Anti-exchange double-lock-catch binding tape with two-dimensional code label and method

Abstract

The invention discloses an anti-exchange double-lock-catch binding tape with a two-dimensional code label and a method. The method comprises the following steps: S1, providing a double-lock-catch binding tape; s2, integrating a two-dimensional code label on the binding tape, wherein a two-dimensional code is dynamically generated based on quantum key distribution and a distributed depth deterministic strategy gradient algorithm; s3, transmitting the collected state data to a digital twin model for synchronization and analysis; s4, identifying a potential damage or tampering behavior, and sending out an early warning signal; s5, performing real-time analysis by using an algorithm based on combination of a graph neural network and Bayesian optimization; s6, when an abnormal behavior or system early warning is detected, a multi-level anti-counterfeiting verification measure is triggered; and S7, when serious damage or tampering behaviors are detected, the self-repairing material of the binding tape triggers irreversible color change, and a new two-dimensional code label is generated. According to the invention, the quantum key distribution, the Internet of Things sensor and the digital twinning technology are combined, so that the intelligent anti-exchange binding tape is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-counterfeiting and anti-substitution, and in particular to a double-lock binding belt with a QR code label for anti-substitution and a method thereof. Background Art

[0002] In the field of construction project quality inspection, ensuring the authenticity and integrity of samples is one of the important links to ensure project quality. With the expansion of the scale of construction projects and the improvement of quality requirements, how to prevent samples from being replaced or tampered with during the flow from on-site sampling to laboratory testing has become a key issue in quality management. However, in the existing sample management and testing processes, problems such as sample replacement and tampering often occur, bringing potential risks and hidden dangers to project quality.

[0003] In traditional construction engineering quality inspections, samples are usually sealed in sealed bags or ordinary binding tapes, and are attached with identification labels, such as barcodes or QR codes, to identify information such as the source of the sample, sampling time, and sampling personnel. These labels are usually static, generated when the sample is sealed, and then remain unchanged throughout the transportation and storage process. However, this method has obvious shortcomings. Since the label information cannot be updated dynamically and there is no effective means to prevent tampering, attackers can easily replace or tamper with samples by copying or forging label information. In addition, ordinary binding tapes or sealed bags cannot provide sufficient physical protection and can be easily opened by force or resealed by clever methods without leaving any traces.

[0004] With the development of digital technology, some engineering quality inspection agencies have begun to introduce electronic identification and Internet of Things technology to improve the reliability of sample management. For example, some systems try to track the flow of samples through RFID tags or electronic chips, and record the environmental conditions of samples in real time. However, these methods are often limited by technical complexity and cost, and in practical applications, it is still difficult to completely avoid interference from human factors. Especially when samples are stored for a long time or go through multiple circulation links, how to ensure the authenticity of samples and prevent substitution is still a problem that needs to be solved.

[0005] In the existing quality inspection of construction projects, sample management still has other shortcomings. For example, although IoT sensors can monitor the environmental conditions of samples, such as temperature and humidity, these data are usually only passively recorded and cannot be dynamically associated with the label information of the samples, lacking real-time analysis and early warning mechanisms. In addition, many sample management systems lack automated and intelligent anti-counterfeiting functions and cannot dynamically adjust security strategies based on real-time monitoring data, resulting in insufficient protection capabilities when faced with complex replacement methods.

[0006] The application of digital twin technology in the industrial and manufacturing fields has gradually matured. It creates a virtual model of the physical sample to achieve real-time monitoring and analysis of the sample status. However, in the field of construction project quality inspection, the application of digital twin technology is still relatively small, mainly focusing on the monitoring of large equipment or complex structures, and the application of single sample anti-replacement is relatively limited. Even if there are related applications, they are mostly limited to data recording and simple threshold alarms, lacking in-depth intelligent analysis and dynamic response mechanisms, and unable to provide comprehensive security in all aspects of sample circulation.

[0007] Another urgent problem to be solved is that when existing systems detect sample anomalies, they usually rely on a single verification step, such as confirming the identity of the sample through simple QR code scanning or barcode recognition. This method is easily exploited by attackers to bypass the verification system by forging or copying label information. Existing anti-substitution measures are usually taken only after sample anomalies are discovered, lacking continuous monitoring and early warning capabilities, and unable to provide continuous security throughout the entire sample circulation process.

[0008] In general, the current sample management in the field of construction engineering quality inspection faces many challenges. Traditional sample identification and sealing methods are difficult to cope with the high requirements of modern engineering projects for quality assurance, and the risk of sample substitution during circulation still exists. The application of the Internet of Things and digital twin technology has not yet fully realized its potential, and the automation and intelligence level of anti-counterfeiting systems is still low. In addition, the lack of multi-level verification and dynamic response mechanisms makes it difficult for existing anti-substitution measures to cope with complex and diverse substitution methods.

[0009] Therefore, how to provide a double-locking binding belt with a QR code label and a method for preventing replacement is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0010] One purpose of the present invention is to propose a double-locking binding belt with a QR code label and a method for anti-substitution. The present invention cleverly combines quantum key distribution, distributed deep learning, IoT sensors and digital twin technology, and constructs an intelligent anti-substitution system by dynamically generating and encrypting QR codes, real-time monitoring of binding belt status and multi-level anti-counterfeiting verification. The system can not only identify and warn potential tampering behaviors in real time, but also has a high degree of anti-counterfeiting and flexible security policy adjustment capabilities, ensuring the safety and integrity of items in the entire logistics and supply chain, and significantly improving the protection effect and reliability of the system.

[0011] According to an embodiment of the present invention, a double-locking tie strap with a QR code label for anti-replacement and a method thereof include the following steps:

[0012] S1. Provide a double-locking tie strap, which is made of a polymer material with self-repairing function, automatically repairs after minor damage, and triggers color change when severely damaged;

[0013] S2. Integrate a QR code label on the binding belt. The QR code is dynamically generated based on quantum key distribution and distributed deep deterministic policy gradient algorithm, and the strategy is optimized according to real-time environmental data and user behavior patterns to dynamically adjust the generation and update frequency of the QR code;

[0014] S3, monitor the physical status of the binding straps in real time through IoT sensors, and transmit the collected status data to the digital twin model for synchronization and analysis;

[0015] S4. Based on digital twin technology, the real-time physical status of the lashing strap is compared with the virtual model to identify potential damage or tampering and issue an early warning signal;

[0016] S5. Use an algorithm based on graph neural network combined with Bayesian optimization to perform real-time analysis on QR code scanning behavior and user operation patterns;

[0017] S6. When abnormal behavior or system warning is detected, multi-level anti-counterfeiting verification measures are triggered;

[0018] S7. When serious damage or tampering is detected, the self-healing material of the binding tape triggers an irreversible color change, automatically updates the QR code and encrypts the data, and generates a new QR code label.

[0019] Optionally, the S2 specifically includes:

[0020] S21. Generate a quantum state key for encrypting the QR code information through quantum key distribution technology;

[0021] S22, based on the distributed deep deterministic policy gradient algorithm, the current state of the system S is analyzed through the policy network t Evaluate the system status including environmental data t and user behavior pattern B t , output the optimal action A t ;

[0022] S23. Use the value network to evaluate the optimal action A t The expected value Q(S t ,A t ), optimize the weight of the strategy network through back propagation and dynamically adjust the QR code generation strategy;

[0023] S24. Generate and encrypt QR code information:

[0024]

[0025] Among them, C t Indicates the encrypted QR code information, K t represents the quantum key, E(·) represents the encryption function, H(·) represents the hash function, Represents a bitwise exclusive OR operation;

[0026] S25. Dynamically adjust the frequency of generating the QR code F t and the key update frequency K update :

[0027]

[0028] K update =γ1·|T t -T avg |+γ2·|H t -H avg |+γ3·|N t -N avg |+γ4·|I t -I avg |;

[0029] Among them, T t Indicates the current ambient temperature, H t Indicates the current ambient humidity, L t Indicates the current light intensity, N t Indicates the number of user scans, I t Represents the scanning time interval, T avg Indicates the average ambient temperature set by the system, H avg Indicates the average ambient humidity set by the system, N avg Indicates the average number of user scans set by the system, I avg represents the average value of the scanning time interval set by the system, α1 represents the current ambient temperature weight coefficient, α2 represents the current ambient humidity weight coefficient, α3 represents the current light intensity weight coefficient, β1 represents the user scanning times weight coefficient, β2 represents the scanning time interval weight coefficient, γ1, γ2, γ3 and γ4 represent the weight coefficients used for key update frequency calculation;

[0030] S26, after the adjustment, the generation frequency F t and the key update frequency K update Based on the above, generate and update the QR code label;

[0031] S27. Integrate the generated unique and dynamically updated QR code label into the double-lock buckle binding belt, and use the IoT sensor to scan and verify the QR code label.

[0032] Optionally, the S3 specifically includes:

[0033] S31. Embed IoT sensors in the double-lock tie strap, including a temperature sensor, a humidity sensor, a pressure sensor, and an acceleration sensor, to monitor the physical state of the tie strap in real time, collect environmental data and mechanical data, and construct a data set;

[0034] S32. Combining the sensor data degradation rate and historical data trend, the data set is fused by an improved data fusion algorithm:

[0035]

[0036] Among them, V s Represents the integrated state data after fusion, ω i (s) represents the dynamic weight of the i-th sensor, R i (s) represents the data collected by the i-th sensor at time s, η represents the adjustment coefficient that controls the change rate of sensor data, n represents the total number of sensors, and defines ΔR i (s) = R i (s)-R i (s-1);

[0037] S33, the integrated state data V s The data is transmitted to the cloud server or local computing device through the wireless communication module, and the encryption protocol is used to protect the data during the transmission process to ensure that the data is not tampered with or stolen during the transmission process;

[0038] S34, in the cloud server or local computing device, by mapping function Φ(V s ,Γ) will integrate the integrated state data V s Mapping to the digital twin model M s In the digital twin model M s reflects the real-time physical state of the binding belt, where Γ represents the mapping parameter set;

[0039] S35, the mapped comprehensive status data V s Synchronize and analyze.

[0040] Optionally, the S4 specifically includes:

[0041] S41, calibrating the initial state of the digital twin model according to the bound initial state vector;

[0042] S42. Use the mapping function to update the mapping between real-time data and model status:

[0043]

[0044] Among them, N trepresents the state of the digital twin model at time t, Y tj represents the real-time data of the jth sensor at time t, ΔY tj represents the rate of change of sensor data, ω j represents the weight of each sensor data, κ represents the adjustment coefficient, and n represents the total number of sensors;

[0045] By introducing the sensor data change rate ΔY into the mapping function tj , the model can respond more sensitively to real-time changes in the environment and status, making the update of the digital twin model more accurate and dynamic;

[0046] S43, update the digital twin model state N t Compare with the preset reference model N ref , calculate the deviation vector ΔN t :

[0047]

[0048] Among them, α j and β j represents the regression coefficient, ζ represents the time weighting factor, λ represents the historical deviation impact factor, ∈ k Represents the coefficients for weighting the deviations of the previous p time steps. These coefficients and factors can better reflect the impact of historical data on the current deviation and improve the ability to detect abnormal situations that change gradually;

[0049] S44, according to the deviation vector ΔN t Calculate the comprehensive anomaly score Z t :

[0050] Z t =∥ΔN t ∥+η·∥ΔN t -ΔN t-1 ∥+ρ·max(ΔN t );

[0051] Among them, η represents the weighted coefficient of the deviation change trend, ρ represents the weighted coefficient of the maximum deviation, max(ΔN t ) represents the absolute value of the maximum deviation component at time t, which improves the sensitivity of anomaly detection from multiple dimensions, especially in detecting rapid changes or sudden anomalies;

[0052] S45, when the comprehensive abnormality score Z t When the safety threshold θ is exceeded, the system will trigger the early warning mechanism, generate an early warning signal, and record abnormal status data;

[0053] S46. After the warning is triggered, the abnormal status data is associated with the bound QR code information and stored in the database.

[0054] Optionally, the S5 specifically includes:

[0055] S51, preprocess the user's QR code scanning behavior and operation mode, map the original operation data into a graph structure G = (U, E), where U represents the user's operation behavior node, E represents the associated edge between the operation behaviors, and for each node U i Assign initial eigenvector

[0056] S52. Use graph neural network to model the user operation network and use multi-layer message passing mechanism to gradually update the feature vector of each node

[0057]

[0058] in, represents the updated feature vector, σ represents the activation function, W (k) and represents the weight matrix of the kth layer, d i Represents node U i The degree, d j Represents node U j The degree of node U i The set of neighbor nodes of

[0059] S53, after iterative update through the multi-layer graph neural network, the final embedding vector of each operation node is obtained The final embedding vector Input into the Bayesian optimization model for strategy optimization to maximize the anti-counterfeiting effect of QR code generation and update:

[0060]

[0061] Among them, L(θ) represents the objective function, θ represents the model parameter set, represents a prediction function that predicts the correlation between the user operation mode and the security of the QR code; represents the expected value, λ v represents the weight parameter for adjusting the variance effect, represents the variance term;

[0062] S54. In the Bayesian optimization process, the acquisition function is used to select the next set of QR code generation parameters and key update strategies to be evaluated:

[0063] AF(θ)=μ(θ)+κ1·σ(θ);

[0064] Where AF(θ) represents the acquisition function, μ(θ) represents the expected value predicted by the current model, κ1 represents the parameter that controls the trade-off between exploration and exploitation, and σ(θ) represents the prediction uncertainty;

[0065] S55, inputting the QR code update frequency and key update strategy after Bayesian optimization into the system;

[0066] S56, the system monitors the generation process of the QR code and the update process of the key in real time, and adjusts the model parameter θ according to the real-time monitoring data to achieve dynamic optimization.

[0067] Optionally, the S6 specifically includes:

[0068] S61. After triggering the multi-level anti-counterfeiting verification, the dynamic QR code is updated according to the optimized update frequency F opt Generate a new QR code and encrypt and update the QR code:

[0069]

[0070] Among them, Q new represents the updated QR code, E(·) represents the encryption function, H(·) represents the hash function, Indicates bitwise XOR operation, K opt represents the optimized key, Q old Represents the original QR code, Z t represents the comprehensive abnormality score;

[0071] S62, the system starts a multi-factor authentication mechanism, sends a one-time password to the user's bound device, requires the user to provide biometric information for verification, answer preset security questions, and check and verify the user's device information, IP address, and geographic location;

[0072] S63, combining the results of the verification steps to generate a comprehensive verification score V t , and compares it with the advanced threshold δ2 set by the system to determine whether to allow continued access. If the comprehensive verification score V t If the threshold is lower than δ2, the system will deny access and record relevant information;

[0073] S64. After completing all verification steps, the system will decide whether to allow the user to continue the operation based on the final result, and will store the relevant data of the verification process with the current QR code information Q new Bind and store in the system database.

[0074] The beneficial effects of the present invention are:

[0075] First, the present invention uses a polymer material with self-repairing function, so that the binding belt can automatically repair itself after slight damage, thereby extending the service life of the binding belt, and triggering irreversible color changes when severely damaged, providing an intuitive and effective indication for tampering detection. The application of this material not only improves the physical protection capability, but also greatly increases the difficulty of illegal tampering, making it difficult for attackers to destroy or replace the binding belt without leaving traces.

[0076] Secondly, the present invention adopts a dynamic two-dimensional code generation technology that combines quantum key distribution with a distributed deep deterministic policy gradient algorithm, which significantly improves the anti-counterfeiting performance of the two-dimensional code. Quantum key distribution technology ensures the security of the key and avoids the risk of the key being stolen or tampered with during transmission. At the same time, the distributed deep deterministic policy gradient algorithm dynamically adjusts the generation and update frequency of the two-dimensional code according to real-time environmental data and user behavior patterns, so that each generated two-dimensional code is unique and difficult to forge. In this way, the present invention effectively prevents the two-dimensional code from being copied or forged, and greatly improves the security and reliability of the anti-counterfeiting label.

[0077] The introduction of IoT sensors enables real-time monitoring of the physical state of the lashing straps, and through an improved data fusion algorithm, the data from multiple sensors is effectively integrated to generate comprehensive state data. These data are mapped and analyzed in the digital twin model, which can accurately identify potential damage or tampering and issue early warning signals. Through digital twin technology, the present invention can identify anomalies in advance before problems occur, provide predictive maintenance functions, and avoid potential security threats.

[0078] When abnormal behavior is detected, the present invention further triggers multi-level anti-counterfeiting verification measures, including dynamic QR code updates and multi-factor verification mechanisms. By updating QR codes and keys in real time, encryption processing, and the combined use of multiple verification methods, the present invention effectively improves the system's ability to resist complex attack methods. Multi-level verification not only takes into account the user's immediate behavior, but also integrates multiple sources of information to ensure reliable anti-counterfeiting verification in different scenarios.

[0079] In addition, the system of the present invention has the ability to dynamically adjust, and can flexibly adjust security policies according to real-time monitoring data and verification results, and continuously provide a high level of protection. This dynamic and intelligent security management method not only adapts to the complexity of modern logistics and supply chain environments, but also provides a more comprehensive solution for anti-substitution and anti-counterfeiting, ensuring the safety and integrity of items throughout the entire circulation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0081] Figure 1 A flowchart of a double-locking binding belt with a QR code label and a method for preventing replacement proposed by the present invention;

[0082] Figure 2 A schematic diagram of a dynamic QR code generation and encryption structure of a double-locking tie strap with a QR code label for anti-exchange and a method thereof proposed by the present invention;

[0083] Figure 3 A schematic structural diagram of a double-locking tie strap with a QR code label for anti-replacement and a method thereof proposed in the present invention. DETAILED DESCRIPTION

[0084] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0085] refer to Figure 1-3 A double-locking tie strap with a QR code label for anti-replacement and a method thereof, comprising the following steps:

[0086] S1. Provide a double-locking tie strap, which is made of a polymer material with self-repairing function, automatically repairs after minor damage, and triggers color change when severely damaged;

[0087] S2. Integrate a QR code label on the binding belt. The QR code is dynamically generated based on quantum key distribution and distributed deep deterministic policy gradient algorithm, and the strategy is optimized according to real-time environmental data and user behavior patterns to dynamically adjust the generation and update frequency of the QR code;

[0088] S3, monitor the physical status of the binding straps in real time through IoT sensors, and transmit the collected status data to the digital twin model for synchronization and analysis;

[0089] S4. Based on digital twin technology, the real-time physical status of the lashing strap is compared with the virtual model to identify potential damage or tampering and issue an early warning signal;

[0090] S5. Use an algorithm based on graph neural network combined with Bayesian optimization to perform real-time analysis on QR code scanning behavior and user operation patterns;

[0091] S6. When abnormal behavior or system warning is detected, multi-level anti-counterfeiting verification measures are triggered;

[0092] S7. When serious damage or tampering is detected, the self-healing material of the binding tape triggers an irreversible color change, automatically updates the QR code and encrypts the data, and generates a new QR code label.

[0093] In this implementation, S2 specifically includes:

[0094] S21. Generate a quantum state key for encrypting the QR code information through quantum key distribution technology;

[0095] S22, based on the distributed deep deterministic policy gradient algorithm, the current state of the system S is analyzed through the policy network t Evaluate the system status including environmental data t and user behavior pattern B t , output the optimal action A t ;

[0096] S23. Use the value network to evaluate the optimal action A t The expected value Q(S t ,A t ), optimize the weight of the strategy network through back propagation and dynamically adjust the QR code generation strategy;

[0097] S24. Generate and encrypt QR code information:

[0098]

[0099] Among them, C t Indicates the encrypted QR code information, K t represents the quantum key, E(·) represents the encryption function, H(·) represents the hash function, Represents a bitwise exclusive OR operation;

[0100] S25. Dynamically adjust the frequency of generating the QR code F t and the key update frequency K update :

[0101]

[0102] K update =γ1·|T t -T avg |+γ2·|H t -H avg |+γ3·|N t -N avg |+γ4·|I t -I avg |;

[0103] Among them, T t Indicates the current ambient temperature, Ht Indicates the current ambient humidity, L t Indicates the current light intensity, N t Indicates the number of user scans, I t Represents the scanning time interval, T avg Indicates the average ambient temperature set by the system, H avg Indicates the average ambient humidity set by the system, N avg Indicates the average number of user scans set by the system, I avg represents the average value of the scanning time interval set by the system, α1 represents the current ambient temperature weight coefficient, α2 represents the current ambient humidity weight coefficient, α3 represents the current light intensity weight coefficient, β1 represents the user scanning times weight coefficient, β2 represents the scanning time interval weight coefficient, γ1, γ2, γ3 and γ4 represent the weight coefficients used for key update frequency calculation;

[0104] S26, after the adjustment, the generation frequency F t and the key update frequency K update Based on the above, generate and update the QR code label;

[0105] S27. Integrate the generated unique and dynamically updated QR code label into the double-lock buckle binding belt, and use the IoT sensor to scan and verify the QR code label.

[0106] In this implementation, S3 specifically includes:

[0107] S31. Embed IoT sensors in the double-lock tie strap, including a temperature sensor, a humidity sensor, a pressure sensor, and an acceleration sensor, to monitor the physical state of the tie strap in real time, collect environmental data and mechanical data, and construct a data set;

[0108] S32. Combining the sensor data degradation rate and historical data trend, the data set is fused by an improved data fusion algorithm:

[0109]

[0110] Among them, V s Represents the integrated state data after fusion, ω i (s) represents the dynamic weight of the i-th sensor, R i (s) represents the data collected by the i-th sensor at time s, η represents the adjustment coefficient that controls the change rate of sensor data, n represents the total number of sensors, and defines ΔR i (s) = R i (s)-R i (s-1);

[0111] S33, the integrated state data V s The data is transmitted to the cloud server or local computing device through the wireless communication module, and the encryption protocol is used to protect the data during the transmission process to ensure that the data is not tampered with or stolen during the transmission process;

[0112] S34, in the cloud server or local computing device, by mapping function Φ(V s ,Γ) will integrate the integrated state data V s Mapping to the digital twin model M s In the digital twin model M s reflects the real-time physical state of the binding belt, where Γ represents the mapping parameter set;

[0113] S35, the mapped comprehensive status data V s Synchronize and analyze.

[0114] In this implementation, S4 specifically includes:

[0115] S41, calibrating the initial state of the digital twin model according to the bound initial state vector;

[0116] S42. Use the mapping function to update the mapping between real-time data and model status:

[0117]

[0118] Among them, N t represents the state of the digital twin model at time t, Y tj represents the real-time data of the jth sensor at time t, ΔY tj represents the rate of change of sensor data, ω j represents the weight of each sensor data, κ represents the adjustment coefficient, and n represents the total number of sensors;

[0119] By introducing the sensor data change rate ΔY into the mapping function tj , the model can respond more sensitively to real-time changes in the environment and status, making the update of the digital twin model more accurate and dynamic;

[0120] S43, update the digital twin model state N t Compare with the preset reference model N ref , calculate the deviation vector ΔN t :

[0121]

[0122] Among them, α j and β jrepresents the regression coefficient, ζ represents the time weighting factor, λ represents the historical deviation impact factor, ∈ k Represents the coefficients for weighting the deviations of the previous p time steps. These coefficients and factors can better reflect the impact of historical data on the current deviation and improve the ability to detect abnormal situations that change gradually;

[0123] S44, according to the deviation vector ΔN t Calculate the comprehensive anomaly score Z t :

[0124] Z t =∥ΔN t ∥+η·∥ΔN t -ΔN t-1 ∥+ρ·max(ΔN t );

[0125] Among them, η represents the weighted coefficient of the deviation change trend, ρ represents the weighted coefficient of the maximum deviation, max(ΔN t ) represents the absolute value of the maximum deviation component at time t, which improves the sensitivity of anomaly detection from multiple dimensions, especially in detecting rapid changes or sudden anomalies;

[0126] S45, when the comprehensive abnormality score Z t When the safety threshold θ is exceeded, the system will trigger the early warning mechanism, generate an early warning signal, and record abnormal status data;

[0127] S46. After the warning is triggered, the abnormal status data is associated with the bound QR code information and stored in the database.

[0128] In this implementation manner, S5 specifically includes:

[0129] S51, preprocess the user's QR code scanning behavior and operation mode, map the original operation data into a graph structure G = (U, E), where U represents the user's operation behavior node, E represents the associated edge between the operation behaviors, and for each node U i Assign initial eigenvector

[0130] S52. Use graph neural network to model the user operation network and use multi-layer message passing mechanism to gradually update the feature vector of each node

[0131]

[0132] in, represents the updated feature vector, σ represents the activation function, W (k) and represents the weight matrix of the kth layer, d i Represents node U i The degree, d j Represents node U j The degree of node U i The set of neighbor nodes of

[0133] S53, after iterative update through the multi-layer graph neural network, the final embedding vector of each operation node is obtained The final embedding vector Input into the Bayesian optimization model for strategy optimization to maximize the anti-counterfeiting effect of QR code generation and update:

[0134]

[0135] Among them, L(θ) represents the objective function, θ represents the model parameter set, represents a prediction function that predicts the correlation between the user operation mode and the security of the QR code; represents the expected value, λ v represents the weight parameter for adjusting the variance effect, represents the variance term;

[0136] S54. In the Bayesian optimization process, the acquisition function is used to select the next set of QR code generation parameters and key update strategies to be evaluated:

[0137] AF(θ)=μ(θ)+κ1·σ(θ);

[0138] Where AF(θ) represents the acquisition function, μ(θ) represents the expected value predicted by the current model, κ1 represents the parameter that controls the trade-off between exploration and exploitation, and σ(θ) represents the prediction uncertainty;

[0139] S55, inputting the QR code update frequency and key update strategy after Bayesian optimization into the system;

[0140] S56, the system monitors the generation process of the QR code and the update process of the key in real time, and adjusts the model parameter θ according to the real-time monitoring data to achieve dynamic optimization.

[0141] In this implementation manner, S6 specifically includes:

[0142] S61. After triggering the multi-level anti-counterfeiting verification, the dynamic QR code is updated according to the optimized update frequency F opt Generate a new QR code and encrypt and update the QR code:

[0143]

[0144] Among them, Q newrepresents the updated QR code, E(·) represents the encryption function, H(·) represents the hash function, Indicates bitwise XOR operation, K opt represents the optimized key, Q old Represents the original QR code, Z t represents the comprehensive abnormality score;

[0145] S62, the system starts a multi-factor authentication mechanism, sends a one-time password to the user's bound device, requires the user to provide biometric information for verification, answer preset security questions, and check and verify the user's device information, IP address, and geographic location;

[0146] S63, combining the results of the verification steps to generate a comprehensive verification score V t , and compares it with the advanced threshold δ2 set by the system to determine whether to allow continued access. If the comprehensive verification score V t If the threshold is lower than δ2, the system will deny access and record relevant information;

[0147] S64. After completing all verification steps, the system will decide whether to allow the user to continue the operation based on the final result, and will store the relevant data of the verification process with the current QR code information Q new Bind and store in the system database.

[0148] Embodiment 1:

[0149] In order to verify the feasibility of the present invention in practice, the present invention is applied to a large construction engineering company. The company is responsible for the construction of an important commercial complex project, which involves the transportation and storage of a large number of high-end building materials and precision equipment. Each link of the procurement, transportation and installation of these materials and equipment has a crucial impact on the progress and quality of the project.

[0150] In order to ensure the safety of these high-value building materials during transportation and storage, the construction engineering company decided to apply the double-lock tie system with QR code labels of the present invention to protect these important materials. First, the company selected this double-lock tie for a batch of high-grade marble slabs imported from Europe. Each of these marble slabs is valuable and has extremely high requirements for appearance and quality. During transportation, any possible tampering, substitution or external damage will make the materials unusable for the project, and even cause construction delays and additional costs.

[0151] In the process of implementing the present invention, each marble slab uses the double-locking tie strap of the present invention. The tie strap is made of a polymer material with self-healing function, which can automatically repair minor damage during transportation and trigger irreversible color changes when severely damaged, providing an intuitive tampering indication. These tie straps are integrated with dynamically generated and updated QR code labels, which are generated based on quantum key distribution and distributed deep deterministic policy gradient algorithms, and can be optimized according to real-time environmental data and operating modes to ensure the uniqueness and non-replicability of the QR code.

[0152] During the transportation of marble slabs, from stone processing plants in Europe to construction sites in Asia, IoT sensors embedded in the lashings continuously monitored transportation conditions. These sensors collected temperature, humidity, pressure, and vibration data, and transmitted this data to the company's cloud server in real time through wireless communication modules. In the first few days of transportation, the transportation process was smooth, and the sensor data showed that the environment was stable and there were no abnormalities in the lashings. On the seventh day, when the transport ship passed through the Suez Canal, due to the sudden change in the external environment, the temperature in the cabin rose by 5 degrees Celsius and the vibration frequency increased significantly. The sensors immediately uploaded this data to the cloud and processed it through an improved dynamic fusion algorithm. These abnormal data were mapped to the digital twin model, and the system calculated the comprehensive abnormality score through deviation analysis. The results showed that the state of the lashings deviated significantly from the preset normal state.

[0153] After the comprehensive anomaly score exceeded the security threshold, the system automatically triggered multi-level anti-counterfeiting verification measures. The system first generated a new QR code and encrypted it with an optimized key. At the same time, the system remotely verified the transportation team through a multi-factor verification mechanism, including sending a one-time password (OTP) to the operator's device, requiring them to perform biometric verification and answer preset security questions. After these verifications are successful, the system allows the transportation to continue and binds the updated QR code information to the electronic waybill of the materials.

[0154] After arriving in Asia, the marble slabs were inspected by customs at the port in Shanghai, China. Customs officers scanned the QR code using a barcode scanner, and the system showed that the QR code matched the previously recorded content perfectly, with no signs of tampering. At the same time, historical data from IoT sensors showed that in the last few days of transportation, although environmental conditions changed significantly, the lashings remained intact without damage or abnormalities. Ultimately, the marble passed customs inspection and was safely delivered to the construction site.

[0155] At the construction site, the construction workers unpacked and inspected the marble slabs and scanned the QR code again to ensure that each slab was consistent with the information in the purchase record. After the construction was completed, the company reviewed and analyzed the data of the entire transportation and storage process. The results showed that since the adoption of the double-locking tie-down system of the present invention, the transportation loss rate of high-value building materials has been reduced by 90%, and the abnormal alarm rate has been reduced by 75%. At the same time, due to the intelligent processing capability of the system, the customs clearance speed of the transported materials has also increased by 20%, effectively reducing the risk of project delays.

[0156] These data clearly show that the application of the present invention greatly enhances the safety and reliability of the construction material transportation process. By implementing the double-locking tie-down system of the present invention, the construction engineering company effectively solved the risk of tampering and replacement of high-value materials during long-distance transportation, ensuring the integrity of the materials and the smooth implementation of the project. The present invention not only improves transportation safety, but also improves the overall logistics efficiency, providing a strong guarantee for the smooth completion of the construction project.

[0157] Table 1 Comparative analysis of safety and efficiency of transportation of high-value construction materials

[0158]

[0159]

[0160] From the comparative data in Table 1 above, it can be seen that the transportation loss rate has dropped significantly from 10% under the traditional method to 1%, which shows that the present invention effectively reduces the material loss caused by tampering, replacement or damage. The abnormal alarm rate has been reduced from 20 times / 100 transportations to 5 times / 100 transportations, further illustrating the excellent performance of the system in identifying and preventing potential risks.

[0161] In terms of customs clearance speed, after applying the present invention, the average customs clearance time was shortened from 2 hours / batch to 1.6 hours / batch, reducing the customs clearance time by 20%, significantly improving logistics efficiency. The incidence of tampering incidents was reduced to 0 times / year after applying the present invention, completely eliminating the risk of tampering or substitution caused by human factors, and ensuring the safety of materials during transportation.

[0162] In addition, the material integrity inspection pass rate increased from 85% to 99%, and almost all materials that arrived at the destination were judged to be intact, showing the effectiveness of the invention in ensuring material quality. The verification time was shortened from 30 minutes / batch to 15 minutes / batch, greatly reducing the operation time during customs inspection or unpacking on the construction site, and improving overall work efficiency.

[0163] The customer complaint rate has been significantly reduced from 15% to 2% with the traditional method, reflecting the high customer satisfaction with transportation safety and service quality. The logistics cost has been reduced by 10% compared with the traditional method, and this cost saving is mainly due to the more efficient customs clearance and transportation process. Finally, the number of project delays has been reduced from 3 times / year to 0 times / year, ensuring the project is completed on time and avoiding delays caused by material transportation problems.

[0164] In summary, the double-locking tie strap system of the present invention not only performs well in ensuring the safety of transportation of high-value construction materials, but also significantly improves overall logistics efficiency and customer satisfaction, while reducing project costs and construction period risks.

[0165] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A double-locking tie belt with a QR code label for anti-replacement and a method, characterized in that: The steps include: S1. Provide a double-locking tie strap, which is made of a polymer material with self-repairing function, automatically repairs after minor damage, and triggers color change when severely damaged; S2. Integrate a QR code label on the binding belt. The QR code is dynamically generated based on quantum key distribution and distributed deep deterministic policy gradient algorithm, and the strategy is optimized according to real-time environmental data and user behavior patterns to dynamically adjust the generation and update frequency of the QR code; S3, monitor the physical status of the binding straps in real time through IoT sensors, and transmit the collected status data to the digital twin model for synchronization and analysis; S4. Based on digital twin technology, the real-time physical status of the lashing strap is compared with the virtual model to identify potential damage or tampering and issue an early warning signal; S5. Use an algorithm based on graph neural network combined with Bayesian optimization to perform real-time analysis on QR code scanning behavior and user operation patterns; S6. When abnormal behavior or system warning is detected, multi-level anti-counterfeiting verification measures are triggered; S7. When serious damage or tampering is detected, the self-healing material of the binding tape triggers an irreversible color change, automatically updates the QR code and encrypts the data, and generates a new QR code label.

2. A double-locking tie strap with a QR code label and method for anti-replacement according to claim 1, characterized in that: The S2 specifically includes: S21. Generate a quantum state key for encrypting the QR code information through quantum key distribution technology; S22, based on the distributed deep deterministic policy gradient algorithm, the current state of the system S is analyzed through the policy network t Evaluate the system status including environmental data t and user behavior pattern B t , output the optimal action A t ; S23. Use the value network to evaluate the optimal action A t The expected value Q(S t ,A t ), optimize the weight of the strategy network through back propagation and dynamically adjust the QR code generation strategy; S24. Generate and encrypt QR code information: Among them, C t Indicates the encrypted QR code information, K t represents the quantum key, E(·) represents the encryption function, H(·) represents the hash function, Represents a bitwise exclusive OR operation; S25. Dynamically adjust the frequency of generating the QR code F t and the key update frequency K update : K update =γ1·|T t -T avg |+γ2·|H t -H avg |+γ3·|N t -N avg |+γ4·|I t -I avg |; Among them, T t Indicates the current ambient temperature, H t Indicates the current ambient humidity, L t Indicates the current light intensity, N t Indicates the number of user scans, I t Represents the scanning time interval, T avg Indicates the average ambient temperature set by the system, H avg Indicates the average ambient humidity set by the system, N avg Indicates the average number of user scans set by the system, I avg represents the average value of the scanning time interval set by the system, α1 represents the current ambient temperature weight coefficient, α2 represents the current ambient humidity weight coefficient, α3 represents the current light intensity weight coefficient, β1 represents the user scanning times weight coefficient, β2 represents the scanning time interval weight coefficient, γ1, γ2, γ3 and γ4 represent the weight coefficients used for key update frequency calculation; S26, after the adjustment, the generation frequency F t and the key update frequency K update Based on the above, generate and update the QR code label; S27. Integrate the generated unique and dynamically updated QR code label into the double-lock buckle binding belt, and use the IoT sensor to scan and verify the QR code label.

3. A double-locking binding belt with a QR code label and method for anti-replacement according to claim 1, characterized in that: The S3 specifically includes: S31. Embed IoT sensors in the double-lock tie strap, including a temperature sensor, a humidity sensor, a pressure sensor, and an acceleration sensor, to monitor the physical state of the tie strap in real time, collect environmental data and mechanical data, and construct a data set; S32. Combining the sensor data degradation rate and historical data trend, the data set is fused by an improved data fusion algorithm: Among them, V s Represents the integrated state data after fusion, ω i (s) represents the dynamic weight of the i-th sensor, R i (s) represents the data collected by the i-th sensor at time s, η represents the adjustment coefficient that controls the change rate of sensor data, n represents the total number of sensors, and defines ΔR i (s) = R i (s)-R i (s-1); S33, the integrated state data V s The data is transmitted to the cloud server or local computing device through the wireless communication module, and the encryption protocol is used to protect the data during the transmission process; S34, in the cloud server or local computing device, by mapping function Φ(V s ,Γ) The fused comprehensive state data V s Mapping to the digital twin model M s In the digital twin model M s reflects the real-time physical state of the binding belt, where Γ represents the mapping parameter set; S35, the mapped comprehensive status data V s Synchronize and analyze.

4. A double-locking tie strap with a QR code label and method for anti-replacement according to claim 1, characterized in that: The S4 specifically includes: S41, calibrating the initial state of the digital twin model according to the bound initial state vector; S42. Use the mapping function to update the mapping between real-time data and model status: Among them, N t represents the state of the digital twin model at time t, Y tj represents the real-time data of the jth sensor at time t, ΔY tj represents the rate of change of sensor data, ω j represents the weight of each sensor data, κ represents the adjustment coefficient, and n represents the total number of sensors; S43, update the digital twin model state N t Compare with the preset reference model N ref , calculate the deviation vector ΔN t : Among them, α j and β j represents the regression coefficient, ζ represents the time weighting factor, λ represents the historical deviation impact factor, ∈ k Represents the coefficient for weighting the deviations of the previous p time steps; S44, according to the deviation vector ΔN t Calculate the comprehensive anomaly score Z t : Z t =∥ΔN t ∥+η·∥ΔN t -ΔN t-1 ∥+ρ·max(ΔN t ); Among them, η represents the weighted coefficient of the deviation change trend, ρ represents the weighted coefficient of the maximum deviation, max(ΔN t ) represents the absolute value of the maximum deviation component at time t; S45, when the comprehensive abnormality score Z t When the safety threshold θ is exceeded, the system will trigger the early warning mechanism, generate an early warning signal, and record abnormal status data; S46. After the warning is triggered, the abnormal status data is associated with the bound QR code information and stored in the database.

5. A double-locking tie strap with a QR code label and method for anti-replacement according to claim 1, characterized in that: The S5 specifically includes: S51, preprocess the user's QR code scanning behavior and operation mode, map the original operation data into a graph structure G = (U, E), where U represents the user's operation behavior node, E represents the associated edge between the operation behaviors, and for each node U i Assign initial eigenvector S52. Use graph neural network to model the user operation network and use multi-layer message passing mechanism to gradually update the feature vector of each node in, represents the updated feature vector, σ represents the activation function, W (k) and represents the weight matrix of the kth layer, d i Represents node U i The degree, d j Represents node U j The degree of node U i The set of neighbor nodes of S53, after iterative update through the multi-layer graph neural network, the final embedding vector of each operation node is obtained The final embedding vector Input into the Bayesian optimization model for strategy optimization to maximize the anti-counterfeiting effect of QR code generation and update: Among them, L(θ) represents the objective function, θ represents the model parameter set, represents a prediction function that predicts the correlation between the user operation mode and the security of the QR code; represents the expected value, λ v represents the weight parameter for adjusting the variance effect, represents the variance term; S54. In the Bayesian optimization process, the acquisition function is used to select the next set of QR code generation parameters and key update strategies to be evaluated: AF(θ)=μ(θ)+κ1·σ(θ); Where AF(θ) represents the acquisition function, μ(θ) represents the expected value predicted by the current model, κ1 represents the parameter that controls the trade-off between exploration and exploitation, and σ(θ) represents the prediction uncertainty; S55, inputting the QR code update frequency and key update strategy after Bayesian optimization into the system; S56, the system monitors the generation process of the QR code and the update process of the key in real time, and adjusts the model parameter θ according to the real-time monitoring data to achieve dynamic optimization.

6. A double-locking tie strap with a QR code label and method for anti-replacement according to claim 1, characterized in that: The S6 specifically includes: S61. After triggering the multi-level anti-counterfeiting verification, the dynamic QR code is updated according to the optimized update frequency F opt Generate a new QR code and encrypt and update the QR code: Among them, Q new represents the updated QR code, E(·) represents the encryption function, H(·) represents the hash function, Indicates bitwise XOR operation, K opt represents the optimized key, Q old Represents the original QR code, Z t represents the comprehensive abnormality score; S62, the system starts a multi-factor authentication mechanism, sends a one-time password to the user's bound device, requires the user to provide biometric information for verification, answer preset security questions, and check and verify the user's device information, IP address, and geographic location; S63, combining the results of the verification steps to generate a comprehensive verification score V t , and compares it with the advanced threshold δ2 set by the system to determine whether to allow continued access. If the comprehensive verification score V t If the threshold is lower than δ2, the system will deny access and record relevant information; S64. After completing all verification steps, the system will decide whether to allow the user to continue the operation based on the final result, and will store the relevant data of the verification process with the current QR code information Q new Bind and store in the system database.

7. A double-locking tie strap for anti-replacement, characterized in that: include: A two-dimensional code label and a double-locking binding belt, wherein the two-dimensional code label is used to execute any one of the anti-replacement methods in claims 1 to 6.

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