A safe AI glasses device production management identification method and system

Abstract

The application discloses a safe AI glasses device production management identification method and system, and relates to the technical field of AI glasses device safety management, and comprises the following steps: step one: obtaining the basic information of each device in a factory and generating a device serial number; the device serial number comprises a basic code and a check bit; the check bit comprises a first check bit C1 and a second check bit C2; the first check bit C1 is obtained by taking the modulus of a preset check modulus after summing the basic code bit values according to the positive position weight; the second check bit C2 is obtained by taking the modulus of a preset check modulus after summing the basic code bit values according to the reverse position weight; the check bit of the device serial number is expanded from a self-check function to a key derivation factor, so that the replacement or loss of any device will cause the verification key and the decryption key to be unmatched, the tamper-proof verification of the device list is realized, and the online mode and the offline mode can be switched, so that the device can work in the network environment and the non-network environment.

Description

Technical Field

[0001] This invention belongs to the field of AI glasses device safety management technology, specifically a safe production management and identification method and system for AI glasses devices. Background Technology

[0002] In modern intelligent manufacturing environments, accurate identification, status tracking, and compliant management of equipment assets are core aspects of ensuring production safety and efficiency. Traditional equipment management methods primarily rely on paper work orders, handheld barcode scanners, or simple mobile applications to collect and record equipment information. With the development of augmented reality technology, AI glasses are gradually being introduced into the field of production management, assisting workers in equipment identification and operation guidance through augmented reality interfaces and machine vision capabilities.

[0003] Currently, device management solutions based on AI glasses typically store device list data on the glasses or a companion terminal. Operators scan the QR codes or barcodes on the devices using the glasses to obtain device numbers, and then compare these numbers with the list to confirm device identity. Regarding device number generation, existing solutions mostly use simple sequential coding or single check digit coding rules. For task execution, existing solutions usually store the device list directly on the execution terminal; after the operator completes the scan, the system records the operation result and sends it back to the backend management system.

[0004] However, existing AI glasses device management and identification solutions lack sufficient anti-counterfeiting verification capabilities for device serial numbers. Simple coding rules or single check bits are insufficient to effectively detect errors in serial numbers generated during transmission, input, or human tampering. Furthermore, it is difficult for the system to detect when the identifier of a single device is forged or replaced. Simultaneously, there is a lack of effective verification mechanisms for the overall integrity of the device list. Existing solutions verify devices through one-to-one comparison; when some devices in the list are replaced, deleted, or added, the system cannot recognize that the list has been tampered with as long as the number format of the replaced device is correct. Additionally, the programs and data at the task execution end lack anti-tampering protection; malicious modifications to the execution end program or embedded data cannot be automatically detected by the system, posing a security risk. Based on these findings, a secure AI glasses device production management and identification method and system are proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a secure production management and identification method and system for AI glasses devices to solve the problems mentioned in the background art.

[0006] A secure production management and identification method and system for AI glasses devices, comprising: Step 1: Obtain the basic information of each piece of equipment in the factory and generate the equipment serial number. The equipment serial number includes a basic code and a check bit. The check bit includes a first check bit C1 and a second check bit C2. The first check bit C1 is obtained by summing the values ​​of each bit of the basic code according to their forward position weights and then taking the modulus of the preset check modulus. The second check bit C2 is obtained by summing the values ​​of each bit of the basic code according to their reverse position weights and then taking the modulus of the preset check modulus. Step 2: Extract the device list involved in the task to be processed. Add the values ​​corresponding to the first check bit C1 of all devices in the device list to obtain the first check bit sum S1. Add the values ​​corresponding to the second check bit C2 of all devices to obtain the second check bit sum S2. Calculate the decryption key K based on the first check bit sum S1, the second check bit sum S2 and the number of devices N. Encapsulate the task to be processed, the device list and the decryption key K into the task execution terminal. Step 3: After starting the task execution terminal, perform self-verification and task timeliness verification. After the verification is successful, load the device list data. Step 4: The operator scans the identifier of each execution device through AI glasses to obtain the device serial number. The verification key V is calculated for all the scanned devices according to the method in Step 2. The verification key V is compared with the decryption key K. If they are equal, a device list is generated to identify the correct identifier. Step 5: After correctly identifying the equipment, generate an operation record containing the equipment serial number, operation time, and operation result, and link multiple records together to form a record chain; Step Six: Receive the record chain data, verify it, update the device status, and archive it to complete the data loop.

[0007] As a further aspect of the present invention: the device serial number is generated using a composite encoding rule, specifically in the following format: a 3-digit device type code, a 2-digit year code, a 4-digit batch number, a 5-digit serial number, and a 2-digit check digit are sequentially concatenated to form a 16-digit complete device serial number, wherein the first 14 digits are the basic encoding, the 15th digit is the first check digit C1, and the 16th digit is the second check digit C2.

[0008] As a further aspect of the present invention: the first check bit C1 is calculated as follows: Multiply the first value of the basic code by weight 1, the second value by weight 2, and so on until the 14th value is multiplied by weight 14. Add all the products together and take the modulo 36. Convert the result of the modulo operation into the corresponding character according to the base-36 rule and use it as the first check bit C1. The numbers 0 to 9 correspond to the values ​​0 to 9, and the letters A to Z correspond to the values ​​10 to 35. The preset check modulus is 36.

[0009] As a further aspect of the present invention: the second check bit C2 is calculated as follows: Multiply the first value of the basic code by weight 14, the second value by weight 13, and so on until the 14th value is multiplied by weight 1. Add all the products using a reverse weighting method and take the modulo 36. The result of the modulo operation is converted into the corresponding character according to the base-36 rules and used as the second check bit C2.

[0010] As a further aspect of the present invention: the decryption key K is calculated as follows: Multiply the sum of the first check bits S1 by the number of devices N, and add the sum of the second check bits S2 to obtain the original value of the decryption key; take the modulo of the original value of the decryption key with the preset key modulus to obtain the decryption key K; the preset key modulus is a prime number 9973.

[0011] As a further aspect of the present invention: Step 2 further includes: calculating the hash value of the whole to be executed, which consists of the task to be processed, the device list, and the decryption key K, and embedding the hash value as the expected hash value into the task execution program; Step 3, the self-verification specifically involves: after the task execution end starts, calculating the current hash value of the whole to be executed, comparing the current hash value with the expected hash value, and if they match, the self-verification passes; if they do not match, it is determined that the program has been tampered with and the program is terminated.

[0012] As a further aspect of the present invention: In step three, after verification, the operating mode is selected according to the network status, and a probe request is sent to the background. If a response is received, it is determined to be in an online state and the online mode is selected, and the latest device list data is requested from the background. If no response is received, it is determined to be in an offline state and the offline mode is selected, and the data is loaded from the device list embedded in the task execution terminal.

[0013] As a further aspect of the present invention: In step five, the way to connect multiple records to form a record chain is as follows: each operation record contains the hash value of the previous operation record as the preceding hash value, the preceding hash value of the first operation record is set as the hash value of the task identifier, and the preceding hash value of the second and subsequent operation records is the hash value of the previous operation record, thereby forming a chain structure with sequential association.

[0014] As a further aspect of the present invention: in step six, the specific methods for verification include: The integrity of the record chain is verified by checking whether the preceding hash value of each record is consistent with the hash value of the previous record. Starting from the first record whose preceding hash value is equal to the task identifier hash value, the verification proceeds sequentially. If the preceding hash value of each record is consistent with the hash value of the previous record, the integrity verification of the record chain is considered successful. The device serial number in each record is then verified by checking the check code. The basic code of the device serial number is extracted, and the first check bit and the second check bit are calculated respectively according to the method described in step one. The calculation results are compared with the check bits in the device serial number. If the calculated first check bit is consistent with the 15th bit of the device serial number and the calculated second check bit is consistent with the 16th bit of the device serial number, the check code verification of the device serial number is considered successful. When the integrity verification of the record chain is successful and the check code verification of all device serial numbers is successful, the verification in step six is ​​considered successful.

[0015] A secure production management and identification system for AI glasses devices includes: The device serial number generation module is used to obtain the device type code, year code, batch number and serial number of each device to form a 14-bit basic code. The first check bit C1 is calculated by taking the modulo 36 of the forward weighted summation, and the second check bit C2 is calculated by taking the modulo 36 of the reverse weighted summation. The two check bits are appended to the basic code to generate a complete 16-bit device serial number. The task encapsulation and key generation module is used to extract the equipment list involved in the task to be processed, calculate the sum of the first check bit C1 and the sum of the second check bit C2 of each device in the equipment list, and then calculate the decryption key K. The task to be processed, the equipment list, the validity period and the decryption key are encapsulated into an execution whole, and the hash value of the execution whole is calculated as the expected hash value and embedded into the production line execution program. The dual-layer verification and mode selection module is used to calculate the overall hash value to be executed and compare it with the expected hash value for self-verification when the production line execution end starts, obtain the current time and compare it with the task validity period for timeliness verification, and send a probe request to the background. Based on the response, it can choose the online mode to obtain the latest equipment list from the background or choose the offline mode to load data from the program's embedded list. The AI ​​glasses recognition and key verification module is used to establish a communication connection with the AI ​​glasses worn by the operator, receive the device serial number obtained by scanning the QR code of the AI ​​glasses, verify the device serial number of each device by checking the check code, calculate the verification key V based on the check bit after all devices have been scanned, compare the verification key V with the decryption key K, and if they are equal, generate a device list to identify the correct identification. The record chain generation and feedback module is used to generate an operation record for each device after correctly identifying the device list. Each record contains the device serial number, operation time, and operation result. Each record contains the hash value of the previous record to form a record chain. In online mode, the record chain is transmitted to the backend in real time. In offline mode, it is packaged and exported as an offline data package with a packet verification code. The background verification and status update module is used to receive online transmission operation records or import offline data packets, verify the packet checksum and record chain integrity, verify the device serial number of each record with a checksum, and update the device status after all verifications are successful.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention extends the verification bit of the device serial number from a simple self-verification function to a key derivation factor, ensuring that the replacement or loss of any device will result in a mismatch between the verification key and the decryption key. This achieves tamper-proof verification of the device list and solves the technical problem that traditional one-to-one comparison methods cannot detect overall tampering. Intelligent switching between online and offline modes ensures reliable operation in both network-enabled and network-free environments, meeting the complex and ever-changing network conditions in factory workshops. Visual operation guidance is achieved through the augmented reality interface of AI glasses, freeing up operators' hands and improving work efficiency and accuracy. A chained hash structure protects the continuity and immutability of operation records, forming a complete operation audit chain that meets the requirements of production compliance management and quality traceability. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method framework structure of the present invention; Figure 2 This is a schematic diagram of the system framework structure of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Please refer to Figure 1 This application provides a secure production management identification method for AI glasses devices, including the following steps: Step 1: Obtain basic information for each piece of equipment in the factory. This basic information includes the equipment type code, year code, batch number, and serial number. Based on this basic information, generate a 16-digit complete equipment serial number for each piece of equipment. The equipment serial number is generated using a composite coding rule, specifically in the following format: 3-digit equipment type code + 2-digit year code + 4-digit batch number + 5-digit serial number + 2 check digits. The first 14 digits are the basic code, and the last 2 digits are check digits, designated as check digit C1 and check digit C2. The specific method is as follows: When generating a device serial number for each device, the device type code is taken as 3 characters, the year code as 2 digits, the batch number as 4 digits, and the serial number as 5 digits, for a total of 14 basic codes. Then, the 14 basic codes are analyzed and calculated to obtain 2 check bits. The 2 check bits are added to the 14 basic codes to form a complete device serial number, thus generating the device serial number for each device. The 2 check bits are the first check bit C1 and the second check bit C2. The first check digit is calculated as follows: multiply the first value of the basic code by weight 1, the second value by weight 2, and so on until the 14th value is multiplied by weight 14. Add all the products together and take the modulo 36. The result of the modulo operation is converted into a base-36 character as the first check digit C1. The numbers 0 to 9 correspond to values ​​0 to 9, and the letters A to Z correspond to values ​​10 to 35, 0-A, 11-B, ..., 35-Z. The second check bit is calculated as follows: multiply the first value of the basic code by weight 14, the second value by weight 13, and so on until the 14th value is multiplied by weight 1. Using the reverse weighting method, add all the products together and take the modulo 36. The result of the modulo operation is converted into a base-36 character as the second check bit C2. The preset check modulo is 36. For example: A device's equipment type code is PMI, indicating a precision instrument; the year code is 24, indicating 2024; the batch number is 0315, indicating the 15th batch in March; and the serial number is 00001, indicating the first unit in that batch. The basic code is PMI2403150001, a total of 14 digits. The first check digit is calculated as follows: The value of P (25) is multiplied by the position weight 1, the value of M (22) by the position weight 2, the value of I (18) by the position weight 3, and so on, performing a weighted sum. The sum is then modulo 36 to obtain 17, corresponding to the character H. The second check digit is calculated similarly, resulting in 5. Therefore, the final device serial number is PMI240315000001H5.

[0020] For each piece of equipment in the factory, its basic information, such as equipment type code, year code, batch number, and serial number, is first obtained. This information is then concatenated according to a preset format to form a 14-digit basic code. Next, a double check digit calculation is performed on this basic code. The first check digit uses a forward position weighting method: the first digit of the basic code is multiplied by weight 1, the second by weight 2, and so on until the 14th digit is multiplied by weight 14. All products are summed and the result is modulo a preset check modulus to obtain the first check digit. The second check digit uses a reverse position weighting method: the first digit of the basic code is multiplied by weight 14, the second by weight 13, and so on until the 14th by weight 1. All products are summed. The second check bit is obtained by taking the modulus of the preset check modulus. Finally, the two check bits are appended to the basic code to form a complete 16-bit device serial number. The dual check bits are generated through two different calculation methods, forward weighting and reverse weighting, which gives the device serial number a strong self-verification capability. It can effectively detect unit errors, transposition errors, multi-digit errors, and random errors that occur during the transmission, input, scanning, or human tampering of the serial number. Compared with a single check bit, the dual check bit mechanism significantly improves the error detection rate and provides a reliable verification basis for the serial number's legality verification in subsequent steps. At the same time, the existence of dual check bits significantly increases the difficulty of forging a device serial number that can pass the verification, thus improving the anti-counterfeiting capability of the device identification.

[0021] Step Two: Extract the equipment list involved in the task to be processed. Based on the two check digits in the serial number of each device in the equipment list, obtain the decryption key corresponding to the equipment list of the task to be processed. Encapsulate the task to be processed, the equipment list involved, and the validity period of the task to be processed as a whole into the production line execution terminal. At the same time, calculate the hash value of the whole to be processed as the expected hash value and embed it into the production line execution terminal. The specific method is as follows: First, from the device serial number database generated in step one, a list of devices involved in this task is extracted according to the conditions of the task to be processed. The device list contains N devices, each device corresponding to a complete 16-bit device serial number. Then, based on the first check bit C1 and the second check bit C2 in the device serial number of each device in the device list, the decryption key K corresponding to the device list of the task to be processed is calculated. The decryption key K is calculated as follows: Add the value corresponding to the first check bit C1 of the first device in the device list to the value corresponding to the first check bit C1 of the second device, and so on, to add the values ​​corresponding to the first check bit C1 of all N devices to obtain the first check bit sum S1. Add the value corresponding to the second check bit C2 of the first device in the device list to the value corresponding to the second check bit C2 of the second device, and so on, to add the values ​​corresponding to the second check bit C2 of all N devices to obtain the second check bit sum S2. Multiply the sum of the first check bits S1 by the number of devices N, and add the sum of the second check bits S2 to obtain the original value of the decryption key; take the modulo of the original value of the decryption key with 9973 to obtain the decryption key K; The task identifier, task type, operator identifier, equipment list, and task validity period of the task to be processed are taken as the whole to be executed, and the decryption key K is encapsulated into the production line execution program; the hash value of the whole to be executed is calculated, and the hash value is embedded into the production line execution program as the expected hash value for subsequent self-verification; For example: The task to be processed is an incoming inspection task. We select equipment with batch number 0315 from the equipment serial number database, extracting a list of 3 devices for illustration. The first device's serial number is PMI240315000001H5, with the first check bit H corresponding to the value 17 and the second check bit 5 corresponding to the value 5. The second device's serial number is PMI240315000002J7, with the first check bit J corresponding to the value 19 and the second check bit 7 corresponding to the value 7. The third device's serial number is PMI240315000003K2, with the first check bit K corresponding to the value 20 and the second check bit 2 corresponding to the value 2. The sum of the first check bits, S1, is 17 + 19 + 20 = 56. The sum of the second check bits, S2, is 5 + 7 + 2 = 14. The original decryption key is 56 multiplied by 3 plus 14, which equals 182. Taking the modulo 9973, we get the decryption key K as 182. The task identifier, task type entry verification, operator Wang Gong, list of 3 equipment, validity period from 08:00 to 18:00 on March 15, 2024, and decryption key 182 are encapsulated into the production line execution terminal, the overall hash value to be executed is calculated and embedded into the program.

[0022] By using the sum of the check bits of all devices in the device list as the key derivation factor, the overall characteristics of N devices are creatively compressed into a single decryption key K, realizing the transformation from individual verification to overall verification. This decryption key K is mathematically related to the check bit of each device in the device list. The replacement, deletion, or addition of any device will cause the sum of the check bits to change, resulting in a mismatch between the verification key calculated based on the sum and the original decryption key, thus enabling the detection of any tampering with the device list. By calculating the hash value of the entire process to be executed and embedding it into the program, a benchmark value is provided for the self-verification of program integrity in subsequent steps, ensuring that the task execution program and its encapsulated data are not tampered with after deployment.

[0023] Step 3: After the operator starts the program, a two-layer verification process is performed on the production line execution end, involving self-verification and task timeliness verification. After successful verification, the online or offline mode is selected based on the network status to load the equipment list data for the task to be processed. The specific method is as follows: After the operator double-clicks to start the production line execution program, a self-verification is first performed. The production line execution terminal calculates the hash value of the entire program file to be executed and compares the calculation result with the expected hash value embedded in step two. If the two match, the self-verification passes and the program continues to run. If the two do not match, it is determined that the program has been tampered with, and the program terminates and displays a message indicating that the program integrity verification failed. After the self-verification passes, the task timeliness verification is performed by obtaining the current system time and comparing it with the task validity period encapsulated in step two. If the current time is within the validity period, the timeliness verification passes and the program continues to run. If the current time exceeds the validity period, the program displays a message indicating that the task has expired and terminates. After both self-verification and task timeliness verification pass, network probing is performed, sending a probe request to the production line execution backend. If a response is received, the system is determined to be online, online mode is selected, and the system requests the latest equipment list data from the production line execution backend. If no response is received, the system is determined to be offline, offline mode is selected, and data is loaded from the equipment list embedded in the production line execution program. For example: On March 15, 2024, at 09:00, Engineer Wang started the production line execution program on the production line PC. The program first calculated the hash value of the entire task to be executed as A3B5C7D9, which matched the embedded expected value A3B5C7D9, thus passing the self-verification. It then compared the current time (09:00) with the task's validity period (08:00 to 18:00), finding the current time to be within the validity period, thus passing the timeliness verification. The program sent a probe request to the backend and received a response, determining it to be online. It then selected online mode and retrieved the latest equipment list from the backend, totaling 3 devices. The interface displayed that the system was ready and online mode had loaded 3 devices.

[0024] Through a self-verification mechanism based on hash value comparison, it is ensured that the task execution program and its encapsulated core data, such as pending tasks, equipment lists, and decryption keys, are not tampered with during the process from backend deployment to production line execution. Any modification to the program or data will result in a change in the hash value and will be detected, effectively preventing security risks caused by malicious tampering. Through task timeliness verification, it is ensured that tasks can only be executed within the time window specified by the administrator, and expired tasks are automatically invalidated, preventing the risk of expired programs being abused. Through network detection, intelligent switching between online and offline modes is achieved, enabling the system to obtain the latest data and maintain data synchronization in a network environment, and to work normally in a workshop environment without network or with an unstable network, relying on embedded data, significantly enhancing the system's environmental adaptability and reliability.

[0025] Step 4: Obtain each execution device from the equipment list data of the task to be processed loaded in Step 3. The operator wears AI glasses and establishes a communication connection with the production line execution end. The operator scans the QR code on each execution device using the AI ​​glasses to obtain the corresponding device serial number. The obtained device serial number is analyzed according to the method in Step 2 to obtain the verification key. The verification key is compared with the decryption key of the equipment list of the task to be processed. Based on the comparison result, a correct device list identification mark is generated. The specific method is as follows: The operator wears AI glasses and establishes a communication connection between the AI ​​glasses and the production line execution terminal via USB or wirelessly. After the production line execution terminal detects the connection of the AI ​​glasses, it enters the recognition working state. The operator aligns the AI ​​glasses with the QR code labels affixed to each execution device in turn. The camera of the AI ​​glasses captures the QR code image and decodes it, and transmits the 16-bit device serial number obtained by decoding to the production line execution terminal. After receiving the equipment serial number, the production line execution terminal first performs a checksum verification on the equipment serial number according to the checksum calculation method described in step one: extract the first 14 bits of the equipment serial number as the basic code, calculate the first checksum using a forward weighting method, and calculate the second checksum using a reverse weighting method. Compare the two calculated checksums with the 15th and 16th bits of the equipment serial number; if they match, the checksum verification passes; if either matches, the checksum verification fails, and the AI ​​glasses will display a message indicating that the serial number verification failed and requesting a rescan. After the operator completes scanning of all N devices in the equipment list, the production line execution end calculates the verification key V by calculating the check bits in the device serial numbers of the scanned N devices according to the decryption key calculation method described in step two: the values ​​corresponding to the first check bits C1 of the scanned N devices are added together to obtain the scanned first check bit sum S1V; the values ​​corresponding to the second check bits C2 of the scanned N devices are added together to obtain the scanned second check bit sum S2V; S1V is multiplied by the number of devices N and then added to S2V to obtain the original value of the verification key, and the modulo of 9973 is taken to obtain the verification key V; Compare the verification key V with the decryption key K encapsulated in step two. If the verification key V and the decryption key K are exactly equal, it indicates that the scanned device matches the task device list perfectly. A device list identification mark is generated, and the production line execution end displays all devices verified successfully through the augmented reality interface of the AI ​​glasses. If the verification key V and the decryption key K are not equal, it indicates that there is a device mismatch. The AI ​​glasses will prompt that the device list verification failed and ask you to check the device. For example: Engineer Wang wears AI glasses and connects them to the production line execution terminal. He scans the QR codes of three devices sequentially. Scanning the first device yields PMI240315000001H5, verification successful; scanning the second device yields PMI240315000002J7, verification successful; scanning the third device yields PMI240315000003K2, verification successful. After all scans are complete, the verification key is calculated: the sum of the first verification bits (S1V) is 17 + 19 + 20 = 56; the sum of the second verification bits (S2V) is 5 + 7 + 2 = 14. The original verification key value is 56 multiplied by 3 + 14 = 182. Taking the modulo of 9973, the verification key V is 182. Comparing the verification key 182 with the decryption key 182, they are equal, generating a device list and identifying the correct device. The AI ​​glasses display that all three devices have been verified and request further operation.

[0026] By performing double checksum verification on the serial number of each device, it ensures that every serial number obtained through scanning and identification is legitimate and valid, and can promptly detect serial number errors caused by QR code corruption, scanning misreading, or label forgery. By comparing the verification key V calculated after scanning all devices with the preset decryption key K, a one-time verification of the overall integrity of the device list is achieved. Since the calculation of V and K depends on the sum of the checksums of all devices, the replacement, absence, or extraness of any device will cause a change in the sum, resulting in V and K being unequal, thus accurately detecting any tampering of the device list. This verification mechanism upgrades the traditional one-to-one comparison method to a holistic feature verification method, which not only verifies the correctness of the identity of each device, but also verifies the integrity of the device list as a whole, effectively preventing attacks that evade detection by replacing some devices.

[0027] Step 5: After correctly identifying the equipment, the tools within the production line execution terminal immediately generate operation records containing the equipment serial number, operation time, and operation result. Multiple records are then chained together chronologically to form a record chain. Depending on the network status, the record chain is either transmitted back to the backend in real time or packaged into an offline data packet. The specific method is as follows: After generating the equipment list and correctly identifying the identifiers, the operator uses AI glasses to confirm the operation results for each device one by one. For each device confirmed, the production line execution end immediately generates an operation record. The operation record includes the record number, device serial number, operation type, operator identifier, operation time, and operation result. The operation result includes three states: passed, failed, and pending review. Multiple operation records are chained together in chronological order to form a record chain. Each record contains the hash value of the previous record as a pre-hash value. The pre-hash value of the first record is set to the hash value of the task identifier; the pre-hash value of the second record is the hash value of the first record; and so on, forming a complete chain of interconnected records. Data feedback is executed based on the network status determined in step three: in online mode, each generated operation record is transmitted to the production line execution end backend in real time via the network; in offline mode, after all operations are completed, the entire record chain is packaged, the hash value of the data packet is calculated to generate a packet checksum, and the data packet and packet checksum are exported together as an offline data packet file. For example: After the equipment list verification is passed, Engineer Wang confirms the operation results of the three devices one by one. Upon confirming the first device's success, the production line execution end generates operation record 1: Record number 1, equipment serial number PMI240315000001H5, operation type: inbound inspection, operator: Engineer Wang, operation time: March 15, 2024, 09:05, operation result: passed, and the preceding hash value is the task identifier hash value. Upon confirming the second device's success, operation record 2 is generated, with the preceding hash value being the hash value of record 1. Upon confirming the third device's success, operation record 3 is generated, with the preceding hash value being the hash value of record 2. These three records form a complete record chain. In online mode, the three records are transmitted to the backend in real time. In offline mode, they are packaged into an offline data package file.

[0028] By linking each operation record to the previous record using hash values ​​to form a chain structure, the record chain possesses immutability and continuity protection capabilities. Any deletion, modification, or insertion operation on a record will cause a change in the hash value of that record and all subsequent records, thus breaking the chain association and being detected. The preceding hash value of the first record is anchored to the task identifier, ensuring that the starting point of the record chain is traceable and cannot be forged. Real-time transmission in online mode ensures instant synchronization of operation data, while packaged export in offline mode ensures that operation data can be completely saved and subsequently imported even in environments without a network. Support for both modes allows the system to adapt to various network environments. The calculation of packet checksums provides integrity protection for offline data packets, ensuring that data packets are not tampered with during physical transmission.

[0029] Step Six: Receive the record chain data returned from Step Five or import the offline data packet. Perform integrity verification and device serial number validity verification on the record chain. After successful verification, update the status of the corresponding device and archive to complete the data loop. The specific method is as follows: The production line execution backend receives data according to the feedback method in step five: if it is in online mode, the backend automatically receives the real-time transmitted operation records; if it is in offline mode, the administrator imports the offline data packet file into the backend; performs packet checksum verification on the offline data packet, recalculates the hash value of the data packet and compares it with the packet checksum; if they match, the data packet is complete and has not been tampered with; if they do not match, the data packet is rejected; performs integrity verification on the record chain, checks whether the preceding hash value of each record is consistent with the hash value of the previous record; starting from the preceding hash value of the first record being equal to the task identifier hash value, verifies each record sequentially to confirm that the entire record chain is complete and has not been deleted or modified; performs checksum verification on the equipment serial number in each record according to the method described in step one: extracts the first 14 basic codes of the 16-bit equipment serial number, calculates the first check digit using a forward weighting method, calculates the second check digit using a reverse weighting method, and compares the calculation results with the 15th and 16th digits of the serial number to confirm that the equipment serial number is legal and valid; After all verifications are passed, the status of the corresponding equipment is updated according to the operation records: the status of equipment that passes the warehousing inspection is updated to "warehousing", the status of equipment that passes the spot check is updated to the last spot check time, and the status of equipment that is confirmed by the asset inventory is updated to the inventory status; a task report containing task information, equipment list, operator, operation time and operation results is generated and archived, completing the data closed loop from equipment identification to status update. For example: The backend receives three operation records submitted by Engineer Wang. The integrity of the record chain is checked: Record 1's preceding hash is the correct task identifier hash value; Record 2's preceding hash matches Record 1's hash value; Record 3's preceding hash matches Record 2's hash value; the record chain is complete. The device serial number of each record is verified using a checksum. Taking PMI240315000001H5 as an example, the first checksum is calculated as H, and the second checksum is calculated as 5, which matches the 15th and 16th bits of the serial number; the verification passes. All three records pass verification. The device status is updated: the status of the three devices is updated from "Pending Inbound" to "Inbound". A task report is generated: Task Inbound Verification, Operator: Engineer Wang, Date: March 15, 2024, All three devices passed. The report is archived, and the data loop is complete. The backend receives data based on the data transmission method. In online mode, it automatically receives real-time transmitted operation records; in offline mode, the administrator imports the offline data packet file into the backend. For offline data packets, a packet checksum verification is performed first. The hash value of the data packet is recalculated and compared with the packet checksum. If they match, the data packet is complete; otherwise, it is rejected. Next, the record chain is verified for integrity. Starting from the first record whose preceding hash value equals the task identifier hash value, each record is checked sequentially to verify if its preceding hash value matches the hash value of the previous record. If all match, the record chain integrity verification passes. Then, the device serial number in each record is verified for checksum. The basic serial number code is extracted, and a double checksum is calculated using the method in step one and compared with the checksum in the serial number. If they match, the serial number is valid. When the record chain integrity verification passes and all device serial number checksum verifications pass, the verification is considered successful. The status of the corresponding device is updated according to the operation records, and a task report is generated and archived, completing the data loop from device identification to status update.

[0030] A multi-level verification mechanism ensures the authenticity, integrity, and legality of the returned data. Packet checksum verification ensures that offline data packets are not tampered with during transmission, record chain integrity verification ensures that operation records are not deleted, modified, or inserted, and device serial number checksum verification ensures that the device identifier in the record is legal and valid. Only data that passes all verifications is accepted by the backend and used to update device status, effectively preventing the impact of forged or tampered data on equipment asset management. Automatic updates of device status and archiving of task reports realize a complete data closed loop from on-site operation to backend management, ensuring that the digital records of equipment assets are consistent with the physical reality, and providing reliable data support for equipment lifecycle management and operation audit traceability.

[0031] Example 2: Please refer to Figure 2 As shown, this embodiment also provides a secure production management and identification system for AI glasses devices. This method is implemented through the aforementioned secure production management and identification method for AI glasses devices, specifically including the following steps; The device serial number generation module is used to obtain the device type code, year code, batch number and serial number of each device to form a 14-bit basic code. The first check bit C1 is calculated by taking the modulo 36 of the forward weighted summation, and the second check bit C2 is calculated by taking the modulo 36 of the reverse weighted summation. The two check bits are appended to the basic code to generate a complete 16-bit device serial number. The task encapsulation and key generation module is used to extract the equipment list involved in the task to be processed, calculate the sum of the first check bit C1 and the sum of the second check bit C2 of each device in the equipment list, and then calculate the decryption key K. The task to be processed, the equipment list, the validity period and the decryption key are encapsulated into an execution whole, and the hash value of the execution whole is calculated as the expected hash value and embedded into the production line execution program. The dual-layer verification and mode selection module is used to calculate the overall hash value to be executed and compare it with the expected hash value for self-verification when the production line execution end starts, obtain the current time and compare it with the task validity period for timeliness verification, and send a probe request to the background. Based on the response, it can choose the online mode to obtain the latest equipment list from the background or choose the offline mode to load data from the program's embedded list. The AI ​​glasses recognition and key verification module is used to establish a communication connection with the AI ​​glasses worn by the operator, receive the device serial number obtained by scanning the QR code of the AI ​​glasses, verify the device serial number of each device by checking the check code, calculate the verification key V based on the check bit after all devices have been scanned, compare the verification key V with the decryption key K, and if they are equal, generate a device list to identify the correct identification. The record chain generation and feedback module is used to generate an operation record for each device after correctly identifying the device list. Each record contains the device serial number, operation time, and operation result. Each record contains the hash value of the previous record to form a record chain. In online mode, the record chain is transmitted to the backend in real time. In offline mode, it is packaged and exported as an offline data package with a packet verification code. The background verification and status update module is used to receive online transmission operation records or import offline data packets, verify the packet checksum and record chain integrity, verify the device serial number of each record, update the device status and archive the task report after all verifications are successful to complete the data loop.

[0032] A three-layer security mechanism was constructed based on dual-checksum serial numbers, checksum accumulation key derivation, and verification key comparison verification. The first layer, during device serial number generation, establishes a unique digital identity with self-verification capabilities for each device through forward and reverse weighted dual-checksums. The second layer, during task encapsulation, calculates the decryption key K by summing the checksums of all devices in the device list as a key derivation factor, compressing the overall characteristics of the list into a single key value, and embedding the overall hash value of the target program into the execution process. The third layer, during on-site identification, involves the operator scanning all devices using AI glasses and calculating the verification key V using the same algorithm. The comparison between V and K verifies the overall integrity of the device list. Based on this, program self-verification ensures execution-end security, chained hash records ensure reliable operation auditing, and multi-level backend verification ensures reliable data closure.

[0033] By extending the device serial number's verification bit from a simple self-verification function to a key derivation factor, the replacement or loss of any device will result in a mismatch between the verification key and the decryption key, achieving tamper-proof verification of the device list and solving the technical problem that traditional one-to-one comparison methods cannot detect overall tampering. Intelligent switching between online and offline modes ensures reliable operation in both network-connected and offline environments, meeting the complex and ever-changing network conditions in factory workshops. Visual operation guidance is achieved through the augmented reality interface of AI glasses, freeing up operators' hands and improving work efficiency and accuracy. A chained hash structure protects the continuity and immutability of operation records, forming a complete operation audit chain, meeting the requirements of production compliance management and quality traceability. In summary, this invention effectively solves the technical deficiencies of existing AI glasses device management solutions in terms of device identification anti-counterfeiting, list integrity verification, program tamper-proofing, and record reliability, significantly improving the security, reliability, and traceability of production management identification.

[0034] Example 3: As Example 3 of the present invention, in specific implementation, compared with Example 1 and Example 2, the technical solution of this example is to combine the solutions of Example 1 and Example 2.

[0035] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0036] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A secure production management and identification method for AI glasses devices, characterized in that, include: Step 1: Obtain the basic information of each piece of equipment in the factory and generate the equipment serial number. The equipment serial number includes a basic code and a check bit. The check bit includes a first check bit C1 and a second check bit C2. The first check bit C1 is obtained by summing the values ​​of the basic code according to their forward position weights and taking the modulus of the preset check modulus; the second check bit C2 is obtained by summing the values ​​of the basic code according to their reverse position weights and taking the modulus of the preset check modulus; the device serial number is generated using a composite coding rule, specifically in the following format: a 3-digit device type code, a 2-digit year code, a 4-digit batch number, a 5-digit serial number, and a 2-digit check bit are concatenated to form a 16-digit complete device serial number, where the first 14 digits are the basic code, the 15th digit is the first check bit C1, and the 16th digit is the second check bit C2; Step 2: Extract the device list involved in the task to be processed. Add the values ​​corresponding to the first check bit C1 of all devices in the device list to obtain the first check bit sum S1. Add the values ​​corresponding to the second check bit C2 of all devices to obtain the second check bit sum S2. Calculate the decryption key K based on the first check bit sum S1, the second check bit sum S2, and the number of devices N. Encapsulate the task to be processed, the device list, and the decryption key K into the task execution terminal. The calculation method for the first check bit C1 is as follows: multiply the first value of the basic code by weight 1, the second value by weight 2, and so on until the 14th value by weight 14. Add all the products and take the modulo 36. Convert the modulo operation result to the corresponding character according to the base-36 rules as the first check bit. The first check bit is C1; where the digits 0 to 9 correspond to the values ​​0 to 9, and the letters A to Z correspond to the values ​​10 to 35, with a preset check modulus of 36. The second check bit C2 is calculated as follows: multiply the first value of the basic code by a weight of 14, the second value by a weight of 13, and so on until the 14th value is multiplied by a weight of 1. All products are added using a reverse weighting method, and the result is modulo 36. The modulo result is converted to the corresponding character according to base-36 rules and used as the second check bit C2. The decryption key K is calculated as follows: multiply the sum of the first check bits S1 by the number of devices N, and add the sum of the second check bits S2 to obtain the original decryption key value. The original decryption key value is then modulo the preset key modulus to obtain the decryption key K. The preset key modulus is a prime number 9973. Step 3: After starting the task execution terminal, perform self-verification and task timeliness verification. After the verification is successful, load the device list data. Step 4: The operator scans the identifier of each execution device through AI glasses to obtain the device serial number. The verification key V is calculated for all the scanned devices according to the method in Step 2. The verification key V is compared with the decryption key K. If they are equal, a device list is generated to identify the correct identifier. Step 5: After correctly identifying the equipment, generate an operation record containing the equipment serial number, operation time, and operation result, and link multiple records together to form a record chain; Step Six: Receive the record chain data, verify it, update the device status, and archive it to complete the data loop.

2. The production management and identification method for a secure AI glasses device according to claim 1, characterized in that, Step two also includes: calculating the hash value of the whole to be executed, consisting of the task to be processed, the device list, and the decryption key K, and embedding the hash value as the expected hash value into the task execution program; the self-verification in step three is as follows: after the task execution program starts, it calculates the current hash value of the whole to be executed, compares the current hash value with the expected hash value, and if they match, the self-verification passes; if they do not match, it is determined that the program has been tampered with and terminates its operation.

3. The production management and identification method for a secure AI glasses device according to claim 1, characterized in that, In step three, after successful verification, the operating mode is selected based on the network status, and a probe request is sent to the backend. If a response is received, it is determined to be in an online state and the online mode is selected. The backend is then requested to obtain the latest device list data. If no response is received, the system is determined to be offline and offline mode is selected, loading data from the device list embedded in the task execution terminal.

4. The production management and identification method for a secure AI glasses device according to claim 1, characterized in that, In step five, the way to chain multiple records together to form a record chain is as follows: each operation record contains the hash value of the previous operation record as the preceding hash value. The preceding hash value of the first operation record is set as the hash value of the task identifier. The preceding hash values ​​of the second and subsequent operation records are the hash values ​​of the previous operation record, thus forming a chain structure with sequential relationships.

5. The production management and identification method for a secure AI glasses device according to claim 4, characterized in that, In step six, the specific methods for successful verification include: The integrity of the record chain is verified by checking whether the preceding hash value of each record is consistent with the hash value of the previous record. Starting from the first record whose preceding hash value is equal to the task identifier hash value, the verification proceeds sequentially. If the preceding hash value of each record is consistent with the hash value of the previous record, the integrity verification of the record chain is considered successful. The device serial number in each record is then verified by checking the check code. The basic code of the device serial number is extracted, and the first check bit and the second check bit are calculated respectively according to the method described in step one. The calculation results are compared with the check bits in the device serial number. If the calculated first check bit is consistent with the 15th bit of the device serial number and the calculated second check bit is consistent with the 16th bit of the device serial number, the check code verification of the device serial number is considered successful. When the integrity verification of the record chain is successful and the check code verification of all device serial numbers is successful, the verification in step six is ​​considered successful.

6. A secure production management and identification system for AI glasses devices, characterized in that, The system is implemented through a production management and identification method for secure AI glasses devices as described in any one of claims 1-5, comprising: The device serial number generation module is used to obtain the device type code, year code, batch number and serial number of each device to form a 14-bit basic code. The first check bit C1 is calculated by taking the modulo 36 of the forward weighted summation, and the second check bit C2 is calculated by taking the modulo 36 of the reverse weighted summation. The two check bits are appended to the basic code to generate a complete 16-bit device serial number. The task encapsulation and key generation module is used to extract the equipment list involved in the task to be processed, calculate the sum of the first check bit C1 and the sum of the second check bit C2 of each device in the equipment list, and then calculate the decryption key K. The task to be processed, the equipment list, the validity period and the decryption key are encapsulated into an execution whole, and the hash value of the execution whole is calculated as the expected hash value and embedded into the production line execution program. The dual-layer verification and mode selection module is used to calculate the overall hash value to be executed and compare it with the expected hash value for self-verification when the production line execution end starts, obtain the current time and compare it with the task validity period for timeliness verification, and send a probe request to the background. Based on the response, it can choose the online mode to obtain the latest equipment list from the background or choose the offline mode to load data from the program's embedded list. The AI ​​glasses recognition and key verification module is used to establish a communication connection with the AI ​​glasses worn by the operator, receive the device serial number obtained by scanning the QR code of the AI ​​glasses, verify the device serial number of each device by checking the check code, calculate the verification key V based on the check bit after all devices have been scanned, compare the verification key V with the decryption key K, and if they are equal, generate a device list to identify the correct identification. The record chain generation and feedback module is used to generate an operation record for each device after correctly identifying the device list. Each record contains the device serial number, operation time, and operation result. Each record contains the hash value of the previous record to form a record chain. In online mode, the record chain is transmitted to the backend in real time. In offline mode, it is packaged and exported as an offline data package with a packet verification code. The background verification and status update module is used to receive online transmission operation records or import offline data packets, verify the packet checksum and record chain integrity, verify the device serial number of each record with a checksum, and update the device status after all verifications are successful.

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