End-to-end traceability monitoring method and system for temperature-controlled goods

Abstract

The application provides an end-to-end traceability monitoring method and system for supporting temperature-controlled goods, which comprises the following steps: based on the Internet of Things label configured on the distribution box of the temperature-controlled goods, real-time collection of geographical position data and temperature data of the temperature-controlled goods in the whole distribution process; real-time uploading of the collected geographical position data and temperature data to a block chain platform, storage of the data on the block chain through block chain technology, and formation of a credible traceability record; generation of an initial key matrix based on the label information of the Internet of Things label and the type information of the distributed goods; deformation of the initial key matrix based on the distribution order information of the temperature-controlled goods, obtaining of a target key matrix; generation of a target key based on the target key matrix, encryption of the geographical position data and the temperature data, and association to generate a traceability two-dimensional code. In the application, the security of the key generation mechanism is improved, and the uniqueness of the traceability information is ensured.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for end-to-end traceability monitoring of temperature-controlled goods. Background Technology

[0002] With the increasing market demand for temperature-controlled goods such as pharmaceuticals and fresh produce, ensuring product quality in the last-mile delivery process has become a core concern for the industry. Temperature-controlled goods have strict requirements for the delivery environment temperature. If temperatures exceed the limit or the delivery chain breaks during transit, it can not only lead to product spoilage and failure but also potentially cause serious consequences such as food safety risks and reduced efficacy of medical supplies, resulting in economic losses for both businesses and consumers. Furthermore, last-mile delivery involves multiple distribution points, requiring a traceable and reliable record of the product's geographical location and temperature changes.

[0003] To achieve delivery monitoring and traceability of temperature-controlled goods, existing technologies primarily involve configuring temperature sensors and positioning modules on delivery boxes to collect temperature and geographic location data, uploading this data to a management platform, and generating traceability QR codes for users to query. However, these existing technologies still have significant technical shortcomings and fail to meet the industry's high requirements for the security, reliability, and monitoring accuracy of traceability data.

[0004] On the one hand, existing traceability data encryption methods are relatively simple, often using static keys to encrypt collected data or directly associating unencrypted data with traceability QR codes. Because static keys lack dynamic correlation, they are easily cracked or reused, leading to the risk of traceability data being tampered with or forged. Furthermore, traceability QR codes are mostly fixed information carriers, only associated with basic location and temperature data, without deep binding to core identifiers such as products, orders, and IoT tags. This makes it difficult to ensure the uniqueness and correspondence of traceability information, and fails to fundamentally solve the data trust issue.

[0005] On the other hand, in existing technologies, key generation often relies on single-dimensional information (such as order number or tag ID alone). The key generation logic is simple and lacks a dynamic transformation mechanism, which makes it easy for keys for different orders and different products to be duplicated or follow a pattern, further reducing the security of data encryption.

[0006] Therefore, how to design a last-mile delivery monitoring method with dynamic correlation characteristics, a highly secure key generation mechanism, and the ability to achieve reliable traceability of encrypted data has become a pressing technical problem for the current temperature-controlled goods delivery industry. Summary of the Invention

[0007] The main objective of this invention is to provide a method and system for end-to-end traceability monitoring of temperature-controlled goods, aiming to overcome the shortcomings of current key generation mechanisms in terms of security and difficulty in ensuring the uniqueness of traceability information.

[0008] To achieve the above objectives, the present invention provides a method for end-to-end traceability and monitoring of temperature-controlled goods during last-mile delivery, comprising the following steps:

[0009] Based on the IoT tags configured on the delivery boxes of temperature-controlled goods, the geographical location data and temperature data of the temperature-controlled goods are collected in real time throughout the delivery process;

[0010] The collected geographic location data and temperature data are uploaded to the blockchain platform in real time, and the data is stored on the chain through blockchain technology to form a reliable traceability record;

[0011] An initial key matrix is ​​generated based on the tag information of IoT tags and the type of goods being delivered. The initial key matrix is ​​then transformed based on the delivery order information of temperature-controlled goods to obtain the target key matrix.

[0012] A target key is generated based on the target key matrix, and after encrypting the geographic location data and temperature data, a traceability QR code is generated.

[0013] Furthermore, the IoT tag integrates a GPS module and a temperature sensor; the IoT tag uploads the collected geographic location data and temperature data to the blockchain platform in real time via an NB-IoT network or a 5G network.

[0014] Furthermore, after collecting the geographic location data and temperature data of temperature-controlled goods in real time throughout the delivery process, it also includes:

[0015] When the collected temperature data exceeds the preset temperature threshold, an early warning message is sent to the deliveryman's terminal, the dispatch center platform, and the customer's terminal.

[0016] Furthermore, by scanning the traceability QR code, the customer terminal can query the geographical location trajectory and temperature change trajectory of the entire last-mile delivery of temperature-controlled goods.

[0017] Furthermore, an initial key matrix is ​​generated based on the tag information of IoT tags and the type of goods being delivered. Based on the delivery order information of temperature-controlled goods, the initial key matrix is ​​transformed to obtain a target key matrix, including:

[0018] Extract the tag information of the Internet of Things (IoT) tag, which is a combination of the IoT tag's unique hardware ID and the sensor's real-time calibration coefficient. Construct a basic feature matrix based on the tag information.

[0019] Extract the type feature information of the delivered goods, including the product temperature control priority and the category-specific check code. Convert the type feature information into binary numbers and perform an XOR operation to generate a fused feature sequence, which is then split into a dynamic weight matrix.

[0020] A data sequence is generated based on the product temperature control priority. The data sequence is then multiplied by the corresponding elements of the basic feature matrix and the dynamic weight matrix to obtain the initial key matrix.

[0021] Obtain delivery order information for temperature-controlled goods and construct a digital matrix of deformation factors;

[0022] Based on the element values ​​of the transformation factor digital matrix, the elements of the initial key matrix are transformed to obtain an intermediate matrix; based on the matrix properties of the transformation factor digital matrix, the number of rows and columns of the intermediate matrix are transformed and adjusted to obtain the target key matrix.

[0023] Furthermore, based on the element values ​​of the transformation factor digital matrix, the elements of the initial key matrix are transformed to obtain an intermediate matrix, including:

[0024] Construct a curve based on the element values ​​of the deformation factor numerical matrix;

[0025] The curve is added to the initial key matrix according to a preset rule. Based on the positional relationship between each matrix element of the initial key matrix and the curve, each matrix element is offset to obtain the intermediate matrix.

[0026] Furthermore, an initial key matrix is ​​generated based on the tag information of the IoT tags and the type of goods being delivered, including:

[0027] Extract tag information from IoT tags, and construct a basic feature matrix based on the tag information;

[0028] Extract the type feature information of the delivered goods, convert the type feature information into binary numbers, split it into multiple sets of number combinations according to rules, and add them to each node of the preset undirected graph to obtain a digital undirected graph;

[0029] According to preset rules, the basic feature matrix is ​​superimposed on the digital undirected graph; wherein the center of the basic feature matrix and the center of the digital undirected graph completely coincide.

[0030] For each node in the digital undirected graph, the number on the node is combined with the matrix elements of the basic feature matrix within a preset range to obtain the corresponding subkey; each subkey is added to the matrix in sequence to construct the initial key matrix.

[0031] Furthermore, based on the delivery order information of temperature-controlled goods, the initial key matrix is ​​transformed to obtain the target key matrix, including:

[0032] Obtain the delivery order information for temperature-controlled goods, extract the numeric characters, and combine them into multiple numbers; based on each number, simulate and generate a closed polygonal shape.

[0033] The polygonal closed shape is added to the initial key matrix according to a preset rule. Based on the length of the overlapping characters between each subkey of the initial key matrix and the polygonal closed shape, the positions of each subkey are reordered to obtain the intermediate matrix.

[0034] Based on the graphic properties of the polygonal closed graph, the number of rows and columns of the intermediate matrix are deformed and adjusted to obtain the target key matrix.

[0035] This invention also provides a last-mile traceability and monitoring system for temperature-controlled goods, comprising:

[0036] The data acquisition module is used to collect real-time geographic location data and temperature data of temperature-controlled goods from IoT tags configured on the delivery boxes of temperature-controlled goods throughout the delivery process.

[0037] The upload module is used to upload the collected geographic location data and temperature data to the blockchain platform in real time, so as to realize the on-chain storage of data through blockchain technology and form a reliable traceability record;

[0038] The generation module is used to generate an initial key matrix based on the tag information of IoT tags and the type information of delivered goods, and to transform the initial key matrix based on the delivery order information of temperature-controlled goods to obtain a target key matrix;

[0039] The traceability module is used to generate a target key based on the target key matrix, and after encrypting the geographical location data and temperature data, it generates a traceability QR code.

[0040] This invention provides a method and system for end-to-end traceability monitoring of temperature-controlled goods, comprising: real-time collection of geographic location and temperature data of the temperature-controlled goods throughout the delivery process based on IoT tags configured on the delivery boxes; real-time uploading of the collected geographic location and temperature data to a blockchain platform, achieving on-chain data storage through blockchain technology to form a trusted traceability record; generating an initial key matrix based on the tag information of the IoT tags and the type of goods being delivered; transforming the initial key matrix based on the delivery order information of the temperature-controlled goods to obtain a target key matrix; generating a target key based on the target key matrix, and then encrypting the geographic location and temperature data to generate a traceability QR code. In this invention, the generation of an initial key matrix using the tag information of the IoT tags and the type of goods being delivered, the transformation of the initial key matrix based on the delivery order information of the temperature-controlled goods to obtain the target key matrix, and the generation of the target key based on the target key matrix enhances the security of the key generation mechanism. Simultaneously, the traceability information is deeply associated with the goods, orders, and IoT tags, ensuring the uniqueness of the traceability information. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the steps of a method for end-to-end traceability monitoring of temperature-controlled goods in one embodiment of the present invention;

[0042] Figure 2 This is a structural block diagram of a last-mile delivery traceability and monitoring system for temperature-controlled goods, according to one embodiment of the present invention.

[0043] Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0044] The implementation, functional features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] It is particularly important to note that all technical steps, algorithm applications, and parameter settings in the technical solution of this application have clear technical objectives and application value. They do not utilize complex steps and algorithmic formulas to achieve simple functions. To provide detailed explanations of each step and avoid ambiguity, some conventional algorithms are used for illustration. However, this does not mean that the algorithms and technical features listed herein are the only way to implement the technical solution of this application, nor is it intended to limit the scope of protection of this application. This application is not a combination or stacking of the listed algorithms and technical features; its essence is to exemplify the implementation methods of this application to fully explain it. It does not pursue formal complexity by adding meaningless technical steps, nor does it involve the accumulation of technologies divorced from practical needs; it conforms to the conventional logic of technical improvement and design.

[0047] Reference Figure 1 One embodiment of the present invention provides a method for end-to-end traceability and monitoring of temperature-controlled goods, comprising the following steps:

[0048] Step S1: Based on the IoT tags configured on the delivery boxes of temperature-controlled goods, collect the geographical location data and temperature data of the temperature-controlled goods in real time throughout the delivery process;

[0049] Step S2: The collected geographic location data and temperature data are uploaded to the blockchain platform in real time. The data is stored on the chain through blockchain technology to form a reliable traceability record.

[0050] Step S3: Generate an initial key matrix based on the tag information of IoT tags and the type of goods being delivered; transform the initial key matrix based on the delivery order information of temperature-controlled goods to obtain the target key matrix.

[0051] Step S4: Generate a target key based on the target key matrix, encrypt the geographic location data and temperature data, and then generate a traceability QR code.

[0052] In this embodiment, as described in step S1 above, the core objective is to acquire key environmental and location data related to the quality of temperature-controlled goods throughout the entire delivery process. Specifically, an IoT tag is pre-installed on the delivery box carrying the temperature-controlled goods. This IoT tag integrates a GPS positioning module and a high-precision temperature sensor, and features low power consumption, making it suitable for long-term operation in last-mile delivery. Throughout the entire delivery process from when the temperature-controlled goods leave the warehouse to when they are delivered to the customer, the GPS positioning module of the IoT tag captures the geographical coordinates of the delivery box in real time at a preset sampling frequency (e.g., every 30 seconds, which can be adjusted according to delivery distance and monitoring accuracy requirements), forming continuous geographical location data. Simultaneously, the temperature sensor penetrates deep into the delivery box to directly collect real-time temperature data of the surrounding environment of the goods, ensuring consistency between the temperature data and the actual environment in which the goods are located. Through this design, synchronous and continuous acquisition of location-temperature dual-dimensional data of temperature-controlled goods is achieved, providing raw data support for subsequent traceability and quality monitoring.

[0053] As described in step S2 above, the aim is to solve the problems of easy tampering and difficulty in verification of traditional data storage using blockchain technology. IoT tags collect geographic location and temperature data and transmit it in real-time to a pre-set blockchain platform via an NB-IoT network or a 5G network (adaptively selected based on network coverage in the delivery scenario). During the data upload process, the blockchain platform performs format verification and integrity verification on the received data to ensure that the data is not lost or tampered with during transmission. After successful data verification, the blockchain platform, following the core logic of distributed ledger technology, records the batch of data into the ledgers of multiple nodes and generates a unique hash value for each data entry using an encryption algorithm, while simultaneously associating it with the hash information of the previous data block, forming a chain-like storage structure. Due to the decentralized nature of blockchain and its hash value association mechanism, any tampering with the data by any node will cause a change in the corresponding hash value, and it is impossible to synchronously modify the ledger records of all nodes. This achieves the immutability and traceability of the data, ultimately forming a complete and reliable traceability record, providing authoritative evidence for subsequent data verification and dispute resolution.

[0054] As described in step S3 above, a unique and dynamic target key matrix is ​​generated through multi-dimensional information fusion and dynamic deformation mechanisms. First, the core tag information of the IoT tags is extracted. This information includes the tag's unique hardware identifier (used to distinguish IoT devices in different delivery boxes) and sensor calibration parameters (basic information to ensure data collection accuracy). At the same time, the type information of the delivered goods is extracted, including the product category (such as fresh fruits and vegetables, pharmaceutical preparations, etc.), temperature control level (such as refrigerated, frozen, constant temperature, etc.), and freshness sensitivity (reflecting the product's tolerance to temperature changes). After digitizing the above tag information and product type information, an initial key matrix is ​​generated according to preset matrix construction rules (such as dimensional splitting after assigning weights based on information importance). This matrix has the basic characteristics of being bound to specific products and specific IoT devices. Subsequently, the delivery order information for temperature-controlled goods is extracted, including the order's unique identifier (distinguishing different delivery orders), delivery start timestamp, estimated delivery timestamp, and delivery route planning nodes. Using this order information as the trigger condition and parameters for matrix transformation, a pre-defined matrix transformation algorithm (such as a chaotic permutation algorithm based on order timestamps or a dimension adjustment algorithm based on order nodes) is employed to perform row / column permutations, element weight adjustments, and dimension optimization on the initial key matrix, ultimately yielding the target key matrix. Because the initial key matrix is ​​associated with fixed information about the goods and devices, while the transformation process is linked to the dynamic information of the orders, the target key matrix corresponding to each delivery order is unique and cannot be derived from a single piece of information, greatly enhancing the security of subsequent data encryption.

[0055] As described in step S4 above, this is a crucial step connecting data encryption and user traceability queries, binding encrypted data to the traceability entry point. First, based on the obtained target key matrix, a target key is generated using a key extraction algorithm (such as extracting diagonal elements of the matrix or concatenating matrix elements in a preset order). This target key corresponds one-to-one with the target key matrix and possesses the characteristic of deep binding with specific products, orders, and IoT devices. Subsequently, the collected geographic location data and temperature data are encrypted using this target key to ensure that the data is not illegally obtained or tampered with during subsequent queries and transmissions. Finally, the encrypted geographic location data and encrypted temperature data are associated and integrated with key index information (IoT tag information, delivered product type information, and delivery order information, etc.) to generate a unique traceability QR code according to the QR code generation rules. This traceability QR code not only serves as the entry point for users to query traceability information but also implicitly contains the key index information required for data decryption, ensuring that only users who scan the QR code through legitimate means can complete data decryption and traceability information queries in subsequent steps, achieving a closed-loop design of encryption-traceability-decryption.

[0056] In one embodiment, the IoT tag integrates a GPS module and a temperature sensor; the IoT tag uploads the collected geographic location data and temperature data to a blockchain platform in real time via an NB-IoT network or a 5G network.

[0057] In one embodiment, after real-time collection of the geographic location data and temperature data of temperature-controlled goods throughout the delivery process, the method further includes:

[0058] When the collected temperature data exceeds the preset temperature threshold, an early warning message is sent to the deliveryman's terminal, the dispatch center platform, and the customer's terminal.

[0059] In one embodiment, the customer terminal can scan the traceability QR code to query the geographical location trajectory and temperature change trajectory of the entire last-mile delivery of temperature-controlled goods.

[0060] In this embodiment, after the customer terminal scans the traceability QR code, it will automatically match the key index information hidden in the QR code or the encrypted traceability data of the blockchain platform, generate the target key to complete the data decryption, and intuitively present the continuous geographical location trajectory (including key node coordinates) and real-time temperature change trajectory (including temperature fluctuation curve and whether it exceeds the limit) of the temperature-controlled product from leaving the warehouse to delivery to the customer terminal, so as to realize the rapid query and visualization of traceability information.

[0061] In one embodiment, an initial key matrix is ​​generated based on tag information from IoT tags and information about the type of goods being delivered. Then, based on delivery order information for temperature-controlled goods, the initial key matrix is ​​transformed to obtain a target key matrix, including:

[0062] Extract the tag information of the Internet of Things (IoT) tag, which is a combination of the IoT tag's unique hardware ID and the sensor's real-time calibration coefficient. Construct a basic feature matrix based on the tag information.

[0063] Extract the type feature information of the delivered goods, including the product temperature control priority and the category-specific check code. Convert the type feature information into binary numbers and perform an XOR operation to generate a fused feature sequence, which is then split into a dynamic weight matrix.

[0064] A data sequence is generated based on the product temperature control priority. The data sequence is then multiplied by the corresponding elements of the basic feature matrix and the dynamic weight matrix to obtain the initial key matrix.

[0065] Obtain delivery order information for temperature-controlled goods and construct a digital matrix of deformation factors;

[0066] Based on the element values ​​of the transformation factor digital matrix, the elements of the initial key matrix are transformed to obtain an intermediate matrix; based on the matrix properties of the transformation factor digital matrix, the number of rows and columns of the intermediate matrix are transformed and adjusted to obtain the target key matrix.

[0067] In this embodiment, firstly, two types of core data are precisely extracted from the IoT tags configured in the delivery box: one is the unique hardware ID of the IoT tag (this ID is an unmodifiable identifier fixed at the factory, used to uniquely distinguish different IoT devices and avoid device confusion); the other is the real-time calibration coefficient of the sensor (this coefficient is dynamically generated by the self-calibration module built into the IoT tag according to the ambient temperature and device operating time, used to correct the acquisition error of the temperature sensor and ensure the accuracy of temperature data acquisition). The unique hardware ID and the real-time calibration coefficient of the sensor are concatenated according to a preset format to form combined data. Then, the combined data is Base64 decoded and converted into a binary sequence of fixed length (such as a 48-bit binary number, the length of which can be adjusted according to the number of bits in the hardware ID and the accuracy of the calibration coefficient). According to the matrix construction rules, the binary sequence is split into preset dimensions according to the row priority principle, with each matrix element corresponding to a fixed number of bits in the binary sequence, and finally a basic feature matrix is ​​constructed. Because this matrix is ​​bound to the unique hardware ID and the real-time calibration coefficient, it has the dual characteristics of being device-specific and dynamically calibration-related.

[0068] Next, two core feature information types of the delivered goods are extracted: first, the product temperature control priority (divided into 1-5 levels based on the product's tolerance to temperature fluctuations and preservation requirements, with level 1 being the highest priority for extremely temperature-sensitive products such as biological agents, and level 5 being the lowest priority for products with strong temperature resistance such as some fruits and vegetables); second, the category-specific check code (generated by industry-unified coding rules, used to uniquely identify the category to which the product belongs, such as a dedicated pharmaceutical code check code for pharmaceutical products and a fresh produce category check code for fresh produce). The product temperature control priority is converted into an 8-bit binary number, and the category-specific check code is converted into a 16-bit binary number. An XOR operation is performed on the two binary numbers (0 for the same bits, 1 for different bits) to eliminate redundant information and fuse the core features, generating a fixed-length 16-bit fused feature sequence. According to the preset matrix dimension requirements, the 16-bit fused feature sequence is split sequentially, and each split subsequence is converted into a decimal integer as a matrix element, ultimately obtaining a dynamic weight matrix. Because this matrix links product temperature control priority with a unique verification code, the subsequently generated keys have the ability to dynamically adjust to adapt to product characteristics.

[0069] Then, based on the extracted product temperature control priority and combined with preset generation rules, a data sequence with the same length as the total number of elements in the basic feature matrix and dynamic weight matrix is ​​generated (e.g., a 6x8 matrix with 48 elements results in a data sequence length of 48). This data sequence is then split according to the dimensions of the basic feature matrix, forming a sequence matrix with the same dimensions as the basic feature matrix and dynamic weight matrix. Following the principle of one-to-one matrix element correspondence, each element of the basic feature matrix and each element of the dynamic weight matrix are multiplied by the corresponding element of the sequence matrix to obtain intermediate results. All intermediate results are then modulo 256 processed (ensuring element values ​​are within the integer range of 0-255 to meet the requirements of subsequent encryption algorithms), ultimately yielding an initial key matrix with the same dimensions as the basic feature matrix (e.g., 6x8). This matrix, through triple data fusion, achieves deep binding between IoT devices, product types, and temperature control requirements, ensuring the uniqueness and specificity of the key from the source.

[0070] Then, based on the dynamic information of delivery orders, a transformation factor digital matrix with order-specific characteristics is constructed to provide order-dimensional parameter support for the dynamic transformation of the initial key matrix. Three core dynamic information types are extracted from temperature-controlled product delivery orders: first, the order's unique identifier (a platform-generated, non-repeatable order number used to uniquely distinguish different delivery orders); second, the delivery start timestamp (a precise time record of the product leaving the warehouse, accurate to the second); and third, the estimated delivery timestamp (an estimated delivery time generated based on delivery route planning, accurate to the second). The order's unique identifier is hashed, and the first 24 characters of the hash value are extracted and converted into a 32-bit binary number. The delivery start timestamp and the estimated delivery timestamp are also converted into 32-bit binary numbers. The difference between the two is calculated, and the absolute value is taken to obtain a 32-bit time difference binary number. These three types of 32-bit binary numbers are concatenated into a 96-bit binary sequence, split according to a preset matrix dimension, and each subsequence is converted into a decimal integer as a matrix element to construct the transformation factor digital matrix. Because this matrix is ​​associated with the order's unique identifier and dynamic time information, the transformation factor digital matrix corresponding to each order is unique.

[0071] Finally, by performing element-level transformation and dimension-level adjustment on the initial key matrix, a target key matrix with device-product-order triple binding characteristics is generated, maximizing key security. First, based on the element values ​​of the transformation factor digital matrix, transformation processing is performed on each element of the initial key matrix: for example, for each element in the initial key matrix, the value of the corresponding element in the transformation factor digital matrix is ​​extracted. If the value is odd, the corresponding element in the initial key matrix is ​​left-shifted by 3 bits; if the value is even, the corresponding element in the initial key matrix is ​​right-shifted by 2 bits. Dynamic adjustment of element values ​​is achieved through bitwise operations to obtain the intermediate matrix. Subsequently, based on the matrix properties of the transformation factor digital matrix (including the greatest common divisor of the number of rows and columns, the range of the average element value, etc.), dimensional transformation adjustment is performed on the intermediate matrix: if the greatest common divisor of the number of rows and columns of the transformation factor digital matrix is ​​2, and the average element value is in the range [0, 127], then the number of rows of the intermediate matrix is ​​halved and the number of columns is doubled (e.g., 6 rows and 8 columns are adjusted to 3 rows and 16 columns); if the greatest common divisor is 3, and the average element value is in the range [128, 255], then the number of rows of the intermediate matrix is ​​doubled and the number of columns is halved (e.g., 6 rows and 8 columns are adjusted to 12 rows and 4 columns). Dynamic changes in the matrix structure are achieved through dimensional adjustment. After the dual transformation processing of element transformation and dimensional adjustment, the target key matrix is ​​finally obtained. Because this matrix integrates the triple core information of device, product, and order, and has undergone dynamic transformation processing, it possesses extremely high uniqueness and anti-cracking capabilities, providing secure and reliable key support for subsequent data encryption.

[0072] In one embodiment, based on the element values ​​of the transformation factor digital matrix, the elements of the initial key matrix are transformed to obtain an intermediate matrix, including:

[0073] Construct a curve based on the element values ​​of the deformation factor numerical matrix;

[0074] The curve is added to the initial key matrix according to a preset rule. Based on the positional relationship between each matrix element of the initial key matrix and the curve, each matrix element is offset to obtain the intermediate matrix.

[0075] In this embodiment, the deformation factor digital matrix undergoes element extraction and sorting: all elements are extracted sequentially according to row priority, forming a one-dimensional discrete numerical sequence with a sequence length consistent with the total number of elements in the deformation factor digital matrix; simultaneously, a unique coordinate index value is assigned to each element, with the horizontal axis representing the element's position in the sequence and the vertical axis representing the element's specific numerical value, thus constructing a discrete data coordinate set. Subsequently, an interpolation fitting algorithm is used to perform curve fitting on this discrete data coordinate set, specifically cubic spline interpolation, using the coordinate index value as the horizontal axis and the element value as the vertical axis. Interpolation operations supplement the continuous data between discrete points, ultimately generating a smooth and continuous fitting curve. The shape of this curve is directly determined by the element value distribution characteristics of the deformation factor digital matrix. Since the deformation factor digital matrix is ​​associated with the unique order identifier and dynamic time information, the curve shape corresponding to different orders will show significant differences, thereby ensuring the order-specific characteristics of the curve.

[0076] Next, the fitted curve is embedded into the spatial coordinate system of the initial key matrix according to a preset mapping rule: a dedicated matrix spatial coordinate system is established using the number of rows of the initial key matrix as the ordinate dimension and the number of columns as the abscissa dimension; simultaneously, the generated fitted curve is scaled proportionally to this matrix spatial coordinate system, ensuring that the abscissa range of the curve matches the number of columns in the matrix, and the ordinate range matches the number of rows in the matrix, ensuring that the curve can completely cover the entire spatial region of the initial key matrix. Subsequently, the positional relationship between each element in the initial key matrix and the fitted curve is determined one by one: for any element in the matrix, the corresponding coordinate point of that element in the matrix spatial coordinate system is extracted, the vertical distance between that coordinate point and the fitted curve is calculated, and it is determined whether the coordinate point is above or below the curve. The element values ​​are dynamically adjusted according to preset offset rules: if the coordinate point is above the curve, the calculated vertical distance is used as the offset coefficient, and the element value is added to this offset coefficient. The offset coefficient is obtained by multiplying the vertical distance by a preset weight coefficient, which is set between 0.5 and 1.0 and can be flexibly adjusted according to the actual encryption strength requirements. If the coordinate point is below the curve, the vertical distance is also used as the offset coefficient, and the element value is subtracted from this offset coefficient. If the coordinate point falls exactly on the curve, the element value remains unchanged. After all elements have been offset adjusted, a modulo operation is performed on the adjusted element values ​​to ensure that the element values ​​are always within a preset reasonable integer range, meeting the format requirements of subsequent encryption algorithms, and finally obtaining the intermediate matrix. In this process, the offset of each element is dynamically determined by its positional relationship with the fitted curve, making the element distribution of the intermediate matrix have curve correlation and randomness, which greatly improves the anti-cracking capability of the key matrix.

[0077] In one embodiment, an initial key matrix is ​​generated based on the tag information of the IoT tag and the type information of the delivered goods, including:

[0078] Extract tag information from IoT tags, and construct a basic feature matrix based on the tag information;

[0079] Extract the type feature information of the delivered goods, convert the type feature information into binary numbers, split it into multiple sets of number combinations according to rules, and add them to each node of the preset undirected graph to obtain a digital undirected graph;

[0080] According to preset rules, the basic feature matrix is ​​superimposed on the digital undirected graph; wherein the center of the basic feature matrix and the center of the digital undirected graph completely coincide.

[0081] For each node in the digital undirected graph, the number on the node is combined with the matrix elements of the basic feature matrix within a preset range to obtain the corresponding subkey; each subkey is added to the matrix in sequence to construct the initial key matrix.

[0082] In this embodiment, firstly, a basic feature matrix with unique device characteristics is constructed based on the core identification information of the IoT tag, providing a foundation for subsequent fusion with product type characteristics. Core tag information is accurately extracted from the IoT tags configured in the delivery box. This tag information is a combination of the IoT tag's unique hardware identifier and the sensor's real-time calibration coefficients. The unique hardware identifier is a dedicated identification information fixed at the tag's factory, used to absolutely distinguish different IoT devices and avoid device confusion. The sensor's real-time calibration coefficients are dynamically generated by the tag's built-in self-calibration module according to the working environment, ensuring the accuracy of temperature data acquisition. Subsequently, the extracted combined data is standardized to eliminate interference from data format differences. The processed combined data is then converted into a continuous numerical sequence. Following a preset matrix dimension rule (the dimension is reasonably set according to encryption requirements and data volume), this numerical sequence is sequentially split and filled into the matrix structure. Each split value corresponds to an element of the matrix, ultimately constructing the basic feature matrix. This matrix, deeply bound to the IoT tag's exclusive information and dynamic calibration data, possesses the dual characteristics of device uniqueness and precise data correlation, laying the foundation for the uniqueness of the initial key matrix.

[0083] Next, through the graphical transformation of product type features, a digital undirected graph with product-specific characteristics is generated, achieving deep integration of product features and matrix elements. Core type feature information of the delivered products is extracted, including product temperature control priority and category-specific checksum. Product temperature control priority is determined based on the product's tolerance to temperature fluctuations and preservation requirements, directly reflecting the product's core temperature control needs; the category-specific checksum is a unique identifier under industry-unified coding rules, used to uniquely identify the product's category. Subsequently, the extracted two types of type feature information are converted into corresponding binary data, and then, according to preset splitting rules, both types of binary data are split into multiple sets of number combinations of equal length (the number of splitting sets matches the number of nodes in the undirected graph). Simultaneously, an undirected graph model is pre-constructed, with the number of nodes and node distribution density set according to the complexity of the product type features. Nodes are connected by undirected edges to ensure the correlation between nodes. Finally, the multiple sets of number combinations obtained from the splitting are added one by one to each node of the undirected graph, with each node uniquely carrying one set of number combinations, forming the digital undirected graph. This undirected graph, due to the core temperature control and category characteristics of the associated products, possesses significant product-specific attributes, providing differentiated support for subsequent integration with the basic feature matrix at the product dimension.

[0084] Then, through precise spatial alignment, the physical fusion of the basic feature matrix and the digital undirected graph is achieved, providing a spatial basis for subsequent element combinations. First, the geometric center of the basic feature matrix is ​​determined, using the center of the matrix element distribution as the core reference point; simultaneously, the geometric center of the digital undirected graph is determined, using the central intersection point of all nodes in the undirected graph as the core reference point. Following a pre-defined mapping rule, the digital undirected graph is superimposed on top of the basic feature matrix, ensuring that the geometric centers of both completely coincide, achieving precise spatial alignment. During the superposition process, the node distribution range of the digital undirected graph matches the element coverage range of the basic feature matrix, forming a clear spatial correspondence between the nodes of the undirected graph and the elements of the basic feature matrix. This ensures that all nodes in the undirected graph can be associated with matrix elements, while avoiding fusion omissions due to range mismatches, thus constructing a complete spatial association system for subsequent subkey generation.

[0085] Finally, by combining node numbers with matrix elements, subkeys are generated and an initial key matrix is ​​constructed, achieving deep fusion of device and product information. First, for each node in the undirected digital graph, the association range of that node is determined according to preset rules. This involves selecting basic feature matrix elements within a preset distance around the node as association elements (the association range is set based on fusion accuracy requirements to ensure the association between nodes and matrix elements without redundancy). Then, the combination of numbers carried by each node is combined with the basic feature matrix elements within the node's association range. The operation method prioritizes data association and randomness. Through this combination operation, device-specific information and product feature information are deeply fused, generating a subkey corresponding to each node. Each subkey uniquely corresponds to a set of device-product association information. Finally, according to preset sorting rules (such as the distribution order of nodes in the undirected graph or the element order of the basic feature matrix), all generated subkeys are added one by one to a preset matrix structure. Each subkey serves as an element of the matrix, ultimately constructing the initial key matrix. Because this matrix integrates device information from IoT tags with product type characteristics, it possesses the uniqueness and differentiation of dual device-product binding, providing a secure and reliable foundation for the subsequent generation of the target key matrix.

[0086] In one embodiment, based on the delivery order information of temperature-controlled goods, the initial key matrix is ​​transformed to obtain a target key matrix, including:

[0087] Obtain the delivery order information for temperature-controlled goods, extract the numeric characters, and combine them into multiple numbers; based on each number, simulate and generate a closed polygonal shape.

[0088] The polygonal closed shape is added to the initial key matrix according to a preset rule. Based on the length of the overlapping characters between each subkey of the initial key matrix and the polygonal closed shape, the positions of each subkey are reordered to obtain the intermediate matrix.

[0089] Based on the graphic properties of the polygonal closed graph, the number of rows and columns of the intermediate matrix are deformed and adjusted to obtain the target key matrix.

[0090] In this embodiment, firstly, complete delivery order information for temperature-controlled goods is obtained. This order information includes core content such as the order's unique identifier, delivery start and estimated delivery time, and delivery route planning nodes. All numeric characters are precisely extracted from this order information, and redundant non-numeric information is removed. Then, according to preset combination rules, the extracted numeric characters are split into multiple numbers of equal length (the combination rules are set based on the total length of the numeric characters in the order information and the required number of sides of the polygonal closed figure, ensuring that the number of split numbers matches the number of sides of the figure). Subsequently, each split number is used as the side length parameter of the polygonal closed figure, combined with preset drawing rules (such as determining the side length ratio according to the number size and the connection order of the sides according to the number arrangement order), to simulate and draw the polygonal closed figure in a virtual coordinate system. The number of sides, side length, and overall shape of this figure are directly determined by the combination of numeric characters in the order information. Since the numeric information of each delivery order is unique, the generated polygonal closed figure also has exclusive characteristics, providing a foundation for subsequent order binding in matrix transformation.

[0091] Next, by spatially associating the polygonal closed figure with the initial key matrix, dynamic rearrangement of subkeys is achieved based on the length of overlapping characters, overcoming the limitations of traditional fixed-order arrangement. First, the generated polygonal closed figure is embedded into the spatial coordinate system of the initial key matrix according to a preset mapping rule: using the geometric center of the initial key matrix as a reference, the polygonal closed figure is scaled proportionally to perfectly fit the coverage area of ​​the initial key matrix, ensuring that the figure completely covers all subkey elements of the matrix while maintaining the original morphological characteristics of the figure. Then, the spatial overlap relationship between each subkey in the initial key matrix and the polygonal closed figure is analyzed one by one: for each subkey, its character distribution area in the matrix spatial coordinate system is extracted, and the length of overlapping characters between this area and the outline and interior area of ​​the polygonal closed figure (i.e., the intersection of the subkey character sequence and the character length corresponding to the area covered by the figure) is calculated, and the numerical value of the overlapping character length for each subkey is recorded. All subkeys are reordered according to a preset sorting rule: the core sorting criterion is the length of overlapping characters, and the subkeys are arranged in descending order of length; if subkeys with the same overlapping character length exist, they are supplemented according to their original position order in the initial key matrix. The sorted subkeys are then refilled into a preset matrix structure, with each subkey corresponding to one element of the matrix, ultimately resulting in an intermediate matrix. This process guides the subkey reordering through an order-specific graphic, deeply associating the element distribution of the intermediate matrix with order information, thus enhancing the dynamism and uniqueness of the key matrix.

[0092] Furthermore, by leveraging the inherent properties of polygonal closed figures, the intermediate matrix is ​​dimensionally modified to further enhance the key matrix's anti-cracking capabilities. First, the core graphical attributes of the polygonal closed figure are extracted, including key parameters such as the number of sides, the sum of interior angles, and the ratio of the figure's area to the matrix's coverage area. The number of sides reflects the basic structural features of the figure, the sum of interior angles reflects the figure's morphological regularity, and the area ratio reflects the figure's coverage density within the matrix. Based on preset dimension adjustment rules and extracted graphic attribute parameters, the dimension adjustment scheme of the intermediate matrix is ​​determined. For example, in a specific embodiment, if the number of edges of the graphic is even and the area ratio is in a preset low coverage range, the number of rows in the intermediate matrix is ​​reduced by half the number of edges, and the number of columns is increased proportionally, keeping the total number of matrix elements unchanged. If the number of edges of the graphic is odd and the area ratio is in a preset high coverage range, the number of rows in the intermediate matrix is ​​increased by an integer multiple of the number of edges, and the number of columns is decreased proportionally, again keeping the total number of matrix elements unchanged. If the sum of the interior angles of the graphic conforms to a preset regularity threshold, the element arrangement direction of the matrix is ​​rotated clockwise after adjusting the number of rows and columns. According to the determined dimension adjustment scheme, the row and column number transformation operations are performed on the intermediate matrix, while ensuring that all subkey elements are not lost or duplicated during the adjustment process, finally obtaining the target key matrix. Because this matrix integrates information on devices, products, and orders, and has undergone graphically guided rearrangement and dimensional adjustments, it possesses extremely high uniqueness, dynamism, and anti-cracking capabilities, providing secure and reliable key support for subsequent data encryption.

[0093] In one embodiment, generating a target key based on the target key matrix includes:

[0094] Extract the feature parameters of each subkey in the target key matrix. The feature parameters include the character repetition frequency of the subkey. At the same time, combine the polygonal closed graph to extract the coverage area ratio of each subkey in the graph.

[0095] Using each subkey as a network node, connect adjacent subkeys with character repetition frequency similarity ≥ a preset threshold or graphic coverage area ratio through edges, and calculate the association strength of each edge; association strength = character similarity weight × graphic position weight;

[0096] Using association strength as an indicator, the top N core subkeys are selected, and each core subkey is split according to rules to obtain multiple key fragments. Each key fragment is marked with the corresponding percentage of the graphic coverage area.

[0097] Extract the graphic attributes of the polygonal closed graph as mapping parameters, and convert the mapping parameters into a sorting sequence of key fragments to perform preliminary sorting of all key fragments;

[0098] Obtain the type information of the temperature-controlled product, convert it into an adjustment coefficient, and perform local position swapping on the initially sorted key fragments; calculate the character matching degree of adjacent fragments, and based on the character matching degree, select the corresponding characters from the adjacent key fragments according to the rules, and concatenate them in order to generate the target key.

[0099] In this embodiment, firstly, the subkey feature parameters and graphic association data are extracted: for each subkey in the target key matrix, the frequency of character repetition is first counted. This frequency is the number of repetitions of the character that appears most frequently in the subkey, which is used to reflect the character distribution pattern of the subkey; at the same time, the polygonal closed graphic generated in the association step is used to determine the coverage area of ​​each subkey in the graphic through spatial coordinate matching, and the proportion of the coverage area to the total area of ​​the graphic is calculated to obtain the graphic coverage area ratio of each subkey, thereby realizing the spatial association binding between the subkey and the order-specific graphic.

[0100] Next, a subkey association network is constructed and the association strength is calculated: an undirected association network model is established with each subkey as an independent network node; a preset similarity threshold is set (set according to the encryption strength requirements, such as 80%). If the similarity of the character repetition frequency of two subkeys reaches or exceeds the threshold, or the graphic coverage areas of the two subkeys are spatially adjacent (i.e., the coverage areas do not overlap but the boundaries intersect), then a connection edge is established between the corresponding two network nodes; then the association strength of each connection edge is calculated. The association strength is determined by the product of the character similarity weight and the graphic position weight. The character similarity weight is dynamically allocated according to the difference in the character repetition frequency of the two subkeys (the smaller the difference, the greater the weight), and the graphic position weight is allocated according to the proportion of the adjacent length of the coverage areas of the two subkeys (the larger the proportion of the adjacent length, the greater the weight). The accuracy and uniqueness of the association strength are ensured through dual weight coupling.

[0101] Next, core subkeys are selected and key fragments are generated: using association strength as the core selection criterion, the subkeys corresponding to all network nodes are sorted, and the top N subkeys with the highest total association strength are selected as core subkeys (N is the preset total number of key fragments, set according to the target key length requirement); for each core subkey, it is split according to the character length equalization rule, that is, the core subkey is split into multiple key fragments of equal length (the split length is set according to the encryption algorithm adaptation requirements), and at the same time, the corresponding graphic coverage area ratio of each key fragment is marked to ensure the continuous association between the fragment and the graphic features.

[0102] Then, a sorting sequence is generated based on the graphic attributes to complete the initial sorting: the core graphic attributes of the polygonal closed figure are extracted as mapping parameters. These graphic attributes include the number of sides of the figure, the length ratio of each side, and the distribution of interior angles. These mapping parameters are converted into an ordered numerical sequence according to preset rules. This numerical sequence is the sorting sequence of key fragments. The number of sides of the figure determines the basic length of the sorting sequence, the length ratio of each side corresponds to the sorting priority, and the distribution of interior angles is used to correct the sorting order. According to this sorting sequence, all key fragments marked with the proportion of the graphic coverage area are arranged in sequence to complete the initial sorting of key fragments, so that the sorting logic is deeply bound to the graphic features of the order.

[0103] Furthermore, the order of key fragments is adjusted based on product type information: the type information of temperature-controlled products is extracted, including product temperature control priority and category-specific attributes, and this information is converted into corresponding adjustment coefficients according to preset encoding rules (different types of information correspond to different coefficient ranges, such as high temperature control priority corresponding to high coefficient values); the local position exchange rules of key fragments are determined according to the adjustment coefficients. The larger the adjustment coefficient, the more fragments are exchanged locally and the wider the exchange range. During the exchange process, it is ensured that the correlation between the proportion of the marked graphic coverage area and the sorting sequence is not destroyed; through the dynamic intervention of product type information, the key fragment sorting is adapted to product characteristics, improving the targeting of the key.

[0104] Finally, characters are filtered and concatenated based on character matching degree to generate the target key: the character matching degree of two adjacent key segments after preliminary sorting and local swapping is calculated, and the character matching degree is the proportion of the number of identical characters in adjacent segments; a matching degree threshold (e.g., 70%) is set. If the character matching degree of adjacent segments reaches or exceeds the threshold, the character at the first preset position is selected from the two segments for retention; if the character matching degree is lower than the threshold, the character at the second preset position is selected from the two segments for retention; then, according to the adjusted segment order, the filtered characters are concatenated in sequence to finally generate a target key with triple binding of product characteristics, order graphic features, and subkey association network.

[0105] In the above embodiments, this application incorporates some existing algorithms and technical features for explanation and description to make the specification more detailed, clear, and complete, thus complying with the provisions of the Patent Law. However, this is not achieved by using a series of complex steps and algorithmic formulas, nor by complicating the technical solution, nor by combining or stacking conventional or simple features. The existing algorithms and technical features listed are for the purpose of disclosing the specific implementation methods of each step of this application (not to limit this application) and to avoid situations where this application cannot be implemented.

[0106] Reference Figure 2In another embodiment of the present invention, a last-mile traceability and monitoring system for temperature-controlled goods is also provided, comprising:

[0107] The data acquisition module is used to collect real-time geographic location data and temperature data of temperature-controlled goods from IoT tags configured on the delivery boxes of temperature-controlled goods throughout the delivery process.

[0108] The upload module is used to upload the collected geographic location data and temperature data to the blockchain platform in real time, so as to realize the on-chain storage of data through blockchain technology and form a reliable traceability record;

[0109] The generation module is used to generate an initial key matrix based on the tag information of IoT tags and the type information of delivered goods, and to transform the initial key matrix based on the delivery order information of temperature-controlled goods to obtain a target key matrix;

[0110] The traceability module is used to generate a target key based on the target key matrix, and after encrypting the geographical location data and temperature data, it generates a traceability QR code.

[0111] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0112] Reference Figure 3 This invention also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0113] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0114] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0115] In summary, the method and system for end-to-end traceability monitoring of temperature-controlled goods provided in this embodiment of the invention includes: real-time collection of geographic location data and temperature data of the temperature-controlled goods throughout the delivery process based on IoT tags configured on the delivery boxes of the temperature-controlled goods; real-time uploading of the collected geographic location data and temperature data to a blockchain platform, achieving on-chain data storage through blockchain technology to form a trusted traceability record; generating an initial key matrix based on the tag information of the IoT tags and the type information of the delivered goods; transforming the initial key matrix based on the delivery order information of the temperature-controlled goods to obtain a target key matrix; generating a target key based on the target key matrix, and encrypting the geographic location data and temperature data to generate a traceability QR code. In this invention, the initial key matrix is ​​generated through the tag information of the IoT tags and the type information of the delivered goods; the initial key matrix is ​​transformed based on the delivery order information of the temperature-controlled goods to obtain the target key matrix; and the target key is generated based on the target key matrix, which improves the security of the key generation mechanism. Simultaneously, the traceability information is deeply associated with the goods, orders, and IoT tags to ensure the uniqueness of the traceability information.

[0116] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0117] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0118] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for end-to-end traceability and monitoring of temperature-controlled goods during last-mile delivery, characterized in that, Includes the following steps: Based on the IoT tags configured on the delivery boxes of temperature-controlled goods, the geographical location data and temperature data of the temperature-controlled goods are collected in real time throughout the delivery process; The collected geographic location data and temperature data are uploaded to the blockchain platform in real time, and the data is stored on the chain through blockchain technology to form a reliable traceability record; An initial key matrix is ​​generated based on the tag information of IoT tags and the type information of delivered goods. The target key matrix is ​​then transformed based on the delivery order information of temperature-controlled goods. Specifically, this includes: extracting tag information from IoT tags, which is a combination of the unique hardware ID of the IoT tag and the real-time calibration coefficient of the sensor; constructing a basic feature matrix based on the tag information; extracting type feature information of delivered goods, including product temperature control priority and category-specific checksum; converting the type feature information into binary numbers and performing an XOR operation to generate a fused feature sequence, which is then split into a dynamic weight matrix; generating a data sequence based on the product temperature control priority; multiplying the data sequence by the corresponding elements of the basic feature matrix and the dynamic weight matrix to obtain the initial key matrix; obtaining the delivery order information of temperature-controlled goods and constructing a transformation factor digital matrix; transforming each element of the initial key matrix based on the element values ​​of the transformation factor digital matrix to obtain an intermediate matrix; and adjusting the number of rows and columns of the intermediate matrix based on the matrix attributes of the transformation factor digital matrix to obtain the target key matrix. A target key is generated based on the target key matrix, and after encrypting the geographic location data and temperature data, a traceability QR code is generated.

2. The end-to-end traceability monitoring method for temperature-controlled goods according to claim 1, wherein, The IoT tag integrates a GPS module and a temperature sensor; the IoT tag uploads the collected geographic location data and temperature data to the blockchain platform in real time via an NB-IoT network or a 5G network.

3. The end-to-end traceability monitoring method for temperature-controlled goods according to claim 1, wherein, After collecting the geographic location data and temperature data of temperature-controlled goods throughout the delivery process in real time, the following is also included: When the collected temperature data exceeds the preset temperature threshold, an early warning message is sent to the deliveryman's terminal, the dispatch center platform, and the customer's terminal.

4. The end-to-end traceability monitoring method for temperature-controlled goods according to claim 1, wherein, Customers can scan the traceability QR code to check the geographical location and temperature change trajectory of the entire last-mile delivery of temperature-controlled goods.

5. The end-to-end traceability monitoring method for temperature-controlled goods according to claim 1, wherein, Based on the element values ​​of the transformation factor digital matrix, the elements of the initial key matrix are transformed to obtain an intermediate matrix, including: Construct a curve based on the element values ​​of the deformation factor numerical matrix; The curve is added to the initial key matrix according to a preset rule. Based on the positional relationship between each matrix element of the initial key matrix and the curve, each matrix element is offset to obtain the intermediate matrix.

6. An end-to-end distribution traceability monitoring system for temperature-controlled goods, characterized in that, include: The data acquisition module is used to collect real-time geographic location data and temperature data of temperature-controlled goods from IoT tags configured on the delivery boxes of temperature-controlled goods throughout the delivery process. The upload module is used to upload the collected geographic location data and temperature data to the blockchain platform in real time, so as to realize the on-chain storage of data through blockchain technology and form a reliable traceability record; A generation module is used to generate an initial key matrix based on the tag information of IoT tags and the type information of delivered goods. Based on the delivery order information of temperature-controlled goods, the initial key matrix is ​​transformed to obtain a target key matrix. Specifically, this includes: extracting tag information from IoT tags, where the tag information is a combination of the unique hardware ID of the IoT tag and the real-time calibration coefficient of the sensor; constructing a basic feature matrix based on the tag information; extracting type feature information of delivered goods, including product temperature control priority and category-specific checksum; converting the type feature information into binary numbers and performing an XOR operation to generate a fused feature sequence, which is then split into a dynamic weight matrix; generating a data sequence based on the product temperature control priority; multiplying the data sequence by the corresponding elements of the basic feature matrix and the dynamic weight matrix to obtain the initial key matrix; obtaining the delivery order information of temperature-controlled goods and constructing a transformation factor digital matrix; transforming each element of the initial key matrix based on the element values ​​of the transformation factor digital matrix to obtain an intermediate matrix; and adjusting the number of rows and columns of the intermediate matrix based on the matrix attributes of the transformation factor digital matrix to obtain the target key matrix. The traceability module is used to generate a target key based on the target key matrix, and after encrypting the geographical location data and temperature data, it generates a traceability QR code.

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