Fiber fabric production tracing system and method based on Internet of Things

Abstract

The invention relates to the technical field of production management traceability, in particular to a fiber fabric production traceability system and method based on the Internet of Things. The system is characterized in that a data acquisition processing unit acquires fiber fabric production data, a fiber fabric state image and machine operation state data of each production stage; the main double-linked list construction unit constructs a fiber fabric main double-linked list of each batch of fiber fabrics; the grid tracing chain table construction unit is used for bidirectionally linking comprehensive label nodes in all fiber fabric main double chain tables in the same machine production and the same stage according to a time batch sequence to construct a fabric production grid tracing chain table; and the fabric chain type tracing unit monitors whether the information of each batch of fiber fabric is tampered or not in real time by using an entropy change chain type tracing mechanism and traces the source of the quality problem of the fiber fabric. According to the method, an entropy change chain type tracing mechanism is used on the basis of a fabric production grid tracing chain table, and cross-batch and cross-equipment full-stage tracing of the fiber fabric is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of production management traceability, and particularly relates to a fiber fabric production traceability system and method based on the Internet of Things. Background Art

[0002] With the increasing attention of consumers to product quality and origin, the textile industry has also paid more and more attention to the transparency and traceability of the production process. The fiber fabric production traceability technology aims to improve the transparency of the production process and ensure the traceability of product quality. By collecting and recording various data in the production process, each link of fabric production is controlled to achieve comprehensive traceability from raw materials to finished products. The goal is to ensure product quality, optimize the production process, and provide consumers with credible production source information.

[0003] Traditional fiber fabric production traceability technologies mainly rely on barcodes, RFID, ERP systems, etc. to trace product data. Most of these systems operate independently and lack effective data integration means, resulting in scattered data storage and difficulty in achieving integration across links and devices. Moreover, RFID scanners usually require manual data entry to read tags, which poses a risk of manual tampering with tag marks. Since physical tags require manual operation, cannot record the production process in real time, and there are multiple factors in product quality problems, the quality information of products is at risk of being tampered with manually during the production process, and product quality problems and machine failures cannot be discovered in a timely manner, resulting in difficulties in full-stage quality monitoring and traceability of products throughout the process. Summary of the Invention

[0004] The purpose of the present invention is to provide a fiber fabric production traceability system and method based on the Internet of Things to solve the problems mentioned in the above background art, namely, due to the need for manual operation of physical tags, the inability to record the production process in real time, and multiple factors in product quality problems, the quality information of products is at risk of being tampered with manually during the production process, and product quality problems and machine failures cannot be discovered in a timely manner, resulting in difficulties in full-stage quality monitoring and traceability of products throughout the process.

[0005] To achieve the above purpose, the invention aims to provide a fiber fabric production traceability system based on the Internet of Things, including: A data acquisition and processing unit, which uses Internet of Things sensors to collect fiber fabric production data, fiber fabric status images, and machine operation status data at each production stage; A main double-linked list construction unit, which constructs comprehensive label nodes of each batch of fiber fabrics at each production stage based on the fiber fabric production data and fiber fabric status images, and inserts all the comprehensive label nodes of this batch of fiber fabrics into the head node in the order of fiber fabric batches and production time to construct the main double-linked list of each batch of fiber fabrics; A grid traceability linked list construction unit, which unidirectionally links the head nodes of the main double linked list of each batch of fiber fabrics in the order of time batches to obtain a single linked list of fiber fabric batches, and bidirectionally links the comprehensive label nodes produced by the same machine and in the same stage in all the main double linked lists of fiber fabrics in the order of time batches to obtain a sub double linked list of fiber fabrics, and constructs a machine label node for each machine based on the machine operation state data and inserts it at the end of each sub double linked list of fiber fabrics, and finally constructs a fabric production grid traceability linked list; A fabric chain traceability unit, which, based on the fabric production grid traceability linked list, uses an entropy change chain traceability mechanism to monitor in real time whether the information of each batch of fiber fabrics has been tampered with and trace the source of the fiber fabric quality problem when a quality problem occurs in the fiber fabric.

[0006] Preferably, the production stages include a raw material preparation stage, a spinning stage, a weaving stage, a dyeing and finishing stage, and a finished product processing and packaging stage; The fiber fabric production data includes the temperature, humidity, tension, pressure, production speed of each production stage, and the basic information of the fabric raw materials; The fiber fabric state image is the fabric image data obtained in real time by a camera and is used to record the quality state of the fiber fabric; The machine operation state data includes the machine temperature, vibration signal, load power data, and start-stop state data.

[0007] Preferably, the main double linked list construction unit includes a comprehensive label node construction module and a fiber fabric main linked list construction module; The comprehensive label node construction module constructs comprehensive label nodes for each batch of fiber fabrics in each production stage based on the fiber fabric production data and the fiber fabric state image; Among them, the comprehensive label node is used to store the fiber fabric information of this node in each production stage and the state information of the previous and subsequent comprehensive label nodes.

[0008] Preferably, the fiber fabric main linked list construction module inserts all the comprehensive label nodes of each batch of fiber fabrics at the end of the head node in the order of fiber fabric batches and production time to construct the main double linked list of each batch of fiber fabrics; Among them, the head node is the first node for constructing the main double linked list of fiber fabrics. The head node is used to store the batch information of the fiber fabrics, and the number of head nodes is the same as the number of fiber fabric batches.

[0009] Preferably, the grid traceability linked list construction unit includes a fiber fabric batch single linked list construction module, a fiber fabric sub linked list construction module, and a fabric production grid traceability linked list generation module; Among them, the fiber fabric batch single-linked list construction module unidirectionally links the head nodes of the main double-linked lists of fiber fabrics in each production batch in the order of time batches to construct a fiber fabric batch single-linked list.

[0010] Preferably, the fiber fabric sub-linked list construction module bidirectionally links the comprehensive label nodes produced by the same machine and at the same stage in all the main double-linked lists of fiber fabrics in the order of time batches to obtain a fiber fabric sub-double-linked list; The fabric production grid traceability linked list generation module tail-insert the machine label nodes of each machine at the end of the fiber fabric sub-double-linked list respectively, and finally obtains a fabric production grid traceability linked list with the main double-linked list of fiber fabrics horizontally, the fiber fabric batch single-linked list vertically, and the fiber fabric sub-linked list; Among them, the machine label node stores the machine operation state data, which is used to record and monitor the machine state at each production stage of the fiber fabric; Among them, the fabric production grid traceability linked list is used for traceability of fiber fabrics without quality problems and traceability of fiber fabrics with quality problems.

[0011] Preferably, the fabric chain traceability unit uses the entropy change chain traceability mechanism based on the fabric production grid traceability linked list to trace the reasons for fabric quality problems caused by the quality of the fabric itself and the machine quality, and to trace the reasons for fiber fabric quality problems caused by human factors; Among them, fabric quality problems include fabric itself problems, machine problems, and human factors.

[0012] Preferably, the entropy change chain traceability mechanism is implemented based on information entropy, blockchain, and Internet of Things technologies, and is used to monitor the fabric production status in real time and trace the source of fabric quality problems; The entropy change chain traceability mechanism is specifically as follows: S4.1. Trace the source of quality problems caused by the fabric itself: Calculate the information entropy values of the comprehensive label nodes of all batches, and calculate the composite entropy value of the main double-linked list of fiber fabrics by weighted calculation. Screen the main double-linked list of fiber fabrics with the largest jump in the composite entropy value of the next main double-linked list of fiber fabrics. The batch of fiber fabrics recorded in this linked list is the batch with fabric quality problems. Then, by analyzing the fiber fabric state image and the basic information of the fabric raw materials recorded in the comprehensive label nodes of the main double-linked list of fiber fabrics in this batch, determine the batch with fabric quality problems; S4.2. If the quality problems caused by the fabric itself in S4.1 are excluded, then trace the source of quality problems caused by machine problems: Based on the batches with fabric quality problems in S4.1, in the main doubly linked list of fiber fabrics of the batch where the fabric quality problem first appears, traverse all comprehensive label nodes in sequence, and screen a quality problem comprehensive label node with the largest jump in the information entropy value between the comprehensive label node and the adjacent comprehensive label nodes in the sub-doubly linked list of the fiber fabric it belongs to. That is, it is determined that there is a problem with the production stage and machine where this quality problem comprehensive label node is located. Traverse to the machine label node based on the sub-doubly linked list of the fiber fabric where the quality problem comprehensive label node is located, and analyze the machine operation status data of the machine label node to determine the problem machine; S4.3. If the fabric quality problems caused by the fabric itself in S4.1 and the quality problems caused by machine problems in S4.2 are excluded, then trace the source of the quality problems caused by human factors: Combined with blockchain technology, based on the batches with fabric quality problems in S4.1, screen the main doubly linked list of fiber fabrics with the largest jump in the composite entropy value of the next main doubly linked list of fiber fabrics, and analyze the status information and the change of information entropy value of the pre-order and post-order comprehensive label nodes recorded in all comprehensive label nodes in the main doubly linked list of fiber fabrics with the largest jump in the composite entropy value; If both the pre-order comprehensive label node and the post-order comprehensive label node in a comprehensive label node record that the comprehensive label node has received information changes, and the information entropy value of this comprehensive label node changes, it proves that this comprehensive label node has been tampered with by humans.

[0013] Preferably, in S4.2, after determining the machine problem, continue to calculate the information entropy value of the subsequent comprehensive label nodes in the sub-doubly linked list of the fiber fabric after the selected quality problem comprehensive label node, and compare it with the information entropy value of the selected quality problem comprehensive label node. If the difference is less than the error threshold, it proves that the fiber fabric of the subsequent batch where the comprehensive label node is located also has the same machine problem.

[0014] On the other hand, the present invention provides an Internet of Things-based fiber fabric production traceability method for an Internet of Things-based fiber fabric production traceability system described above, including the following steps: S10.1. Use Internet of Things sensors to collect fiber fabric production data, fiber fabric status images, and machine operation status data at each production stage; S10.2. Based on the fiber fabric production data and fiber fabric status images, construct comprehensive label nodes for each batch of fiber fabrics at each production stage, and insert all the comprehensive label nodes of this batch of fiber fabrics into the head node in the order of fiber fabric batch and production time to construct the main doubly linked list of each batch of fiber fabrics; S10.3. Unidirectionally link the head nodes of the main doubly linked list of each batch of fiber fabrics in the order of time batches to obtain a singly linked list of fiber fabric batches. Bidirectionally link the comprehensive label nodes of the same machine production and the same stage in all the main doubly linked lists of fiber fabrics in the order of time batches to obtain a sub-doubly linked list of fiber fabrics. Based on the machine operation status data, construct machine label nodes for each machine and insert them at the end of each sub-doubly linked list of fiber fabrics. Finally, construct a fabric production grid traceability linked list; S10.4. Based on the fabric production grid traceability linked list, use the entropy change chain traceability mechanism to monitor in real time whether the information of each batch of fiber fabrics has been tampered with and trace the source of the fiber fabric quality problems when quality problems occur in the fiber fabrics; Among them, the fabric quality problems include fabric itself problems, machine problems, and human factors.

[0015] Compared with the prior art, the above technical solutions of the present invention have the following beneficial technical effects: 1. In the present invention, based on the fabric production grid traceability linked list, through the data collected in real time by the Internet of Things devices and sensors, the production quality fluctuations of each batch of fabrics at each stage during the production process can be monitored in real time. The production data of each batch of fabrics at each stage are closely linked and record the real-time production information with each other, realizing real-time monitoring and control of the record at the end of production; 2. In the present invention, by constructing the entropy change chain traceability mechanism and combining it with the fabric production grid traceability linked list, calculating the information entropy change, discovering problems in the production process in a timely manner and efficiently tracing the reasons for the fiber fabric production quality problems, it can not only ensure the authenticity of the production data, avoid quality problems caused by data tampering, but also achieve comprehensive traceability across batches and devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a principle block diagram of an embodiment proposed by the present invention; Reference numerals: 1. Data acquisition and processing unit; 2. Main doubly linked list construction unit; 21. Comprehensive label node construction module; 22. Fiber fabric main linked list construction module; 3. Grid traceability linked list construction unit; 31. Fiber fabric batch singly linked list construction module; 32. Fiber fabric sub-linked list construction module; 33. Fabric production grid traceability linked list generation module; 4. Fabric chain traceability unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Embodiment 1, as Figure 1 shown, provides a fiber fabric production traceability system based on the Internet of Things, including: A data acquisition and processing unit 1, and the data acquisition and processing unit 1 uses Internet of Things sensors to collect fiber fabric production data, fiber fabric status images, and machine operation status data at each production stage; The production stage includes a raw material preparation stage, a spinning stage, a weaving stage, a dyeing and finishing stage, and a finished product processing and packaging stage; The fiber fabric production data includes the temperature, humidity, tension, pressure, production speed of each production stage, and the basic information of the fabric raw materials; The fiber fabric status image is the fabric image data obtained in real time by a camera, which is used to record the quality status of the fiber fabric; The machine operation status data includes the machine temperature, vibration signal, load power data, and start-stop status data; In this embodiment, the Internet of Things sensors include temperature and humidity sensors, tension and pressure sensors, camera vision sensors, and production speed sensors; The temperature and humidity sensors are used in the raw material preparation stage, spinning stage, weaving stage, and dyeing and finishing stage. In the dyeing and finishing stage, the temperature and humidity sensors are installed around the dyeing machine to monitor the changes in temperature and humidity in real time, ensure the uniformity of dyes and the stability of the fabric, record the temperature and humidity data every minute, and upload it to the local and cloud servers for storage; The tension and pressure sensors are used in the spinning stage and the weaving stage. In the warp tension control system of the weaving machine, the tension sensor monitors the tension of the warp yarn in real time during the weaving process, and the data is uploaded to the central control system to adjust the machine settings to ensure that the fabric does not have stretching or shrinking problems; The production speed sensors are used to monitor the production speeds of the spinning machines and the weaving machines. The production speed sensors are installed on the spinning machines to monitor the production speed of the yarn and record the production speed data; The camera vision sensors are high-definition cameras installed at various important links on the production line to collect the status images of the fabric in each production stage; All the collected data is uploaded to the local and cloud servers in real time through the Internet of Things devices for processing and storage.

[0018] The main double-linked list construction unit 2, the main double-linked list construction unit 2 constructs the comprehensive label nodes of each batch of fiber fabrics in each production stage based on the fiber fabric production data and the fiber fabric status images, and inserts all the comprehensive label nodes of this batch of fiber fabrics into the head node in the order of fiber fabric batches and production time to construct the main double-linked list of each batch of fiber fabrics; The main double-linked list construction unit 2 includes a comprehensive label node construction module 21 and a fiber fabric main list construction module 22; The comprehensive label node construction module 21 constructs the comprehensive label nodes of each batch of fiber fabrics in each production stage based on the fiber fabric production data and the fiber fabric status images; Among them, the comprehensive label node is used to store the fiber fabric information of this node in each production stage, as well as the status information of the previous and subsequent comprehensive label nodes.

[0019] In this embodiment, the data node structure of the comprehensive label node includes: ID (unique identifier of the comprehensive label node): Use the batch number + production stage number + equipment number to construct a unique ID; Production stage information: Record the production stage information represented by the current node; Basic fiber fabric information: Record the fiber fabric information related to the current production stage, including the raw material type, fabric strength, thickness, texture, etc. of the fabric; Fiber fabric status image: The fabric status image data collected by the camera, record the fabric quality of each production stage, and identify the surface defects of the fabric through image processing technology; Two previous node pointers: One of the previous node pointers points to the previous comprehensive label node of the main double linked list of the fiber fabric, and the other previous node pointer points to the previous comprehensive label node of the sub double linked list of the fiber fabric; Two successor node pointers: One of the successor node pointers points to the successor comprehensive label node of the main double linked list of the fiber fabric, and the other successor node pointer points to the successor comprehensive label node of the sub double linked list of the fiber fabric; Node status information: Store the status information of the current node, previous node and successor node, including two states of "normal" and "abnormal", and identify whether there is an abnormality in this node through the change of entropy value; Production timestamp: Each node will also record the time information of the production stage, which is used to trace the time sequence of fabric production.

[0020] In this embodiment, the data of each comprehensive label node is recorded in the blockchain using blockchain technology to ensure that the data of each link in the production process is tamper-proof. If it is found that the entropy value of a certain node changes greatly and the blockchain record shows that the data of this node has a modification record, it can be traced back to the specific operator and modification time to ensure the integrity and non-tamperability of the data; combined with blockchain technology, the system can not only query the historical data of each production batch, but also analyze the machine status, operators and specific conditions of each production node; Through the fabric production grid trace linked list structure, users can quickly query the specific information of each production batch, including the detailed data of each production stage, equipment status, fabric images, etc.; Through the previous and successor pointers of the node, the system supports flexible forward and backward tracing to help quickly locate quality problems that occur in the production process; The fiber fabric main chain list construction module 22 inserts the tail of all comprehensive label nodes of each batch of fiber fabrics into the head node according to the order of fiber fabric batches and production time, and constructs the main double chain list of each batch of fiber fabrics; Among them, the head node is the first node to construct the main double linked list of fiber fabrics. The head node is used to store the batch information of fiber fabrics. The number of head nodes is the same as the batch of fiber fabrics.

[0021] A grid traceability linked list construction unit 3, wherein the grid traceability linked list construction unit 3 unidirectionally links the head node of each batch of fiber fabric main double linked list in a time batch order to obtain a fiber fabric batch single linked list, and bidirectionally links the comprehensive label nodes produced by the same machine and at the same stage in all fiber fabric main double linked lists in a time batch order to obtain a fiber fabric sub-double linked list, and constructs a machine label node for each machine based on the machine operation status data and inserts the tail into each fiber fabric sub-double linked list, and finally constructs a fabric production grid traceability linked list; The grid traceability chain table construction unit 3 includes a fiber fabric batch single chain table construction module 31, a fiber fabric sub-chain table construction module 32 and a fabric production grid traceability chain table generation module 33; The fiber fabric batch single linked list construction module 31 unidirectionally links the head nodes of the fiber fabric main double linked list of each production batch according to the time batch sequence to construct a fiber fabric batch single linked list.

[0022] In this embodiment, the main double linked list of each production batch contains multiple comprehensive label nodes. The head node is extracted from the main double linked list of each batch. This node contains the basic information of the batch. According to the production time and batch sequence, the tail of the head node of each batch is inserted into the batch linked list in chronological order to construct a fiber fabric batch single linked list; the batch sorting can be achieved through the production timestamp to ensure that each batch is inserted into the linked list in the order of the actual production time; the nodes of each batch are linked together through the linked list pointer to facilitate subsequent query and traceability.

[0023] The fiber fabric sub-link list construction module 32 bidirectionally links all the comprehensive label nodes in the fiber fabric main double-link list that are produced by the same machine and at the same stage according to the time batch sequence to obtain a fiber fabric sub-double-link list.

[0024] In this embodiment, the main double-linked list of each production batch is analyzed to identify the production equipment and production stage. If the spinning machine is responsible for the production of multiple batches, the module will extract the data in these batches and sort them in chronological order. The comprehensive label nodes of the same equipment and the same production stage are bidirectionally linked in the order of time batches to obtain the fiber fabric sub-double-linked list; Each comprehensive label node of the fiber fabric doubly linked list contains the production data of a certain piece of equipment in this production stage; each comprehensive label node of the fiber fabric doubly linked list is connected by a two-way pointer to ensure that it can be traced forward or backward from any node. If a quality problem is found at a certain node, it can be traced back to other relevant nodes in this batch, or the production data of different batches of the same equipment can be analyzed; The fabric production grid traceability linked list generation module 33 inserts the machine label nodes of each machine at the end of the fiber fabric sub-doubly linked list respectively, and finally obtains the fabric production grid traceability linked list with the fiber fabric main doubly linked list horizontally, the fiber fabric batch singly linked list vertically, and the fiber fabric sub-linked list; Among them, the machine label node stores the machine operation status data, which is used to record and monitor the machine status of each production stage of the fiber fabric; Among them, the fabric production grid traceability linked list is used for traceability of fiber fabric without quality problems and traceability of fiber fabric with quality problems.

[0025] In this embodiment, the fiber fabric main doubly linked list, the fiber fabric batch singly linked list, and the fiber fabric sub-doubly linked list are combined to form the final fabric production grid traceability linked list, and the operation status data of each machine is inserted as a machine label node at the end of the sub-linked list: By unidirectionally linking the head nodes of the fiber fabric main doubly linked list of each production batch in the order of time batches, and at the same time bidirectionally linking the sub-linked lists of each production equipment in the order of production batches; inserting a machine label node at the end of each sub-linked list, which is used to store the operation status data of each machine; The content recorded by each machine label node includes: the temperature, load, vibration signal, start-stop status data of the machine.

[0026] The fabric chain traceability unit 4, the fabric chain traceability unit 4 is based on the fabric production grid traceability linked list, and uses the entropy change chain traceability mechanism to monitor in real time whether the information of each batch of fiber fabric is tampered with and trace the source of the fiber fabric quality problem when the fiber fabric has a quality problem; The fabric chain traceability unit 4 traces the reasons for the fabric quality problems caused by the quality of the fabric itself and the machine quality, as well as the reasons for the fiber fabric quality problems caused by human factors based on the fabric production grid traceability linked list using the entropy change chain traceability mechanism; Among them, the fabric quality problems include fabric itself problems, machine problems and human factors.

[0027] The entropy change chain traceability mechanism is implemented based on information entropy, blockchain and Internet of Things technology, and is used to monitor the fabric production status in real time and trace the source of fabric quality problems; The entropy change chain traceability mechanism is specifically as follows: S4.1. Trace the source of quality problems in the fabric itself: Calculate the information entropy value of the comprehensive label nodes of all batches, and calculate the composite entropy value of the main doubly linked list of fiber fabrics by weighted calculation. Screen the main doubly linked list of fiber fabrics with the largest jump in the composite entropy value of the main doubly linked list of the next fiber fabric. The batch of fiber fabrics recorded in this linked list is the batch with fabric quality problems. Then, by analyzing the fabric state image and the basic information of fabric raw materials recorded in the comprehensive label nodes of the main doubly linked list of this batch of fiber fabrics, determine the batch with fabric quality problems; In this embodiment, analyze the fabric state image and the basic information of fabric raw materials recorded in the comprehensive label nodes of the main doubly linked list of this batch of fiber fabrics, use the deep learning algorithm to detect defects in the fabric image, and combine the results of entropy value changes to further determine whether it is caused by the quality problems of the fabric itself; S4.2. If the quality problems caused by the fabric itself in S4.1 are excluded, trace the source of quality problems caused by machine problems: Based on the batch of fabric quality problems in S4.1, in the main doubly linked list of fiber fabrics of the batch where fabric quality problems first appear, traverse all comprehensive label nodes in sequence, and screen a quality problem comprehensive label node with the largest jump in the information entropy value of a comprehensive label node and the information entropy value of the adjacent comprehensive label node of its corresponding sub-doubly linked list of fiber fabrics. That is, it is screened out that there are problems in the production stage and machine where this quality problem comprehensive label node is located. Based on the sub-doubly linked list of fiber fabrics where the quality problem comprehensive label node is located, traverse to the machine label node, and analyze the machine operation state data of the machine label node to determine the problem machine; In this embodiment, if the machine state data is abnormal, the system will record the specific failure time and equipment state to help locate the specific faulty machine; in the machine label node, the change in the entropy value of the machine label node can reveal whether the machine has a fault; S4.3. If the quality problems caused by the fabric itself in S4.1 and the quality problems caused by machine problems in S4.2 are excluded, trace the source of quality problems caused by human factors: Combined with blockchain technology, based on the batch of fabric quality problems in S4.1, screen the main doubly linked list of fiber fabrics with the largest jump in the composite entropy value of the main doubly linked list of the next fiber fabric, and analyze the state information and information entropy value changes of the recorded previous and subsequent comprehensive label nodes in all comprehensive label nodes of this main doubly linked list of fiber fabrics with the largest jump in the composite entropy value; If both the previous comprehensive label node and the subsequent comprehensive label node in a comprehensive label node record that this comprehensive label node has received information changes, and the information entropy value of this comprehensive label node changes, it proves that this comprehensive label node has been tampered with by humans.

[0028] In S4.2, after determining the machine problem, continue to calculate the information entropy value of the subsequent comprehensive label nodes of the fiber fabric double linked list after the comprehensive label nodes of the quality problems screened out, and compare it with the information entropy value of the comprehensive label nodes of the quality problems screened out. If the difference is less than the error threshold, it proves that the fiber fabric in the batch where the subsequent comprehensive label nodes are located also has the same machine problem.

[0029] Embodiment 2. The present invention proposes an Internet of Things-based fiber fabric production traceability method for an Internet of Things-based fiber fabric production traceability system in Embodiment 1 above, including the following steps: S10.1. Use Internet of Things sensors to collect fiber fabric production data, fiber fabric status images, and machine operation status data at each production stage; S10.2. Based on the fiber fabric production data and fiber fabric status images, construct comprehensive label nodes for each batch of fiber fabric at each production stage. According to the fiber fabric batch and production time sequence, tail-insert all the comprehensive label nodes of this batch of fiber fabric into the head node to construct the main double linked list of each batch of fiber fabric; S10.3. Unidirectionally link the head nodes of the main double linked lists of each batch of fiber fabric according to the time batch sequence to obtain a fiber fabric batch single linked list, and bidirectionally link the comprehensive label nodes of the same machine production and the same stage in all the main double linked lists of fiber fabric according to the time batch sequence to obtain a fiber fabric sub double linked list. And construct machine label nodes for each machine based on the machine operation status data and tail-insert them into each fiber fabric sub double linked list. Finally, construct a fabric production grid traceability linked list; S10.4. Based on the fabric production grid traceability linked list, use the entropy change chain traceability mechanism to monitor in real time whether the information of each batch of fiber fabric is tampered with and trace the source of the fiber fabric quality problem when the fiber fabric has a quality problem; Among them, the fabric quality problems include fabric itself problems, machine problems, and human factors.

[0030] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to this. Various changes can be made without departing from the spirit of the present invention within the knowledge scope of those skilled in the art.

Claims

1. An Internet of Things-based fiber fabric production traceability system, characterized in that, Including: A data acquisition and processing unit (1), which uses Internet of Things sensors to collect fiber fabric production data, fiber fabric status images, and machine operating status data at each production stage; A main double-linked list construction unit (2), which constructs comprehensive label nodes of each batch of fiber fabrics at each production stage based on the fiber fabric production data and fiber fabric status images, and inserts all the comprehensive label nodes of this batch of fiber fabrics into the head node in the order of fiber fabric batches and production time to construct the main double-linked list of each batch of fiber fabrics; A grid traceability linked list construction unit (3), which unidirectionally links the head nodes of the main double-linked lists of each batch of fiber fabrics in the order of time batches to obtain a single-linked list of fiber fabric batches, and bidirectionally links the comprehensive label nodes of the same machine production and the same stage in all the main double-linked lists of fiber fabrics in the order of time batches to obtain a sub double-linked list of fiber fabrics, and constructs machine label nodes for each machine based on the machine operating status data and inserts them at the end of each sub double-linked list of fiber fabrics, and finally constructs a fabric production grid traceability linked list; A fabric chain traceability unit (4), which based on the fabric production grid traceability linked list, uses an entropy change chain traceability mechanism to monitor in real time whether the information of each batch of fiber fabrics has been tampered with and trace the source of the fiber fabric quality problem when the fiber fabric has a quality problem.

2. The fiber fabric production traceability system based on the Internet of Things according to claim 1, wherein The production stages include a raw material preparation stage, a spinning stage, a weaving stage, a dyeing and finishing stage, and a finished product processing and packaging stage; The fiber fabric production data includes the temperature, humidity, tension, pressure, production speed, and basic information of the fabric raw materials at each production stage; The fiber fabric status image is fabric image data obtained in real time through a camera and is used to record the quality status of the fiber fabric; The machine operating status data includes machine temperature, vibration signals, load power data, and start-stop status data.

3. The fiber fabric production traceability system based on the Internet of Things according to claim 2, characterized in that, The main double-linked list construction unit (2) includes a comprehensive label node construction module (21) and a fiber fabric main linked list construction module (22); The comprehensive label node construction module (21) constructs comprehensive label nodes of each batch of fiber fabrics at each production stage based on the fiber fabric production data and fiber fabric status images; Among them, the comprehensive label node is used to store the fiber fabric information of this node in each production stage and the status information of the previous and subsequent comprehensive label nodes.

4. The fiber fabric production traceability system based on the Internet of Things according to claim 3, characterized in that, The fiber fabric main linked list construction module (22) inserts all the comprehensive label nodes of each batch of fiber fabrics into the head node in the order of fiber fabric batches and production time to construct the main double-linked list of each batch of fiber fabrics; Among them, the head node is the first node for constructing the main double-linked list of fiber fabrics, and the head node is used to store the batch information of the fiber fabrics, and the number of head nodes is the same as the number of fiber fabric batches.

5. The fiber fabric production traceability system based on the Internet of Things according to claim 4, characterized in that The grid traceability linked list construction unit (3) includes a fiber fabric batch single-linked list construction module (31), a fiber fabric sub-linked list construction module (32), and a fabric production grid traceability linked list generation module (33); Among them, the fiber fabric batch single-linked list construction module (31) unidirectionally links the head nodes of the main double-linked lists of fiber fabrics in each production batch in the order of time batches to construct a fiber fabric batch single-linked list.

6. The fiber fabric production traceability system based on the Internet of Things according to claim 5, characterized in that, The fiber fabric sub-linked list construction module (32) bidirectionally links the comprehensive label nodes produced by the same machine and at the same stage in all the main double-linked lists of fiber fabrics in the order of time batches to obtain a fiber fabric sub-double-linked list; The fabric production grid traceability linked list generation module (33) tail-inserts the machine label nodes of each machine at the end of the fiber fabric sub-double-linked list respectively, and finally obtains a fabric production grid traceability linked list with the main double-linked list of fiber fabrics horizontally, the fiber fabric batch single-linked list vertically, and the fiber fabric sub-linked list; Among them, the machine label node stores the machine operation status data, which is used to record and monitor the machine status in each production stage of the fiber fabric; Among them, the fabric production grid traceability linked list is used for traceability of fiber fabrics without quality problems and traceability of fiber fabrics with quality problems.

7. The fiber fabric production traceability system based on the Internet of Things according to claim 6, characterized in that, The fabric chain traceability unit (4) traces the reasons for fabric quality problems caused by the quality of the fabric itself and the machine quality, and traces the reasons for fiber fabric quality problems caused by human factors based on the fabric production grid traceability linked list using the entropy change chain traceability mechanism; Among them, fabric quality problems include fabric itself problems, machine problems, and human factors.

8. The fiber fabric production traceability system based on the Internet of Things according to claim 7, characterized in that, The entropy change chain traceability mechanism is implemented based on information entropy, blockchain, and Internet of Things technologies, and is used to monitor the fabric production status in real time and trace the source of fabric quality problems; The entropy change chain traceability mechanism is specifically as follows: S4.

1. Trace the source of quality problems in the fabric itself: Calculate the information entropy values of the comprehensive label nodes of all batches, and calculate the composite entropy value of the main double-linked list of fiber fabrics by weighted calculation. Screen the main double-linked list of fiber fabrics with the largest jump in the composite entropy value of the next main double-linked list of fiber fabrics. The batch of fiber fabrics recorded in this linked list is the batch with fabric quality problems. Then, by analyzing the fabric status image and the basic information of the fabric raw materials recorded in the comprehensive label nodes of the main double-linked list of this batch of fiber fabrics, determine the batch with fabric quality problems; S4.

2. If the quality problems caused by the fabric itself in S4.1 are excluded, then trace the source of quality problems caused by machine problems: Based on the batch with fabric quality problems in S4.1, in the main double-linked list of fiber fabrics in the batch where fabric quality problems first appear, traverse all the comprehensive label nodes in turn, and screen a quality problem comprehensive label node with the largest jump in the information entropy value of a comprehensive label node and the information entropy value of the adjacent comprehensive label node in its fiber fabric sub-double-linked list, that is, screen out that there are problems in the production stage and machine where this quality problem comprehensive label node is located. Based on the fiber fabric sub-double-linked list where the quality problem comprehensive label node is located, traverse to the machine label node, and analyze the machine operation status data of the machine label node to determine the problem machine; S4.

3. If the quality problems caused by the fabric itself in S4.1 and the quality problems caused by machine problems in S4.2 are excluded, then trace the source of quality problems caused by human factors: Combined with blockchain technology, based on the batches with fabric quality problems that occurred in S4.1, screen the main doubly linked list of fiber fabrics with the largest jump in the composite entropy value of the next main doubly linked list of fiber fabrics, and analyze whether the status information and information entropy value of the pre-order and successor comprehensive label nodes recorded in all comprehensive label nodes in the main doubly linked list of fiber fabrics with the largest jump in the composite entropy value have changed; If both the pre-order comprehensive label node and the successor comprehensive label node in a comprehensive label node record that the comprehensive label node has received information changes, and the information entropy value of the comprehensive label node has changed, it proves that the comprehensive label node has been tampered with manually.

9. The fiber fabric production traceability system based on the Internet of Things according to claim 8, characterized in that In S4.2, after determining the machine problem, continue to calculate the information entropy value of the subsequent comprehensive label nodes in the sub-doubly linked list of fiber fabrics after the comprehensive label nodes of the selected quality problems, and compare it with the information entropy value of the comprehensive label nodes of the selected quality problems. If the difference is less than the error threshold, it proves that the fiber fabrics in the batches where the subsequent comprehensive label nodes are located also have the same machine problem.

10. A production traceability method for fiber fabrics based on the Internet of Things, which is used for a production traceability system for fiber fabrics based on the Internet of Things as described in any one of claims 1-9, characterized in that: It includes the following steps: S10.

1. Use Internet of Things sensors to collect fiber fabric production data, fiber fabric status images, and machine operation status data at each production stage; S10.

2. Based on the fiber fabric production data and fiber fabric status images, construct comprehensive label nodes for each batch of fiber fabrics at each production stage. According to the fiber fabric batch and production time sequence, insert all the comprehensive label nodes of this batch of fiber fabrics into the head node at the tail to construct the main doubly linked list of each batch of fiber fabrics; S10.

3. Unidirectionally link the head nodes of the main doubly linked lists of each batch of fiber fabrics in time batch sequence to obtain a singly linked list of fiber fabric batches, and bidirectionally link the comprehensive label nodes in the same machine production and at the same stage in all the main doubly linked lists of fiber fabrics in time batch sequence to obtain a sub-doubly linked list of fiber fabrics. And construct machine label nodes for each machine based on the machine operation status data and insert them at the tail of each sub-doubly linked list of fiber fabrics. Finally, construct a fabric production grid traceability linked list; S10.

4. Based on the fabric production grid traceability linked list, use the entropy change chain traceability mechanism to monitor in real time whether the information of each batch of fiber fabrics has been tampered with and trace the source of the fiber fabric quality problem when the fiber fabric has quality problems; Among them, the fabric quality problems include fabric itself problems, machine problems, and human factors.

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