Securable cold chain transportation tracking system based on electronic tags and method thereof

Abstract

The application discloses a cold chain transportation tracking system and method based on an electronic tag, and particularly relates to the technical field of cold chain transportation tracking and information security, and the system comprises an encrypted electronic tag module, an intelligent reader-writer module, an edge computing gateway module, a cloud management platform module and a multi-subject permission management module; the edge computing gateway module is arranged on a cold chain transportation vehicle or a transfer node. The application has the advantages of significantly improved security performance: through a dynamic key encryption mechanism, two-way identity authentication and an optimized ring signature algorithm, the application realizes full-process encryption protection of collected data, transmitted data and stored data, effectively prevents data eavesdropping, forgery, tampering and unauthorized access, and improves the anti-quantum security of cross-chain data sharing based on a ring signature scheme of lattice assumption.

Description

Technical Field

[0001] This invention relates to the technical field of cold chain transportation tracking and information security, and more specifically, to a confidential cold chain transportation tracking system and method based on electronic tags. Background Technology

[0002] Cold chain transportation is a crucial link in maintaining the quality of perishable and highly sensitive goods (such as vaccines, fresh produce, and biological agents). Its core requirement is to achieve real-time tracking of transportation routes and environmental parameters (temperature, humidity, vibration, etc.), while ensuring that sensitive data such as the identity information of the transported goods, transportation routes, and recipient information are not leaked or tampered with. Currently, cold chain tracking technologies based on electronic tags (such as RFID and NFC) are widely used, but existing technologies still have the following core shortcomings, making it difficult to meet the requirements for high-security and high-precision tracking: 1. Insufficient Information Security: Existing electronic tags mostly use a single encryption algorithm (such as AES) to encrypt data. The encryption key is fixed and easily cracked. Furthermore, data transmission between tags and readers, and between readers and management platforms, is often in plaintext or with simple encryption, making them vulnerable to security risks such as data eavesdropping, forgery, and tampering. This is especially true for pharmaceuticals and military-grade precision cold chain goods, where leakage of sensitive information could have serious consequences. Simultaneously, in multi-entity collaborative transportation scenarios, the division of authority among different logistics companies and regulatory departments is often ambiguous, easily leading to unauthorized access to sensitive data, creating a dual dilemma of "data silos" and privacy breaches.

[0003] 2. Poor tracking accuracy and environmental adaptability: In existing technologies, the data acquisition frequency of electronic tags is fixed and cannot be dynamically adjusted according to changes in the cold chain transportation environment (such as temperature fluctuations and transportation bumps). This results in either redundant environmental parameter acquisition (increasing energy consumption and data transmission pressure) or missing data (failing to capture key environmental anomalies). At the same time, in complex cold chain environments such as low temperature, high humidity, and metal interference, tag signals are prone to attenuation and missed readings. Combined with the limitations of traditional static frame time slot algorithms, the accuracy is low when reading multiple tags in batches, making it impossible to achieve accurate positioning and status tracking of items.

[0004] 3. Insufficient algorithm optimization and low data processing efficiency: Existing cold chain tracking systems often use fixed threshold judgment methods for detecting environmental parameter anomalies, failing to consider the temporal and spatial correlations of environmental parameters, which easily leads to false alarms and missed alarms. Data fusion algorithms mostly use simple weighted average methods, which cannot effectively filter noisy data collected by sensors, resulting in low data accuracy. Furthermore, they lack the ability to predict the cold chain environment, only able to passively respond to anomalies and unable to proactively avoid transportation risks. In addition, in existing cross-chain data sharing solutions, the signature algorithm is inefficient and difficult to adapt to the real-time data interaction needs of multiple entities and nodes in cold chain transportation.

[0005] Therefore, a secure cold chain transportation tracking system and method based on electronic tags are proposed to address the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a confidential cold chain transportation tracking system and method based on electronic tags to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a secure cold chain transportation tracking system based on electronic tags, the system comprising an encrypted electronic tag module, an intelligent reader module, an edge computing gateway module, a cloud management platform module, and a multi-entity permission management module; The encrypted electronic tag module has a built-in encryption chip, environmental sensor and positioning module, which is used to collect environmental parameters and location information of cold chain goods and encrypt them locally. It automatically updates the encryption key according to changes in transportation nodes, and the encrypted electronic tag module and the smart reader module have two-way identity authentication. The intelligent reader module is deployed at nodes related to cold chain transportation and has the ability to read multiple tags in batches. It adopts an optimized dynamic frame time slot algorithm and dual-band switching technology to receive and decrypt encrypted data sent by the encrypted electronic tag module and upload it to the edge computing gateway module. At the same time, it encrypts the control commands issued by the cloud management platform module and sends them to the encrypted electronic tag module. The edge computing gateway module is deployed on cold chain transport vehicles or transit nodes. It is used to preprocess and temporarily store the data uploaded by the smart reader module. It uses an improved data fusion algorithm to fuse data collected by multiple tags and multiple sensors, and then encrypts the data using an optimized ring signature algorithm before uploading it to the cloud management platform module.

[0008] Furthermore, the cloud management platform module includes a data storage unit, an algorithm processing unit, an anomaly warning unit, and a trajectory display unit, which are used for encrypted data storage, algorithm processing, anomaly warning and trajectory display, and can automatically optimize the parameters of each algorithm.

[0009] Furthermore, the multi-entity permission management module uses a role-based access control mechanism combined with cross-chain integration technology to assign different access permissions to different entities. It also adopts a ring signature scheme based on the lattice assumption to achieve cross-chain data sharing and hierarchical management of sensitive data.

[0010] Furthermore, the environmental sensors include a temperature sensor, a humidity sensor, and a vibration sensor, which are used to collect temperature, humidity, and vibration data during cold chain transportation, respectively. The positioning module adopts a Beidou positioning module, which supports real-time location acquisition.

[0011] Furthermore, the preprocessing of the edge computing gateway module includes data completion and adaptive noise filtering using linear interpolation, and the data fusion algorithm is an improved clustering algorithm to suppress the influence of noise and outliers; it supports 5G / BeiDou dual-mode communication and has local temporary storage function.

[0012] Furthermore, the algorithm processing unit of the cloud management platform module integrates a prediction algorithm combining improved clustering and long short-term memory networks, as well as an adaptive anomaly detection algorithm; the anomaly warning unit supports three-level warnings and pushes warning information and handling suggestions; the data storage unit adopts encrypted storage technology and supports data traceability; and the trajectory display unit displays the transportation trajectory of cold chain goods and environmental parameter change curves in real time.

[0013] On the other hand, a secure cold chain transportation tracking method based on electronic tags includes the following steps: S1: System initialization and tag activation. The shipper enters the basic information of cold chain goods through the cloud management platform module, generates a unique item identifier, generates an initial encryption key and permission list in the cloud, and writes it into the encrypted electronic tag module to complete the activation. S2: Dynamic encrypted data acquisition. The encrypted electronic tag module adjusts the acquisition frequency according to the transportation stage and environmental fluctuations, collects environmental parameters and location information, and sends them to the intelligent reader module after encryption with a dynamic key. S3: Multi-tag collaborative reading and data preprocessing. The intelligent reader module adopts the optimized dynamic frame time slot algorithm and dual-band switching technology to realize batch reading and identity authentication of multiple tags. After decryption, the data is uploaded to the edge computing gateway module, which completes the data and filters noise. S4: Data fusion and encrypted transmission. The edge computing gateway module uses an improved data fusion algorithm to fuse standardized data, encrypts it using an optimized ring signature algorithm, uploads it to the cloud management platform module, and performs local temporary storage. S5: Cloud-based algorithm processing and anomaly warning. After the cloud management platform module decrypts the data, it predicts changes in environmental parameters through a prediction algorithm, identifies anomalies through an adaptive anomaly detection algorithm, issues warnings based on the anomaly level, and pushes processing suggestions. S6: Multi-entity access control and data traceability. The multi-entity access control module assigns access permissions to different entities according to the access list, realizes hierarchical control of sensitive data and cross-chain data sharing. After the transportation is completed, the data of the whole process is encrypted and archived, and data traceability is supported. The encrypted electronic tag module can be reused after being reset. S7: System closed-loop optimization. The cloud management platform module regularly analyzes the data throughout the entire process, automatically optimizes the parameters of each algorithm and the energy consumption control strategy of the tag, and improves system performance.

[0014] Furthermore, the dynamic acquisition algorithm in S2 dynamically adjusts the acquisition frequency based on the fluctuation range of environmental parameters and the remaining power of the tag: when the environmental parameters are stable, the acquisition frequency is reduced to balance data integrity and tag power consumption.

[0015] Furthermore, the improved data fusion algorithm in S4 introduces environmental parameter weight factors on the basis of traditional clustering algorithms, and allocates weights according to the degree of influence of different environmental parameters on the quality of cold chain goods, thereby improving the accuracy of data fusion.

[0016] Furthermore, the prediction algorithm in S5 combines historical environmental data, current data, and transportation road condition information to achieve multi-step prediction of environmental parameters; the adaptive anomaly detection algorithm combines preset thresholds and data change trends to avoid false alarms and missed alarms, and can identify environmental parameters exceeding standards, data tampering, and abnormal tags. The multi-entity access control in S6 adopts a role-based access control mechanism, combined with a ring signature scheme based on the lattice assumption, to achieve quantum-resistant security for cross-chain data sharing, ensuring that different entities can only access data within their own permission scope; the encrypted electronic tag module achieves reuse through key updates, reducing transportation costs.

[0017] The technical effects and advantages of this invention are as follows: Compared with existing technologies, this secure cold chain transportation tracking system and method based on electronic tags significantly improves security performance: through a dynamic key encryption mechanism (based on a dynamic key generation formula), two-way authentication (based on an authentication formula), an optimized ring signature algorithm (based on a signature generation and verification formula), and multi-entity access control (based on an access allocation and verification formula), it achieves end-to-end encryption protection for data collection, transmission, and storage, effectively preventing data eavesdropping, forgery, tampering, and unauthorized access; the ring signature scheme based on the lattice assumption enhances the quantum-resistant security of cross-chain data sharing, solves the privacy leakage and "data silo" problems in multi-entity collaborative transportation, meets the security requirements of high-end cold chains such as pharmaceuticals and military applications, and creatively realizes an integrated security design of "encryption-access control-sharing".

[0018] Significantly improved tracking accuracy and environmental adaptability: Through optimized dynamic acquisition algorithms (based on acquisition frequency adjustment formulas), improved data fusion algorithms (based on distance and fusion formulas), and multi-tag reading algorithms (based on frame time slot adjustment and collision probability formulas), accurate acquisition, fusion, and analysis of environmental parameters and location information are achieved, effectively improving acquisition accuracy, enhancing multi-tag reading efficiency, and reducing the missed reading rate. Combined with low-temperature resistant flexible packaging and anti-metal design, the tags can adapt to extreme environments ranging from -40℃ to 85℃, solving the problem of existing tags being prone to failure and missed reading in complex cold chain environments. The improved prediction algorithm combining K-medoids and LSTM (based on LSTM prediction formulas and loss functions) enables accurate prediction of environmental parameters, allowing for early avoidance of transportation risks and significantly reducing the loss rate of cold chain goods.

[0019] The algorithm is innovative and highly practical: This invention has made targeted optimizations and innovations to the encryption algorithm, acquisition algorithm, data fusion algorithm, and anomaly detection algorithm, clearly defining the formula definition and application logic of each algorithm. The algorithms work together to balance information security, tracking accuracy, and system efficiency. The combination of dynamic acquisition algorithm and low-power control strategy (based on energy balance adjustment formula) extends the tag's battery life. The tag can be reused more than 50 times, reducing the cost per use and significantly reducing cold chain transportation costs. The algorithm parameters can be automatically optimized through the cloud platform to adapt to different types of cold chain transportation scenarios, making it highly versatile.

[0020] Full-process traceability and strong controllability: It enables full-process tracking and data recording of cold chain transportation from dispatch, warehousing, transportation, transshipment and receiving. The encrypted and archived traceability files support multi-authorized entities for querying, realizing the traceability, accountability and responsibility of the transportation process; the hierarchical anomaly early warning mechanism can promptly detect and handle transportation anomalies, reduce product damage and improve the reliability of cold chain transportation; multi-entity access control realizes hierarchical control of sensitive data, which not only ensures the legitimate data access needs of each entity, but also prevents unauthorized leakage of sensitive information, breaks the "data silo" dilemma in multi-entity collaborative transportation, and achieves the control goal of "collaborative efficiency and privacy controllability"; Excellent system compatibility and scalability: The algorithms used in this invention have good compatibility and can be adapted to different types of electronic tags (such as RFID, NFC), smart readers and cloud platforms. No large-scale modification of existing cold chain transportation equipment is required, reducing system deployment costs. At the same time, the system architecture adopts a modular design, which can flexibly add functional modules (such as cold chain goods quality assessment module, transportation route optimization module) according to actual cold chain transportation needs, optimize algorithm parameters, and adapt to the transportation needs of different types of cold chain goods such as fresh food, pharmaceuticals, and precision instruments. It has strong scalability and a wide range of applications. Green and energy-saving with controllable operating costs: Through the synergistic effect of dynamic acquisition algorithms and low-power control strategies (based on energy balance adjustment formulas), the energy consumption of electronic tags is effectively reduced, the tag battery life is extended, and the tags can be reused more than 50 times, significantly reducing the cost of using disposable tags. At the same time, the data fusion algorithm effectively reduces data redundancy, reduces the pressure on data transmission and storage, and reduces the operating costs of network bandwidth and cloud storage. Combined with the system closed-loop optimization mechanism, the algorithm parameters and energy consumption control strategies can be continuously optimized to further reduce the overall operating costs of cold chain transportation and improve the economic benefits of enterprises. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the system flow of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention; In the diagram: 1. Encrypted electronic tag module; 2. Intelligent reader / writer module; 3. Edge computing gateway module; 4. Cloud management platform module; 401. Data storage unit; 402. Algorithm processing unit; 403. Anomaly warning unit; 404. Trajectory display unit; 5. Multi-subject permission management module. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0023] Example 1 As attached Figure 1 The illustrated secure cold chain transportation tracking system based on electronic tags includes an encrypted electronic tag module 1, an intelligent reader / writer module 2, an edge computing gateway module 3, a cloud management platform module 4, and a multi-entity access control module 5. The encrypted electronic tag module 1 incorporates an encryption chip, environmental sensors, and a positioning module. It collects environmental parameters and location information of cold chain goods and performs local encryption. The environmental sensors include a temperature sensor, a humidity sensor, and a vibration sensor, which collect temperature, humidity, and vibration data during cold chain transportation, respectively. The positioning module uses a BeiDou positioning module, supporting real-time location acquisition and automatically updating the encryption key based on changes in transportation nodes. It also features two-way authentication between the encrypted electronic tag module 1 and the intelligent reader / writer module 2. The encrypted electronic tag module 1 uses a low-temperature resistant flexible encapsulation material, polyimide substrate, suitable for extreme cold chain environments ranging from -40℃ to 85℃. It has an anti-metal interference design and can be reused more than 50 times. The encrypted electronic tag module 1 uses a dynamic key generation algorithm to achieve automatic key updates and encryption. The formula and application are as follows: Dynamic key generation formula: ,in, The encryption key for the current transportation node. This is the historical key of the previous transport node. This serves as the identifier for the current transportation node (such as a warehousing node, transportation node, or transit node, represented by a unique code). This is the current timestamp (accurate to the second). The current remaining battery level of the electronic tag (0-100). For XOR operation, A hash function (using the SM3 hash algorithm) is used to compress the mixed input information into a fixed-length (128-bit) key, ensuring the uniqueness and security of the key. Application scenario: When an electronic tag moves from one transportation node (such as a warehouse node in City A) to another transportation node (such as a transport vehicle), the tag automatically collects the identifier of the current node. Current timestamp and its own battery level, i.e., the current remaining battery level of the electronic tag. Combined with the key of the previous node A new key is generated using the above formula. The data is then synchronized to the smart reader module 2 to enable dynamic key updates, preventing data leakage due to the cracking of a fixed key. Simultaneously, the battery status is incorporated into the key generation process. When the tag's battery is too low, energy consumption can be reduced by adjusting the key generation frequency; The complete process of two-way authentication is as follows: The first step is for the tag to authenticate with the reader: Before sending the collected data to the smart reader, the encrypted electronic tag first calls the encryption key of the current transportation node. ,Will With its own unique identifier Perform an XOR operation to obtain the mixed result; then input the mixed result into the SM3 hash function. Generate identity authentication request information and will The first step involves synchronously sending the data to the smart reader module to initiate the authentication request on the tag side; the second step is for the reader module to verify the tag's legitimacy: the smart reader module receives the data sent by the tag. Then, it immediately retrieves the current node key stored within itself and updated synchronously with the tag. and the pre-entered unique identifier of the tag. Following the exact same operational logic as the labels, and Perform an XOR operation, then input the SM3 hash function to calculate the verification value. Send the tag With its own calculation A consistency comparison is performed. If the two are completely equal, it means that the tag is a legal tag authorized by the system, and the tag completes the initial authentication of the reader (that is, the tag confirms that the current reader is a system-authorized device and can perform subsequent interactions). If the two are not equal, it is determined to be an illegal tag, and any data interaction with the tag is immediately refused. The abnormal information is recorded and reported to the cloud management platform.

[0024] The third step involves the reader initiating authentication with the tag: To further ensure communication security and achieve two-way verification (preventing unauthorized readers from forging identities and deceiving the tag), after verifying the tag's legitimacy, the smart reader module calls its own unique identifier. With the current node key The two are XORed together and then input into the SM3 hash function to generate the authentication information on the reader side. and will Send to encrypted electronic tag module 1.

[0025] Step 4: Verify the legitimacy of the reader / writer: The encrypted electronic tag module receives the data sent by the reader / writer. Then, it retrieves the current node key stored within itself. The unique identifier of the reader / writer (This identifier was written synchronously when the tag was activated); Following the same computational logic as the reader, the verification value is calculated and compared with the received... A consistency comparison is performed. If the two are completely equal, it means that the reader is a legitimate device authorized by the system, and the reader completes the authentication of the tag (that is, the reader confirms that the current tag is a system-authorized tag). If the two are not equal, it is determined to be an illegal reader, communication is immediately terminated, and it enters a sleep state to avoid data leakage.

[0026] Only after all four steps are completed, i.e., after successful two-way authentication, can a secure and legitimate communication link be established between the encrypted electronic tag and the smart reader, enabling subsequent data transmission and control command interaction. This two-way authentication mechanism, combined with dynamically updated keys... With unique identifier ( This approach eliminates security risks such as data leakage and tampering caused by illegal label forgery and unauthorized reader access from both directions, laying a solid foundation for the security of cold chain transportation data collection.

[0027] The intelligent reader module 2 is deployed at nodes related to cold chain transportation and has the ability to read multiple tags in batches. It adopts an optimized dynamic frame time slot algorithm and dual-band switching technology to receive and decrypt encrypted data sent by the encrypted electronic tag module 1 and upload it to the edge computing gateway module 3. At the same time, it encrypts the control commands (such as collection frequency adjustment and key update) issued by the cloud management platform module 4 and sends them to the encrypted electronic tag module 1. The relevant nodes of cold chain transportation include cold chain transportation vehicles, warehousing nodes, and transfer nodes. The dual-band switching technology uses 920MHz and 865MHz to avoid local frequency band interference and improve reading efficiency and accuracy. The intelligent reader module 2 applies an optimized dynamic frame time slot algorithm for batch reading of multiple tags, resolving the signal collision problem. The formula and application are as follows: Frame slot adjustment formula: ,in, This represents the number of time slots in the current frame. This represents the number of electronic tags detected in the current area. The preset missed read rate threshold is (0.1% in this invention). This represents the number of time slots in the previous frame. It is a rounding function; Formula for calculating signal collision probability: ; in, The signal collision probability of the current frame, when At that time, frame time slot adjustment is triggered, through the above... The formula recalculates the number of time slots until the collision probability is ≤0.3, ensuring the accuracy of multi-tag reading.

[0028] Application scenario: Intelligent readers and writers detect the number of tags in the current area in real time. Combined with the number of time slots in the previous frame The system calculates the number of time slots in the current frame using a frame time slot adjustment formula, and verifies the adjustment effect using a collision probability formula. It dynamically optimizes time slot allocation to avoid collisions caused by multiple tags sending signals simultaneously, thereby improving reading efficiency. Combined with dual-band switching technology, when interference occurs in one frequency band (such as 920MHz), it automatically switches to another frequency band (865MHz) to further reduce the missed read rate and ensure the stability of batch reading of multiple tags.

[0029] The edge computing gateway module 3 is deployed on cold chain transport vehicles or transit nodes. It is used to preprocess and temporarily store the data uploaded by the smart reader module 2. It uses an improved data fusion algorithm to fuse data collected by multiple tags and multiple sensors. After encrypting the data with an optimized ring signature algorithm, it uploads it to the cloud management platform module 4. The preprocessing of the edge computing gateway module 3 includes data completion using linear interpolation and adaptive noise filtering. The data fusion algorithm is an improved clustering algorithm to suppress the influence of noise and outliers. It supports 5G / BeiDou dual-mode communication and has local temporary storage function. Edge computing gateway module 3 applies an improved K-medoids data fusion algorithm, an adaptive noise filtering algorithm, a linear interpolation data completion algorithm, and an optimized ring signature algorithm. The formulas and applications of each algorithm are as follows: Adaptive noise filtering algorithm (used to filter noise data acquired by sensors): formula: ,in, ,in, These are the filtered data values. This is the original collected data. The sliding window size (set to 3 in this invention) is the maximum size of the sliding window. These are weighting coefficients. For smoothing coefficients, Using the index value within the sliding window, the algorithm smooths out noisy data with abnormal fluctuations through a weighted average of the sliding window. The weight coefficient decays with the distance from the current data point, thus preserving the true trend of data change while effectively filtering out noise.

[0030] Linear interpolation data completion algorithm (used to repair short-term missing data): formula: , in, These are the imputation values ​​for missing data points. The previous valid data value before the missing point. The next valid data value after the missing point. These are the collection time indexes for the corresponding data points. When there is a short-term gap in the data collected by the sensor (such as 1-2 collection cycles), this formula is used to fill in the data and ensure its integrity.

[0031] Improved K-medoids data fusion algorithm (for fusing multi-label, multi-sensor data to improve data accuracy): Step 1: Initialize cluster centers ,in The number of clusters (set according to the number of labels, set to 5-10 in this invention). For the first The initial centers of each cluster (randomly selected from the preprocessed standardized data); Step 2: Calculate each data point To each cluster center Distance, distance formula: ,in, For the first The first data point One environmental parameter ( For temperature, For humidity, (for vibration) For the first The first cluster center One environmental parameter, Environmental parameter weighting factors (as defined in this invention) ), allocated according to the degree of influence of each parameter on the quality of cold chain goods; Step 3: Assign each data point to the nearest cluster and calculate the cost function for each cluster: ,in For the first One cluster; Step 4: Iteratively update the cluster centers, selecting the clusters within each cluster that maximize the cost function. The smallest data point is used as the new cluster center. Repeat steps 2-3 until the cluster centers no longer change or the number of iterations reaches a preset threshold (20 times in this invention). Step 5: Data Fusion Output: ,in For the final data after fusion, the mean of each cluster center is calculated to effectively suppress the influence of noise and outliers and reduce data redundancy.

[0032] Optimized ring signature algorithm (used for data transmission encryption, ensuring secure cross-chain data sharing): Based on the lattice assumption, the core formula is as follows: Signature generation: ,in ; Signature verification: ; in, For the data to be encrypted and transmitted, This involves multiple entities (such as shippers, logistics companies, and regulatory authorities). For the first The public key of each subject, It is a random vector. For the first The private key of each entity, Index the signers (hide the signer's identity). The algorithm uses the SM3 hash function. This algorithm simplifies the traditional ring signature process, improves signing and verification efficiency, and, based on the lattice assumption, possesses quantum-resistant security. It is suitable for cross-chain data sharing in multi-entity collaborative transportation, ensuring that data transmission is not eavesdropped on or tampered with, and that the signer's identity cannot be traced, thus protecting privacy.

[0033] Application Scenario: After receiving multi-tag data uploaded by a smart reader, the edge computing gateway first filters noise from vibration and temperature data using an adaptive noise filtering algorithm, and then completes short-term missing data using a linear interpolation algorithm to obtain standardized data. Subsequently, an improved K-medoids data fusion algorithm is used to cluster and fuse the standardized data collected from multiple tags and sensors to obtain high-precision fused data. Finally, an optimized ring signature algorithm is used to encrypt the fused data, which is then uploaded to the cloud management platform via 5G / BeiDou dual-mode communication and simultaneously temporarily stored locally to prevent data loss due to network interruptions.

[0034] The cloud management platform module 4 includes a data storage unit 401, an algorithm processing unit 402, an anomaly warning unit 403, and a trajectory display unit 404, which are used for encrypted data storage, algorithm processing, anomaly warning and trajectory display, and can automatically optimize the parameters of each algorithm. The algorithm processing unit 402 of the cloud management platform module 4 integrates a prediction algorithm that combines improved clustering and long short-term memory networks, as well as an adaptive anomaly detection algorithm; the anomaly warning unit 403 supports three-level warnings and pushes warning information and handling suggestions; the data storage unit 404 adopts encrypted storage technology and supports data traceability; the trajectory display unit 404 displays the transportation trajectory of cold chain goods and the environmental parameter change curves in real time. Module 4 of the cloud management platform applies an improved prediction algorithm combining K-medoids and LSTM, and an adaptive anomaly detection algorithm. The formulas and applications of each algorithm are as follows: An improved environmental parameter prediction algorithm combining K-medoids and LSTM (used to predict changes in environmental parameters over a future period and mitigate transportation risks in advance): Step 1: Preprocess the historical environmental data using the improved K-medoids algorithm to obtain standardized historical data after denoising and redundancy removal. ,in For the length of historical data, (Temperature, humidity, and vibration, respectively); Step 2: Construct an LSTM prediction model, with the input layer being historical data sequences. ,in The time step is set to 60 (in this invention, it is set to 60, meaning that the prediction is based on historical data from the past 60 minutes). Step 3: LSTM cell state update formula: (Forget Gate, controls the state of cells that have forgotten history). (Input gate, controls the input of current information) (Candidate cell status) (Cell status update) (Output gate, controls the output of cell state). (Hidden layer output); Step 4: Output layer prediction formula: ; in, It is the sigmoid activation function. The hyperbolic tangent activation function is used. This is element-wise multiplication. Here are the weight matrices for each layer. For each layer of bias terms, Output for the current hidden layer. The current cell state, This provides predicted environmental parameters for the next minute; through iterative calculations, multi-step predictions for the next 30 minutes can be achieved. ).

[0035] Step 5: Prediction Error Correction: The mean squared error (MSE) is used as the loss function, formula: ,in To determine the amount of data in the validation set, For predicted values, The model weights and biases are updated using gradient descent to minimize the loss function and improve prediction accuracy.

[0036] Adaptive anomaly detection algorithm (used to identify situations such as excessive environmental parameters, data tampering, and abnormal labels): Formula for detecting abnormal environmental parameters: Dynamic threshold calculation: ; Anomaly detection: If If so, it is determined that the environmental parameters are abnormal; in, For the first Time of the first Dynamic threshold of each environmental parameter ( (corresponding to temperature, humidity, and vibration respectively). For the first Preset baseline thresholds for each parameter, This is the threshold adjustment coefficient (set to 0.8 in this invention). The standard deviation of the i-th parameter over the past 30 minutes. These are the minimum and maximum values ​​of the dynamic threshold, respectively. For the current moment, the first The formula dynamically adjusts the threshold by combining the standard deviation of historical data, thus avoiding false alarms and missed alarms caused by fixed thresholds.

[0037] Data tampering detection formula: Data integrity verification: ,like If so, it is determined to be data tampering; in, The hash value of the current data. For the currently collected data, The current encryption key. The original hash value before data transmission (generated by the edge computing gateway and synchronized to the cloud) is used to quickly identify data tampering by comparing the hash values.

[0038] Tag anomaly detection formula: Signal strength anomaly detection: And duration If so, it is determined that the tag is abnormal (signal loss or forgery); in, This represents the signal strength value of the current tag. The preset minimum signal strength threshold (set to -85dBm in this invention) is used. The duration during which the signal strength is below a threshold. The preset anomaly detection time is 30 seconds in this invention.

[0039] Specifically, after the cloud management platform receives and decrypts the encrypted data uploaded by the edge computing gateway, the algorithm processing unit 402 calls a prediction algorithm combining improved K-medoids and LSTM to predict changes in environmental parameters within the next 30 minutes based on historical environmental data, current data, and road condition information of the transportation route, thereby enabling advance prediction of transportation risks. At the same time, it calls an adaptive anomaly detection algorithm to detect the current environmental parameters, data integrity, and tag signal strength in real time. If an anomaly is detected, the anomaly warning unit 403 automatically issues a warning signal according to the anomaly level (general, severe, emergency), notifies the corresponding responsible parties through SMS, APP push, etc., and generates anomaly handling suggestions. The trajectory display unit updates the transportation trajectory and environmental parameter change curves in real time for all authorized parties to view.

[0040] The multi-entity permission management module 5 uses a role-based access control mechanism (RBAC) combined with cross-chain integration technology to assign different access permissions to different entities. It adopts a ring signature scheme based on the lattice assumption to achieve cross-chain data sharing and hierarchical control of sensitive data. Different entities include, but are not limited to, logistics companies, regulatory authorities, consignees, and shippers. The multi-entity permission management module 5 applies the RBAC permission allocation algorithm, working in conjunction with the optimized ring signature algorithm of the edge computing gateway module. The permission allocation formula and its application are as follows: ,in, For the first The set of permissions for each entity. For the first Does the entity possess the [number]? Types of roles (1 indicates ownership, 0 indicates non-ownership). For the first The set of permissions corresponding to each role (e.g., the shipper role corresponds to full-process data access permissions, and the logistics company role corresponds to transportation trajectory and environmental parameter access permissions). This represents the total number of characters.

[0041] (2) Permission verification formula: When the first Individual entity requests access to data At that time, check If the condition is met, access is allowed, and the access request is signed using an optimized ring signature algorithm to ensure the security of the access process; if the condition is not met, access is denied, thus achieving hierarchical control of sensitive data.

[0042] Specifically, the multi-entity permission management module 5 assigns corresponding permissions to each entity based on a preset list of roles and permissions using a permission allocation formula. When entities such as logistics companies, regulatory authorities, and consignees request access to data, their access permissions are verified using a permission verification formula. At the same time, a ring signature algorithm is used to achieve cross-chain data sharing, ensuring that different entities can only access data within their own permission scope, thus preventing unauthorized access and leakage of sensitive data.

[0043] Example 2 Please see Figure 2 A secure cold chain transportation tracking method based on electronic tags includes the following steps: S1: System initialization and tag activation. The shipper enters basic information about the cold chain goods (goods name, specifications, preset environmental thresholds, transportation route, and multi-entity permission information) through module 4 of the cloud management platform, generating a unique goods identifier (ID). Based on the goods identifier and transportation node information, module 4 of the cloud management platform generates an initial encryption key. (Using the dynamic key generation formula, take) Preset initial key, The initial key, permission information, and preset environmental threshold are written into the encrypted electronic tag module 1 through the intelligent reader / writer module 2 to complete the tag activation. The encrypted electronic tag module 1 adopts a low-temperature resistant flexible packaging and anti-metal design. After activation, it enters a low-power standby state and waits for the collection command. S2: Dynamic encrypted data acquisition. The encrypted electronic tag module 1 automatically adjusts its working mode according to the transportation stage (warehousing, transportation, transshipment). Environmental sensors collect environmental parameters (temperature, humidity, vibration) according to the optimized dynamic acquisition algorithm, and the positioning module collects real-time location information. After acquisition, the encrypted electronic tag module 1 uses a dynamic key encryption mechanism (through a dynamic key generation formula). Generate the current node key The collected data is encrypted locally, and the dynamic key is automatically updated according to changes in the transportation node (such as from the warehouse node to the transportation vehicle) to avoid data security risks caused by key leakage; the encrypted data is sent to the intelligent reader module 2 via wireless communication.

[0044] The dynamic acquisition algorithm is an innovative and optimized algorithm. Its core is to dynamically adjust the acquisition frequency according to the fluctuation range of environmental parameters, and balance energy consumption by combining the remaining power of the tag. The specific formula and application are as follows: Formula for adjusting sampling frequency: , in, This is the current sampling frequency. The minimum sampling frequency is set at 10 minutes per sampling in this invention. This is the highest sampling frequency (set to 1 minute / time in this invention). The fluctuation range of the current environmental parameters ( (i.e., the difference between the maximum and minimum values ​​of the parameter within the current acquisition period). The maximum fluctuation range is preset (set according to the type of cold chain goods, such as setting the maximum temperature fluctuation range of vaccines to 2℃).

[0045] Energy balance adjustment: When the tag has remaining power At that time, the sampling frequency was adjusted to Appropriately reduce the sampling frequency and extend the battery life; when At that time, the sampling frequency is maintained This ensures the integrity of the data collection.

[0046] Application logic: When environmental parameters are within a preset threshold range and the fluctuation is small... , near Reduce tag energy consumption; when environmental parameters are close to the preset threshold or fluctuate significantly ( ), near To ensure that key environmental changes are captured; S3: Multi-tag collaborative reading and data preprocessing. The intelligent reader module 2 uses an optimized dynamic frame time slot algorithm and dual-band switching technology to achieve batch reading and authentication of multiple tags. After decryption, the data is uploaded to the edge computing gateway module 3. The edge computing gateway module 3 completes the data and filters noise, based on the number of tags in the current area. Adjustment formula via frame time slot Dynamically adjusting the signal transmission time window, combined with dual-band switching technology to avoid local frequency band interference, and using the collision probability formula Verify the adjustment effect to achieve accurate batch reading of multiple tags and reduce the missed reading rate; after reading encrypted data, the intelligent reader module uses a two-way authentication formula. (Comparison) Confirms the tag's legitimacy. Once confirmed, the data is decrypted using the corresponding key, and the decrypted data is uploaded to the edge computing gateway module. The edge computing gateway module preprocesses the uploaded data, using a linear interpolation data completion formula. Repairing short-term data gaps using an adaptive noise filtering formula The noise data collected by the filter sensor is used to complete and purify the data, resulting in standardized data. S4: Data Fusion and Encrypted Transmission. Edge computing gateway module 3 uses an improved data fusion algorithm to fuse standardized data. After encryption using an optimized ring signature algorithm, the data is uploaded to cloud management platform module 4 and temporarily stored locally. The improved data fusion algorithm in S4 introduces environmental parameter weight factors on the basis of traditional clustering algorithms. It allocates weights according to the degree of influence of different environmental parameters on the quality of cold chain goods, thereby improving the accuracy of data fusion. An innovative, improved K-medoids data fusion algorithm is employed to fuse standardized data collected from multiple labels and sensors in both spatial and temporal dimensions. The specific process is as follows: First, cluster centers are initialized, and then... (The sentence is incomplete and requires more context to translate accurately.) Calculate the distance from each data point to the cluster center, assign the data points to the corresponding clusters, and apply the cost function. Iterative updates of cluster centers, ultimately achieved through a fusion formula. The fused core data effectively suppresses the influence of noise and outliers, reducing data redundancy. Furthermore, by incorporating time-series correlation, the fused data is smoothed to improve accuracy. After fusion, the edge computing gateway module 3 employs an optimized ring signature algorithm, generated using the signature formula... The data is encrypted and uploaded to the cloud management platform module 4 via 5G / BeiDou dual-mode communication, while also achieving local temporary storage to avoid data loss due to network interruption; S5: Cloud-based algorithm processing and anomaly warning. After decrypting the data, the cloud management platform module 4 predicts changes in environmental parameters using a prediction algorithm and identifies anomalies using an adaptive anomaly detection algorithm. Based on the anomaly level, it issues warnings and pushes handling suggestions. The prediction algorithm in S5 combines historical environmental data, current data, and transportation road condition information to achieve multi-step prediction of environmental parameters. The adaptive anomaly detection algorithm combines preset thresholds and data change trends to avoid false alarms and missed alarms. It can identify situations such as environmental parameters exceeding limits, data tampering, and abnormal tags. The cloud management platform module 4 receives encrypted data uploaded by the edge computing gateway module 3. After decryption using the corresponding key, the algorithm processing unit 402 further processes the data: it employs a prediction algorithm combining improved K-medoids and LSTM, first preprocessing historical data using the improved K-medoids algorithm, and then updating the data using the LSTM cell state formula (…). (etc.) and output layer prediction formula Based on historical environmental data, current environmental data, and transportation route road condition information, multi-step predictions are made on changes in environmental parameters over a future period, using a loss function. Correcting prediction errors enables early prediction of transportation risks; an adaptive anomaly detection algorithm is employed, using a dynamic threshold formula. Determine if environmental parameters are abnormal using the data integrity verification formula. To determine if data has been tampered with, use a formula to detect abnormal signal strength. The system determines whether a tag is abnormal by combining preset environmental thresholds and data change trends to accurately identify various anomalies and avoid false alarms and missed alarms.

[0047] When an anomaly is detected, the anomaly warning unit 403 automatically issues an early warning signal according to the anomaly level (general, severe, emergency) and notifies the corresponding responsible parties (logistics personnel, regulatory departments, consignees) via SMS, APP push, etc., while generating anomaly handling suggestions; the trajectory display unit 404 updates the transportation trajectory of cold chain goods and environmental parameter change curves in real time for all authorized parties to view. S6: Multi-entity access control and data traceability. The multi-entity access management module 5 assigns access permissions to different entities based on the access list, realizing hierarchical control of sensitive data and cross-chain data sharing. After transportation, the entire process data is encrypted and archived, supporting data traceability. The encrypted electronic tag module 1 can be reused after being reset. The multi-entity access control in S6 adopts a role-based access control mechanism, combined with a ring signature scheme based on the lattice assumption, to achieve quantum-resistant security for cross-chain data sharing, ensuring that different entities can only access data within their own access scope. The encrypted electronic tag module 1 achieves reuse through key updates, reducing transportation costs. The multi-entity permission management module 5 is based on the RBAC mechanism and combines cross-chain integration technology. According to the preset permission list, it allocates permissions through a permission formula. Different access permissions are assigned to different entities: shippers can view the entire process information of the goods, logistics companies can view the transportation trajectory and environmental parameters, regulatory authorities can view compliance data, and recipients can view the arrival time of the goods and terminal environmental parameters; each entity must pass the permission verification formula when accessing data. Verification is performed, and a ring signature scheme based on the lattice assumption (through signature generation and verification formulas) is adopted to achieve cross-chain data sharing, ensuring that sensitive data is not accessed or leaked without authorization.

[0048] After the cold chain transportation is completed, the cloud management platform module 4 encrypts and archives the data of the entire process to form a complete transportation traceability file, which supports authorized entities to trace the data according to the item identification, so as to realize the traceability and accountability of the transportation process; the encrypted electronic tag module 1 performs a reset process, and the key can be reused after being updated by the dynamic key generation formula, which reduces transportation costs; S7: System closed-loop optimization, cloud management platform module (4) regularly analyzes the data of the whole process, automatically optimizes the parameters of each algorithm and the energy consumption control strategy of the tag, and improves the system performance; The specific strategy is as follows: Module 4 of the cloud management platform regularly performs statistical analysis on the entire process data (collection frequency, anomaly detection accuracy, tag energy consumption, and data transmission efficiency), and automatically optimizes the parameters of each algorithm based on the analysis results: adjusting the dynamic acquisition algorithm... Optimize the number of clusters in the improved K-medoids algorithm With weighting factors Update the weight matrix and bias terms of the LSTM prediction model, and adjust the adaptive anomaly detection algorithm. and At the same time, the energy consumption control strategy of the tags is adjusted to achieve continuous optimization of system performance, improve tracking accuracy and confidentiality, and reduce operating costs.

Claims

1. A secure cold chain transportation tracking system based on electronic tags, characterized in that, The system includes an encrypted electronic tag module (1), an intelligent reader module (2), an edge computing gateway module (3), a cloud management platform module (4), and a multi-subject permission management module (5). The encrypted electronic tag module (1) has a built-in encryption chip, environmental sensor and positioning module, which is used to collect environmental parameters and location information of cold chain goods and perform local encryption. The encryption key is automatically updated according to the change of transportation node, and the encrypted electronic tag module (1) and the intelligent reader module (2) have two-way identity authentication. The intelligent reader module (2) is deployed at nodes related to cold chain transportation. It has the ability to read multiple tags in batches. It adopts an optimized dynamic frame time slot algorithm and dual frequency band switching technology to receive encrypted data sent by the encrypted electronic tag module (1) and decrypt it, then upload it to the edge computing gateway module (3). At the same time, it encrypts the control commands issued by the cloud management platform module (4) and sends them to the encrypted electronic tag module (1). The edge computing gateway module (3) is deployed on cold chain transport vehicles or transit nodes to preprocess and temporarily store the data uploaded by the smart reader module (2). It uses an improved data fusion algorithm to fuse the data collected by multiple tags and multiple sensors, and uploads the data to the cloud management platform module (4) after encrypting the data through an optimized ring signature algorithm.

2. The secure cold chain transportation tracking system based on electronic tags according to claim 1, characterized in that: The cloud management platform module (4) includes a data storage unit (401), an algorithm processing unit (402), an anomaly warning unit (403), and a trajectory display unit (404), which are used for encrypted data storage, algorithm processing, anomaly warning and trajectory display, and can automatically optimize the parameters of each algorithm.

3. The secure cold chain transportation tracking system based on electronic tags according to claim 2, characterized in that: The multi-subject permission management module (5) uses a role-based access control mechanism, combined with cross-chain integration technology, to assign different access permissions to different subjects. It adopts a ring signature scheme based on the lattice assumption to realize cross-chain data sharing and hierarchical management of sensitive data.

4. The secure cold chain transportation tracking system based on electronic tags according to claim 3, characterized in that: The environmental sensors include a temperature sensor, a humidity sensor, and a vibration sensor, which are used to collect temperature, humidity, and vibration data during cold chain transportation. The positioning module uses a Beidou positioning module to support real-time location acquisition.

5. The secure cold chain transportation tracking system based on electronic tags according to claim 1, characterized in that: The preprocessing of the edge computing gateway module (3) includes data completion and adaptive noise filtering using linear interpolation. The data fusion algorithm is an improved clustering algorithm to suppress the influence of noise and isolated points. It supports 5G / BeiDou dual-mode communication and has local temporary storage function.

6. The secure cold chain transportation tracking system based on electronic tags according to claim 2, characterized in that: The algorithm processing unit (402) of the cloud management platform module (4) integrates a prediction algorithm combining improved clustering and long short-term memory network, as well as an adaptive anomaly detection algorithm; the anomaly warning unit (403) supports three-level warnings and pushes warning information and processing suggestions; the data storage unit (401) adopts encrypted storage technology and supports data traceability; the trajectory display unit (404) displays the transportation trajectory of cold chain goods and the environmental parameter change curve in real time.

7. A secure cold chain transportation tracking method based on electronic tags, characterized in that, The system implementation based on any one of claims 1-5 includes the following steps: S1: System initialization and tag activation. The shipper enters the basic information of cold chain items through the cloud management platform module (4), generates a unique item identifier, generates an initial encryption key and permission list in the cloud, and writes it into the encrypted electronic tag module (1) to complete the activation. S2: Dynamic encrypted data acquisition, encrypted electronic tag module (1) adjusts the acquisition frequency through dynamic acquisition algorithm according to the transportation stage and environmental fluctuation range, acquires environmental parameters and location information, and sends them to the intelligent reader module (2) after being encrypted with dynamic key. S3: Multi-tag collaborative reading and data preprocessing. The intelligent reader module (2) adopts the optimized dynamic frame time slot algorithm and dual frequency band switching technology to realize multi-tag batch reading and identity authentication. After decryption, the data is uploaded to the edge computing gateway module (3). The edge computing gateway module (3) completes the data and filters noise. S4: Data fusion and encrypted transmission. The edge computing gateway module (3) uses an improved data fusion algorithm to fuse standardized data, encrypts it through an optimized ring signature algorithm, and then uploads it to the cloud management platform module (4) for local temporary storage. S5: Cloud-based algorithm processing and anomaly warning. After the cloud management platform module (4) decrypts the data, it predicts changes in environmental parameters through a prediction algorithm, identifies anomalies through an adaptive anomaly detection algorithm, issues warnings based on the anomaly level, and pushes processing suggestions. S6: Multi-subject permission control and data traceability, multi-subject permission management module (5) assigns access permissions to different subjects according to the permission list, realizes hierarchical control of sensitive data and cross-chain data sharing, encrypts and archives the entire process data after transportation, supports data traceability, and the encrypted electronic tag module (1) can be reused after reset; S7: System closed-loop optimization, cloud management platform module (4) regularly analyzes the data of the whole process, automatically optimizes the parameters of each algorithm and the energy consumption control strategy of the tag, and improves the system performance.

8. The secure cold chain transportation tracking method based on electronic tags according to claim 1, characterized in that: The dynamic acquisition algorithm in S2 dynamically adjusts the acquisition frequency based on the fluctuation range of environmental parameters and the remaining power of the tag: when the environmental parameters are stable, the acquisition frequency is reduced to balance data integrity and tag power consumption.

9. The secure cold chain transportation tracking system based on electronic tags according to claim 1, characterized in that: The improved data fusion algorithm in S4 introduces environmental parameter weight factors on the basis of traditional clustering algorithms, and allocates weights according to the degree of influence of different environmental parameters on the quality of cold chain goods, thereby improving the accuracy of data fusion.

10. The secure cold chain transportation tracking system and method based on electronic tags according to claim 1, characterized in that: The prediction algorithm in S5 combines historical environmental data, current data, and transportation road condition information to achieve multi-step prediction of environmental parameters; The adaptive anomaly detection algorithm combines preset thresholds with data change trends to avoid false alarms and missed alarms, and can identify situations such as environmental parameters exceeding the standard, data tampering, and abnormal tags. The multi-subject access control in S6 adopts a role-based access control mechanism, combined with a ring signature scheme based on the lattice hypothesis, to achieve quantum-resistant security for cross-chain data sharing, ensuring that different subjects can only access data within their own permission scope; the encrypted electronic tag module (1) achieves reuse through key updates, reducing transportation costs.

Images