Garment anti-counterfeiting traceability management system and method based on Internet of Things technology

Abstract

The invention discloses a clothing anti-counterfeiting traceability management system and method based on an internet of things technology, particularly relates to the technical field of anti-counterfeiting traceability, and is used for solving the problem that clothing traceability management is not clear. According to the invention, the data is encrypted and the authentication label is generated through the AES-GCM mode, then the RSA public key is used to encrypt the AES secret key and generate the ciphertext secret key, the ciphertext secret key is transmitted to the cloud through the Internet of Things network, and the cloud uses the RSA private key to decrypt and recover the AES secret key and decrypt the clothing data, so that safe and efficient data interaction is realized. By combining clothing data verification and anti-counterfeiting detection in a data transmission process, extracting traceability stable information and analyzing the performance of the data in the aspects of integrity, consistency and tampering prevention, the authenticity and reliability of the data are ensured, an anti-counterfeiting traceability strategy is dynamically adjusted based on an analysis result of the traceability stable information, the safety and anti-counterfeiting capability of a clothing traceability system are improved, and the safety of the clothing traceability system is improved. And data counterfeiting and tampering are prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of clothing anti-counterfeiting and tracing technology, and more specifically, to a clothing anti-counterfeiting and tracing management system and method based on the Internet of Things technology. Background Art

[0002] RFID-based clothing anti-counterfeiting is to use RFID chips to store product information for product anti-counterfeiting, and use radio frequency technology to upload the information stored in the chip to the system server through the terminal for confirmation. Large data storage, good confidentiality and strong anti-interference ability are several major features of RFID. At the same time, it can complete data transmission over long distances without contact. Therefore, this technology is mainly used in logistics, production, transportation, medical care, asset management and other fields; The clothing anti-counterfeiting identification technology based on two-dimensional barcodes on the market directly stores product information in the QR code, and consumers can directly obtain it by scanning the code. This type of anti-counterfeiting method requires the QR code to be encrypted and a decryption device to be used to obtain the information. The actual effect is often not ideal due to the low security of the encryption algorithm. However, this type of technology is low-cost and easy to promote in the current market environment.

[0003] Deficiencies of existing technologies: In clothing anti-counterfeiting traceability, the data collection and encryption processes are often separated, lacking real-time and systematicity, which makes the data vulnerable to tampering or leakage during transmission; secondly, although traditional encryption methods can protect the privacy of data, they lack effective strategies for key management, and the key life cycle and update mechanism are not flexible and secure enough, increasing the risk of key leakage or abuse. In addition, anti-counterfeiting capabilities mostly rely on static database verification, lacking a dynamic, transparent and tamper-proof verification mechanism, and unable to guarantee the authenticity and anti-counterfeiting of data in real time. Especially when facing the application of large-scale Internet of Things devices, the implementation of clothing anti-counterfeiting technology becomes more complicated and inefficient. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a clothing anti-counterfeiting traceability management system and method based on the Internet of Things technology to solve the problem of unclear clothing traceability management in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The clothing anti-counterfeiting traceability management method based on Internet of Things technology includes the following steps: Collect clothing data in real time based on IoT devices, convert it into a unified format through a standardized interface, encrypt it using AES-GCM mode, and generate an authentication tag; Use the RSA public key to encrypt the AES key, generate AES ciphertext, and transmit it to the cloud. The cloud uses the RSA private key to decrypt the recovery key and decrypt the clothing data. The verification and anti-counterfeiting process of clothing data in the transmission process is analyzed, the traceability stability information generated by the clothing data verification process is obtained, the traceability stability information is analyzed, and the clothing anti-counterfeiting traceability strategy is adjusted according to the analysis results.

[0006] In a preferred embodiment, clothing data is collected in real time based on IoT devices and converted into a unified format through a standardized interface. The specific process is as follows: Clothing data is obtained through RFID and QR code scanners to obtain clothing information during the production process. Clothing information includes production batch number, production time, raw material source, and production factory ID; The collected clothing data is processed and stored in JSON or XML format.

[0007] In a preferred embodiment, the AES-GCM mode is used to encrypt data and generate an authentication tag. The specific process is as follows: Perform AES key generation. After each data collection, a new AES key is generated through a pseudo-random number generator; The collected clothing data is encrypted according to the generated AES key to generate ciphertext data, and then the ciphertext data is used to generate an authentication tag through the AES-GCM mode; The generated ciphertext data and authentication tag are transmitted to the cloud.

[0008] In a preferred embodiment, the AES key is encrypted using the RSA public key to generate the AES ciphertext, which is then transmitted to the cloud. The cloud uses the RSA private key to decrypt the recovery key and decrypt the clothing data. The specific steps are as follows: Use RSA key to encrypt AES key. RSA key consists of public key and private key. RSA public key is used to encrypt data, and RSA private key is used to decrypt data. RSA public key is distributed to each IoT device, and RSA private key is held by the cloud server. The IoT device uses the RSA public key to encrypt the AES key and generate AES ciphertext; The cloud uses the RSA private key to decrypt the transmitted AES ciphertext and restore the original AES key. The restored AES key is then used to decrypt the transmitted ciphertext data and restore the original clothing data.

[0009] In a preferred embodiment, the verification and anti-counterfeiting process of the clothing data during the transmission process is analyzed to obtain the traceability stability information generated by the clothing data verification process. The specific process is as follows: Obtain the traceability stability information generated by the clothing data verification process, which includes the decryption consistency index and the temporal trend continuity index; The obtained decryption consistency index and time series trend continuity index are analyzed to determine the anti-counterfeiting traceability of the clothing.

[0010] In a preferred embodiment, the traceability stability information is analyzed and the strategy of clothing anti-counterfeiting traceability is adjusted according to the analysis results. The specific process is as follows: Set the decryption consistency security threshold to determine whether the decryption consistency index meets the expected requirements, and set the time series trend continuity security threshold to determine whether the time series trend continuity index meets the expected standards; When the decryption consistency index reaches or exceeds the decryption consistency security threshold, and the temporal trend consistency index reaches or exceeds the temporal trend consistency security threshold, the anti-counterfeiting information of the clothing product is authentic and reliable, and no management is required; When the decryption consistency index is lower than the decryption consistency security threshold, or the temporal trend continuity index is lower than the temporal trend continuity security threshold, there is an anti-counterfeiting risk, triggering an alarm mechanism to verify the clothing data.

[0011] The clothing anti-counterfeiting and traceability management system based on the Internet of Things technology is used to implement the clothing anti-counterfeiting and traceability management method based on the Internet of Things technology, including: Clothing data encryption module, which is used to collect clothing data in real time based on IoT devices, convert it into a unified format through a standardized interface, encrypt the data using AES-GCM mode, and generate an authentication tag; The decryption analysis module is used to use the RSA public key to perform AES key encryption, generate AES ciphertext, and transmit it to the cloud. The cloud uses the RSA private key to decrypt the recovery key and decrypt the clothing data; The traceability management module is used to analyze the verification and anti-counterfeiting process of clothing data during the transmission process, obtain the traceability stability information generated by the clothing data verification process, analyze the traceability stability information and adjust the clothing anti-counterfeiting traceability strategy based on the analysis results.

[0012] Technical effects and advantages of the present invention: The invention collects clothing data in real time based on the Internet of Things device, performs format conversion through a standardized interface, encrypts the data in AES-GCM mode and generates an authentication tag to ensure the confidentiality and integrity of the data, encrypts the AES key with an RSA public key, generates a ciphertext key, and transmits the data to the cloud through the Internet of Things network. The cloud uses an RSA private key to decrypt and restore the AES key, and decrypts the clothing data to achieve safe and efficient data interaction. In combination with clothing data verification and anti-counterfeiting detection in the data transmission process, traceability stability information is extracted, and the performance of the data in terms of integrity, consistency and anti-tampering is analyzed to ensure that the data is authentic and reliable. Based on the analysis result of the traceability stability information, the anti-counterfeiting traceability strategy is dynamically adjusted to improve the security and anti-counterfeiting ability of the clothing traceability system, prevent data forgery and tampering, enhance the traceability of clothing production and circulation processes, and protect the rights and interests of brands and consumers. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a flowchart of the clothing anti-counterfeiting traceability management method based on the Internet of Things technology.

[0014] Figure 2 It is a structural schematic diagram of the clothing anti-counterfeiting and traceability management system based on the Internet of Things technology of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Example 1: Figure 1 As shown, the clothing anti-counterfeiting traceability management method based on the Internet of Things technology includes the following steps: Collect clothing data in real time based on IoT devices, convert it into a unified format through a standardized interface, encrypt it using AES-GCM mode, and generate an authentication tag; Use the RSA public key to encrypt the AES key, generate AES ciphertext, and transmit it to the cloud. The cloud uses the RSA private key to decrypt the recovery key and decrypt the clothing data. The verification and anti-counterfeiting process of clothing data in the transmission process is analyzed, the traceability stability information generated by the clothing data verification process is obtained, the traceability stability information is analyzed, and the clothing anti-counterfeiting traceability strategy is adjusted according to the analysis results.

[0017] Step 1: Collect clothing data and generate AES keys. All key information related to clothing (such as production batch, production date, source of raw materials, etc.) is accurately collected and provides raw data for subsequent encryption. This data will be the basis for clothing anti-counterfeiting. The specific steps are as follows: Clothing traceability is to obtain key clothing information in the production process through integrated sensors (such as RFID, QR code scanners, etc.), such as production batch number, production time, raw material source, production factory ID, etc. Each clothing product will have a unique identifier (such as RFID tag or QR code) during the production process, which contains various information about the product; Specifically, IoT devices (such as clothing traceability inventory stations, RFID tag scanners, QR code readers, etc.) are used to collect production information related to clothing. Each piece of clothing is attached with an RFID tag during production. The tag has a unique identifier embedded in it and can store information including production batch, production time, raw material source, production factory, etc. Each production batch of clothing corresponds to a unique RFID tag; The data collected by each sensor is transmitted to the data processing unit in the IoT gateway or device through the data acquisition interface. The original data is obtained through the RFID scanner, QR code scanner or other IoT sensors, and transmitted to the clothing traceability inventory station through the interface. The data includes but is not limited to: Production batch number, used to identify the unique number of each garment production; production time, used to record the production time of the garment, used to determine the time range of its production; raw material source, used to track the source of the raw materials of the garment to ensure the quality and traceability of the garment; production factory, used to record the production factory ID of the garment to ensure that the production location and production process of each garment can be traced; The data format includes text data, timestamps, numbers, etc., which are used to represent the unique identity and production information of the garment; The collected data will be processed according to a standardized data format (such as JSON or XML format) to ensure that there will be no format incompatibility issues during subsequent encryption, transmission and storage. The formatted clothing data will be transmitted to the encryption module for subsequent encryption processing; By generating an AES (Advanced Encryption Standard) session key and using it to encrypt the clothing data, the confidentiality and integrity of the data during transmission is ensured to prevent tampering or leakage during the data transmission process. At the same time, the AES-GCM mode is adopted to ensure that the encrypted data can be decrypted and integrity checked in the cloud. The specific steps are as follows: AES key generation is performed. After each data collection, a new AES key is generated. The generation of AES key uses a pseudo-random number generator to ensure the unpredictability and security of the key. In order to ensure encryption strength and data protection, the length of the AES key is 256 bits. The specific generation formula is: ,in, is the generated AES key, Indicates that a 256-bit AES key is generated through a pseudo-random number generator; The collected clothing data is encrypted using the generated AES key. AES is a symmetric encryption algorithm, which means that the same key is used in the encryption and decryption processes. Therefore, the security of the key is of vital importance. In addition, the AES-GCM mode (encryption and authentication integrated mode) is used to generate an authentication tag for the ciphertext data generated after the AES key is encrypted to ensure that the data is not only encrypted, but also the integrity of the data during transmission is verified. The encryption formula is: , where C is the encrypted ciphertext data, M is the original plaintext data (such as production batch number, production time, etc.); T is the authentication tag, which is used to verify whether the data has been tampered with during transmission; The AES-GCM mode generates an authentication tag T while encrypting the data. The tag is used to verify the integrity of the data when decrypting the data. If the data is tampered with, the authentication tag will not match, thereby avoiding the transmission of erroneous data. The generated ciphertext data and the authentication tag will be transmitted together to the cloud or data storage system.

[0018] In step 1, the clothing data is encrypted using the AES encryption algorithm to ensure the confidentiality of the data. However, since AES is a symmetric encryption algorithm, its key itself (i.e., the AES key) also needs to be protected. In order to ensure the security of the AES key during transmission, the AES key must be encrypted using the RSA encryption algorithm, and then the encrypted AES key and clothing data are transmitted to the cloud through the IoT network. This step ensures the security of the AES key and prevents key leakage or tampering that may occur in the IoT environment.

[0019] Step 2: encrypt the AES key with RSA and transmit it through the Internet of Things. That is, encrypt the AES key generated in step 1 using the RSA encryption algorithm. Since RSA is an asymmetric encryption algorithm, it uses a public key to encrypt data and a private key to decrypt data. In this embodiment, the cloud system holds the RSA private key, which is used to decrypt the encrypted AES key transmitted from the Internet of Things device. The specific steps are as follows: Use RSA to encrypt the AES key. The encryption process is as follows: The RSA key pair consists of a public key and a private key. The RSA public key is used to encrypt data, and the RSA private key is used to decrypt data. In the clothing anti-counterfeiting traceability management, the RSA public key will be distributed to each IoT device, while the RSA private key is held by the cloud server. The cloud system uses the private key to decrypt the data transmitted by the IoT device. The RSA key pair generation expression is: ,in, is the public key, which is distributed to IoT devices. It is a private key, stored in the cloud; The AES key encryption process is as follows: IoT devices use RSA public keys ( ) to encrypt the AES key generated in step 1 ( ), at this time, the AES key is encrypted into AES ciphertext and can be securely transmitted to the cloud. Even if the encrypted AES key is intercepted during transmission, the attacker cannot decrypt it. Only the RSA private key owned by the cloud can decrypt it. The RSA encryption expression is: ,in, is the encrypted AES key (AES ciphertext); is the original AES key; The RSA public key.

[0020] It should be noted that the AES key is encrypted using the RSA public key to ensure that the AES key remains confidential when transmitted between the IoT device and the cloud, preventing the AES key from being intercepted or tampered with. The RSA asymmetric encryption mechanism ensures the one-way nature of the encryption process, and only the cloud system with the corresponding private key can decrypt it.

[0021] During the transmission of encrypted data and keys, in order to ensure the security of information, the transmission will use the TLS protocol (Transport Layer Security Protocol) to further encrypt the data. TLS provides end-to-end encryption and identity authentication mechanisms to ensure that data will not be eavesdropped or tampered with during transmission between IoT devices and the cloud; Once the encrypted AES key and clothing data are transmitted to the cloud, the cloud will use the RSA private key to decrypt the AES ciphertext, recover the original AES key, and use the key to decrypt the encrypted clothing data (ciphertext data). This is a key step to ensure the integrity and authenticity of the data during transmission; The cloud uses the RSA private key to decrypt the transmitted encrypted AES key, restore the original AES key, and then use the restored AES key to decrypt the transmitted encrypted data to restore the original clothing data (such as production batches, raw material sources, etc.).

[0022] The decrypted data will be compared with the records in the cloud database to determine whether the data matches and ensure the authenticity of the clothing data. In addition, the AES-GCM authentication tag is used to verify the integrity of the data during the decryption process. If the decrypted authentication tag does not match the original tag, the system will prompt that the data has been tampered with.

[0023] Each time data is uploaded, a new AES key is generated and transmitted to the IoT device after being encrypted by RSA. The storage, update, and destruction of all keys are managed uniformly to ensure the validity of the keys.

[0024] Step 3: Perform data verification and anti-counterfeiting process analysis. After data transmission and decryption are completed, the cloud obtains the clothing data and needs to verify the clothing data to determine whether the clothing data has been tampered with during the transmission process and whether it conforms to the expected production information. The specific steps are as follows: Obtain the traceability stability information generated by the clothing data verification process, which includes the decryption consistency index calibrated as DCI and the time series trend continuity index calibrated as TTCI; The decryption consistency index measures the consistency between the decrypted data and the original data or expected records in terms of content, structure, and authentication tags. By comparing the authentication tags generated during the encryption process with the authentication tags recalculated in the cloud bit by bit, and measuring the semantic consistency of key fields, the decryption consistency index can quantitatively reflect whether the data maintains integrity and accuracy during transmission and decryption. The value of the decryption consistency index is between 0 and 1. The higher the value, the higher the data integrity and the greater the possibility of not being tampered with, thereby enhancing the reliability of product anti-counterfeiting. During the encryption process, the AES-GCM mode generates an authentication tag. The decryption consistency index evaluates the match between the authentication tag recalculated by the cloud and the original authentication tag, thereby reflecting whether the data maintains integrity during transmission and decryption; The logic for obtaining the decryption consistency index is as follows: In the data encryption stage, when the clothing data is encrypted using the AES-GCM mode, an authentication tag is generated at the same time ; After decrypting the data in the cloud, use the same AES-GCM algorithm and the decrypted AES key to recalculate the authentication tag for the decrypted data ; Compare and match the authentication tag bit by bit: , for all i=1, 2, ..., N, the matching rate is calculated after comparison: , N is the total length of the authentication tag, and the power function is used to map the authentication tag consistency factor: , where ; Extract fields from the decrypted clothing data and record them as sets , and obtain the corresponding expected field value set from the cloud database , calculate the similarity score for each field j , calculate the semantic consistency of all fields, and use the geometric mean method to calculate the similarity of the aggregated fields. The expression is: , where F represents the total number of fields, and the decryption consistency index is calculated as: .

[0025] It should be noted that the parameters Control the matching strictness; if the matching rate r is low, the exponential effect will significantly reduce the consistency factor of the authentication tag; extract a set of key fields from the decrypted clothing data, such as production batch number, production factory number, raw material source, etc., and record them as a set; the similarity score can be calculated by a string similarity function, for example, a cosine similarity function.

[0026] The temporal trend consistency index is used to quantify the consistency between the decrypted data and the expected records in the background in terms of time and production trends. The index first compares the production time recorded in the clothing data with the expected value in the database from the time dimension, and calculates the temporal consistency factor through the exponential decay function; secondly, it analyzes the deviation between the production indicators (such as output per unit time or quality indicators) and the expected trend from the trend dimension, and also uses the exponential decay function to obtain the trend consistency factor. Finally, these two factors are multiplied to form the temporal trend consistency index. The closer the value is to 1, the more it indicates that the clothing production data is in line with expectations in terms of time and trend, reflecting a stable production process and true traceability information. By comparing the production time recorded in the decrypted data with the expected production time in the database, the matching of the data in the time dimension can be evaluated. By comparing the differences between the decrypted data and the historical expected records of key indicators reflecting production trends (such as output per unit time, quality indicators, data update frequency, etc.), the consistency of data trend changes can be quantitatively reflected. It can provide quantitative basis of temporal and spatial information for anti-counterfeiting verification, ensuring that the decrypted data is not only consistent with expectations in content, but also in production sequence and trend, reflecting the normal law of clothing production; The logic for obtaining the time series trend continuity index is as follows: Get production time from decrypted data ; Get the expected production time from the database , calculate the absolute difference between the two: , and calculate the timing consistency factor, the calculation expression is: , where is the time decay parameter, ; Get production trend indicators from decrypted data and calculate current trend values With expected trend value The absolute difference between: , calculate the trend deviation value , calculate the time series trend continuity index, the calculation expression is: .

[0027] It should be noted that in the clothing production process, product production volume, quality indicators or other key production data usually show a certain trend of change over time, so the trend value is analyzed. The trend value can be obtained by exponential smoothing, that is, the exponential smoothing method is used to perform weighted smoothing on historical data, giving recent data a higher weight, thereby obtaining a smooth trend sequence that reflects the changing trend in the production process.

[0028] Set the decryption consistency security threshold to determine whether the decryption consistency index meets the expected requirements, and set the time series trend continuity security threshold to determine whether the time series trend continuity index meets the expected standards; When the decryption consistency index reaches or exceeds the decryption consistency security threshold, and the time series trend consistency index reaches or exceeds the time series trend consistency security threshold; It shows that the decrypted clothing data is highly consistent with the expected records in all key dimensions. Specifically, there are no anomalies in the data in terms of authentication label matching, key field semantic consistency, production time records and production trends. This status indicates that the data has not been tampered with during the collection, encryption, transmission, decryption and verification process. The product's anti-counterfeiting information is authentic and reliable, and can provide consumers and regulatory authorities with a highly reliable verification basis; When the decryption consistency index is lower than the decryption consistency safety threshold, or the time series trend consistency index is lower than the time series trend consistency safety threshold: If the decryption consistency index is lower than the decryption consistency security threshold, it means that there are obvious deviations in authentication tag matching, data content consistency or time recording, which may mean that the data has been tampered with during transmission or decryption, or there is a risk of forgery, thus affecting the integrity and authenticity of the data; If the temporal trend consistency index is lower than the temporal trend consistency security threshold, it indicates that there is a large difference between the production time and production trend in the decrypted data and the expected records, which may reflect an abnormal production process, inaccurate data collection or forgery; If any indicator is below the safety threshold, further safety review or alarm mechanism should be triggered to conduct detailed verification of the data to prevent counterfeit and shoddy products from entering the market.

[0029] The decryption consistency security threshold and the time trend continuity security threshold are used as preset standards, providing a clear basis for security judgment for comprehensive verification. When both indicators reach or exceed their respective security thresholds, it means that the clothing data meets expectations in all key dimensions and the anti-counterfeiting verification results are credible.

[0030] It should be noted that the thresholds involved in the embodiments can be determined according to specific scenarios and requirements.

[0031] The invention collects clothing data in real time based on the Internet of Things device, performs format conversion through a standardized interface, encrypts the data in AES-GCM mode and generates an authentication tag to ensure the confidentiality and integrity of the data, encrypts the AES key with an RSA public key, generates a ciphertext key, and transmits the data to the cloud through the Internet of Things network. The cloud uses an RSA private key to decrypt and restore the AES key, and decrypts the clothing data to achieve safe and efficient data interaction. In combination with clothing data verification and anti-counterfeiting detection in the data transmission process, traceability stability information is extracted, and the performance of the data in terms of integrity, consistency and anti-tampering is analyzed to ensure that the data is authentic and reliable. Based on the analysis result of the traceability stability information, the anti-counterfeiting traceability strategy is dynamically adjusted to improve the security and anti-counterfeiting ability of the clothing traceability system, prevent data forgery and tampering, enhance the traceability of clothing production and circulation processes, and protect the rights and interests of brands and consumers.

[0032] Example 2: Clothing anti-counterfeiting traceability management system based on Internet of Things technology, such as Figure 2 As shown, specifically including: Clothing data encryption module, which is used to collect clothing data in real time based on IoT devices, convert it into a unified format through a standardized interface, encrypt the data using AES-GCM mode, and generate an authentication tag; The decryption analysis module is used to use the RSA public key to perform AES key encryption, generate AES ciphertext, and transmit it to the cloud. The cloud uses the RSA private key to decrypt the recovery key and decrypt the clothing data; The traceability management module is used to analyze the verification and anti-counterfeiting process of clothing data during the transmission process, obtain the traceability stability information generated by the clothing data verification process, analyze the traceability stability information and adjust the clothing anti-counterfeiting traceability strategy based on the analysis results.

[0033] The above formulas are all dimensionless and calculated numerically. Specific dimension removal can be achieved by various means such as standardization, which will not be elaborated here. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0034] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, an ATA hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state ATA hard disk.

[0035] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0036] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0037] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0038] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0039] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0040] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A clothing anti-counterfeiting traceability management method based on Internet of Things technology, characterized in that: The steps include: Collect clothing data in real time based on IoT devices, convert it into a unified format through a standardized interface, encrypt it using AES-GCM mode, and generate an authentication tag; Use the RSA public key to encrypt the AES key, generate AES ciphertext, and transmit it to the cloud. The cloud uses the RSA private key to decrypt the recovery key and decrypt the clothing data. Analyze the verification and anti-counterfeiting process of clothing data during transmission, obtain the traceability stability information generated by the clothing data verification process, analyze the traceability stability information and adjust the clothing anti-counterfeiting traceability strategy based on the analysis results; The verification and anti-counterfeiting process of clothing data during the transmission process is analyzed to obtain the traceability stability information generated by the clothing data verification process. The specific process is as follows: Obtain the traceability stability information generated by the clothing data verification process, which includes the decryption consistency index and the temporal trend continuity index; The obtained decryption consistency index and temporal trend continuity index are analyzed to determine the anti-counterfeiting traceability of clothing; The decryption consistency index is used to indicate the degree of match between the authentication tag recalculated by the cloud and the original authentication tag; The temporal trend consistency index is used to indicate the degree of match between the production time recorded in the decrypted data and the expected production time data in the database in terms of time dimension.

2. The clothing anti-counterfeiting and traceability management method based on Internet of Things technology according to claim 1 is characterized by: The clothing data is collected in real time based on IoT devices and converted into a unified format through a standardized interface. The specific process is as follows: Clothing data is obtained through RFID and QR code scanners to obtain clothing information during the production process. Clothing information includes production batch number, production time, raw material source, and production factory ID; The collected clothing data is processed and stored according to JSON or XML data format.

3. The clothing anti-counterfeiting and traceability management method based on Internet of Things technology according to claim 2 is characterized by: Use AES-GCM mode to encrypt data and generate authentication tags. The specific process is as follows: Perform AES key generation. After each data collection, a new AES key is generated through a pseudo-random number generator; The collected clothing data is encrypted according to the generated AES key to generate ciphertext data, and then the ciphertext data is used to generate an authentication tag through the AES-GCM mode; The generated ciphertext data and authentication tag are transmitted to the cloud.

4. The clothing anti-counterfeiting and traceability management method based on Internet of Things technology according to claim 3 is characterized by: Use the RSA public key to encrypt the AES key, generate the AES ciphertext, and transmit it to the cloud. The cloud uses the RSA private key to decrypt the recovery key and decrypt the clothing data. The specific steps are as follows: Use RSA key to encrypt AES key. RSA key consists of public key and private key. RSA public key is used to encrypt data, and RSA private key is used to decrypt data. RSA public key is distributed to each IoT device, and RSA private key is held by the cloud server. The IoT device uses the RSA public key to encrypt the AES key and generate AES ciphertext; The cloud uses the RSA private key to decrypt the transmitted AES ciphertext and restore the original AES key. The restored AES key is then used to decrypt the transmitted ciphertext data and restore the original clothing data.

5. The clothing anti-counterfeiting and traceability management method based on Internet of Things technology according to claim 4 is characterized by: The logic for obtaining the decryption consistency index is as follows: In the data encryption stage, when the clothing data is encrypted using the AES-GCM mode, an authentication tag is generated at the same time ; After decrypting the data in the cloud, use the same AES-GCM algorithm and the decrypted AES key to recalculate the authentication tag for the decrypted data ; Compare and match the authentication tag bit by bit: , for all i=1, 2, ..., N, the matching rate is calculated after comparison: , N is the total length of the authentication tag, and the power function is used to map the authentication tag consistency factor: , where ; Extract fields from the decrypted clothing data and record them as sets , and obtain the corresponding expected field value set from the cloud database , calculate the similarity score for each field j , calculate the semantic consistency of all fields, and use the geometric mean method to calculate the similarity of the aggregated fields. The expression is: , where F represents the total number of fields, and the decryption consistency index is calculated as: ; The logic for obtaining the time series trend continuity index is as follows: Get production time from decrypted data ; Get the expected production time from the database , calculate the absolute difference between the two: , and calculate the timing consistency factor, the calculation expression is: , where is the time decay parameter, ; Get production trend indicators from decrypted data and calculate current trend values With expected trend value The absolute difference between: , calculate the trend deviation value , calculate the time series trend continuity index, the calculation expression is: .

6. The clothing anti-counterfeiting and traceability management method based on Internet of Things technology according to claim 5 is characterized by: Analyze the traceability stability information and adjust the clothing anti-counterfeiting traceability strategy based on the analysis results. The specific process is as follows: Set the decryption consistency security threshold to determine whether the decryption consistency index meets the expected requirements, and set the time series trend continuity security threshold to determine whether the time series trend continuity index meets the expected standards; When the decryption consistency index reaches or exceeds the decryption consistency security threshold, and the temporal trend consistency index reaches or exceeds the temporal trend consistency security threshold, the anti-counterfeiting information of the clothing product is authentic and reliable, and no management is required; When the decryption consistency index is lower than the decryption consistency security threshold, or the temporal trend continuity index is lower than the temporal trend continuity security threshold, there is an anti-counterfeiting risk, triggering an alarm mechanism to verify the clothing data.

7. A clothing anti-counterfeiting and traceability management system based on the Internet of Things technology, used to implement the clothing anti-counterfeiting and traceability management method based on the Internet of Things technology as described in any one of claims 1 to 6, characterized in that: include: Clothing data encryption module, which is used to collect clothing data in real time based on IoT devices, convert it into a unified format through a standardized interface, encrypt the data using AES-GCM mode, and generate an authentication tag; The decryption analysis module is used to use the RSA public key to perform AES key encryption, generate AES ciphertext, and transmit it to the cloud. The cloud uses the RSA private key to decrypt the recovery key and decrypt the clothing data; The traceability management module is used to analyze the verification and anti-counterfeiting process of clothing data during the transmission process, obtain the traceability stability information generated by the clothing data verification process, analyze the traceability stability information and adjust the clothing anti-counterfeiting traceability strategy based on the analysis results.

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