E-commerce supply chain full-process traceability anti-counterfeiting method and platform based on Internet of Things

Abstract

The invention discloses an e-commerce supply chain full-process traceability anti-counterfeiting method and platform based on the Internet of Things, and the method comprises the steps: encoding commodity basic information through a two-dimensional code encryption algorithm to generate an initial two-dimensional code, collecting and processing a commodity transportation track through a GPS track fusion model, and dynamically updating the two-dimensional code in combination with e-commerce supply chain parameters; and the authenticity and the circulation compliance of the commodity are judged through decryption verification at the point-of-sale terminal, and a result is fed back. A corresponding platform comprises six modules including a multi-dimensional information encryption coding module and a spatio-temporal trajectory sensing processing module, all the modules operate cooperatively, information encryption, real-time tracking, dynamic updating, accurate verification and closed-loop management of commodities in the whole process from production to sales are achieved, the problems that in the prior art, information management is outdated, and dynamic monitoring is lacked are effectively solved, and the service life of the commodities is prolonged. A reliable traceability anti-counterfeiting solution is provided for the e-commerce supply chain, and the supply chain management level and the commodity safety are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of e-commerce supply chain traceability and anti-counterfeiting, and particularly relates to an e-commerce supply chain whole-process traceability and anti-counterfeiting method and platform based on Internet of Things. BACKGROUND

[0002] With the rapid development of e-commerce industry, the supply chain system is increasingly large and complex, and the circulation of goods in the production, transportation, warehousing, and sales links involves multiple parties and a large amount of information. Consumers' concern about the origin, quality, and authenticity of goods is increasing, and merchants also need effective means to ensure supply chain transparency and prevent counterfeit and substandard products from entering the market, which makes e-commerce supply chain whole-process traceability and anti-counterfeiting an urgent need for industry development.

[0003] However, the current e-commerce supply chain traceability and anti-counterfeiting technology has obvious shortcomings. Firstly, the information management method is backward. Traditional traceability technology mostly uses manual input and static barcode identification, which is not only inefficient, but also prone to errors or omissions in data collection, storage, and transmission, making it difficult to ensure the integrity and accuracy of the goods information. Once the goods information is tampered with, there is also a lack of effective identification and prevention mechanism. Secondly, the dynamic monitoring capability is missing. The existing technology cannot comprehensively track the real-time status of goods at each link of the supply chain, making it difficult to analyze the combination of the goods logistics track and the operation parameters of the supply chain. When the goods transportation path deviates or the warehouse environment is not up to standard, it is difficult to detect abnormalities in a timely manner, which may lead to counterfeit and substandard goods mixing into the normal circulation channel, damaging the rights and interests of consumers and the reputation of merchants. SUMMARY

[0004] In order to overcome the shortcomings and deficiencies of the prior art, the present application provides an e-commerce supply chain whole-process traceability and anti-counterfeiting method and platform based on Internet of Things.

[0005] The technical solution adopted by the present application is an e-commerce supply chain whole-process traceability and anti-counterfeiting method based on Internet of Things, comprising the following steps:

[0006] Step S1: using a preset two-dimensional code encryption algorithm to encode the basic information of the goods in the e-commerce supply chain, including the category, production batch, and specification and model, to generate an initial two-dimensional code, wherein the two-dimensional code encryption algorithm performs irreversible encryption conversion of information through multi-dimensional key permutation and chaotic mapping mechanism;

[0007] Step S2: binding the goods with an Internet of Things terminal device with GPS positioning function, and in the process of goods circulation, the Internet of Things terminal device collects the geographical position information of the goods at fixed time intervals and generates corresponding GPS track data sequence;

[0008] Step S3: Construct a GPS trajectory fusion model to perform feature matching and deviation calculation between the GPS trajectory data sequence collected in step S2 and the standard transportation route data preset by the e-commerce supply chain, to obtain a difference index of the actual transportation trajectory of the goods and the standard transportation route;

[0009] Step S4: Based on different parameters of the e-commerce supply chain, including the supplier qualification level, the logistics node processing timeliness, the warehouse environment temperature and humidity threshold, dynamically update the initial two-dimensional code generated in step S1, and embed real-time state parameters in the two-dimensional code during the goods circulation process;

[0010] Step S5: At the goods sales terminal, use the corresponding two-dimensional code decryption algorithm to analyze the goods two-dimensional code, perform consistency check on the goods information obtained by analysis and the updated information in step S4, and judge the authenticity and circulation compliance of the goods in combination with the difference index obtained in step S3;

[0011] Step S6: Feed back the judgment result of step S5 to the e-commerce supply chain management center database through the Internet of Things communication network, and perform closed-loop management on the goods whole-process traceability information.

[0012] Further, in step S3, the GPS trajectory fusion model calculates the difference index of the actual transportation trajectory of the goods and the standard transportation route by the following formula:

[0013]

[0014] Wherein, D is the difference index; α and β are weight coefficients, and α+β=1; n is the number of sampling points in the GPS trajectory data sequence; is the coordinate of the actual transportation trajectory of the i-th sampling point goods; is the coordinate of the i-th sampling point standard transportation route; t a is the actual transportation time of the goods; t s is the standard transportation time.

[0015] Further, in step S4, the process of dynamically updating the initial two-dimensional code based on the parameters of the e-commerce supply chain is expressed as:

[0016]

[0017] Wherein, Q new is the updated two-dimensional code information; Q old is the initial two-dimensional code information; represents an information fusion operation; f(S q , T p , H t ) is an update information function generated according to the parameters of the e-commerce supply chain; S qrepresents the supplier qualification level parameter, quantifying the supplier reputation level in numerical form; T p represents the logistics node processing timeliness parameter, recording the cargo processing time of each logistics node; H t represents the warehouse environment temperature and humidity threshold parameter, including real-time monitoring values of temperature and humidity.

[0018] Further, in step S1, the key generation process of the two-dimensional code encryption algorithm adopts the following formula:

[0019] K=g(P id ,B np ,B and )

[0020] Wherein, K is the generated encryption key; g is the key generation function; P id is the unique identification code of the product; B no is the product production batch number; R and is a random number seed, which is generated by the Internet of Things terminal device in real time.

[0021] Further, in step S5, the judgment process of the product authenticity and flow compliance is based on the following formula to calculate the comprehensive judgment index:

[0022] C=γ×V c +(1-γ)×(1-D)

[0023] Wherein, C is the comprehensive judgment index; γ is the weight coefficient; V c is the two-dimensional code information consistency check result, taking value 0 or 1, 0 indicating inconsistency and 1 indicating consistency.

[0024] Further, in step S2, the frequency of GPS trajectory data collected by the Internet of Things terminal device is dynamically adjusted according to the logistics transportation speed parameter V s and the transportation distance parameter L d of the e-commerce supply chain through the following formula:

[0025]

[0026] Wherein, F is the GPS trajectory data collection frequency; τ is the preset time-distance adjustment coefficient.

[0027] Further, in step S4, the operation of embedding the real-time state parameter of the product flow process in the initial two-dimensional code is expressed as:

[0028]

[0029] Wherein, Q embed is the two-dimensional code information after embedding the real-time state parameter; w jS is the weight of the jth real-time state parameter; S j S is the jth e-commerce supply chain real-time state parameter, and m is the total number of real-time state parameters.

[0030] Further, in step S3, the feature matching process of the GPS trajectory data sequence and the standard transportation route data, the feature similarity is calculated by the following formula:

[0031]

[0032] S is the feature similarity. sim

[0033] Further, in step S6, the judgment result feedback process to the e-commerce supply chain management center database adopts the following data transmission priority formula:

[0034] P r = δ × C + (1- δ) × U l

[0035] P is the data transmission priority; δ is the weight coefficient; U r l

[0036] The e-commerce supply chain whole-process traceability anti-counterfeiting platform based on Internet of Things includes:

[0037] Multi-dimensional information encryption coding module: through the preset two-dimensional code encryption algorithm, the category, production batch, specification and model of the commodity in the e-commerce supply chain are coded and processed, and the multi-dimensional key substitution and chaotic mapping mechanism are used for information irreversible encryption conversion to generate an initial two-dimensional code;

[0038] Space-time trajectory sensing processing module: after being bound with the commodity, the geographical position information of the commodity in the circulation process is acquired in real time according to the dynamically adjusted acquisition frequency to generate a GPS trajectory data sequence; at the same time, a GPS trajectory fusion model is constructed to perform feature matching and deviation calculation on the collected trajectory data and the preset standard transportation route data to obtain the difference degree index of the actual transportation trajectory of the commodity and the standard transportation route;

[0039] Dynamic parameter fusion updating module: real-time acquisition of the supplier qualification grade, logistics node processing time limit, storage environment temperature and humidity threshold parameters in the e-commerce supply chain, dynamic updating of the initial two-dimensional code, embedding of the real-time state parameters in the commodity circulation process into the two-dimensional code through information fusion operation;

[0040] ​​​The bidirectional information verification and analysis module: at the commodity sales terminal, the corresponding two-dimensional code decryption algorithm is used to analyze the commodity two-dimensional code, the commodity information obtained by analysis is consistent with the updated two-dimensional code information, the difference degree index of the transportation track is combined to judge the authenticity and circulation compliance of the commodity;

[0041] The data hierarchical feedback transmission module: according to the comprehensive judgment index of the authenticity and circulation compliance of the commodity and the real-time load rate of the Internet of Things communication network, the data transmission priority is determined, and the commodity judgment result is fed back to the e-commerce supply chain management center database through the Internet of Things communication network;

[0042] The global collaborative management module: respectively with the multi-dimensional information encryption and coding module, the space-time track sensing processing module, the dynamic parameter fusion updating module, the bidirectional information verification and analysis module, the data hierarchical feedback transmission module, the communication connection is established, through the preset management strategy, the running parameters, data interaction process and collaborative work logic of each module are uniformly managed, and the stable operation of the platform is ensured.

[0043] Beneficial effects: the method and platform for tracing and preventing counterfeiting of the e-commerce supply chain based on the Internet of Things are proposed, the multi-dimensional information encryption and coding module is used to reversibly encrypt and convert the basic information of the commodity by using advanced two-dimensional code encryption algorithm, compared with traditional manual input and static bar code, the accuracy and security of the information are greatly improved, and the information is effectively prevented from being tampered with. The space-time track sensing processing module and the dynamic parameter fusion updating module work collaboratively, the geographic position information of the commodity is collected in real time, combined with the supply chain parameters such as supplier qualification, logistics timeliness, storage environment, the two-dimensional code is dynamically updated, the real-time monitoring of the commodity circulation process is realized, and the shortcomings of traditional technology that cannot track the real-time state of the commodity and combine multiple parameters for analysis are made up. At the commodity sales terminal, the bidirectional information verification and analysis module decrypts and analyzes the two-dimensional code and verifies the information, and judges the authenticity and circulation compliance of the commodity by combining the transportation track difference degree, so that consumers can purchase genuine products. The data hierarchical feedback transmission module efficiently feeds back the judgment result to the management center database, forming an information closed loop. The global collaborative management module uniformly manages the operation of each module, and ensures the stable operation of the platform. Through the close cooperation of each module, the method and platform realize the accurate tracing and reliable anti-counterfeiting of the e-commerce supply chain, significantly improve the supply chain management level, and maintain the legal rights and interests of consumers and merchants. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The method steps of the present application are shown in the figure;

[0045] Figure 2 The platform module composition diagram of the present application is shown in the figure. DETAILED DESCRIPTION

[0046] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict, and the present application will be further described in detail below in combination with the drawings and specific embodiments.

[0047] As shown in the figure, the e-commerce supply chain whole-process traceability anti-counterfeiting method and platform based on Internet of Things includes the following steps: Figure 1

[0048] Step S1: using a preset two-dimensional code encryption algorithm to encode the basic information of the commodity in the e-commerce supply chain, including commodity category, production batch, specification and model, to generate an initial two-dimensional code, wherein the two-dimensional code encryption algorithm performs irreversible encryption conversion of information through multi-dimensional key permutation and chaotic mapping mechanism.

[0049] Specifically, step S1 generates an initial two-dimensional code by encoding the basic information of the commodity in the e-commerce supply chain through a preset two-dimensional code encryption algorithm. This step uses multi-dimensional key permutation and chaotic mapping mechanism to realize irreversible encryption conversion of information. Specifically, the basic information includes commodity category, production batch, specification and model, etc. These parameters are converted into binary data, then rearranged by multi-dimensional key permutation algorithm, and further disturbed by chaotic mapping mechanism to form ciphertext information. This encryption process has high complexity and uniqueness, and the key is generated by the commodity identification, batch information and random number seed, ensuring that the encryption path of each two-dimensional code is unique.

[0050] In the implementation process, the system first performs format standardization processing on the input basic information, unifies the data length and coding rules. Then, the multi-dimensional key permutation algorithm performs multiple transposition operations on the binary data according to the preset permutation rule to form intermediate ciphertext. The chaotic mapping mechanism maps the intermediate ciphertext to a higher-dimensional chaotic space based on a deterministic chaotic system to generate the final encrypted data. The encrypted data is converted into a specific two-dimensional code pattern containing error correction code and positioning identifier to ensure accurate identification in different environments. The significance of this step is to establish a unique digital identity for the commodity, ensuring the security and traceability of the information from the source.

[0051] Step S2: binding the commodity with an Internet of Things terminal device with GPS positioning function, and in the commodity circulation process, the Internet of Things terminal device collects the geographic position information of the commodity at fixed time intervals and generates corresponding GPS trajectory data sequence.

[0052] ​Specifically, step S2 binds the commodity with the Internet of Things terminal device with GPS positioning function. In the commodity circulation process, the terminal device collects geographic position information at fixed time intervals to generate a GPS trajectory data sequence. The technical parameters of this step involve collection frequency, positioning accuracy, and data format. The collection frequency is dynamically adjusted according to the speed and distance of commodity transportation to ensure that enough density of trajectory points can be obtained during high-speed transportation; the positioning accuracy needs to meet the error range of centimeter to meter to accurately reflect the actual position of the commodity; and the data format uses standardized latitude and longitude coordinates, with time stamp and device identification.

[0053] In implementation, the Internet of Things terminal device obtains real-time position information through the Global Positioning System and samples at a preset time interval. The sampling data is preliminarily processed locally, including denoising, coordinate conversion, and time synchronization, and then packaged into a standard data format. To ensure the reliability of data transmission, the terminal device uses multi-channel communication technology and transmits data through 4G / 5G network and low-power wide-area network to realize dual-link backup. The generated GPS trajectory data sequence contains continuous position point information, forming a space-time trajectory record of commodity circulation, providing basic data for subsequent path analysis and anomaly detection.

[0054] Step S3: Construct a GPS trajectory fusion model to perform feature matching and deviation calculation on the GPS trajectory data sequence collected in step S2 and the standard transportation route data preset in the e-commerce supply chain to obtain the difference index of the actual transportation trajectory of the commodity and the standard transportation route.

[0055] Specifically, step S3 constructs a GPS trajectory fusion model to perform feature matching and deviation calculation on the GPS trajectory data sequence collected and the standard transportation route data preset in the e-commerce supply chain to obtain the difference index of the actual transportation trajectory of the commodity and the standard transportation route. The technical core of this step lies in the trajectory feature extraction and matching algorithm. The standard transportation route data is pre-stored in the system, including key node coordinates, allowed path range, and time window parameters. The model first extracts features from the actual trajectory and the standard route, including speed change rate, direction turning point, and stay time distribution.

[0056] In the implementation process, the system uses the dynamic time warping algorithm to align the two trajectories and calculates the spatial distance and time deviation between the corresponding points. By weighted summation, the deviations in each feature dimension are integrated into a comprehensive difference index. This index not only reflects the spatial deviation degree of the trajectory, but also considers the compliance in the time dimension, such as whether the key node is reached within the specified time. The calculation result of the difference index is used to judge whether there is an anomaly in the commodity transportation process, such as path deviation and excessive stay time, providing a key basis for subsequent authenticity verification.

[0057] Step S4: Based on different parameters of the e-commerce supply chain, including supplier qualification level, logistics node processing timeliness, warehouse environment temperature and humidity threshold, the initial two-dimensional code generated in step S1 is dynamically updated, and real-time state parameters in the commodity circulation process are embedded in the two-dimensional code;

[0058] Specifically, step S4 dynamically updates the initial two-dimensional code based on various parameters of the e-commerce supply chain, and embeds real-time state parameters in the two-dimensional code during the commodity circulation process. These parameters include supplier qualification level, logistics node processing timeliness, warehouse environment temperature and humidity threshold, etc. The system updates the initial two-dimensional code incrementally by collecting data from each link in real time, ensuring that the two-dimensional code always reflects the latest state of the commodity. The update process uses information fusion technology to securely integrate new parameters with original encrypted information, maintaining the integrity of the original information while adding real-time dynamic data.

[0059] In implementation, the system first standardizes the collected supply chain parameters, converting data of different sources and formats into uniform numerical representations. Then, through a specific information fusion algorithm, these parameters are encoded into binary data blocks, and a symmetric encryption algorithm is used to encrypt the data blocks. The encrypted data blocks are embedded in a specific area of the two-dimensional code, which has enough space reserved for dynamic updates. To ensure that the updated two-dimensional code can still be correctly identified, the system recalculates the check code and adjusts the error correction level of the two-dimensional code. This step makes the two-dimensional code a real-time carrier of commodity state, enhancing the timeliness and integrity of the traceability information.

[0060] Step S5: At the commodity sales terminal, use the corresponding two-dimensional code decryption algorithm to analyze the commodity two-dimensional code, and perform consistency check on the commodity information obtained by analysis and the updated information in step S4, and combine the difference index obtained in step S3 to judge the authenticity and circulation compliance of the commodity;

[0061] Specifically, step S5 analyzes the commodity two-dimensional code at the commodity sales terminal, and performs consistency check on the analysis information and the updated information, and combines the difference index of the transportation track to judge the authenticity and circulation compliance of the commodity. The technical key of this step lies in the two-dimensional code decryption algorithm and the multi-dimensional verification mechanism. The sales terminal device first obtains the two-dimensional code image through image recognition technology, and then uses the decryption algorithm corresponding to the encryption algorithm to restore the basic information and real-time state parameters of the commodity.

[0062] In the verification process, the system compares the parsed information with the latest information stored in the database, checking the consistency and integrity of the data. For the transportation trajectory difference index, the system sets a threshold range, and if it exceeds the range, it is determined to be abnormal. The verification process covers multiple dimensions, including the consistency of commodity basic information, the timeliness of supply chain parameters, and the compliance of transportation trajectory. Only when the verification results of all dimensions meet the preset standards, the commodity is confirmed to be real and compliant. This step effectively identifies counterfeit and substandard commodities and illegal circulation behaviors through a multi-dimensional verification mechanism, protecting consumer rights and market order.

[0063] Step S6: The judgment result of step S5 is fed back to the e-commerce supply chain management center database through the Internet of Things communication network for closed-loop management of commodity whole-process traceability information.

[0064] Specifically, step S6 feeds back the judgment result to the e-commerce supply chain management center database through the Internet of Things communication network, realizing closed-loop management of commodity whole-process traceability information. The technical key of this step lies in the reliability of data transmission and the efficient management of the database. The feedback information includes the verification result of the commodity, detailed traceability information, and time stamp, etc. To ensure the reliability of data transmission, the system uses encryption communication protocol and data redundancy technology to encrypt the transmitted data and send them through multiple communication links at the same time, improving the data arrival rate.

[0065] The management center database adopts a distributed architecture with high concurrent processing capability and data redundancy backup mechanism. After the feedback data enters the database, the system first performs format verification and integrity check, and then stores it according to the preset classification rules. The database will index each feedback information for fast query and statistical analysis. At the same time, the system will trigger the corresponding business processes according to the feedback results, such as tracking and processing of abnormal commodities, adjustment and optimization of supply chain parameters, etc. Through this step, the closed-loop management of commodity whole-process information from production to sales is realized, providing data support for the optimization and quality control of the supply chain.

[0066] Preferably, in step S3, the GPS trajectory fusion model calculates the difference index of the actual transportation trajectory of the commodity and the standard transportation route by the following formula:

[0067]

[0068] Where D is the difference index; a, b are weight coefficients, and a+b=1; n is the number of sampling points in the GPS trajectory data sequence; is the coordinate of the actual transportation trajectory of the commodity at the ith sampling point; is the coordinate of the standard transportation route at the ith sampling point; t a is the actual transportation time of the commodity; t sFor standard transportation time-consuming.

[0069] Specifically, the application of the GPS trajectory fusion model in transportation path verification, the model calculates the difference index of the actual transportation trajectory and the standard transportation route, realizes the dynamic monitoring of the transportation process. The model considers the deviation of space and time two dimensions: the spatial deviation is measured by comparing the coordinate distance of the actual trajectory point and the standard route point, reflecting the path deviation degree; the time deviation is evaluated by comparing the actual transportation time-consuming and the standard time-consuming, embodying the timeliness difference. The weight coefficients α and β adjust the influence degree of space and time factors respectively, and their values are dynamically adjusted according to the characteristics of goods and transportation requirements. In implementation, the system first preprocesses the collected GPS trajectory data, including coordinate system and noise filtering. Then, the preprocessed trajectory sequence is matched with the standard route point by point, and the spatial distance of each matched point is calculated. At the same time, the arrival time of the key node is recorded, and the overall transportation time-consuming deviation is calculated. These deviation values are normalized and weighted to get the final difference index. The index is used to judge whether there is an abnormality in the transportation process, such as detour, retention and other behaviors, to provide quantitative basis for subsequent authenticity verification and enhance the supervision ability of the system to the logistics link.

[0070] Preferably, in step S4, the process of dynamically updating the initial two-dimensional code based on the e-commerce supply chain parameters is expressed as:

[0071]

[0072] Q new is the updated two-dimensional code information; Q old is the initial two-dimensional code information; represents information fusion operation; f(S q , T p , H t ) is an update information function generated according to e-commerce supply chain parameters; S q represents the supplier qualification level parameter, which quantifies the supplier's reputation level in numerical form; T p represents the logistics node processing timeliness parameter, which records the processing time of goods at each logistics node; H t represents the warehouse environment temperature and humidity threshold parameter, including real-time monitoring values of temperature and humidity.

[0073] Specifically, the process of dynamically updating the two-dimensional code based on the e-commerce supply chain parameters converts the parameters such as supplier qualification, logistics timeliness and storage environment into update information that can be embedded in the two-dimensional code through a specific information fusion function. The supplier qualification level parameter quantifies the enterprise reputation level in numerical form, the logistics node processing timeliness records the time consumption of each link operation, and the storage environment temperature and humidity threshold reflects the commodity storage condition. The system collects these parameters in real time, which are standardized and combined with the initial two-dimensional code information through a preset fusion algorithm. The update process uses incremental encoding technology to preserve the integrity of the original information while adding new state data. In implementation, the system first establishes the mapping relationship between the parameters and the data bits of the two-dimensional code to determine the storage location of each parameter in the two-dimensional code. Then, the newly collected parameters are encrypted to generate an update key associated with the original key. The key is used to encrypt and convert the parameters to form an embeddable information block. Finally, a special encoding algorithm is used to integrate the information block into the reserved area of the two-dimensional code, and the check code is recalculated to ensure data integrity. This mechanism enables the two-dimensional code to dynamically reflect the latest status of the commodity in the supply chain, improving the real-time and accuracy of the traceability information.

[0074] Preferably, in step S1, the key generation process of the two-dimensional code encryption algorithm uses the following formula:

[0075] K = g(P id , B no , R and )

[0076] Where K is the generated encryption key; g is the key generation function; P id is the unique identification code of the commodity; B no is the commodity production batch number; R and is a random number seed generated in real time by the Internet of Things terminal device.

[0077] Specifically, the key generation mechanism of the two-dimensional code encryption algorithm generates an encryption key through the combination of the product identifier, batch number, and random number seed, ensuring that each product's encryption path is unique. The product unique identifier code is the basic code for distinguishing different products, the production batch number reflects the production batch information of the product, and the random number seed is generated in real time by the Internet of Things terminal device, ensuring the dynamic nature of the key. The key generation function uses a multi-layer transformation algorithm. First, the input parameters are subjected to a hash operation to generate an intermediate key. Then, the intermediate key is rearranged according to a pre-set permutation rule to form the final encryption key. This key is used to control the subsequent multi-dimensional permutation and chaotic mapping process, ensuring the complexity and anti-cracking ability of the encryption result. In implementation, the system generates the unique identifier code and batch number at the product production stage and binds them with the terminal device. During the encryption process, the terminal device generates a random number seed in real time, which is input into the key generation function together with the pre-stored parameters. The generated key is only stored and used locally and is not transmitted with the two-dimensional code, effectively preventing the risk of key leakage. This mechanism fundamentally guarantees the security of two-dimensional code encryption and provides a reliable identity authentication basis for product traceability.

[0078] Preferably, in step S5, the judgment process of the authenticity and compliance of the product is based on the following formula to calculate the comprehensive judgment index:

[0079] C = γ × V c + (1 - γ) × (1 - D)

[0080] Wherein, C is the comprehensive judgment index; γ is the weight coefficient; V c is the two-dimensional code information consistency verification result, taking the value of 0 or 1, 0 indicating inconsistency and 1 indicating consistency.

[0081] Specifically, the comprehensive judgment method of the authenticity and compliance of the product fuses the two-dimensional code information consistency verification result and the transportation trajectory difference degree index through weighted fusion to form a unified judgment standard. The two-dimensional code information consistency verification ensures the authenticity of the product identity by comparing the parsed information with the database stored information; the transportation trajectory difference degree index reflects the compliance of the product in the logistics link. The weight coefficient γ is dynamically adjusted according to the product type and risk level to balance the verification intensity of the two dimensions. In implementation, the system first decrypts and parses the two-dimensional code to extract the product basic information and dynamic parameters. Then, these information is compared with the latest records stored in the database to calculate the information matching degree. At the same time, the GPS trajectory fusion model is called to calculate the transportation trajectory difference degree. After normalization processing, the two results are weighted and summed according to the pre-set weight to obtain the comprehensive judgment index. The system determines whether the product is real and compliant according to the comparison result of the index and the pre-set threshold. This method combines static information verification with dynamic trajectory analysis to form a multi-dimensional verification system, effectively improving the accuracy and reliability of the anti-fake judgment.

[0082] Preferably, in step S2, the Internet of Things terminal device collects GPS trajectory data at a frequency determined by the logistics transportation speed parameter V s and the transportation distance parameter L d , which is dynamically adjusted by the following formula:

[0083]

[0084] where F is the GPS trajectory data collection frequency; τ is a preset time-distance adjustment coefficient.

[0085] Specifically, the dynamic adjustment mechanism of the GPS trajectory collection frequency optimizes the data collection frequency in real time according to the logistics transportation speed and transportation distance, ensuring that sufficient trajectory information is obtained under different transportation scenarios. The logistics transportation speed reflects the speed of commodity movement, and the transportation distance reflects the length of the journey, both of which determine the spatiotemporal density requirement of trajectory data. The time-distance adjustment coefficient τ, as a system parameter, is used to balance the relationship between the collection frequency and energy consumption. In implementation, the system first obtains the initial transportation plan of the commodity, including the expected speed and distance range. Then, the initial collection frequency is dynamically determined according to the preset calculation formula. During transportation, the terminal device monitors the actual transportation speed in real time and automatically adjusts the collection frequency according to the speed change: increasing the sampling interval to reduce energy consumption during high-speed transportation, and shortening the sampling interval to capture detailed trajectories during low-speed or stop. At the same time, the system dynamically optimizes the collection strategy according to the remaining transportation distance to ensure the integrity of the trajectory data throughout the journey. This mechanism not only ensures the quality of trajectory data, but also effectively reduces the energy consumption of the terminal device, prolongs the service life of the device, and improves the operating efficiency of the system.

[0086] Preferably, in step S4, the operation of embedding the real-time state parameter of the commodity circulation process in the initial two-dimensional code is expressed as:

[0087]

[0088] where Q embed is the two-dimensional code information after embedding the real-time state parameter; w j is the weight of the jth real-time state parameter; S j is the jth e-commerce supply chain real-time state parameter, and m is the total number of real-time state parameters.

[0089] Specifically, the specific method of embedding the real-time state parameters of commodity circulation in the two-dimensional code is to assign weights to each parameter, quantify it into a codeable numerical value, and fuse it with the initial two-dimensional code information. The weight of each real-time state parameter is determined according to its influence on the quality and authenticity of the commodity, and important parameters obtain higher weights. The system first standardizes the collected multi-dimensional parameters to eliminate dimensional differences. Then, the processed parameter values are multiplied by the corresponding weights to obtain weighted parameter values. These values are accumulated and embedded in the reserved area of the two-dimensional code through a specific encoding algorithm. When implemented, the system establishes a parameter weight configuration table to dynamically adjust the weights of each parameter according to the characteristics of the commodity and the demand of the supply chain. In the data collection stage, the terminal device acquires real-time state parameters of various types and pre-processes them according to the preset rules. The embedding process uses lossless encoding technology to ensure that the original two-dimensional code information is not damaged. After embedding is completed, the system recalculates the check bits of the two-dimensional code to ensure the integrity and readability of the data. This method makes the two-dimensional code a carrier of real-time state of the commodity, enhancing the comprehensiveness and timeliness of the traceability information.

[0090] Preferably, in step S3, the feature matching process of the GPS trajectory data sequence and the standard transportation route data is calculated by the following formula:

[0091]

[0092] wherein S sim is the feature similarity.

[0093] Specifically, the feature matching algorithm of GPS trajectory data and standard route calculates the ratio of the minimum distance between trajectory points to the maximum coordinate value to quantify the trajectory similarity. This method considers both the absolute difference and the relative proportion of spatial coordinates, avoiding matching errors caused by different coordinate scales. When implemented, the system first aligns the sampling points of the actual trajectory and the standard route to ensure the same data structure. Then, the coordinate distance between the actual trajectory point and the nearest point on the standard route is calculated point by point, and the minimum value of these distances is selected as the basic unit of feature matching. At the same time, the maximum value of all trajectory point coordinate values is calculated as a normalization factor. Divide the minimum distance value by the maximum coordinate value to get the similarity index of each sampling point. Finally, the indices of all sampling points are accumulated and averaged to get the overall trajectory similarity. The closer the similarity value is to 1, the more consistent the actual trajectory is with the standard route. This algorithm ensures matching accuracy while reducing computational complexity, improving the efficiency of trajectory anomaly detection, and providing an effective technical means for logistics path supervision.

[0094] Preferably, in step S6, the process of feeding the judgment result to the e-commerce supply chain management center database uses the following data transmission priority formula:

[0095] P r = δ x C + (1 - δ) x U l

[0096] wherein, P r is the data transmission priority; δ is the weight coefficient; U l is the real-time load rate of the Internet of Things communication network.

[0097] Specifically, the method for determining the data transmission priority comprehensively considers the commodity authenticity determination result and the network load condition, and optimizes the data feedback path. The commodity authenticity comprehensive determination index reflects the credibility of the verification result, and the network real-time load rate reflects the congestion degree of the current communication link. The system dynamically allocates the data transmission priority by weighted combination of the two factors. In implementation, the system first acquires the comprehensive determination index of the commodity and the current network load state. For high-risk commodities (determination index below the threshold value), the system gives a higher transmission priority to ensure that important information is transmitted first. At the same time, the network load condition is monitored, and when the network is congested, the transmission frequency of low-priority data is appropriately reduced to ensure the transmission quality of critical data. After the priority is determined, the system selects the optimal communication link for data transmission according to the preset routing strategy. For high-priority data, a multi-channel backup transmission mechanism is used to ensure reliable data arrival. This method effectively balances the timeliness and reliability of data transmission, improves the running stability of the system in complex network environment, and ensures that the supply chain management center obtains critical information in time.

[0098] As Figure 2 shown, the e-commerce supply chain full-process traceability anti-counterfeiting platform based on the Internet of Things comprises:

[0099] A multi-dimensional information encryption coding module: the category, production batch, specification and model basic information of the commodity in the e-commerce supply chain are coded and processed by a preset two-dimensional code encryption algorithm, and the information is irreversibly encrypted and converted by a multi-dimensional key substitution and chaotic mapping mechanism to generate an initial two-dimensional code.

[0100] A space-time trajectory perception processing module: after being bound with the commodity, the geographic position information of the commodity in the circulation process is acquired in real time according to a dynamically adjusted acquisition frequency to generate a GPS trajectory data sequence; at the same time, a GPS trajectory fusion model is constructed to perform feature matching and deviation calculation on the acquired trajectory data and preset standard transportation route data to obtain a difference index of the actual transportation trajectory of the commodity and the standard transportation route.

[0101] A dynamic parameter fusion updating module: the supplier qualification grade, logistics node processing timeliness, and warehouse environment temperature and humidity threshold parameters in the e-commerce supply chain are acquired in real time to dynamically update the initial two-dimensional code, and real-time state parameters in the commodity circulation process are embedded in the two-dimensional code through information fusion operation.

[0102] The bidirectional information verification and analysis module: at the commodity sales terminal, the corresponding two-dimensional code decryption algorithm is used to analyze the commodity two-dimensional code, the commodity information obtained by analysis is consistent with the updated two-dimensional code information, the difference degree index of the transportation track is combined to judge the authenticity and circulation compliance of the commodity;

[0103] The data hierarchical feedback transmission module: according to the comprehensive judgment index of the authenticity and circulation compliance of the commodity calculated and the real-time load rate of the Internet of Things communication network, the data transmission priority is determined, and the commodity judgment result is fed back to the e-commerce supply chain management center database through the Internet of Things communication network;

[0104] The global collaborative management module: respectively with the multi-dimensional information encryption and coding module, the space-time track perception processing module, the dynamic parameter fusion updating module, the bidirectional information verification and analysis module, the data hierarchical feedback transmission module, the communication connection is established, through the preset management strategy, the running parameters, data interaction process and collaborative work logic of each module are uniformly managed, and the stable operation of the whole process of the platform is guaranteed.

[0105] The e-commerce supply chain whole-process traceability anti-counterfeiting method and platform based on Internet of Things, through the multi-dimensional information encryption and coding module, using advanced two-dimensional code encryption algorithm, the basic information such as commodity category and production batch is irreversibly encrypted and converted to generate initial two-dimensional code. Compared with traditional manual input and static bar code, not only the information input efficiency is improved, but also the information is prevented from being tampered from the root, ensuring the accuracy and security of the commodity information. The dynamic parameter fusion updating module can real-time obtain the supply chain parameters such as supplier qualification and logistics time limit, dynamically update the two-dimensional code, embed the real-time state parameters of commodity circulation, realize the whole-process dynamic management of commodity information, and break the limitations of traditional technology information lag and error.

[0106] In the face of the problem of lack of dynamic monitoring capability of traditional technology, after the space-time track perception processing module is bound with the commodity, the geographic position information of the commodity can be obtained in real time according to the dynamically adjusted frequency, the GPS track data sequence is generated, and the feature matching and deviation calculation are performed with the preset standard transportation route data to obtain the transportation track difference degree index. At the same time, combined with the parameters such as warehouse environment temperature and humidity, the whole process of commodity circulation is monitored. At the sales terminal, the bidirectional information verification and analysis module decrypts and analyzes the two-dimensional code and verifies the information, and combines the transportation track difference degree to accurately judge the authenticity and circulation compliance of the commodity. The data hierarchical feedback transmission module efficiently feeds back the judgment result to the management center database, and the global collaborative management module uniformly manages the operation of each module, forms a complete dynamic monitoring closed loop, and completely solves the problems that traditional technology cannot track the state of the commodity in real time and effectively prevent fake and inferior commodities.

[0107] The present application realizes accurate traceability and reliable anti-counterfeiting of the whole process of the e-commerce supply chain through close cooperation and innovative functions of each module, greatly improves the supply chain management level, and provides a strong guarantee for the healthy development of the e-commerce industry.

[0108] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "arrangement", "installation", "connection", "connection", "fixing" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0109] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various equivalent changes, modifications, replacements and variations of the embodiments can be made without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalent scope.

Claims

1. A method for full-process traceability and anti-counterfeiting in e-commerce supply chains based on the Internet of Things, characterized in that: Includes the following steps: Step S1: Using a preset QR code encryption algorithm, the basic information of goods in the e-commerce supply chain, including product category, production batch, and specifications, is encoded to generate an initial QR code. The QR code encryption algorithm uses a multi-dimensional key substitution and chaotic mapping mechanism to perform irreversible encryption and transformation of information. Step S2: Bind the goods to an IoT terminal device with GPS positioning function. During the circulation of goods, the IoT terminal device collects the geographical location information of the goods at fixed time intervals and generates corresponding GPS trajectory data sequences. Step S3: Construct a GPS trajectory fusion model, perform feature matching and deviation calculation on the GPS trajectory data sequence collected in step S2 and the standard transportation route data preset by the e-commerce supply chain, and obtain the difference index between the actual transportation trajectory of the goods and the standard transportation route. Step S4: Based on different parameters of the e-commerce supply chain, including supplier qualification level, logistics node processing time, and temperature and humidity threshold of the storage environment, dynamically update the initial QR code generated in step S1 and embed real-time status parameters of the product flow process into the QR code. Step S5: At the product sales terminal, the product QR code is parsed using the corresponding QR code decryption algorithm. The parsed product information is then checked for consistency with the information updated in Step S4. At the same time, the authenticity and circulation compliance of the product are determined by combining the difference index obtained in Step S3. Step S6: The judgment result of step S5 is fed back to the e-commerce supply chain management center database through the Internet of Things communication network to carry out closed-loop management of the product traceability information throughout the entire process.

2. The IoT-based e-commerce supply chain traceability and anti-counterfeiting method according to claim 1, characterized in that, In step S3, the GPS trajectory fusion model calculates the difference index between the actual transportation trajectory of the goods and the standard transportation route using the following formula: Where D is the difference index; α and β are weighting coefficients, and α+β=1; n is the number of sampling points in the GPS trajectory data sequence; The coordinates of the actual transportation trajectory of the goods at the i-th sampling point; Let t be the coordinates of the standard transportation route for the i-th sampling point; a The actual time spent transporting the goods; t s This refers to standard transportation time.

3. The IoT-based e-commerce supply chain traceability and anti-counterfeiting method according to claim 1, characterized in that, In step S4, the process of dynamically updating the initial QR code based on e-commerce supply chain parameters is expressed as follows: Q new =Q old ⊕f(S q ,T p ,H t ) Among them, Q new This is the updated QR code information; Q old The initial QR code information; ⊕ represents the information fusion operation; f(S) q T p H t S is a function that generates update information based on e-commerce supply chain parameters; q This represents the supplier qualification level parameter, quantifying the supplier's creditworthiness in numerical form; T p This represents the processing time parameter for logistics nodes, recording the cargo processing time for each logistics node; H t This indicates the temperature and humidity threshold parameters for the storage environment, including real-time monitoring values ​​of temperature and humidity.

4. The IoT-based e-commerce supply chain traceability and anti-counterfeiting method according to claim 1, characterized in that, In step S1, the key generation process of the QR code encryption algorithm adopts the following formula: K=g(P id ,B no ,R and ) Where K is the generated encryption key; g is the key generation function; P id B is a unique identifier code for the product. no For product batch number; R and It serves as a random number seed, generated in real time by IoT terminal devices.

5. The IoT-based e-commerce supply chain traceability and anti-counterfeiting method according to claim 2, characterized in that, In step S5, the determination of the authenticity and compliance of the goods is based on the calculation of a comprehensive judgment index using the following formula: C=γ×V c +(1-γ)×(1-D) Where C is the comprehensive judgment index; γ is the weighting coefficient; V c This is the result of the QR code information consistency verification. The value is 0 or 1, where 0 indicates inconsistency and 1 indicates consistency.

6. The IoT-based e-commerce supply chain traceability and anti-counterfeiting method according to claim 1, characterized in that, In step S2, the frequency at which the IoT terminal device collects GPS trajectory data is determined based on the logistics transportation speed parameter V of the e-commerce supply chain. s and transportation distance parameter L d It can be dynamically adjusted using the following formula: Where F is the GPS trajectory data acquisition frequency; τ is the preset time-distance adjustment coefficient.

7. The IoT-based e-commerce supply chain traceability and anti-counterfeiting method according to claim 1, characterized in that, In step S4, the operation of embedding real-time status parameters of the product circulation process into the initial QR code is expressed as follows: Among them, Q embed To embed the QR code information after real-time status parameters; w j S represents the weight of the k-th real-time state parameter; j Let m be the j-th e-commerce supply chain real-time status parameter, and m be the total number of real-time status parameters.

8. The IoT-based e-commerce supply chain traceability and anti-counterfeiting method according to claim 2, characterized in that, In step S3, the feature matching process between the GPS trajectory data sequence and the standard transportation route data calculates the feature similarity using the following formula: Among them, S sim For feature similarity.

9. The IoT-based e-commerce supply chain traceability and anti-counterfeiting method according to claim 5, characterized in that, In step S6, the process of feeding the judgment result back to the e-commerce supply chain management center database adopts the following data transmission priority formula: P r =δ×C+(1-δ)×U l Among them, P r Data transmission priority; δ is the weighting coefficient; U l This refers to the real-time load rate of the Internet of Things (IoT) communication network.

10. An e-commerce supply chain end-to-end traceability and anti-counterfeiting platform based on the Internet of Things, characterized in that: include: Multidimensional information encryption encoding module: Through a preset QR code encryption algorithm, the basic information of product category, production batch, and specifications in the e-commerce supply chain is encoded, and the information is irreversibly encrypted and converted using a multidimensional key substitution and chaotic mapping mechanism to generate the initial QR code. Spatiotemporal trajectory perception and processing module: After being bound to the product, it acquires the geographical location information of the product in the circulation process in real time according to the dynamically adjusted collection frequency, and generates GPS trajectory data sequence; at the same time, it constructs a GPS trajectory fusion model, performs feature matching and deviation calculation on the collected trajectory data and the preset standard transportation route data, and obtains the difference index between the actual transportation trajectory of the product and the standard transportation route. Dynamic parameter fusion and update module: Real-time acquisition of supplier qualification level, logistics node processing time and warehouse environment temperature and humidity threshold parameters in the e-commerce supply chain, dynamic update of the initial QR code, and embedding real-time status parameters of the product flow process into the QR code through information fusion operation. Two-way information verification and parsing module: At the product sales terminal, the corresponding QR code decryption algorithm is used to parse the product QR code, and the consistency of the parsed product information with the updated QR code information is verified. Combined with the transportation trajectory difference index, the authenticity and circulation compliance of the product are judged. Data hierarchical feedback transmission module: Based on the calculated comprehensive judgment index of product authenticity and circulation compliance, and the real-time load rate of the IoT communication network, the data transmission priority is determined, and the product judgment results are fed back to the e-commerce supply chain management center database through the IoT communication network. The global collaborative management module establishes communication connections with the multi-dimensional information encryption and encoding module, the spatiotemporal trajectory perception and processing module, the dynamic parameter fusion and update module, the two-way information verification and parsing module, and the data hierarchical feedback transmission module. Through preset management strategies, it uniformly manages the operating parameters, data interaction processes, and collaborative work logic of each module.

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