A fiber fabric production tracing system and method based on the Internet of Things

Abstract

The present invention relates to the technical field of production management and traceability, and specifically to a fiber fabric production traceability system and method based on the Internet of Things. The system comprises: a data acquisition and processing unit that collects fiber fabric production data, fiber fabric status images, and machine operation status data at each production stage; a master double-linked list construction unit that constructs a fiber fabric master double-linked list for each batch of fiber fabric; a grid traceability linked list construction unit that bidirectionally links the comprehensive label nodes in all fiber fabric master double-linked lists produced by the same machine and at the same stage in chronological batch order to construct a fabric production grid traceability linked list; and a fabric chain traceability unit that uses an entropy change chain traceability mechanism to monitor in real time whether the information of each batch of fiber fabric has been tampered with and to trace the source of any fiber fabric quality problems. The present invention utilizes an entropy change chain traceability mechanism based on the fabric production grid traceability linked list to achieve full-stage traceability of fiber fabrics across batches and devices.

Description

Technical Field

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

[0002] As consumers pay more attention to product quality and origin, the textile industry is also paying more and more attention to the transparency and traceability of the production process. 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, it controls every link of fabric production and achieves comprehensive traceability from raw materials to finished products. The goal is to ensure product quality, optimize the production process, and provide consumers with reliable production source information.

[0003] Traditional fiber fabric production traceability technology mainly relies on barcodes, RFID, ERP systems, etc. to trace product data. Most of these systems operate independently and lack effective data integration methods, resulting in decentralized data storage and difficulty in achieving cross-link and cross-device integration. In addition, RFID scanners usually require manual data entry to read tags, which poses the risk of human tampering with label marks. Since physical tags require manual operation and cannot record the production process in real time, and product quality issues are caused by various factors, there is a risk of human tampering with product quality information during the production process, and product quality problems and machine failures cannot be discovered in a timely manner, resulting in difficulties in product quality monitoring and traceability throughout the entire 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 raised in the above background technology, namely, since physical labels require manual operation and cannot record the production process in real time, and there are multiple factors causing product quality problems, the quality information of the product is at risk of being tampered with by humans during the production process, and the quality problems of the product itself and machine failures cannot be discovered in time, resulting in difficulties in quality monitoring and traceability of the product throughout the entire process and at all stages.

[0005] To achieve the above objectives, the invention provides a fiber fabric production traceability system based on the Internet of Things, comprising:

[0006] A data acquisition and processing unit, which uses IoT sensors to collect fiber fabric production data, fiber fabric status images, and machine operation status data at each production stage;

[0007] A main double-linked list construction unit, which constructs a comprehensive label node for each batch of fiber fabrics at each production stage based on the fiber fabric production data and the fiber fabric status image, and inserts the tail of all comprehensive label nodes of the batch of fiber fabrics into the head node according to the order of fiber fabric batches and production time, to construct a main double-linked list for each batch of fiber fabrics;

[0008] a grid traceability linked list construction unit, wherein the grid traceability linked list construction unit unidirectionally links the head node of each batch of fiber fabric main double linked list according to the time batch sequence to obtain a fiber fabric batch single linked list, and bidirectionally links the comprehensive label nodes of all fiber fabric main double linked lists produced by the same machine and at the same stage according to the time batch sequence 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, finally constructing a fabric production grid traceability linked list;

[0009] The fabric chain traceability unit is based on the fabric production grid traceability chain table, uses 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 traces the source of the fiber fabric quality problem when the fiber fabric has quality problems.

[0010] Preferably, the production stages include raw material preparation stage, spinning stage, weaving stage, dyeing and finishing stage and finished product processing and packaging stage;

[0011] Fiber fabric production data includes temperature, humidity, tension, pressure, production speed at each production stage, and basic information about fabric raw materials;

[0012] The fiber fabric status image is fabric image data acquired in real time by a camera and is used to record the quality status of the fiber fabric;

[0013] The machine operation status data includes machine temperature, vibration signal, load power data, and start / stop status data.

[0014] Preferably, the main double-linked list construction unit includes a comprehensive label node construction module and a fiber fabric main-linked list construction module;

[0015] The comprehensive label node construction module constructs the comprehensive label node of each batch of fiber fabrics at each production stage based on the fiber fabric production data and fiber fabric status image;

[0016] 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.

[0017] Preferably, the fiber fabric main chain list construction module inserts the tails 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 fiber fabric main double chain list of each batch;

[0018] 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.

[0019] Preferably, the grid traceability chain table construction unit includes a fiber fabric batch single chain table construction module, a fiber fabric sub-chain table construction module and a fabric production grid traceability chain table generation module;

[0020] Among them, the fiber fabric batch single linked list construction module unidirectionally links the head nodes of the main double linked list of each production batch of fiber fabrics according to the time batch sequence to construct a fiber fabric batch single linked list.

[0021] Preferably, the fiber fabric sub-linked list construction module bidirectionally links all comprehensive label nodes in the fiber fabric main double-linked list that are produced by the same machine and at the same stage in a time batch order to obtain a fiber fabric sub-double-linked list;

[0022] The fabric production grid traceability chain table generation module inserts the machine label node of each machine at the end of the fiber fabric sub-double chain table, and finally obtains a fabric production grid traceability chain table with a fiber fabric main double chain table horizontally and a fiber fabric batch single chain table and a fiber fabric sub-chain table vertically;

[0023] Among them, the machine tag node stores the machine operation status data, which is used to record and monitor the machine status at each production stage of fiber fabrics;

[0024] Among them, the fabric production grid traceability chain list is used for tracing fiber fabrics with no quality problems and fiber fabrics with quality problems.

[0025] Preferably, the fabric chain tracing unit uses an entropy change chain tracing mechanism based on the fabric production grid tracing chain table to trace the causes of fabric quality problems caused by the quality of the fabric itself and the quality of the machine, and to trace the causes of fiber fabric quality problems caused by human factors;

[0026] Among them, fabric quality problems include problems with the fabric itself, machine problems and human factors.

[0027] Preferably, 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;

[0028] The entropy change chain tracing mechanism is as follows:

[0029] S4.1. Trace the source of quality problems in the fabric itself:

[0030] Calculate the information entropy of the comprehensive label nodes of all batches, and use weighted calculation to obtain the composite entropy of the fiber fabric master double-linked list. Select the next fiber fabric master double-linked list with the largest jump in composite entropy. The fiber fabric batch recorded in this list is the batch with fabric quality problems. Then, by analyzing the fiber fabric status image and basic information of the fabric raw materials recorded in the comprehensive label nodes of the fiber fabric master double-linked list of this batch, determine the batch with fabric quality problems.

[0031] S4.2: If the quality problem of the fabric itself is not the cause of the problem in S4.1, trace the source of the quality problem to the machine:

[0032] Based on the batch with fabric quality problems in S4.1, in the fiber fabric main double-linked list of the batch with fabric quality problems for the first time, traverse all comprehensive label nodes in sequence, select a comprehensive label node with a quality problem whose information entropy value jumps the most with the information entropy value of the adjacent comprehensive label node in the fiber fabric sub-double-linked list, that is, the production stage and machine where the quality problem comprehensive label node is located are screened out and problems occur. Based on the fiber fabric sub-double-linked list where the quality problem comprehensive label node is located, traverse to the machine label node, analyze the machine operation status data of the machine label node, and determine the problem machine;

[0033] S4.3. If the quality problem caused by the fabric itself in S4.1 and the quality problem caused by the machine in S4.2 are excluded, the source of the quality problem caused by human factors is traced back to:

[0034] In combination with blockchain technology, based on the batch of fabric quality problems in S4.1, the next fiber fabric master double-linked list with the largest jump in composite entropy value is screened, and the status information and information entropy value changes of the preceding and subsequent comprehensive label nodes recorded in all comprehensive label nodes in the fiber fabric master double-linked list with the largest jump in composite entropy value are analyzed;

[0035] If both the preceding and succeeding comprehensive label nodes in a comprehensive label node record that the comprehensive label node has been changed, and the information entropy value of the comprehensive label node has changed, it proves that the comprehensive label node has been tampered with.

[0036] Preferably, in S4.2, after determining the machine problem, the information entropy value of the subsequent comprehensive label node of the fiber fabric sub-double linked list after the screened quality problem comprehensive label node is continued to be calculated, and compared with the information entropy value of the screened quality problem comprehensive label node. If the difference is less than the error threshold, it proves that the fiber fabrics in the batch where the subsequent comprehensive label node is located also have the machine problem.

[0037] On the other hand, the present invention provides a fiber fabric production tracing method based on the Internet of Things, which is used in the fiber fabric production tracing system based on the Internet of Things described above, and includes the following steps:

[0038] S10.1. Use IoT sensors to collect fiber fabric production data, fiber fabric status images, and machine operation status data at each production stage;

[0039] S10.2. Based on the fiber fabric production data and fiber fabric status image, construct a comprehensive label node for each batch of fiber fabric at each production stage. In order of fiber fabric batches and production time, insert the tail of all comprehensive label nodes for that batch of fiber fabric into the head node to construct a master double-linked list for each batch of fiber fabric.

[0040] S10.3. Unidirectionally link the head nodes of each fiber fabric batch master double-linked list in chronological batch order to obtain a fiber fabric batch single-linked list. Bidirectionally link all comprehensive label nodes in the fiber fabric master double-linked list produced by the same machine and at the same stage in chronological batch order to obtain a fiber fabric sub-double-linked list. Based on the machine operating status data, construct a machine label node for each machine and insert it into each fiber fabric sub-double-linked list. Finally, construct a fabric production grid traceability linked list.

[0041] S10.4. Based on the fabric production grid traceability chain table, use the entropy change chain traceability mechanism to monitor in real time whether the fiber fabric information of each batch has been tampered with and trace the source of the fiber fabric quality problem when it occurs;

[0042] Among them, fabric quality problems include problems with the fabric itself, machine problems and human factors.

[0043] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0044] 1. In the present invention, based on the fabric production grid traceability chain table, data collected in real time by IoT devices and sensors can be used to monitor the production quality fluctuations of each batch of fabrics at each stage during the production process. The production data of each batch of fabrics at each stage are closely linked, and real-time production information is recorded with each other, realizing real-time monitoring and control as soon as production is completed;

[0045] 2. In the present invention, by constructing an entropy change chain traceability mechanism and combining it with the fabric production grid traceability chain table, the information entropy change is calculated, problems are discovered in the production process in a timely manner, and the causes of fiber fabric production quality problems are efficiently traced. This not only ensures the authenticity of production data and avoids quality problems caused by data tampering, but also enables comprehensive traceability across batches and devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A functional block diagram of an embodiment of the present invention;

[0047] Figure numerals: 1. Data acquisition and processing unit; 2. Main double-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 single 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

[0048] Example 1, as Figure 1 As shown, a fiber fabric production traceability system based on the Internet of Things is provided, including:

[0049] A data acquisition and processing unit 1, wherein the data acquisition and processing unit 1 uses an Internet of Things sensor to collect fiber fabric production data, fiber fabric status images, and machine operation status data at each production stage;

[0050] The production stages include raw material preparation stage, spinning stage, weaving stage, dyeing and finishing stage and finished product processing and packaging stage;

[0051] Fiber fabric production data includes temperature, humidity, tension, pressure, production speed at each production stage, and basic information about fabric raw materials;

[0052] The fiber fabric status image is fabric image data acquired in real time by a camera and is used to record the quality status of the fiber fabric;

[0053] Machine operating status data includes machine temperature, vibration signal, load power data, and start / stop status data;

[0054] In this embodiment, the IoT sensors include temperature and humidity sensors, tension and pressure sensors, camera vision sensors, and production speed sensors;

[0055] Temperature and humidity sensors are used in the raw material preparation, spinning, weaving, and dyeing and finishing stages. During the dyeing and finishing stage, temperature and humidity sensors are installed around the dyeing and finishing machines to monitor changes in temperature and humidity in real time to ensure the uniformity of dyes and the stability of fabrics. Temperature and humidity data are recorded every minute and uploaded to local and cloud servers for storage.

[0056] Tension and pressure sensors are used in the spinning and weaving stages. 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. The data is uploaded to the central control system so that the machine settings can be adjusted to ensure that the fabric does not stretch or shrink.

[0057] The production speed sensor is used to monitor the production speed of spinning machines and weaving machines. The production speed sensor is installed on the spinning machine to monitor the production speed of the yarn and record the production speed data;

[0058] Camera vision sensors are installed in high-definition cameras at various important links on the production line to collect images of the fabric status at each production stage;

[0059] All collected data is uploaded to local and cloud servers in real time through IoT devices for processing and storage.

[0060] A main double-linked list construction unit 2, which constructs a comprehensive label node for each batch of fiber fabrics at each production stage based on the fiber fabric production data and the fiber fabric status image, and inserts the tail of all comprehensive label nodes of the batch of fiber fabrics into the head node according to the order of fiber fabric batches and production time, to construct a main double-linked list for each batch of fiber fabrics;

[0061] The main double-linked list construction unit 2 includes a comprehensive label node construction module 21 and a fiber fabric main link list construction module 22;

[0062] The comprehensive label node construction module 21 constructs the comprehensive label node of each batch of fiber fabrics at each production stage based on the fiber fabric production data and the fiber fabric state image;

[0063] 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.

[0064] In this embodiment, the data node structure of the comprehensive tag node includes:

[0065] ID (integrated tag node unique identifier): Use batch number + production stage number + equipment number to construct a unique ID;

[0066] Production stage information: records the production stage information represented by the current node;

[0067] Basic information of fiber fabrics: records fiber fabric information related to the current production stage, including raw material type, fabric strength, thickness, texture, etc.

[0068] Fiber fabric status image: The fabric status image data collected by the camera records the fabric quality at each production stage and identifies the surface defects of the fabric through image processing technology;

[0069] Two pre-order node pointers: one pre-order node pointer points to the pre-order comprehensive label node of the fiber fabric main double-linked list, and the other pre-order node pointer points to the pre-order comprehensive label node of the fiber fabric sub-double-linked list;

[0070] Two successor node pointers: one successor node pointer points to the successor comprehensive label node of the fiber fabric main double-linked list, and the other successor node pointer points to the successor comprehensive label node of the fiber fabric sub-double-linked list;

[0071] Node status information: stores the status information of the current node, previous node, and successor node, including "normal" and "abnormal" states. The change in entropy value is used to identify whether the node is abnormal.

[0072] Production timestamp: Each node will also record the time information of the production stage to trace the time series of fabric production.

[0073] In this embodiment, blockchain technology is used to record the data of each comprehensive tag node in the blockchain, ensuring that the data of each link in the production process cannot be tampered with. If the entropy value of a node is found to have changed significantly and the blockchain record shows that the node data has been modified, it can be traced back to the specific operator and modification time, ensuring 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 specific conditions of the machine status, operator and equipment of each production node.

[0074] Through the fabric production grid traceability linked list structure, users can quickly query the specific information of each production batch, including detailed data of each production stage, equipment status, fabric images, etc.

[0075] Through the node's predecessor and successor pointers, the system supports flexible forward and reverse tracing, helping to quickly locate quality problems that occur during the production process;

[0076] The fiber fabric main linked 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 fiber fabric main double linked list of each batch;

[0077] 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.

[0078] The grid traceability linked list construction unit 3 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 fiber fabric batch single-linked list, and bidirectionally links the comprehensive label nodes of all fiber fabric main double-linked lists produced by the same machine and at the same stage in the order of time batches 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, finally constructing a fabric production grid traceability linked list;

[0079] 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;

[0080] 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.

[0081] 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 single linked list of fiber fabric batches; the batches can be sorted by 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 tracing.

[0082] The fiber fabric sub-linked list construction module 32 bidirectionally links all comprehensive label nodes in the fiber fabric main double-linked list that are produced by the same machine and at the same stage in a time batch order to obtain a fiber fabric sub-double-linked list.

[0083] 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 from 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 chronological batch order to obtain the fiber fabric sub-double-linked list;

[0084] Each comprehensive tag node in the fiber fabric sub-double-linked list contains the production data of a certain device in that production stage; each comprehensive tag node in the fiber fabric sub-double-linked list is connected by a bidirectional pointer, ensuring that it can be traced forward or backward from any node. If a quality problem is found at a node, it can be traced back to other related nodes in the batch, or the production data of different batches of the same device can be analyzed;

[0085] The fabric production grid traceability chain table generation module 33 inserts the machine tag node of each machine at the end of the fiber fabric sub-double chain table, and finally obtains a fabric production grid traceability chain table with a fiber fabric main double chain table horizontally and a fiber fabric batch single chain table and a fiber fabric sub-chain table vertically;

[0086] Among them, the machine tag node stores the machine operation status data, which is used to record and monitor the machine status at each production stage of fiber fabrics;

[0087] Among them, the fabric production grid traceability chain list is used for tracing fiber fabrics with no quality problems and fiber fabrics with quality problems.

[0088] In this embodiment, the fiber fabric main double-linked list, the fiber fabric batch single-linked list, and the fiber fabric sub-double-linked list are combined to form the final fabric production grid traceability linked list, and the operating status data of each machine is inserted into the end of the sub-linked list as a machine tag node:

[0089] The fiber fabric main double-linked list header nodes of each production batch are unidirectionally linked according to the time batch order, and the sub-linked list of each production equipment is bidirectionally linked according to the production batch order; a machine tag node is inserted at the end of each sub-linked list to store the operating status data of each machine;

[0090] The content recorded by each machine tag node includes: machine temperature, load, vibration signal, start and stop status data.

[0091] The fabric chain traceability unit 4 uses an entropy change chain traceability mechanism based on the fabric production grid traceability chain table to monitor in real time whether the fiber fabric information of each batch has been tampered with and to trace the source of the fiber fabric quality problem when the fiber fabric has a quality problem;

[0092] The fabric chain tracing unit 4 uses an entropy change chain tracing mechanism based on the fabric production grid tracing chain table to trace the causes of fabric quality problems caused by the quality of the fabric itself and the quality of the machine, and to trace the causes of fiber fabric quality problems caused by human factors;

[0093] Among them, fabric quality problems include problems with the fabric itself, machine problems and human factors.

[0094] The entropy change chain traceability mechanism is 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;

[0095] The entropy change chain tracing mechanism is as follows:

[0096] S4.1. Trace the source of quality problems in the fabric itself:

[0097] Calculate the information entropy of the comprehensive label nodes of all batches, and use weighted calculation to obtain the composite entropy of the fiber fabric master double-linked list. Select the next fiber fabric master double-linked list with the largest jump in composite entropy. The fiber fabric batch recorded in this list is the batch with fabric quality problems. Then, by analyzing the fiber fabric status image and basic information of the fabric raw materials recorded in the comprehensive label nodes of the fiber fabric master double-linked list of this batch, determine the batch with fabric quality problems.

[0098] In this embodiment, the fiber fabric state image and basic information of the fabric raw materials recorded in the comprehensive label node of the main double-linked list of the fiber fabric batch are analyzed, and a deep learning algorithm is used to detect defects in the fabric image. Combined with the results of the entropy value change, it is further determined whether the defect is caused by the quality problem of the fabric itself;

[0099] S4.2: If the quality problem of the fabric itself is not the cause of the problem in S4.1, trace the source of the quality problem to the machine:

[0100] Based on the batch with fabric quality problems in S4.1, in the fiber fabric main double-linked list of the batch with fabric quality problems for the first time, traverse all comprehensive label nodes in sequence, select a comprehensive label node with a quality problem whose information entropy value jumps the most with the information entropy value of the adjacent comprehensive label node in the fiber fabric sub-double-linked list, that is, the production stage and machine where the quality problem comprehensive label node is located are screened out and problems occur. Based on the fiber fabric sub-double-linked list where the quality problem comprehensive label node is located, traverse to the machine label node, analyze the machine operation status data of the machine label node, and determine the problem machine;

[0101] In this embodiment, if the machine status data is abnormal, the system will record the specific fault time and device status to help locate the specific faulty machine; in the machine tag node, the change in the machine tag node entropy value can reveal whether the machine has failed;

[0102] S4.3. If the quality problem caused by the fabric itself in S4.1 and the quality problem caused by the machine in S4.2 are excluded, the source of the quality problem caused by human factors is traced back to:

[0103] In combination with blockchain technology, based on the batch of fabric quality problems in S4.1, the next fiber fabric master double-linked list with the largest jump in composite entropy value is screened, and the status information and information entropy value changes of the preceding and subsequent comprehensive label nodes recorded in all comprehensive label nodes in the fiber fabric master double-linked list with the largest jump in composite entropy value are analyzed;

[0104] If both the preceding and succeeding comprehensive label nodes in a comprehensive label node record that the comprehensive label node has been changed, and the information entropy value of the comprehensive label node has changed, it proves that the comprehensive label node has been tampered with.

[0105] In S4.2, after determining the machine problem, the information entropy value of the subsequent comprehensive label nodes of the fiber fabric sub-double linked list after the screened quality problem comprehensive label node is calculated and compared with the information entropy value of the screened quality problem comprehensive label node. If the difference is less than the error threshold, it proves that the fiber fabrics in the batch where the subsequent comprehensive label node is located also have the machine problem.

[0106] In a second embodiment, the present invention proposes a fiber fabric production tracing method based on the Internet of Things, which is used in the fiber fabric production tracing system based on the Internet of Things in the first embodiment, and includes the following steps:

[0107] S10.1. Use IoT sensors to collect fiber fabric production data, fiber fabric status images, and machine operation status data at each production stage;

[0108] S10.2. Based on the fiber fabric production data and fiber fabric status image, construct a comprehensive label node for each batch of fiber fabric at each production stage. In order of fiber fabric batches and production time, insert the tail of all comprehensive label nodes for that batch of fiber fabric into the head node to construct a master double-linked list for each batch of fiber fabric.

[0109] S10.3. Unidirectionally link the head nodes of each fiber fabric batch master double-linked list in chronological batch order to obtain a fiber fabric batch single-linked list. Bidirectionally link all comprehensive label nodes in the fiber fabric master double-linked list produced by the same machine and at the same stage in chronological batch order to obtain a fiber fabric sub-double-linked list. Based on the machine operating status data, construct a machine label node for each machine and insert it into each fiber fabric sub-double-linked list. Finally, construct a fabric production grid traceability linked list.

[0110] S10.4. Based on the fabric production grid traceability chain table, use the entropy change chain traceability mechanism to monitor in real time whether the fiber fabric information of each batch has been tampered with and trace the source of the fiber fabric quality problem when it occurs;

[0111] Among them, fabric quality problems include problems with the fabric itself, machine problems and human factors.

[0112] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A fiber fabric production traceability system based on the Internet of Things, characterized by: include: A data acquisition and processing unit (1), wherein the data acquisition and processing unit (1) uses an Internet of Things sensor 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 (2) is configured to construct a comprehensive label node for each batch of fiber fabrics at each production stage based on the fiber fabric production data and the fiber fabric state image, and to insert the tail of all comprehensive label nodes of the batch of fiber fabrics into the head node according to the order of fiber fabric batches and production time, thereby constructing a main double-linked list for each batch of fiber fabrics; A grid traceability linked list construction unit (3) is provided, wherein the grid traceability linked list construction unit (3) links the head node of each batch of fiber fabric main double linked list in a unidirectional manner according to the time batch sequence to obtain a fiber fabric batch single linked list, and 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 bidirectional manner according to the time batch sequence 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 fabric chain traceability unit (4) is based on the fabric production grid traceability chain table, 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 traces the source of the fiber fabric quality problem when the fiber fabric has a quality problem.

2. The fiber fabric production tracing system based on the Internet of Things according to claim 1 is characterized in that: The production stages include raw material preparation stage, spinning stage, weaving stage, dyeing and finishing stage and finished product processing and packaging stage; Fiber fabric production data includes temperature, humidity, tension, pressure, production speed at each production stage, and basic information about fabric raw materials; The fiber fabric status image is fabric image data acquired in real time by a camera and is used to record the quality status of the fiber fabric; The machine operation status data includes machine temperature, vibration signal, load power data, and start / stop status data.

3. The fiber fabric production tracing system based on the Internet of Things according to claim 2 is 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 the comprehensive label node of each batch of fiber fabrics at 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 status information of the previous and subsequent comprehensive label nodes.

4. The fiber fabric production tracing system based on the Internet of Things according to claim 3 is characterized in that: The fiber fabric main chain list construction module (22) inserts the tails 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 fiber fabric main double chain list of each batch; 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.

5. The fiber fabric production tracing system based on the Internet of Things according to claim 4 is 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) links the head nodes of the main double linked list of each production batch of fiber fabrics in a unidirectional manner according to the time batch order to construct a fiber fabric batch single linked list.

6. The fiber fabric production tracing system based on the Internet of Things according to claim 5 is characterized in that: The fiber fabric sub-linked list construction module (32) links all the comprehensive label nodes in the fiber fabric main double-linked list produced by the same machine and at the same stage in a bidirectional manner according to the time batch order to obtain a fiber fabric sub-double-linked list; The fabric production grid traceability chain table generation module (33) inserts the machine label node of each machine at the end of the fiber fabric sub-double chain table, and finally obtains the fabric production grid traceability chain table with the fiber fabric main double chain table in the horizontal direction and the fiber fabric batch single chain table and the fiber fabric sub-chain table in the vertical direction; Among them, the machine tag node stores the machine operation status data, which is used to record and monitor the machine status at each production stage of fiber fabrics; Among them, the fabric production grid traceability chain list is used for tracing fiber fabrics with no quality problems and fiber fabrics with quality problems.

7. The fiber fabric production tracing system based on the Internet of Things according to claim 6 is characterized in that: The fabric chain traceability unit (4) uses an entropy change chain traceability mechanism based on the fabric production grid traceability chain table to trace the causes of fabric quality problems caused by the quality of the fabric itself and the quality of the machine, and to trace the causes of fiber fabric quality problems caused by human factors; Among them, fabric quality problems include problems with the fabric itself, machine problems and human factors.

8. The fiber fabric production tracing system based on the Internet of Things according to claim 7 is characterized in that: The entropy change chain traceability mechanism is 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 tracing mechanism is as follows: S4.

1. Trace the source of quality problems in the fabric itself: Calculate the information entropy of the comprehensive label nodes of all batches, and use weighted calculation to obtain the composite entropy of the fiber fabric master double-linked list. Select the next fiber fabric master double-linked list with the largest jump in composite entropy. The fiber fabric batch recorded in this list is the batch with fabric quality problems. Then, by analyzing the fiber fabric status image and basic information of the fabric raw materials recorded in the comprehensive label nodes of the fiber fabric master double-linked list of this batch, determine the batch with fabric quality problems. S4.2: If the quality problem of the fabric itself is not the cause of the problem in S4.1, trace the source of the quality problem to the machine: Based on the batch with fabric quality problems in S4.1, in the fiber fabric main double-linked list of the batch with fabric quality problems for the first time, traverse all comprehensive label nodes in sequence, select a comprehensive label node with a quality problem whose information entropy value jumps the most with the information entropy value of the adjacent comprehensive label node in the fiber fabric sub-double-linked list, that is, the production stage and machine where the quality problem comprehensive label node is located are screened out and problems occur. Based on the fiber fabric sub-double-linked list where the quality problem comprehensive label node is located, traverse to the machine label node, analyze the machine operation status data of the machine label node, and determine the problem machine; S4.

3. If the quality problem caused by the fabric itself in S4.1 and the quality problem caused by the machine in S4.2 are excluded, the source of the quality problem caused by human factors is traced back to: In combination with blockchain technology, based on the batch of fabric quality problems in S4.1, the next fiber fabric master double-linked list with the largest jump in composite entropy value is screened, and the status information and information entropy values ​​of the preceding and subsequent comprehensive label nodes recorded in all comprehensive label nodes in the fiber fabric master double-linked list with the largest jump in composite entropy value are analyzed to see whether they have changed; If both the preceding and succeeding comprehensive label nodes in a comprehensive label node record that the comprehensive label node has been changed, and the information entropy value of the comprehensive label node has changed, it proves that the comprehensive label node has been tampered with.

9. The fiber fabric production tracing system based on the Internet of Things according to claim 8 is characterized in that: In S4.2, after determining the machine problem, the information entropy value of the subsequent comprehensive label nodes of the fiber fabric sub-double linked list after the screened quality problem comprehensive label node is calculated and compared with the information entropy value of the screened quality problem comprehensive label node. If the difference is less than the error threshold, it proves that the fiber fabrics in the batch where the subsequent comprehensive label node is located also have the machine problem.

10. A fiber fabric production tracing method based on the Internet of Things, used in a fiber fabric production tracing system based on the Internet of Things according to any one of claims 1 to 9, characterized in that: The steps include: S10.

1. Use IoT 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 image, construct a comprehensive label node for each batch of fiber fabric at each production stage. In order of fiber fabric batches and production time, insert the tail of all comprehensive label nodes for that batch of fiber fabric into the head node to construct a master double-linked list for each batch of fiber fabric. S10.

3. Unidirectionally link the head nodes of each fiber fabric batch master double-linked list in chronological batch order to obtain a fiber fabric batch single-linked list. Bidirectionally link all comprehensive label nodes in the fiber fabric master double-linked list produced by the same machine and at the same stage in chronological batch order to obtain a fiber fabric sub-double-linked list. Based on the machine operating status data, construct a machine label node for each machine and insert it 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 chain table, use the entropy change chain traceability mechanism to monitor in real time whether the fiber fabric information of each batch has been tampered with and trace the source of the fiber fabric quality problem when it occurs; Among them, fabric quality problems include problems with the fabric itself, machine problems and human factors.

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