Self-adhesive label intelligent tracing method based on RFID electronic label

Abstract

The invention discloses a self-adhesive label intelligent tracing method based on an RFID electronic label, and the method comprises the following steps: S1, obtaining the basic information of a to-be-traced object, writing the basic information into the RFID electronic label of an integrated communication module, and enabling the RFID electronic label to be attached to a self-adhesive label carrier; s2, deploying a sensor at a key node of an article circulation full link, and establishing a data interaction link between the sensor and the tag through a communication module of the RFID electronic tag; and S3, synchronously acquiring RFID electronic tag read data and environment state data of each sensor according to a preset period. The invention relates to the technical field of article tracing. According to the self-adhesive label intelligent tracing method based on the RFID electronic label, multiple types of sensors are deployed in key links such as production, storage, transportation, sales and the like, and a communication link is established with the RFID electronic label, so that dynamic state monitoring and data acquisition of an article in a full life cycle are realized, and the real-time performance and integrity of tracing are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of article tracing, more particularly to an intelligent adhesive label tracing method based on RFID electronic tags. BACKGROUND

[0002] With the increasing requirements of modern supply chain management and product quality supervision, the traditional article tracing method mainly relies on static identification such as bar code and two-dimensional code. Such identification can only record static information and cannot realize dynamic state updating. In addition, it is easy to be contaminated, worn or tampered with in a complex circulation environment, resulting in information loss or distortion, and it is difficult to meet the needs of high-precision and real-time tracing.

[0003] Although the existing RFID electronic tag technology can realize non-contact identification and certain data storage function, its application is mainly concentrated in single-node information identification, and lacks the linkage perception and data fusion capability with environmental parameters. In addition, due to the influence of environmental temperature and humidity, light, vibration and other factors in the multi-link production-circulation-sale of articles, the data in the RFID tag may deviate, and the traditional system often lacks automatic correction and data consistency verification mechanism, resulting in inaccurate tracing results.

[0004] On the other hand, the data of the existing tracing system is mainly stored in the centralized server, which is difficult to support concurrent access and real-time query in a large-scale distributed Internet of Things environment, and also lacks the ability of visual display for multiple terminals. Therefore, how to build an intelligent tracing method combining RFID and multi-sensor collaborative perception, data fusion correction, distributed storage and visual display has become a key technical problem in the current intelligent manufacturing and supply chain digital management. SUMMARY

[0005] The present application aims to provide an intelligent adhesive label tracing method based on RFID electronic tags, which solves the problem that the existing RFID electronic tag technology lacks linkage perception and data fusion capability with environmental parameters, and the data of the tracing system is mainly stored in the centralized server, which is difficult to support concurrent access and real-time query in a large-scale distributed Internet of Things environment, and also lacks the ability of visual display for multiple terminals.

[0006] The present application achieves the above-mentioned purpose through the following technical solutions: an intelligent adhesive label tracing method based on RFID electronic tags, comprising the following steps: S1, obtaining the basic information of the article to be traced and writing it into the RFID electronic tag of the integrated communication module, and the RFID electronic tag is attached to the adhesive label carrier; S2, deploying sensors at key nodes of the whole-link article circulation, and establishing a data interaction link between the sensors and the tags through the communication module of the RFID electronic tag. S3, synchronously collecting RFID electronic tag reading data and environmental state data of each sensor according to a preset period, and structuring and arranging the collected data to construct a multi-dimensional original data set; S4, constructing an intelligent correction model based on a data fusion intelligent correction mechanism, performing preprocessing, interference identification and fusion correction operations on the multi-dimensional original data set through the intelligent correction model, and generating accurate traceability data; S5, establishing a distributed traceability data archive, storing the accurate traceability data according to a preset format, and realizing full-process query and traceability of the article flow state through a visual interface.

[0007] Further, the step of obtaining the basic information of the article to be traced and writing it into the RFID electronic tag comprises: Collecting the basic information of the article to be traced, the basic information at least including an article unique identification code, production-related information, initial state parameters and a preset flow threshold; Converting the basic information into a data format compatible with the RFID tag, and performing encryption processing on the data using an encryption algorithm; Selecting an RFID electronic tag integrated with a communication chip and a data transmission interface, establishing a communication connection through an RFID reader, writing the encrypted basic information into the tag storage area, verifying the data integrity after writing is completed, confirming that the writing is successful if the verification is passed, and re-executing the writing operation if the verification fails.

[0008] Further, the step of deploying sensors at key nodes of the article flow and establishing a data interaction link comprises: Deploying a sensor group at a key position of each of the article production, storage, transportation and sales links, each sensor group at least including an environmental parameter sensor and a state detection sensor, and each sensor being assigned a unique device identification; Initiating a binding request through the communication module of the RFID electronic tag, sending the device identification and communication parameters by the sensor in response to the request, receiving and storing the binding information by the tag, and completing one-to-one binding of the sensor and the tag; Using a preset multiple access method to construct a data interaction link, setting a sensor data reporting period, receiving sensor data by the RFID electronic tag in each period, performing preliminary verification on the received data through a verification method, storing the data in a local cache area of the tag if the verification is passed, and sending a retransmission instruction to the corresponding sensor and recording fault information after retransmission failure if the verification fails.

[0009] Further, the step of synchronously collecting data and constructing a multi-dimensional original data set comprises: Synchronously collecting RFID electronic tag reading data and sensor data, the RFID electronic tag reading data at least including reading timestamp, signal related parameters, storage information check value, and the sensor data including environmental parameter data and article state detection data; Structuring the collected data according to a preset data structure, and using corresponding coding or format specification for different types of data; Transmitting the structured data to a data processing node, establishing a multi-dimensional original data set, and storing the data set file according to a preset format and naming rule.

[0010] Further, the step of constructing an intelligent correction model and generating precise traceability data comprises: Constructing an intelligent correction model comprising a data preprocessing layer, an interference identification layer and a fusion correction layer, each layer being connected by an algorithm model to realize data processing circulation; The data preprocessing layer performs normalization processing on the multi-dimensional original data, maps different types of environmental parameter data and state data to a preset value interval, and converts signal related data into a credibility score index; The interference identification layer uses an anomaly detection algorithm to analyze the preprocessed data, sets an abnormality judgment threshold, calculates a data consistency coefficient, and marks and removes data that meet the abnormality condition; The fusion correction layer calculates a fusion data value based on a weighted fusion algorithm, assigns corresponding weight coefficients to different types of data sources, and obtains a comprehensive data result through a fusion formula; Setting a data deviation threshold, if the deviation between the fusion data value and the RFID tag original data exceeds the threshold, automatically updating the label storage information through a communication module, generating a correction record containing correction related information, and finally outputting precise traceability data.

[0011] Further, the step of establishing a distributed traceability data archive and realizing visual query comprises: Storing the precise traceability data and correction record in a distributed database, the distributed database supporting multi-dimensional data indexing according to article identification, time range and flow link; Building a distributed database using cluster deployment, setting time range indexing accuracy, and specifying flow link classification dimensions; Developing a visual traceability interface, the interface using an architecture design suitable for multi-terminal access, supporting access through a web page or a mobile terminal; The visual interface displays at least article flow trajectory, changes in environmental parameters at each node, correction records and abnormal event alarms; Setting a traceability data retention period, migrating and storing data exceeding the retention period according to a preset rule, and optimizing storage resource allocation.

[0012] Further, the unique identification code of the article adopts a multi-segment digital coding structure, at least including a manufacturer identification segment, a production batch identification segment, and a single product serial number segment; The production-related information includes production batch and production date, and the production date adopts a standardized time format; The initial state parameter includes an article physical characteristic parameter and a qualified state identification; The preset flow threshold includes a temperature threshold and a humidity threshold, the encryption algorithm adopts a symmetric encryption algorithm, and the verification algorithm adopts a cyclic redundancy check algorithm.

[0013] Further, the environmental parameter sensor includes a temperature sensor and a humidity sensor, and the state detection sensor includes an optical sensor; The multiple access mode adopts a time division multiple access mode, and the sensor data reporting period can be adjusted according to actual application scenarios; The local buffer area of the tag has a preset storage capacity, the sending number of the retransmission instruction is set to an upper limit value, and if the verification fails beyond the upper limit value, fault information is recorded and feedback is given.

[0014] Further, the temperature sensor data has a preset precision level, the humidity sensor data has a corresponding precision level, and the optical sensor data includes an article appearance optical parameter and a surface integrity identification; The preset data structure includes fields such as timestamp, tag identification, sensor number, data type, and value, the numerical value type data is reserved according to a preset decimal place, and the state type data is represented by binary coding; The data processing node includes an edge computing node, and the data set storage format adopts a structured data format.

[0015] Further, the normalization processing of the data preprocessing layer maps the temperature data and the humidity data to the [0, 1] interval through a preset formula; The anomaly detection algorithm adopts an isolation forest algorithm, the anomaly judgment threshold is set to a preset multiple of the data standard deviation, the data consistency coefficient is set to a minimum threshold, and data below the threshold and exceeding the preset flow threshold is determined as interference data; The sum of the weight coefficients assigned to each data source by the weighted fusion algorithm is 1, wherein the RFID tag data weight, the temperature sensor data weight, the humidity sensor data weight, and the optical sensor data weight are distributed according to a preset proportion; The correction record includes information such as correction time, data before and after correction, and correction reason, the data deviation threshold is set to a percentage threshold, the automatic update of the tag storage information is realized through a bidirectional communication module, and the accuracy and timeliness of the traceability data are ensured.

[0016] The beneficial effects of the present application are: 1. By deploying multiple types of sensors at key links such as production, storage, transportation, and sales, and establishing communication links with RFID electronic tags, the dynamic state monitoring and data collection of the entire life cycle of the goods are realized, significantly improving the real-time and completeness of the traceability.

[0017] 2. An intelligent correction model including data preprocessing layer, interference identification layer, and fusion correction layer is constructed, isolated forest algorithm is used for anomaly detection, and weighted fusion algorithm is used to realize the fusion and correction of multi-source data, effectively eliminating environmental interference data, and improving the accuracy and reliability of the traceability data.

[0018] 3. By establishing a distributed traceability database, supporting multi-dimensional indexing such as goods identification, time, and circulation link, efficient data storage and fast retrieval are realized; combined with the visual interface adapted to multiple terminals, the goods circulation track, environmental parameter changes, and abnormal alarm can be displayed in real time, improving the data usability and supervision transparency.

[0019] 4. Based on the symmetric encryption algorithm and the cyclic redundancy check mechanism, the integrity and tamper resistance of the data in the RFID tag during writing, transmission, and updating are ensured; at the same time, the automatic retransmission and fault recording mechanism are set to improve the stability of the system in complex application environment.

[0020] 5. The data processing node supports edge computing mode, which can complete preliminary data structuring and filtering processing locally, reducing the load of the central server; at the same time, the system sets data retention period and migration strategy, realizing efficient management of traceability data and optimization of storage resources. BRIEF DESCRIPTION OF DRAWINGS

[0021] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings: Fig. 1 is the overall intelligent traceability flowchart of the present application; Fig. 2 is the data acquisition and processing flowchart of the present application; Fig. 3 is the intelligent correction model flowchart of the present application. DETAILED DESCRIPTION

[0022] The present application is described in detail below with reference to the drawings, and it is necessary to point out here that the following specific embodiments are only used to further illustrate the present application, and cannot be understood as a limitation on the protection scope of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application according to the above application content.

[0023] Embodiment 1: Please refer to Figs. 1-3 The application provides a technical scheme: an intelligent tracing method for adhesive label based on RFID electronic label, which comprises the following specific implementation steps: S1: obtaining the basic information of the to-be-traced article, writing the basic information into the RFID electronic label integrated with a bidirectional communication module, and attaching the RFID electronic label to the adhesive label carrier; Wherein, the to-be-traced article is an article that needs to track and query the whole process information such as production and circulation; the basic information is the basic data about the to-be-traced article, which may cover the initial information related to the article itself such as the name, specification, production date, production batch, and production manufacturer of the article; the RFID electronic label is a radio frequency identification electronic label, which is a wireless communication technology label that automatically identifies target objects and obtains related data by using radio frequency signals. It can realize non-contact two-way data communication through wireless radio frequency to achieve the purpose of identification and data exchange; the bidirectional communication module is a module integrated in the RFID electronic label, which enables the label not only to receive information sent by external devices, but also to send its own stored information to external devices, realizing two-way data transmission; the adhesive label carrier is a label made of adhesive material, which has the characteristics of convenient sticking and flexible use, and serves as the substrate for carrying the RFID electronic label, making it convenient to attach the electronic label to the article; S2: deploying temperature sensors, humidity sensors and optical sensors at key nodes of production, storage, transportation and sales in the whole link of article circulation, and establishing a real-time data interaction link between the sensors and the label through the bidirectional communication module of the RFID electronic label; Wherein, the whole link of the article flow is the whole process of the article from production, through storage, transportation, to the final sales link, each link and path involved; the production node is the link of article manufacturing, which is the key stage of the article from raw materials to finished products; the storage node is the place of article storage, used for storage, management and allocation of articles, to ensure the reasonable storage and supply of articles in the flow process; the transportation node is the link of space transfer of articles between different places, involving the selection of transportation mode, transportation route planning, etc.; the sales node is the link of the article finally reaching the consumer, including retail, wholesale and other sales forms; the temperature sensor is a sensor that can sense temperature and convert it into a usable output signal, used to measure the temperature of the environment where the article is located; the humidity sensor is a sensor used to measure environmental humidity, which can convert humidity information into an electrical signal or other recognizable signal; the optical sensor is a sensor that is sensitive to optical signals and can convert them into electrical signals or other forms of signals, which can be used to detect optical parameters such as light intensity, color, etc.; the real-time data interaction link is a channel that can realize real-time data transmission and exchange between the sensor and the tag through the bidirectional communication module of the RFID electronic tag, ensuring that the data collected by the sensor can be transmitted to the tag in time, and the tag can also feedback accordingly according to the needs; S3: synchronously collecting RFID electronic tag reading data and environmental state data of each sensor according to a preset collection period, structurally arranging the collected data, and constructing a multi-dimensional original data set; Wherein, the preset collection period is a pre-set time interval for collecting data; the RFID electronic tag reading data is the article basic information stored in the RFID electronic tag and other related data that may be recorded, obtained from the RFID electronic tag by a reading device; the environmental state data is the state information about the temperature, humidity, illumination, etc. of the environment where the article is located, collected by temperature sensors, humidity sensors, optical sensors, etc.; the structural arrangement is to organize and arrange the collected RFID electronic tag reading data and environmental state data according to certain rules and formats, so that they have clear structure and logical relationship, facilitating subsequent analysis and processing; the multi-dimensional original data set is a collection containing article basic information and various environmental state data after structural arrangement, which reflects the state of the article in the flow process from different dimensions, providing a basis for subsequent data analysis and tracing; S4: constructing an intelligent correction model based on the intelligent tracing correction mechanism of multi-dimensional data fusion, and sequentially performing preprocessing, interference identification and fusion correction operations on the multi-dimensional original data set through the intelligent correction model to exclude sensor interference and misreading data, and generate accurate tracing data after correction; Among them, the multi-dimensional data fusion intelligent traceability correction mechanism is a mechanism that comprehensively utilizes multiple dimensions of data, corrects and optimizes the traceability data through specific algorithms and models, aiming to improve the accuracy and reliability of the traceability data; the intelligent correction model is a mathematical model constructed based on the multi-dimensional data fusion intelligent traceability correction mechanism, which can automatically identify and exclude interference and errors in the data through learning and analysis of a large amount of data, and perform preprocessing, interference identification and fusion correction on the original data; preprocessing is a series of processing operations performed on the data before formal analysis, such as data cleaning, data normalization, etc., to improve data quality and prepare for subsequent processing; interference identification is to analyze the collected data using the intelligent correction model to determine whether there are abnormal data or error data in the data due to sensor failure, environmental interference and other factors; fusion correction operation is to integrate and correct different dimensions of data after preprocessing and interference identification, eliminate contradictions and errors between data through specific algorithms and rules, and generate more accurate and reliable traceability data; S5: Establishing a distributed traceability data archive, storing the precise traceability data in a preset format, and realizing the full-process query and traceability of the article flow status through a visual interface; Among them, the distributed traceability data archive is an archive system for storing article traceability data established by using distributed storage technology. Distributed storage stores data on multiple nodes to improve data reliability, availability and scalability, and can meet the storage and management needs of large-scale data; the preset format is a pre-defined format for storing precise traceability data, such as a specific file format or database table structure, to ensure that data can be stored and read in a unified and standardized manner, facilitating subsequent data query and analysis; the visual interface is an interface that displays the article flow status and traceability data to users in an intuitive and easy-to-understand manner through visual elements such as graphics, charts and maps. Users can easily query the status information of the article at each link through the interface to realize the full-process query and traceability of the article flow status.

[0024] It should be noted that in use, the basic information is written on the information record, the RFID electronic tag of the integrated bidirectional communication module is pasted on the adhesive carrier, which is convenient and stable, can accurately record the initial information of the goods, in data collection, various sensors are deployed at each key node of the circulation, and a real-time interaction link is established through the bidirectional communication module, so that the environmental state data can be comprehensively and timely obtained, the multi-dimensional original data set is constructed by synchronously collecting and structuring according to the preset period, a rich and accurate data basis is provided for tracing, the intelligent correction model can exclude interference and misreading data, generate accurate tracing data, improve the reliability of tracing, establish a distributed archive and store according to the preset format, cooperate with the visual interface, realize the traceability of the whole process of the circulation of goods, facilitate enterprise management and consumer understanding, and help to improve the supply chain transparency, guarantee product quality and optimize management process.

[0025] In an embodiment, the specific implementation process of the S1 step includes: Collecting the basic information of the goods to be traced, the basic information including the unique identification code of the goods, the production batch, the production date, the initial state parameter and the preset circulation threshold; Among them, the identification code adopts 18-digit coding, the first 6 digits are manufacturer code, the middle 8 digits are production batch number, and the last 4 digits are single product serial number; The production date format is YYYY-MM-DD-HH:MM:SS; The initial state parameter includes the weight, size and qualified state identification of the goods; The preset circulation threshold, the temperature threshold is set to -10℃\45℃, and the humidity threshold is set to 30%\70%; The basic information is converted into binary data compatible with RFID tags through ASCII encoding, and the data is encrypted by using AES-128 encryption algorithm; The RFID electronic tag integrated with ultra-high frequency communication chip and bidirectional data transmission interface is selected, wherein the working frequency of the ultra-high frequency communication chip is 860MHz 960MHz, the communication connection is established through the RS232 interface of the RFID reader, the encrypted basic information is written into the tag storage area, and the data integrity is verified through the CRC32 verification algorithm after the writing is completed. If the verification is passed, a write success signal is output, and if the verification fails, the writing operation is re-executed.

[0026] In this way, the basic information of the collected articles is collected and the coding format and other details are specified, and after coding and encryption, it is written into a specific RFID electronic tag and verified, so as to standardize the collection of basic information and facilitate accurate identification of articles; specific coding and encryption are used to ensure data security and compatibility; appropriate tags and communication methods are selected to ensure reliable data writing and storage, and through strict processes, accurate, safe and complete basic data are provided for subsequent traceability, so that the article information can be effectively tracked and managed in each link of the circulation, and the accuracy and reliability of traceability are improved.

[0027] In an embodiment, the specific implementation process of the S2 step includes: At the end of the article production line, in the partition of the warehouse shelf, in the vehicle compartment of the transport vehicle, and at the shelf of the sales terminal, a sensor group is respectively deployed, each sensor group including 1 temperature sensor, 1 humidity sensor, and 1 optical sensor, and each sensor is assigned a unique 16-bit device number; A binding request is initiated through the bidirectional communication module of the RFID electronic tag, the sensor sends the device number and communication parameters after responding to the request, the tag stores the binding information after receiving, and the one-to-one binding of the sensor and the tag is completed; A time division multiple access method is used to build a data interaction link, the sensor data reporting period is set to 3 minutes, the RFID electronic tag receives the data of each sensor in each period, the received data is preliminarily checked by the parity check method, and if the check is passed, the data is stored in the local 2KB cache area of the tag, if the check fails, a retransmission instruction is sent to the corresponding sensor, the retransmission number does not exceed 3 times, and if it still fails, fault information is recorded.

[0028] In this way, by deploying sensor groups at key positions, binding the sensor and the tag, building a data interaction link and setting data receiving and processing rules, the multi-position deployment of sensors can comprehensively obtain environmental data of articles in each link of the circulation; the binding of the sensor and the tag facilitates data association; the construction of the link and the setting of the rules ensure the orderly reception, accurate checking and storage of data, timely recording of faults, and the integrity and accuracy of data, providing a reliable basis for tracing the environmental information of articles.

[0029] In an embodiment, the specific implementation process of the S3 step includes: The RFID electronic tag reading data and the sensor data are synchronously collected, wherein the RFID electronic tag reading data includes the current reading timestamp of the tag, the signal strength (unit: dBm), and the storage information check value, the temperature sensor data has a precision of ±0.1℃, the humidity sensor data has a precision of ±1%, and the optical sensor data includes the brightness value (unit: lux) of the article appearance and the surface integrity identifier (0 represents no damage, and 1 represents damage); The collected data is structured according to a fixed structure of timestamp-tag identification-sensor number-data type-value, wherein the numerical data retains two decimal places, and the state type data is represented by binary coding; The structured data is transmitted to the edge computing node to establish a multi-dimensional original data set, and the data set is stored in CSV format, and each data file is named according to the rule of "tag identification_collection date.csv".

[0030] In this way, multiple data are synchronously collected, structured and transmitted to the edge computing node to establish a data set and standardize the naming, multiple source data are synchronously collected to comprehensively reflect the state of the goods, the structured data is standardized for subsequent analysis and processing, the data set is established in the edge node to improve the data processing efficiency, the standardized naming facilitates data management and query, provides complete, orderly and easily searchable data support for traceability, and improves the traceability efficiency.

[0031] In an embodiment, the specific implementation process of the S4 step includes: An intelligent correction model is constructed, which includes a data preprocessing layer, an interference identification layer and a fusion correction layer, and each layer is connected through a neural network model; The data preprocessing layer performs normalization processing on the multi-dimensional original data, and the temperature data is normalized through the formula:

[0032] to the interval [0, 1], wherein , , The humidity data is normalized through the formula:

[0033] to the interval [0, 1], wherein , , and the signal strength data is converted to a relative confidence score of 0 100; The interference identification layer uses the Isolation Forest algorithm to detect anomalies in the preprocessed data, sets the anomaly threshold to 3 times the standard deviation, calculates the consistency coefficient of each data with other same type sensor data, and marks and removes the data with consistency less than 80% and exceeding the preset flow threshold as interference data; The fusion correction layer calculates the fusion data value based on the weighted fusion algorithm, gives the RFID tag data a weight of 0.4, the temperature sensor data a weight of 0.3, the humidity sensor data a weight of 0.2, and the optical sensor data a weight of 0.1, and the fusion formula is:

[0034] The data deviation threshold is set to 5%, if the deviation between the fusion data value and the original data of the RFID tag exceeds 5%, the tag storage information is automatically updated through the bidirectional communication module, a correction record containing the correction time, the data before and after correction, and the correction reason is generated, and finally the accurate trace data is output.

[0035] In this way, the intelligent correction model is constructed, the data is processed and identified in layers, the interference is fused and corrected, the data is preprocessed and normalized to eliminate the dimension influence, the abnormal data is removed in the interference identification layer to improve the data quality, the multi-source data is integrated in the fusion correction layer, different weights are given to obtain accurate fusion values, the tag information is automatically updated and recorded when the deviation exceeds the threshold, the accurate and reliable trace data is ensured, the article circulation situation can be truly reflected, and the accuracy and credibility of the trace are enhanced.

[0036] In an embodiment, the specific implementation process of the S5 step includes: The accurate trace data and the correction record are stored in a distributed database, the database supports multi-dimensional indexing according to the unique identification code of the article, the time range, and the circulation link; The distributed database is deployed by using MySQL cluster, the time range is accurate to the hour, and the circulation link includes production, storage, transportation, and sales; A visual trace interface is developed, the interface adopts B / S architecture, supports access through a web terminal or a mobile terminal, and shows the article circulation track map, the change curve of the environmental parameters of each node, the correction record list, and the abnormal event alarm prompt; The trace data retention period is set to 2 years after the shelf life of the article, the data exceeding the retention period is automatically migrated to a cold storage server, the data traceability is ensured, and the storage resources are optimized.

[0037] In this way, the accurate trace data and the record are stored in the distributed database, the visual interface is developed and the data retention period is set, the distributed database supports multi-dimensional indexing, the trace data can be quickly and conveniently queried, the visual interface adopts B / S architecture, the article circulation information can be intuitively displayed through multi-terminal access, the storage resources are reasonably managed by setting the retention period, the data traceability is ensured, the storage is optimized, the user is provided with convenient, efficient, and comprehensive trace services, and the user experience and the practicability of the trace system are improved.

[0038] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment methods can be completed by programs instructing related hardware, therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer usable program codes.

[0039] The above embodiments have been described in detail, and the principles and embodiments of the present application have been described by applying specific examples. The above examples are only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific embodiments and application scope will be changed, and the above description should not be understood as a limitation of the present application.

Claims

1. A method for intelligent traceability of self-adhesive labels based on RFID electronic tags, characterized in that, Includes the following steps: S1. Obtain the basic information of the item to be traced and write it into the RFID electronic tag of the integrated communication module, wherein the RFID electronic tag is attached to the self-adhesive label carrier. S2. Deploy sensors at key nodes in the entire goods circulation chain, and establish a data interaction link between the sensors and tags through the communication module of the RFID electronic tags; S3. Collect RFID electronic tag reading data and environmental status data of each sensor synchronously according to a preset cycle, and organize the collected data in a structured manner to construct a multi-dimensional raw dataset. S4. Construct an intelligent correction model based on a data fusion-based intelligent traceability correction mechanism. Perform preprocessing, interference identification, and fusion correction operations on the multidimensional raw dataset through the intelligent correction model to generate accurate traceability data. S5. Establish a distributed traceability data archive, store accurate traceability data in a preset format, and realize full query and traceability of the status of item circulation through a visual interface.

2. The intelligent traceability method for self-adhesive labels based on RFID electronic tags according to claim 1, characterized in that, The steps of obtaining basic information about the item to be traced and writing it into the RFID electronic tag include: Collect basic information about the items to be traced, including at least the item's unique identifier, production-related information, initial state parameters, and preset circulation thresholds. The basic information is converted into a data format compatible with RFID tags, and the data is encrypted using an encryption algorithm. RFID electronic tags with integrated communication chips and data transmission interfaces are selected. A communication connection is established through an RFID reader / writer. The encrypted basic information is written into the tag's storage area. After writing is completed, the data integrity is verified through a verification algorithm. If the verification passes, the writing is confirmed to be successful. If the verification fails, the writing operation is re-executed.

3. The intelligent traceability method for self-adhesive labels based on RFID electronic tags according to claim 1, characterized in that, The steps of deploying sensors and establishing data interaction links at key nodes in the flow of goods include: Sensor groups are deployed at key locations in the production, storage, transportation and sales of goods. Each sensor group includes at least environmental parameter sensors and status detection sensors, and each sensor is assigned a unique device identifier. The RFID electronic tag initiates a binding request through its communication module. After the sensor responds to the request, it sends the device identifier and communication parameters. The tag receives and stores the binding information, thus completing the one-to-one binding between the sensor and the tag. A data interaction link is constructed using a preset multiple access method. The sensor data reporting cycle is set. The RFID electronic tag receives data from each sensor in each cycle. The received data is initially verified by a verification method. If the verification is successful, the data is stored in the tag's local buffer. If the verification fails, a retransmission command is sent to the corresponding sensor, and the fault information after the retransmission failure is recorded.

4. The intelligent traceability method for self-adhesive labels based on RFID electronic tags according to claim 1, characterized in that, The steps of synchronously collecting data and constructing a multidimensional raw dataset include: Simultaneously collect RFID electronic tag reading data and sensor data. The RFID electronic tag reading data includes at least reading timestamp, signal-related parameters, and stored information verification value. The sensor data includes environmental parameter data and item status detection data. The collected data is processed in a structured manner according to the preset data structure, and the corresponding encoding or format specifications are adopted for different types of data. The structured data is transmitted to the data processing node to create a multidimensional raw dataset, and the dataset file is stored according to the preset format and naming rules.

5. The intelligent traceability method for self-adhesive labels based on RFID electronic tags according to claim 1, characterized in that, The steps for constructing the intelligent correction model and generating accurate traceability data include: An intelligent correction model is constructed, which includes a data preprocessing layer, an interference identification layer, and a fusion correction layer. Each layer is connected in series through an algorithm model to realize data processing and flow. The data preprocessing layer performs normalization processing on the multidimensional raw data, maps different types of environmental parameter data and state data to preset numerical ranges, and converts signal-related data into credibility scoring indicators. The interference identification layer uses an anomaly detection algorithm to analyze the preprocessed data, sets an anomaly judgment threshold, calculates the data consistency coefficient, and marks data that meets the anomaly conditions as interference data and removes them. The fusion correction layer calculates the fused data value based on a weighted fusion algorithm, assigns corresponding weight coefficients to different types of data sources, and obtains the comprehensive data result through the fusion formula. A data deviation threshold is set. If the deviation between the fused data value and the original data of the RFID tag exceeds the threshold, the tag storage information is automatically updated through the communication module to generate a correction record containing correction-related information, and finally output accurate traceability data.

6. The intelligent traceability method for self-adhesive labels based on RFID electronic tags according to claim 1, characterized in that, The steps for establishing a distributed traceability data archive and enabling visual querying include: Accurate traceability data and correction records are stored in a distributed database, which supports multi-dimensional data indexing by item identification, time range, and circulation stage. A distributed database is built using a cluster deployment approach, with time ranges and index precision defined, and the classification dimensions of the workflow stages clearly identified. Develop a visual traceability interface, which adopts an architecture design that adapts to multiple terminal access and supports access via web page or mobile terminal; The visual interface should display at least the item flow trajectory, changes in environmental parameters at each node, calibration records, and alarms for abnormal events. Set a retention period for traceable data. Data that exceeds the retention period will be migrated and stored according to preset rules to optimize storage resource allocation.

7. The intelligent traceability method for self-adhesive labels based on RFID electronic tags according to claim 2, characterized in that: The unique identifier of the item adopts a multi-segment digital coding structure, which includes at least the manufacturer identifier segment, the production batch identifier segment, and the individual item serial number segment; The production-related information includes production batch and production date, and the production date uses a standardized time format. The initial state parameters include the physical property parameters of the item and the qualification status indicator; The preset circulation threshold includes a temperature threshold and a humidity threshold. The encryption algorithm adopts a symmetric encryption algorithm, and the verification algorithm adopts a cyclic redundancy check algorithm.

8. The intelligent traceability method for self-adhesive labels based on RFID electronic tags according to claim 3, characterized in that: The environmental parameter sensor includes a temperature sensor and a humidity sensor, and the state detection sensor includes an optical sensor; The multiple access method adopts time-division multiple access, and the sensor data reporting cycle can be adjusted according to the actual application scenario. The tag local cache has a preset storage capacity, and the number of times the retransmission command is sent is set to an upper limit. If the verification still fails after exceeding the upper limit, the fault information is recorded and feedback is provided.

9. The intelligent traceability method for self-adhesive labels based on RFID electronic tags according to claim 4, characterized in that: The temperature sensor data has a preset accuracy level, the humidity sensor data has a corresponding accuracy level, and the optical sensor data includes the optical parameters of the item's appearance and surface integrity indicators. The preset data structure includes fields such as timestamp, tag identifier, sensor number, data type, and numerical value. Numerical data is retained with a preset number of decimal places, and status data is represented by binary encoding. The data processing nodes include edge computing nodes, and the dataset is stored in a structured data format.

10. The intelligent traceability method for self-adhesive labels based on RFID electronic tags according to claim 5, characterized in that: The normalization process of the data preprocessing layer maps temperature data and humidity data to the [0,1] interval respectively through a preset formula; The anomaly detection algorithm adopts the isolated forest algorithm. The anomaly judgment threshold is set as a preset multiple of the data standard deviation. The data consistency coefficient is set to a minimum threshold. Data that is lower than the minimum threshold and exceeds the preset flow threshold is judged as interference data. The weighted fusion algorithm assigns a total of 1 weight coefficient to each data source, wherein the weights of RFID tag data, temperature sensor data, humidity sensor data, and optical sensor data are allocated according to a preset ratio. The calibration record includes information such as calibration time, data before and after calibration, and calibration reason. The data deviation threshold is set as a percentage threshold. The automatic updating of the tag storage information is achieved through a two-way communication module to ensure the accuracy and timeliness of traceability data.

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