) or reaches the set period, it triggers the digital model's state update (such as model color change, value refresh) in real time through the MQTT protocol, achieving millisecond-level synchronization between virtual and real states.

[0007] Furthermore, an IoT-based method for tracing the quality of school equipment throughout its entire lifecycle is proposed. In step S5, quality reverse traceability is based on the full lifecycle data stored on the blockchain. The specific details of storing the full lifecycle data on the blockchain are as follows: Key data node identification: Identify the key data points that need to be uploaded to the blockchain throughout the product's entire lifecycle, including: Raw material stage: supplier information, raw material batch number, environmental testing report (such as formaldehyde and heavy metal content), mechanical performance parameters; Production stage: production batch, key process parameters (such as pressing temperature and pressure), quality inspection results, and operator information; Logistics and delivery phase: outbound time, logistics trajectory, delivered school, installation location; Maintenance phase: Repair reports, fault symptoms, repair history, and component replacement records; Data on-chain strategy: Not all data is directly on-chain, in order to balance performance and cost. Core data on-chain: The hash value (digital fingerprint) and core summary information (such as supplier ID, batch number, test results) of the above key data are written to the blockchain in real time to form an immutable evidence. Raw data storage: Massive amounts of raw data (such as complete test report PDF, high-frequency sensor data) can be stored in an off-chain database, but its hash value on the chain ensures that the raw data has not been tampered with. Forming a trusted data chain: Through the chain structure of blockchain, every link of a product from raw materials to scrap is connected to form a complete, transparent, and traceable data chain. When traceability is needed, all historical information of the affected product can be retrieved in seconds.

[0008] Furthermore, an IoT-based method for tracing the quality of school equipment throughout its entire lifecycle is proposed. In step S5, the edge intelligent algorithm analyzes the failure rate of raw materials from different suppliers and the repair rate of production batches, accurately locates quality bottlenecks, and discovers environmental and safety hazards. The intelligent algorithm deployed on the edge side—factory server and regional data center—is used for real-time and near real-time analysis. The specific steps are as follows: S51. Multi-dimensional quality indicator calculation: Raw material batch information from different suppliers is read from the blockchain and correlated with repair data from the operation and maintenance phase. This is then used to calculate... This allows us to determine the raw material failure rate for each supplier and accurately identify suppliers with unstable quality. Production batch repair rate analysis: Analyze the repair status of products in different production batches. When the repair rate of a certain batch or several batches is significantly higher than the average level, they are marked as high-risk batches, and the production records of the batch are automatically traced to check whether there are abnormal process parameters or quality inspection omissions. S52. Environmental and Safety Hazard Identification: Continuously monitor the environmental testing data uploaded to the blockchain. When the formaldehyde emission of a batch of boards is qualified but close to the critical value, conduct correlation analysis based on the subsequent repair data of that batch of products. Once a potential correlation is found, an environmental risk warning will be issued immediately, even if the individual data point does not exceed the standard. Combine the digital twin model and real-time collected usage status data (such as load and usage frequency) to predict potential structural safety risks. If it is found that the repair records of a certain model of desk are mostly concentrated on loose connectors under specific high-load usage scenarios, inspection and maintenance reminders will be pushed to schools using this model of desk in advance to prevent problems before they occur.

[0009] An IoT-based school equipment lifecycle quality traceability system is disclosed, comprising: Basic Data and Identifier Resolution Module: Responsible for establishing a unified language and identity ID, initializing digital archives, and establishing the initial binding between physical components and digital archives; Production process transparency monitoring module: This module is responsible for real-time data collection and quality control within the workshop; Smart Logistics and Supply Chain Collaboration Module: This module solves the problems of tracking the flow of products after they leave the factory and preventing cross-selling; Terminal Delivery and Proactive Maintenance Module: This module serves school users, extending traceability to the usage stage. Based on digital twin models and integrated sensor RFID data, it automatically pushes maintenance reminders according to a preset cycle. Data Decision and Security Traceability Module: Utilizing blockchain technology to ensure data immutability, it supports reverse tracing from finished products back to raw materials, suppliers, and production batches. Based on data such as service life, maintenance frequency, and failure rate, it provides data support for school procurement budget formulation and enterprise product iteration.

[0010] The beneficial effects of this invention are as follows: It achieves "one item, one code" identity verification, completely solving the problems of missing and easily counterfeited identification marks in traditional school equipment by assigning each piece of equipment a unique "digital ID card" (enterprise code + category code + batch + serial number). It eliminates the counterfeiting and use of inferior raw materials by binding environmental testing reports for boards and steel pipes to RFID tags, ensuring the authenticity and compliance of raw material sources and guaranteeing the environmental safety of school equipment from the source (such as formaldehyde control). It enables traceable process parameters and improves product consistency: by automatically collecting environmental data such as temperature, humidity, and pressing pressure during production, it solves the pain points of manual recording being prone to errors and tampering. This not only ensures the compliance of the production process but also provides accurate data support for subsequent analysis of product quality issues. It enables rapid and accurate recall, reducing social risks: when an environmental or safety hazard is discovered in a batch of raw materials, the system can use blockchain technology to retrieve the specific flow of the affected products (which warehouse or school) in seconds, achieving accurate recall. Compared to traditional large-scale blind recalls, this greatly reduces the economic losses and negative social impact on enterprises. Attached Figure Description

[0011] Figure 1 A flowchart illustrating a method for tracing the quality of school equipment throughout its entire lifecycle based on the Internet of Things (IoT). Detailed Implementation A method for tracing the quality of school equipment throughout its entire lifecycle based on the Internet of Things, such as... Figure 1 As shown, it includes the following steps; S1. Standard Construction and Source Coding: Before starting traceability, build the hardware and software foundation to support the operation of the system, establish a unique identification system of "enterprise code + category code + batch + serial number" to ensure "one item, one code", and embed anti-metal / high temperature resistant RFID tags into the core raw materials of plates and steel pipes, binding the supplier, environmental protection test report and batch information. When the core components of desktops and bed frames come off the production line, solidify the electronic tags through embedding and high-strength adhesive methods to complete the initial association between physical components and digital files, and record the initial mechanical performance parameters of the components simultaneously; S2. Production Process Transparency and Quality Lock-in: Using readers deployed on the production line, the system automatically records the operators, equipment parameters, and operation times at key assembly and edge-sealing stations, forming a process history and implementing a "quality inspection access control" mechanism. Quality inspectors input data on structural stability and formaldehyde release. If a product fails to meet the requirements, the system automatically locks it, preventing it from entering the next stage. Through miniature environmental sensors deployed in the workshop, the system automatically collects temperature, humidity, and pressing pressure environmental data during production, ensuring traceable process compliance. S3. Multi-level Packaging and Smart Logistics: Establish a four-level data association of "single item-box-pallet-warehouse location". Automatically bind box code to single item during packaging, bind pallet to warehouse location during warehousing, and automatically identify batches when forklifts / AGVs pass through the access gates, achieving seamless entry and exit with 99.9% accuracy. Bind the packing note to the logistics link, and verify the area information through handheld terminals during delivery. The system automatically identifies and warns of cross-regional delivery behavior. S4. Terminal Delivery and Proactive Maintenance: The school's logistics department uses mobile devices for batch scanning, generating asset ledgers in seconds and automatically comparing them with purchase orders, significantly shortening the acceptance cycle. Maintenance personnel can also scan tags to retrieve raw material batches and production records, quickly determining the root cause of faults (design / production / use). Based on a digital twin model combined with real-time usage data of school equipment (collected through RFID tags with integrated sensors), maintenance reminders are automatically pushed according to preset cycles (such as the start of each semester), and maintenance history such as tightening and rust prevention is recorded, realizing the transformation from "passive maintenance" to "proactive maintenance". S5. Data-driven and closed-loop decision-making: Quality reverse traceability, based on blockchain-stored full lifecycle data, combined with edge intelligence algorithms to analyze the failure rate of raw materials from different suppliers and the repair rate of production batches, accurately locate quality bottlenecks, discover environmental or safety hazards, and the system can retrieve the flow of affected products (warehouse / school) in seconds to achieve accurate recall. Full lifecycle evaluation: Based on data such as service life and maintenance frequency, it provides data support for school procurement budgets and enterprise product iteration.

[0012] Furthermore, a method for tracing the quality of school equipment throughout its entire lifecycle based on the Internet of Things, in step S1, establishes the hardware and software foundations that support the operation of the system, as detailed below; Hardware layer deployment: Deploy fixed RFID readers and antennas at key workstations on the production line (such as cutting, edge banding, drilling, and assembly), warehouse entrances and exits, and quality inspection stations; equip warehouse management personnel with handheld terminals (PDAs); and deploy RFID smart tool cabinets / shelves for the management of high-value molds and key components. Software platform setup: Establish or integrate a traceability management system. A "cloud platform + local server" architecture is recommended to ensure data security and real-time synchronization. The system should include modules for school equipment file management, Manufacturing Execution System (MES), Warehouse Management System (WMS), quality traceability, and maintenance management.

[0013] Furthermore, a method for quality traceability of school equipment throughout its entire lifecycle based on the Internet of Things (IoT) establishes a four-level data association of "single item-box-pallet-warehouse location" in step S3. The core of this method lies in establishing and recording data binding relationships at each level during packaging, palletizing, and warehousing through automated means, forming a complete and traceable data chain. The specific steps are as follows: S31. Packaging process: Establish "item-box" association: This is the foundation of the four-level association, and the goal is to bind each individual item to its corresponding packaging box; S311. Individual Product Coding: At the end of the production line, each individual product (such as a desk or a chair) is assigned a unique identification code (usually a QR code or RFID tag). This code is the individual product's "digital ID card" and is associated with its production batch, specifications, and other basic information. S312. Automatic data collection and binding: When a single item enters the automatic packing station, the vision recognition system (such as an industrial camera) deployed here will read the item's identification code at high speed; S313. Establishing a hierarchical relationship: The system simultaneously retrieves the box codes of currently used empty boxes. Subsequently, the traceability system automatically associates the identification codes of all individual items inside the box with this unique box code, forming a hierarchical data relationship where "one box code corresponds to multiple item codes." This process is fully automated, ensuring data accuracy and efficiency. S32. Palletizing process: Establishing a "box-pallet" association: After the packaging boxes come off the production line, they are neatly stacked on pallets for transportation and storage, at which point the second layer of association is established; S321. Pallet coding: Each pallet used to carry goods is also assigned a unique identification code (pallet code). S322. Whole Pallet Batch Data Acquisition: Once the packaging boxes of an entire pallet are stacked, the entire pallet of goods is scanned using a fixed reader or industrial camera, and the system will read the box codes of all the packaging boxes on the pallet at once. S323. Complete hierarchical binding: The system automatically binds these box codes to pallet codes, forming an association relationship of "one pallet code corresponding to multiple box codes"; S33. Inbound Process: Establish "pallet-location" association This is the final link in the chain, binding physical location information with cargo information to achieve digital management of the warehouse; S331. Warehouse location coding: Each storage location (warehouse location) in the warehouse has a unique identification code (warehouse location code), which is usually fixed on the shelf in the form of a barcode or RFID tag; S332. Automatic identification and association: When a forklift or automated guided vehicle (AGV) carrying a pallet passes through the access gate of the warehouse entrance, a fixed reader installed on the gate will automatically read the pallet code; S333. Binding Location Information: The forklift driver or warehouse management system (WMS) will place the pallet in the designated storage location and scan the storage location code using a handheld terminal or vehicle-mounted device. The system will then automatically bind the pallet code to the storage location code and record the storage time. Through the automated operation of the above three steps, a complete four-level data association system of "item-box-pallet-location" is successfully established. Information at any level can be quickly traced. For example, by scanning an item code, you can find out which box it is in, which pallet it is on, and the specific location of the pallet in the warehouse.

[0014] Furthermore, an IoT-based method for tracing the quality of school equipment throughout its entire lifecycle is proposed. In step S4, based on the digital twin model and combined with real-time usage status data of the school equipment: data is collected through RFID tags with integrated sensors. The digital twin model is based on the following two aspects: Precise mapping between physical and digital models: Geometric and Attribute Modeling: CAD / BIM technology is used to construct a three-dimensional geometric model of the training fixture and to assign it physical properties such as material, design load-bearing capacity, and fatigue life, forming a static digital base; Virtual-real identity binding: The unique ID of the integrated sensor RFID tag fixed on the physical training equipment is bound one-to-one with the digital model. This is equivalent to implanting "nerve endings" into the digital model, ensuring that any changes in the physical entity can be accurately mapped to the virtual space through the ID. Real-time data acquisition and synchronization mechanism: Multi-dimensional sensing and data acquisition: Integrated sensor RFID tags collect load pressure, vibration frequency, and ambient temperature and humidity data in real time, and upload them to the cloud via an IoT gateway; Two-way drive synchronization: Adopting an "event-driven + periodic synchronization" mechanism, when the sensor detects abnormal data (such as overload) or reaches the set period, it triggers the digital model's state update (such as model color change, value refresh) in real time through the MQTT protocol, achieving millisecond-level synchronization between virtual and real states.

[0015] Furthermore, an IoT-based method for tracing the quality of school equipment throughout its entire lifecycle is proposed. In step S5, quality reverse traceability is based on the full lifecycle data stored on the blockchain. The specific details of storing the full lifecycle data on the blockchain are as follows: Key data node identification: Identify the key data points that need to be uploaded to the blockchain throughout the product's entire lifecycle, including: Raw material stage: supplier information, raw material batch number, environmental testing report (such as formaldehyde and heavy metal content), mechanical performance parameters; Production stage: production batch, key process parameters (such as pressing temperature and pressure), quality inspection results, and operator information; Logistics and delivery phase: outbound time, logistics trajectory, delivered school, installation location; Maintenance phase: Repair reports, fault symptoms, repair history, and component replacement records; Data on-chain strategy: Not all data is directly on-chain, in order to balance performance and cost. Core data on-chain: The hash value (digital fingerprint) and core summary information (such as supplier ID, batch number, test results) of the above key data are written to the blockchain in real time to form an immutable evidence. Raw data storage: Massive amounts of raw data (such as complete test report PDF, high-frequency sensor data) can be stored in an off-chain database, but its hash value on the chain ensures that the raw data has not been tampered with. Forming a trusted data chain: Through the chain structure of blockchain, every link of a product from raw materials to scrap is connected to form a complete, transparent, and traceable data chain. When traceability is needed, all historical information of the affected product can be retrieved in seconds.

[0016] Furthermore, an IoT-based method for tracing the quality of school equipment throughout its entire lifecycle is proposed. In step S5, the edge intelligent algorithm analyzes the failure rate of raw materials from different suppliers and the repair rate of production batches, accurately locates quality bottlenecks, and discovers environmental and safety hazards. The intelligent algorithm deployed on the edge side—factory server and regional data center—is used for real-time and near real-time analysis. The specific steps are as follows: S51. Multi-dimensional quality indicator calculation: Raw material batch information from different suppliers is read from the blockchain and correlated with repair data from the operation and maintenance phase. This is then used to calculate... This allows us to determine the raw material failure rate for each supplier and accurately identify suppliers with unstable quality. Production batch repair rate analysis: Analyze the repair status of products in different production batches. When the repair rate of a certain batch or several batches is significantly higher than the average level, they are marked as high-risk batches, and the production records of the batch are automatically traced to check whether there are abnormal process parameters or quality inspection omissions. S52. Environmental and Safety Hazard Identification: Continuously monitor the environmental testing data uploaded to the blockchain. When the formaldehyde emission of a batch of boards is qualified but close to the critical value, conduct correlation analysis based on the subsequent repair data of that batch of products. Once a potential correlation is found, an environmental risk warning will be issued immediately, even if the individual data point does not exceed the standard. Combine the digital twin model and real-time collected usage status data (such as load and usage frequency) to predict potential structural safety risks. If it is found that the repair records of a certain model of desk are mostly concentrated on loose connectors under specific high-load usage scenarios, inspection and maintenance reminders will be pushed to schools using this model of desk in advance to prevent problems before they occur.

[0017] An IoT-based school equipment lifecycle quality traceability system is disclosed, comprising: Basic Data and Identifier Resolution Module: Responsible for establishing a unified language and identity ID, initializing digital archives, and establishing the initial binding between physical components and digital archives; Production process transparency monitoring module: This module is responsible for real-time data collection and quality control within the workshop; Smart Logistics and Supply Chain Collaboration Module: This module solves the problems of tracking the flow of products after they leave the factory and preventing cross-selling; Terminal Delivery and Proactive Maintenance Module: This module serves school users, extending traceability to the usage stage. Based on digital twin models and integrated sensor RFID data, it automatically pushes maintenance reminders according to a preset cycle. Data Decision and Security Traceability Module: Utilizing blockchain technology to ensure data immutability, it supports reverse tracing from finished products back to raw materials, suppliers, and production batches. Based on data such as service life, maintenance frequency, and failure rate, it provides data support for school procurement budget formulation and enterprise product iteration.

[0018] Example 2 This embodiment takes the "student dormitory combination bed" produced by a school furniture manufacturing company as an example to describe in detail the actual application process of the traceability method.

[0019] S1. Standard Construction and Source Code Assignment: Before launching the traceability system, the company deployed a private cloud platform and edge computing nodes, and established unified data standards. For a batch of cold-rolled steel pipes purchased from supplier A, the company implanted anti-metal RFID tags before they entered the warehouse. The tags contained the company code (XYZ), category code (BED01), batch number (B20241015), and serial number (0001~5000), and were also linked to the supplier's test report (including mechanical properties and coating thickness) and environmental certification. When the bed frame columns came off the production line, high-strength structural adhesive and embedded slots were used to fix the RFID tags in non-stressed, concealed locations. The initial bending strength (450MPa), weld strength, and other parameters of the component were recorded for the first time using a reader and simultaneously uploaded to the cloud digital archive. S2. Production process transparency and quality assurance: At the assembly station, the edge computing reader automatically records that operator "Zhang Moumou" completed the connection between the crossbeam and the column at 14:23:05 on March 10, 2025, with a tightening torque of 35 N·m. The edge banding process records the hot melt adhesive temperature (180℃) and pressure (0.6MPa). When the bed board enters the quality inspection access control, the quality inspector records the formaldehyde release (0.08mg / m³, lower than the national standard) and structural stability test (no shaking). If the formaldehyde content of a certain batch of bed boards is measured at 0.15mg / m³ (exceeding the standard), the system automatically locks the item, prohibits it from entering the packaging process, and pushes an alarm to the quality supervisor. The workshop temperature and humidity sensor records the temperature as 22℃ and the humidity as 55%RH on that day to ensure that the gluing process is compliant. S3. Multi-level Packaging and Smart Logistics: The company establishes a four-level association: individual item (bed RFID) → box code (carton contains 2 sets of beds) → pallet (20 boxes per pallet) → storage location (3 rows and 5 columns in warehouse A). After scanning the individual item at the packaging station, a box code is automatically generated and affixed. When a forklift passes through the access gate, a fixed reader identifies 40 RFID tags on the pallet within 3 seconds, and the system automatically assigns a storage location. When goods are shipped to the Fifth Middle School of a certain city, the logistics system binds the accompanying tracking number DL20250401-008. Upon delivery, a handheld terminal verifies the delivery address. If the actual delivery address does not match the order, the system automatically issues a "cross-regional shipment warning." S4. Terminal Delivery and Proactive Maintenance: School logistics staff used a mobile app to scan the RFID tags of 50 beds in batches. Within 2 seconds, an asset ledger (including brand, purchase date, and warranty period) was automatically generated and compared with the purchase order. The acceptance time was shortened from 2 days to 20 minutes. Three months later, a bed frame showed slight shaking. The maintenance personnel scanned the tag and immediately retrieved the batch number (B20241015) of the steel pipe used in the bed and the assembly record (operator Zhang, torque 35 N·m). After ruling out production and usage issues, the problem was identified as a design weakness. The system analyzed the usage frequency (5 times a day) based on a digital twin model and automatically pushed a "tighten screws" maintenance reminder in the 6th month, and recorded the maintenance history (including operator, tightening torque, and rust prevention treatment status). S5. Data-driven and closed-loop decision-making: Six months later, the system's edge intelligence algorithm discovered that the failure rate (shaking repair rate) of supplier A's steel pipe batch B20241015 was 12.3%, significantly higher than supplier B's batch of the same model (2.1%). The system automatically identified the quality bottleneck as the deviation in steel pipe wall thickness. On a certain day, the quality inspection found abnormal formaldehyde data. The system retrieved the affected products in seconds and found that they had been sent to the warehouse of "City No. 5 Middle School" (120 sets) and "Sunshine Primary School" (80 sets have been installed). The company immediately initiated a precise recall. At the end of the year, the system comprehensively evaluated a batch of school furniture that had been in service for 9 months and had a repair frequency of 0.15 times / bed, providing data support for the school's procurement budget for the next year (suggesting that supplier B should be preferred) and for the company to improve the design of the bed connection structure.

[0020] As can be seen from the above embodiments, this method realizes transparent traceability and proactive operation and maintenance of the entire life cycle of school equipment, from raw materials, production, logistics, use to scrapping and recycling.

Claims

1. A method for tracing the quality of school equipment throughout its entire lifecycle based on the Internet of Things, characterized in that, Includes the following steps; S1. Standard Construction and Source Coding: Before starting traceability, build the hardware and software foundation to support the operation of the system, establish a unique identification system of enterprise code + category code + batch + serial number, and embed anti-metal / high temperature resistant RFID tags into the core raw materials of plates and steel pipes, bind the supplier, environmental protection test report and batch information, and solidify the electronic tags through embedded and high-strength adhesive methods when the core components of desktops and bed frames are off the production line, complete the initial association between physical components and digital files, and record the initial mechanical performance parameters of the components simultaneously; S2. Production Process Transparency and Quality Lock-in: Using readers deployed on the production line, the system records the operators, equipment parameters, and operation times at key assembly and edge-sealing stations, forming a process history and implementing a quality inspection access control mechanism. Quality inspectors input data on structural stability and formaldehyde release. When a product fails to meet standards, the system automatically locks it, preventing it from entering the next stage. Through miniature environmental sensors deployed in the workshop, the system automatically collects temperature, humidity, and pressing pressure data during production, ensuring process compliance and traceability. S3. Multi-level packaging and smart logistics: Establish a four-level data association between single item, box, pallet, and storage location. When packaging, the box code is automatically bound to the single item. When entering the warehouse, the pallet is bound to the storage location. When forklifts / AGVs pass through the access gate, fixed readers automatically identify the batches. The logistics link is bound to the accompanying note. When delivering, the area information is verified through a handheld terminal. The system automatically identifies and warns of cross-regional delivery behavior. S4. Terminal Delivery and Proactive Maintenance: The school's logistics department uses mobile devices for batch scanning, generating asset ledgers in seconds and automatically comparing them with purchase orders, significantly shortening the acceptance cycle. Maintenance personnel can also scan tags to retrieve raw material batches and production records, quickly determining the root cause of faults: design, production, and use. Based on a digital twin model combined with real-time usage data of school equipment: collected through RFID tags with integrated sensors, maintenance reminders are automatically pushed according to preset cycles, and the history of tightening and rust prevention maintenance is recorded. S5. Data-driven and closed-loop decision-making: Quality reverse traceability, based on blockchain-stored full lifecycle data, combined with edge intelligence algorithms to analyze the failure rate of raw materials from different suppliers and the repair rate of production batches, accurately locates quality bottlenecks, discovers environmental or safety hazards, and the system can retrieve the flow of affected products in seconds: warehouses, schools, to achieve precise recall. Full lifecycle evaluation: based on service life and maintenance frequency data, it provides data support for school procurement budgets and enterprise product iteration.

2. The method for traceability of the entire lifecycle quality of school equipment based on the Internet of Things as described in claim 1, characterized in that, Step S1 involves establishing the hardware and software infrastructure to support the system's operation, as detailed below; Hardware layer deployment: Fixed RFID readers and antennas are deployed at key workstations on the production line: cutting, edge banding, drilling, assembly, warehouse entrances and exits, and quality inspection stations. Handheld PDA terminals are provided for warehouse management personnel. At the same time, RFID smart tool cabinets / shelves are deployed for the management of high-value molds and key components. Software platform setup: Establish and integrate a traceability management system, adopting a cloud platform + local server architecture to achieve data security and real-time synchronization, covering modules such as school equipment file management, production execution MES, warehouse management WMS, quality traceability, and maintenance management.

3. The method for quality traceability of school equipment throughout its entire lifecycle based on the Internet of Things as described in claim 1, characterized in that, Step S3 establishes a four-level data association of "item-box-pallet-warehouse location". The core of this step is to use automation to establish and record data binding relationships at each level of the packaging, palletizing, and warehousing process, forming a complete and traceable data chain. The specific steps are as follows: S31. Packaging process: Establish "item-box" association: bind each individual item to its corresponding packaging box; S311. Individual Product Coding: At the end of the production line, each individual product—a desk or a chair—is assigned a unique identification code. This code is associated with its production batch, specifications, and basic information. S312. Automatic data collection and binding: When a single item enters the automatic packing station, the visual recognition system deployed here reads the item's identification code at high speed; S313. Establish hierarchical relationship: At the same time, the box code of the currently used empty box will be obtained. The traceability system will automatically associate the identification codes of all items in the box with this unique box code to form a hierarchical data relationship of one box code corresponding to multiple item codes. S32. Palletizing process: Establish a "box-pallet" relationship: After the packaging boxes come off the production line, they are neatly stacked on pallets for transportation and storage, establishing a second layer of relationship; S321. Pallet coding: Each pallet used to carry goods is also assigned a unique identification code: pallet code; S322. Whole Pallet Batch Data Acquisition: After a whole pallet of packaging boxes is stacked, the whole pallet of goods is scanned by a fixed reader and an industrial camera. The system will read the box codes of all packaging boxes on the pallet at once. S323. Complete hierarchical binding: The system automatically binds these box codes to pallet codes, forming an association relationship of "one pallet code corresponding to multiple box codes"; S33. Inbound process: Establish "pallet-warehouse location" association: bind physical location information with goods information to achieve digital management of the warehouse; S331. Storage location coding: Each storage location in the warehouse has a unique identification code: storage location code, which is fixed on the shelf in the form of barcode and RFID tag; S332. Automatic identification and association: When a forklift or automated guided vehicle (AGV) carrying a pallet passes through the access gate at the warehouse entrance, a fixed reader installed on the gate will automatically read the pallet code; S333. Binding Location Information: The forklift driver and the Warehouse Management System (WMS) will place the pallet in the designated storage location. By scanning the storage location code with a handheld terminal or vehicle-mounted device, the system will automatically bind the pallet code to the storage location code and record the entry time. Through the automated operation of the above three steps, a four-level data association system of "single item-box-pallet-warehouse location" is established.

4. The method for quality traceability of school equipment throughout its entire lifecycle based on the Internet of Things as described in claim 1, characterized in that, In step S4, based on the digital twin model and combined with real-time usage status data of the school equipment: data is collected through RFID tags with integrated sensors. The digital twin model is based on the following two aspects: Precise mapping between physical and digital models: Geometric and Attribute Modeling: CAD / BIM technology is used to construct a three-dimensional geometric model of the training fixture and to assign it physical properties such as material, design load-bearing capacity, and fatigue life, forming a static digital base; Virtual and physical identity binding: The unique ID of the integrated sensor RFID tag fixed on the physical training equipment is bound one-to-one with the digital model; Real-time data acquisition and synchronization mechanism: Multi-dimensional sensing and data acquisition: Integrated sensor RFID tags collect load pressure, vibration frequency, and ambient temperature and humidity data in real time, and upload them to the cloud via an IoT gateway; Two-way drive synchronization: It adopts an event-driven + periodic synchronization mechanism. When the sensor detects data anomalies, it triggers the state update of the digital model in real time through the MQTT protocol to achieve millisecond-level synchronization between virtual and real states.

5. The method for full lifecycle quality traceability of school equipment based on the Internet of Things as described in claim 1, characterized in that, In step S5, quality reverse traceability is based on the full lifecycle data stored on the blockchain. The specific details of storing the full lifecycle data on the blockchain are as follows: Key data node identification: Identify the key data points that need to be uploaded to the blockchain throughout the product's entire lifecycle, including: Raw material stage: supplier information, raw material batch number, environmental testing report: formaldehyde, heavy metal content, mechanical performance parameters; Production stage: production batch, key process parameters: pressing temperature, pressure, quality inspection results, operator information; Logistics and delivery phase: outbound time, logistics trajectory, delivered school, installation location; Maintenance phase: Repair reports, fault symptoms, repair history, and component replacement records; Data on-chain strategy: Not all data is directly on-chain, in order to balance performance and cost. Core data on-chain: The hash values ​​of the above key data (digital fingerprints) and core summary information (supplier ID, batch number, and test results) are written to the blockchain in real time to form an immutable evidence. Raw data storage: Massive amounts of raw data, such as complete test report PDFs and high-frequency sensor data, are stored in off-chain databases, but their hash values ​​on-chain ensure that the raw data has not been tampered with. Forming a trusted data chain: Through the chain structure of blockchain, every link of a product from raw materials to scrap is connected to form a complete, transparent, and traceable data chain. When traceability is needed, all historical information of the affected product can be retrieved in seconds.

6. The method for full lifecycle quality traceability of school equipment based on the Internet of Things as described in claim 1, characterized in that, In step S5, the edge intelligent algorithm analyzes the failure rate of raw materials from different suppliers and the repair rate of production batches, accurately locates quality bottlenecks, and discovers environmental and safety hazards. The intelligent algorithm deployed on the edge side—factory server and regional data center—is used for real-time and near real-time analysis. The specific steps are as follows: S51. Multi-dimensional quality indicator calculation: Raw material batch information from different suppliers is read from the blockchain and correlated with repair data from the operation and maintenance phase. This is then used to calculate... This allows us to determine the raw material failure rate for each supplier and accurately identify suppliers with unstable quality. Production batch repair rate analysis: Analyze the repair status of products in different production batches. When the repair rate of a certain batch or several batches is significantly higher than the average level, they are marked as high-risk batches, and the production records of the batch are automatically traced to check whether there are abnormal process parameters or quality inspection omissions. S52. Environmental and Safety Hazard Identification: Continuously monitor the environmental testing data uploaded to the blockchain. When the formaldehyde emission of a certain batch of boards is qualified but close to the critical value, conduct correlation analysis in conjunction with the subsequent repair data of the batch of products. Once a potential correlation is found, issue an environmental risk warning immediately, even if the individual data point does not exceed the standard. Combine the digital twin model and real-time collected usage status data: load, usage frequency, to predict potential structural safety risks.

7. A school equipment full lifecycle quality traceability system based on the Internet of Things, characterized in that, The IoT-based school equipment lifecycle quality traceability system is used to implement any one of the IoT-based school equipment lifecycle quality traceability methods as described in claims 1-6; the IoT-based school equipment lifecycle quality traceability system includes: Basic Data and Identifier Resolution Module: Responsible for establishing a unified language and identity ID, initializing digital archives, and establishing the initial binding between physical components and digital archives; Production process transparency monitoring module: This module is responsible for real-time data collection and quality control within the workshop; Smart Logistics and Supply Chain Collaboration Module: This module solves the problems of tracking the flow of products after they leave the factory and preventing cross-selling; Terminal delivery and proactive maintenance module: This module serves school users, extending traceability to the usage stage. Based on digital twin models and integrated sensor RFID data, it automatically pushes maintenance reminders according to a preset cycle. Data Decision and Security Traceability Module: Utilizing blockchain technology to ensure data immutability, it supports reverse tracing from finished products back to raw materials, suppliers, and production batches. Based on data such as service life, maintenance frequency, and failure rate, it provides data support for school procurement budget formulation and enterprise product iteration.

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