Wolfberry production place information traceability management system based on block chain

Abstract

A blockchain-based Chinese wolfberry origin information traceability management system relates to the technical field of blockchains, and divides a supply process of Chinese wolfberry into a plurality of process subsequences, obtains an error level of each process subsequence, and adds a feature matching detection mechanism to a blockchain node according to the error level; performing feature matching detection operation on the wolfberry data received by the block chain node added with the feature matching detection mechanism; obtaining wolfberry batch numbers in the wolfberry data, clustering the wolfberry data stored in the temporary storage spaces of the plurality of block chain nodes according to the wolfberry batch numbers, and constructing a delay detection flow program list of the plurality of wolfberry batch numbers; performing delay detection on the wolfberry data stored in the temporary storage space of each block chain node according to the delay detection flow program list; and data uplink operation is carried out on the Chinese wolfberry data subjected to delay detection, so that the efficiency and the accuracy of Chinese wolfberry production place information traceability management are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of blockchain, and in particular to a wolfberry origin information traceability management system based on blockchain. Background Art

[0002] Prior art CN116843353B "A system and method for tracing agricultural products based on blockchain and the Internet of Things" The Internet of Things device is used to obtain the production information of agricultural products and send the production information to the origin server; the origin server is used to generate first verification information of the production information, and send the first verification information to the blockchain node; the blockchain node is used to store the first verification information in the blockchain; the terminal device is used to respond to the user's traceability operation on the agricultural product and send the traceability information to the origin server; the traceability information is used to request feedback on the production information of the agricultural product; the origin server is used to obtain the first verification information from the blockchain node according to the traceability information; the origin server is used to verify the production information according to the first verification information, and send the production information to the terminal device according to the traceability information when the verification is successful.

[0003] The prior art CN116883026A "Agricultural product origin traceability method and system based on big data" constructs a basic information database; establishes standard agricultural product samples as a reference benchmark, extracts specific features of agricultural products, and constructs a multidimensional feature extraction set; performs sample feature analysis in the same region, and sets feature limit intervals for samples in the same region; establishes an origin traceability network, and based on the establishment results, performs feature similarity evaluation of different regions to determine similar traceability regions; builds a positioning and identification subnetwork for similar traceability regions, and couples the subnetwork to the origin traceability network; extracts a target agricultural product data set, inputs the target agricultural product data set into the origin traceability network, and generates an origin traceability result.

[0004] The use of blockchain technology for wolfberry origin traceability management has many advantages. These advantages are mainly reflected in the fact that once the data is recorded on the blockchain, it cannot be modified or deleted, which ensures the authenticity and integrity of the data. The immutability allows consumers to believe that the information they see is true, enhancing their trust in the product. When a food safety incident occurs, the blockchain can be used to quickly locate the source of the problem and take prompt response measures. However, the use of blockchain technology for wolfberry origin traceability management also has some technical problems that need to be solved urgently, including data input accuracy issues. When human errors or deliberate falsification may lead to inaccurate data input, once this data is written to the blockchain, it is very difficult to modify it, as well as data consistency issues: data in different links may be inconsistent, especially when data is exchanged between multiple independent entities. Summary of the invention

[0005] In order to solve the above technical problems, the object of the present invention is to provide a wolfberry origin information traceability management system based on blockchain, including a wolfberry information management platform, wherein the wolfberry information management platform is communicatively connected with a data analysis module, a full-process prediction module, a feature matching detection module, a batch clustering module, a delay detection module, an identity authentication module and a data uplink module;

[0006] The data analysis module is used to divide the wolfberry supply process into several process subsequences according to the wolfberry supply process information in the wolfberry information management platform, obtain the error level of each process subsequence, and add a feature matching detection mechanism to the blockchain node according to the error level;

[0007] The full-process prediction module is used to output the predicted wolfberry data uploaded by each participant corresponding to each process subsequence;

[0008] The feature matching detection module is used to perform a feature matching detection operation on the wolfberry data received by the blockchain node to which the feature matching detection mechanism is added;

[0009] The batch clustering module obtains the wolfberry batch number in the wolfberry data, clusters the wolfberry data stored in the temporary storage space of several blockchain nodes according to the wolfberry batch number, and constructs a delayed detection flow program list of several wolfberry batch numbers;

[0010] The delay detection module is used to perform delay detection on the wolfberry data stored in the temporary storage space of each blockchain node according to the delay detection flow program list;

[0011] The identity verification module is used to perform the participant identity verification operation according to the participant's data uploading method;

[0012] The data uplink module is used to perform data uplink operation on the wolfberry data that passes the delayed detection.

[0013] Furthermore, the construction process of the wolfberry information management platform includes:

[0014] A wolfberry information management platform is constructed based on blockchain technology. The wolfberry information management platform is communicatively connected to a number of blockchain nodes, and each blockchain node is linked to each other to form a blockchain network. Participants who join the wolfberry information management platform are obtained, and each participant who joins the wolfberry information management platform is communicatively linked to a blockchain node. The blockchain node is used to receive the wolfberry data uploaded by the participants, mark the upload time, and set a collection cycle.

[0015] Furthermore, the data analysis module divides the wolfberry supply process into several process subsequences according to the wolfberry supply process information in the wolfberry information management platform, obtains the error level of each process subsequence, and adds a feature matching detection mechanism to the blockchain node according to the error level, including:

[0016] Obtain the supply process information of wolfberry in the wolfberry information management platform, obtain the supply process characteristics according to the supply process information, split the wolfberry supply process according to the supply process characteristics, and divide it into several process sub-sequences, and use data retrieval to obtain the monitoring indicators of various types of wolfberry that need to be collected by the several process sub-sequences according to the supply process characteristics of the several process sub-sequences, obtain the process sub-sequences to which each participant in the wolfberry information management platform belongs, and obtain the historical wolfberry data uploaded to the blockchain node by several participants corresponding to each process sub-sequence in the wolfberry information management platform, wherein the monitoring indicators of various types of wolfberry include origin indicators, processing indicators, logistics indicators, processed quality indicators and unprocessed quality indicators, and obtain the error input probability and data upload method of the monitoring indicators of various types of wolfberry corresponding to each process sub-sequence according to the historical wolfberry data, wherein the data upload method includes integrated input of Internet of Things devices and manual input;

[0017] The error input probability and data upload method of each type of wolfberry monitoring indicator corresponding to each process subsequence are used as evaluation indicators, and the indicator weight matrix of the evaluation indicator is set. The membership matrix of each process subsequence to the preset error level is judged through fuzzy comprehensive evaluation. The error level of each process subsequence is obtained according to the membership matrix and the indicator weight matrix, and the process subsequences with an error level greater than the preset error level threshold are screened out, the participants corresponding to the process subsequences are obtained, and a feature matching detection mechanism is added to the blockchain nodes of the communication links of the participants.

[0018] Furthermore, the process in which the full-process prediction module outputs the predicted wolfberry data uploaded by each participant corresponding to each process subsequence includes:

[0019] Construct a full-process prediction model based on deep learning, obtain wolfberry data within several historical collection cycles of each blockchain node, use the wolfberry data as a training set and a test set, input the training set into the full-process prediction model for training until the loss function training is stable, save the model parameters, test the full-process prediction model with the test set until it meets the preset requirements, and output the full-process prediction model;

[0020] According to the full-process prediction model, the predicted wolfberry data uploaded by each participant corresponding to each process subsequence in the current collection cycle is output.

[0021] Furthermore, the process of the feature matching detection module performing a feature matching detection operation on the wolfberry data received by the blockchain node to which the feature matching detection mechanism is added includes:

[0022] Obtain the predicted wolfberry data uploaded by each participant corresponding to each process subsequence in the current collection cycle. When the blockchain node receives the wolfberry data uploaded by the participant, obtain the predicted wolfberry data of the participant, perform feature matching on the wolfberry data and the predicted wolfberry data, obtain the feature matching degree of the wolfberry data uploaded by the participant, compare the feature matching degree with a preset feature matching degree threshold, and if the feature matching degree is greater than or equal to the feature matching degree threshold, store the wolfberry data in the temporary storage space of the blockchain node;

[0023] If the feature matching degree is less than the feature matching degree threshold, a data error warning signal of the participant is generated and fed back to the blockchain node. The blockchain node sends a data retransmission instruction to the participant according to the data error warning signal. The participant sends new wolfberry data to the blockchain node according to the data retransmission instruction. If the new wolfberry data is consistent with the wolfberry data, the participant's identity verification operation is performed;

[0024] If the new wolfberry data is inconsistent with the wolfberry data, the new wolfberry data is feature matched with the predicted wolfberry data to obtain the feature matching degree of the new wolfberry data. If the feature matching degree of the new wolfberry data is less than the feature matching degree threshold, the participant identity authentication operation is performed. If the feature matching degree of the new wolfberry data is greater than or equal to the feature matching degree threshold, the new wolfberry data is stored in the temporary storage space of the blockchain node.

[0025] Furthermore, the batch clustering module obtains the wolfberry batch number in the wolfberry data, clusters the wolfberry data stored in the temporary storage space of several blockchain nodes according to the wolfberry batch number, and constructs a delayed detection flow program list of several wolfberry batch numbers, including:

[0026] Acquire wolfberry data stored in the temporary storage space of several blockchain nodes in the wolfberry information management platform, extract the wolfberry batch number from the wolfberry data, screen out wolfberry data with the same wolfberry batch number from the wolfberry data stored in the temporary storage space of several blockchain nodes, acquire the process subsequence to which the wolfberry data belongs, acquire the sequential connection relationship of the process subsequences, sort the wolfberry data of different process subsequences according to the sequential connection relationship, and generate a delay detection flow program list;

[0027] Pre-set the delay detection standard of the last process subsequence in the delay detection flow program list, obtain the processed quality index in the wolfberry data of each process subsequence except the last process subsequence in the delay detection flow program list, obtain the next process subsequence of each process subsequence except the last process subsequence in the delay detection flow program list, and use the processed quality index in the wolfberry data of each process subsequence as the delay detection standard for the next process subsequence of each process subsequence.

[0028] Furthermore, the process of the delay detection module performing delay detection on the wolfberry data stored in the temporary storage space of each blockchain node according to the delay detection flow program list includes:

[0029] Obtain a delay detection flow program list corresponding to the wolfberry data stored in the temporary storage space of the blockchain node, obtain the delay detection standard corresponding to the wolfberry data according to the delay detection flow program list, and simultaneously obtain the unprocessed quality index in the wolfberry data, compare the unprocessed quality index with the delay detection standard for consistency, if they are consistent, perform the wolfberry data chain operation, if not consistent, mark the participant who uploaded the wolfberry data as a malicious participant, and remove the wolfberry data stored in the temporary storage space.

[0030] Furthermore, the process of the identity verification module performing the participant identity verification operation according to the participant's data uploading method includes:

[0031] If the data upload method of the participant is the integrated input of the IoT device, the fault warning information of the participant's IoT device is generated;

[0032] If the participant's data upload method is manual input, a biometric database is pre-built, and the biometric database includes the biometric data of the participant whose data upload method is manual input. The blockchain node sends a biometric verification instruction to the participant, and the participant sends the biometric data to the blockchain according to the biometric verification instruction. The biometric data is matched with the biometric data in the biometric database for consistency. If the match is successful, the wolfberry data uploaded by the participant is stored in the temporary storage space of the blockchain node. If the match fails, the wolfberry data uploaded by the participant received by the blockchain node is removed, and the participant is marked as a malicious participant.

[0033] Furthermore, the process of the data uplink module performing a data uplink operation on the wolfberry data detected by delayed detection includes:

[0034] The mining nodes, verification rules and consensus mechanism of the blockchain network are preset, a new block is created through the mining nodes, the wolfberry data stored in the temporary storage space of the blockchain node is transferred to the new block, and the new block is broadcast to the blockchain network. Other blockchain nodes in the blockchain network verify the new block based on the verification rules and consensus mechanism;

[0035] After the new block is verified, the new block is linked to the blockchain node, and the wolfberry data in the new block is updated to the blockchain copy of all blockchain nodes.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The method you described for managing wolfberry origin information traceability based on blockchain technology aims to solve the problems of data input accuracy and data consistency, and improve the reliability and accuracy of data by introducing technical means such as feature matching detection mechanism and delayed detection process sequence. The specific implementation of this method can bring the following beneficial effects:

[0038] 1. Improve data accuracy:

[0039] Feature matching detection mechanism: By adding a feature matching detection mechanism to blockchain nodes, data that does not meet the preset features can be automatically identified and filtered out, thereby reducing the possibility of human error or deliberate fraud;

[0040] Delayed detection process sequence: Use the delayed detection process sequence to further verify the data to ensure that only data that has been confirmed multiple times can be finally uploaded to the chain, which can effectively avoid the impact of one-time erroneous data.

[0041] 2. Ensure data consistency:

[0042] Process sub-sequence division: The wolfberry supply process is divided into several process sub-sequences, and each sub-sequence is managed specifically to help ensure data consistency between different links;

[0043] Error level assessment: By assessing the error level of each process subsequence, the management and supervision of those links that are more prone to errors can be strengthened in a targeted manner.

[0044] 3. Realize data clustering and tracing:

[0045] Wolfberry batch number: Assign a unique batch number to each batch of wolfberries and cluster the data based on this number, which helps to accurately trace a specific batch of wolfberries;

[0046] Delay detection flow program list: By constructing a delay detection flow program list, the data in the temporary storage space of each blockchain node can be delayed detected to ensure the timeliness and accuracy of the data.

[0047] 4. Strengthening participant identity verification:

[0048] Authentication operation: By authenticating the data upload method of the participants, it can be ensured that only authenticated participants can upload data, thereby reducing the risk of illegal operations.

[0049] 5. Improve the flexibility and scalability of the system:

[0050] Dynamic adjustment: The feature matching detection mechanism based on the error level can be dynamically adjusted according to the actual situation, so that the system can adapt to changing business needs.

[0051] 6. Improve user experience:

[0052] Transparent management: The transparent management of information achieved through blockchain technology can not only improve the efficiency and accuracy of wolfberry origin traceability management, but also largely solve the problems of data input accuracy and data consistency, thereby improving the transparency and reliability of the entire supply chain, allowing consumers to more intuitively understand the origin and production process of wolfberry, thereby enhancing their trust in the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a schematic diagram of a blockchain-based wolfberry origin information traceability management system in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0055] like Figure 1 As shown, a wolfberry origin information traceability management system based on blockchain includes a wolfberry information management platform, and the wolfberry information management platform is communicatively connected with a data analysis module, a full-process prediction module, a feature matching detection module, a batch clustering module, a delay detection module, an identity authentication module and a data chain module;

[0056] The data analysis module is used to divide the wolfberry supply process into several process subsequences according to the wolfberry supply process information in the wolfberry information management platform, obtain the error level of each process subsequence, and add a feature matching detection mechanism to the blockchain node according to the error level;

[0057] The full-process prediction module is used to output the predicted wolfberry data uploaded by each participant corresponding to each process subsequence;

[0058] The feature matching detection module is used to perform a feature matching detection operation on the wolfberry data received by the blockchain node to which the feature matching detection mechanism is added;

[0059] The batch clustering module obtains the wolfberry batch number in the wolfberry data, clusters the wolfberry data stored in the temporary storage space of several blockchain nodes according to the wolfberry batch number, and constructs a delayed detection flow program list of several wolfberry batch numbers;

[0060] The delay detection module is used to perform delay detection on the wolfberry data stored in the temporary storage space of each blockchain node according to the delay detection flow program list;

[0061] The identity verification module is used to perform the participant identity verification operation according to the participant's data uploading method;

[0062] The data uplink module is used to perform data uplink operation on the wolfberry data that passes the delayed detection.

[0063] It should be further explained that, in the specific implementation process, the construction process of the wolfberry information management platform includes:

[0064] A wolfberry information management platform is constructed based on blockchain technology. The wolfberry information management platform is communicatively connected to a number of blockchain nodes, and each blockchain node is linked to each other to form a blockchain network. Participants who join the wolfberry information management platform are obtained. The participants include growers, processing companies, distributors, and retailers, etc., and each participant who joins the wolfberry information management platform is communicatively linked to a blockchain node. The blockchain node is used to receive the wolfberry data uploaded by the participants, mark the upload time, and set a collection cycle.

[0065] It should be further explained that, in the specific implementation process, the data analysis module divides the wolfberry supply process into several process subsequences according to the wolfberry supply process information in the wolfberry information management platform, obtains the error level of each process subsequence, and the process of adding a feature matching detection mechanism to the blockchain node according to the error level includes:

[0066] Obtain the supply process information of wolfberry in the wolfberry information management platform, obtain the supply process characteristics according to the supply process information, split the wolfberry supply process according to the supply process characteristics, and divide it into several process sub-sequences. According to the supply process characteristics of several process sub-sequences, use data retrieval to obtain various types of wolfberry monitoring indicators that need to be collected for several process sub-sequences. The supply process characteristics include planting, fertilization, irrigation, picking, primary processing (such as cleaning, screening), deep processing (such as drying, packaging) and transportation, etc., obtain the process sub-sequences to which each participant in the wolfberry information management platform belongs, obtain the historical wolfberry data uploaded to the blockchain node by several participants corresponding to each process sub-sequence in the wolfberry information management platform, the various types of wolfberry monitoring indicators include origin indicators, processing indicators, logistics indicators, processed quality indicators and unprocessed quality indicators, obtain the error input probability and data upload method of various types of wolfberry monitoring indicators corresponding to each process sub-sequence according to the historical wolfberry data, and the data upload method includes IoT device integrated input and manual input;

[0067] The error input probability and data upload method of each type of wolfberry monitoring indicator corresponding to each process subsequence are used as evaluation indicators, and the indicator weight matrix of the evaluation indicator is set. The membership matrix of each process subsequence to the preset error level is judged through fuzzy comprehensive evaluation. The error level of each process subsequence is obtained according to the membership matrix and the indicator weight matrix, and the process subsequences with an error level greater than the preset error level threshold are screened out, the participants corresponding to the process subsequences are obtained, and a feature matching detection mechanism is added to the blockchain nodes of the communication links of the participants.

[0068] It should be further explained that, in the specific implementation process, the process of obtaining the error input probability of each type of wolfberry monitoring indicator corresponding to each process subsequence is as follows:

[0069] Obtain the cumulative number of times that the participants corresponding to each process subsequence upload wolfberry data in all historical collection cycles, as well as the cumulative number of malicious times that the participants corresponding to each process subsequence are marked as malicious participants when uploading wolfberry data, obtain each type of wolfberry monitoring indicator corresponding to each process subsequence, add the cumulative number of times that the participants corresponding to each process subsequence upload wolfberry data and the cumulative number of malicious times that the participants corresponding to each process subsequence are marked as malicious participants when uploading wolfberry data to each type of wolfberry monitoring indicator corresponding to each process subsequence, obtain the cumulative number and malicious cumulative number corresponding to each type of wolfberry monitoring indicator, and obtain the error input probability of each type of wolfberry monitoring indicator corresponding to each process subsequence according to the cumulative number and malicious cumulative number corresponding to each type of wolfberry monitoring indicator. The calculation formula is:

[0070]

[0071] Among them, fhi represents the probability of incorrect input of type wolfberry monitoring indicator i, NV i Indicates the cumulative number of times corresponding to type wolfberry monitoring indicator i, NS i Indicates the cumulative number of malicious events corresponding to type wolfberry monitoring indicator i.

[0072] It should be further explained that, in the specific implementation process, the data uploading method includes integrated input of IoT devices and manual input. The integrated input of IoT devices is specifically to install IoT devices such as sensors at the planting base to monitor environmental indicators such as soil moisture, temperature, light intensity, etc. in real time, as well as relevant indicators collected by IoT devices in the supply chain process during processing, packaging, storage and transportation, such as batch number, processing date, storage conditions, etc. The manual input is specifically to manually input indicator data of important activities such as planting, fertilization, irrigation, picking, etc. by staff;

[0073] It should be further explained that in the specific implementation process, the monitoring indicators of various types of wolfberries include:

[0074] Origin indicators: including wolfberry planting areas and soil composition, climate conditions, irrigation water, fertilizer and pesticide use, etc.

[0075] Processing indicators: including the index data of each link of wolfberry planting, fertilization, irrigation, picking, primary processing (such as cleaning and screening), deep processing (such as drying and packaging);

[0076] Logistics indicators: including timestamp and location information, batch number, processing date, storage conditions and other indicator data of each logistics node from warehouse to transport vehicle to retailer.

[0077] Processed quality index: The processed quality index indicates the index of wolfberry after the participants of the process subsequence where wolfberry is located receive the wolfberry goods and process the wolfberry in the wolfberry goods according to the supply process characteristics of the process subsequence where wolfberry is located. The index includes color, size, shape, dryness and wetness, impurity (such as branches, leaves, soil, etc.) content, betaine (Betaine), Lycium barbarum polysaccharide (LBP): It is one of the main active ingredients of wolfberry and has immunomodulatory effects, carotenoids (Carotenoids): Such as β-carotene, which is good for vision, flavonoids (Flavonoids): It has antioxidant effects, amino acids (Amino Acids): A variety of essential amino acids for the human body, mineral elements: Such as calcium, iron, zinc and other trace elements, vitamin content, total bacteria count: Ensure that wolfberry is not contaminated by harmful microorganisms, molds and yeasts, etc.;

[0078] Unprocessed quality index: Unprocessed quality index refers to the index of wolfberry when the participants of the process subsequence where wolfberry is located just received the wolfberry goods without any processing of the wolfberry in the wolfberry goods. The initial index includes color, size, shape, dryness and wetness, impurity (such as branches, leaves, soil, etc.) content, betaine (Betaine), Lycium barbarum polysaccharide (LBP): It is one of the main active ingredients of wolfberry and has immunomodulatory effects, carotenoids (Carotenoids): Such as β-carotene, which is good for vision, flavonoids (Flavonoids): It has antioxidant effects, amino acids (Amino Acids): A variety of essential amino acids for the human body, mineral elements: Such as calcium, iron, zinc and other trace elements, vitamin content, total bacteria count: Ensure that wolfberry is not contaminated by harmful microorganisms, molds and yeasts, etc.;

[0079] It should be further explained that, in the specific implementation process, several process sub-sequences include the planting preparation stage, the growth stage, the harvesting stage, the processing stage, the logistics and warehousing stage, and the sales stage;

[0080] It should be further explained that, in the specific implementation process, the process of obtaining the error level of each process subsequence according to the membership matrix and the indicator weight matrix includes:

[0081] The indicator weight matrix and the membership matrix of the evaluation indicator are integrated through a formula to obtain a fuzzy comprehensive evaluation matrix of the evaluation indicator, and the membership of each process subsequence for different error levels is obtained according to the fuzzy comprehensive evaluation matrix, and the error level with the highest membership corresponding to each process subsequence is screened out, and the error level with the highest membership corresponding to each process subsequence is used as the error level of each process subsequence;

[0082] Wherein, the formula is:

[0083] M = αM1 × βM2;

[0084] Among them, M is the fuzzy comprehensive evaluation matrix of the evaluation index, M1 is the indicator weight matrix of the evaluation index, M2 is the membership matrix, "×" represents the multiplication of the elements at the corresponding positions of the weight matrix of the evaluation index and the membership matrix, and α and β are weighting parameters for controlling the balance between the weight matrix and the membership matrix in the fuzzy comprehensive evaluation matrix of the evaluation index.

[0085] It should be further explained that, in the specific implementation process, the process in which the full-process prediction module outputs the predicted wolfberry data uploaded by each participant corresponding to each process subsequence includes:

[0086] Construct a full-process prediction model based on deep learning, obtain wolfberry data within several historical collection cycles of each blockchain node, use the wolfberry data as a training set and a test set, input the training set into the full-process prediction model for training until the loss function training is stable, save the model parameters, test the full-process prediction model with the test set until it meets the preset requirements, and output the full-process prediction model;

[0087] According to the full-process prediction model, the predicted wolfberry data uploaded by each participant corresponding to each process subsequence in the current collection cycle is output.

[0088] It should be further explained that, in the specific implementation process, the process in which the feature matching detection module performs a feature matching detection operation on the wolfberry data received by the blockchain node to which the feature matching detection mechanism is added includes:

[0089] Obtain the predicted wolfberry data uploaded by each participant corresponding to each process subsequence in the current collection cycle. When the blockchain node receives the wolfberry data uploaded by the participant, obtain the predicted wolfberry data of the participant, perform feature matching on the wolfberry data and the predicted wolfberry data, obtain the feature matching degree of the wolfberry data uploaded by the participant, compare the feature matching degree with a preset feature matching degree threshold, and if the feature matching degree is greater than or equal to the feature matching degree threshold, store the wolfberry data in the temporary storage space of the blockchain node;

[0090] Among them, the calculation formula for obtaining the feature matching degree of the wolfberry data uploaded by the participants is:

[0091]

[0092] Among them, g represents the feature matching degree, D i Indicates the value of the monitoring index i of type wolfberry in wolfberry data, DA i It represents the value of the predicted wolfberry monitoring indicator type i in the wolfberry data, and n represents the total number of wolfberry monitoring indicator types in the wolfberry data.

[0093] If the feature matching degree is less than the feature matching degree threshold, a data error warning signal of the participant is generated and fed back to the blockchain node. The blockchain node sends a data retransmission instruction to the participant according to the data error warning signal. The participant sends new wolfberry data to the blockchain node according to the data retransmission instruction. If the new wolfberry data is consistent with the wolfberry data, the participant's identity verification operation is performed;

[0094] If the new wolfberry data is inconsistent with the wolfberry data, the new wolfberry data is feature matched with the predicted wolfberry data to obtain the feature matching degree of the new wolfberry data. If the feature matching degree of the new wolfberry data is less than the feature matching degree threshold, the participant identity authentication operation is performed. If the feature matching degree of the new wolfberry data is greater than or equal to the feature matching degree threshold, the new wolfberry data is stored in the temporary storage space of the blockchain node.

[0095] It should be further explained that, in the specific implementation process, the batch clustering module obtains the wolfberry batch number in the wolfberry data, clusters the wolfberry data stored in the temporary storage space of several blockchain nodes according to the wolfberry batch number, and constructs a delayed detection flow program list of several wolfberry batch numbers. The process includes:

[0096] Acquire wolfberry data stored in the temporary storage space of several blockchain nodes in the wolfberry information management platform, extract the wolfberry batch number from the wolfberry data, screen out wolfberry data with the same wolfberry batch number from the wolfberry data stored in the temporary storage space of several blockchain nodes, acquire the process subsequence to which the wolfberry data belongs, acquire the sequential connection relationship of the process subsequences, sort the wolfberry data of different process subsequences according to the sequential connection relationship, and generate a delay detection flow program list;

[0097] Pre-set the delay detection standard of the last process subsequence in the delay detection flow program list, obtain the processed quality index in the wolfberry data of each process subsequence except the last process subsequence in the delay detection flow program list, obtain the next process subsequence of each process subsequence except the last process subsequence in the delay detection flow program list, and use the processed quality index in the wolfberry data of each process subsequence as the delay detection standard for the next process subsequence of each process subsequence.

[0098] It should be further explained that, in the specific implementation process, the delay detection module performs delay detection on the wolfberry data stored in the temporary storage space of each blockchain node according to the delay detection flow program list, including:

[0099] Obtain a delay detection flow program list corresponding to the wolfberry data stored in the temporary storage space of the blockchain node, obtain the delay detection standard corresponding to the wolfberry data according to the delay detection flow program list, and simultaneously obtain the unprocessed quality index in the wolfberry data, compare the unprocessed quality index with the delay detection standard for consistency, if they are consistent, perform the wolfberry data chain operation, if not consistent, mark the participant who uploaded the wolfberry data as a malicious participant, and remove the wolfberry data stored in the temporary storage space.

[0100] It should be further explained that, in the specific implementation process, the process in which the identity authentication module performs the participant identity authentication operation according to the participant's data uploading method includes:

[0101] If the data upload method of the participant is the integrated input of the IoT device, the fault warning information of the participant's IoT device is generated;

[0102] If the participant's data upload method is manual input, a biometric database is pre-built, and the biometric database includes the biometric data of the participant whose data upload method is manual input. The blockchain node sends a biometric verification instruction to the participant, and the participant sends the biometric data to the blockchain according to the biometric verification instruction. The biometric data is matched with the biometric data in the biometric database for consistency. If the match is successful, the wolfberry data uploaded by the participant is stored in the temporary storage space of the blockchain node. If the match fails, the wolfberry data uploaded by the participant received by the blockchain node is removed, and the participant is marked as a malicious participant.

[0103] It should be further explained that, in the specific implementation process, the process of the data uplink module performing the data uplink operation on the wolfberry data that has passed the delayed detection includes:

[0104] The mining nodes, verification rules and consensus mechanism of the blockchain network are preset, a new block is created through the mining nodes, the wolfberry data stored in the temporary storage space of the blockchain node is transferred to the new block, and the new block is broadcast to the blockchain network. Other blockchain nodes in the blockchain network verify the new block based on the verification rules and consensus mechanism;

[0105] After the new block is verified, the new block is linked to the blockchain node, and the wolfberry data in the new block is updated to the blockchain copy of all blockchain nodes.

[0106] It needs to be further explained that, in the specific implementation process, the wolfberry information management platform sets up a wolfberry product traceability window. Users can log in to the wolfberry information management platform and enter the wolfberry batch number of the wolfberry that needs to be traced through the wolfberry product traceability window. The wolfberry information management platform retrieves the wolfberry data corresponding to each process subsequence corresponding to the wolfberry batch number from the blockchain network and feeds back to the user.

[0107] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A wolfberry origin information traceability management system based on blockchain, characterized in that: It includes a wolfberry information management platform, which is communicatively connected with a data analysis module, a full-process prediction module, a feature matching detection module, a batch clustering module, a delay detection module, an identity authentication module and a data uplink module; The data analysis module is used to divide the wolfberry supply process into several process subsequences according to the wolfberry supply process information in the wolfberry information management platform, obtain the error level of each process subsequence, and add a feature matching detection mechanism to the blockchain node according to the error level; The full-process prediction module is used to output the predicted wolfberry data uploaded by each participant corresponding to each process subsequence; The feature matching detection module is used to perform a feature matching detection operation on the wolfberry data received by the blockchain node to which the feature matching detection mechanism is added; The batch clustering module obtains the wolfberry batch number in the wolfberry data, clusters the wolfberry data stored in the temporary storage space of several blockchain nodes according to the wolfberry batch number, and constructs a delayed detection flow program list of several wolfberry batch numbers; The delay detection module is used to perform delay detection on the wolfberry data stored in the temporary storage space of each blockchain node according to the delay detection flow program list; The identity verification module is used to perform the participant identity verification operation according to the participant's data uploading method; The data uplink module is used to perform data uplink operation on the wolfberry data that passes the delayed detection.

2. According to claim 1, a blockchain-based wolfberry origin information traceability management system is characterized in that: The construction process of the wolfberry information management platform includes: A wolfberry information management platform is constructed based on blockchain technology. The wolfberry information management platform is communicatively connected to a number of blockchain nodes, and each blockchain node is linked to each other to form a blockchain network. Participants who join the wolfberry information management platform are obtained, and each participant who joins the wolfberry information management platform is communicatively linked to a blockchain node. The blockchain node is used to receive the wolfberry data uploaded by the participants, mark the upload time, and set a collection cycle.

3. According to claim 2, a blockchain-based wolfberry origin information traceability management system is characterized in that: The data analysis module divides the wolfberry supply process into several process subsequences according to the wolfberry supply process information in the wolfberry information management platform, obtains the error level of each process subsequence, and adds a feature matching detection mechanism to the blockchain node according to the error level, including: Obtain the supply process information of wolfberry in the wolfberry information management platform, obtain the supply process characteristics according to the supply process information, split the wolfberry supply process according to the supply process characteristics, and divide it into several process sub-sequences, and use data retrieval to obtain the monitoring indicators of various types of wolfberry that need to be collected by the several process sub-sequences according to the supply process characteristics of the several process sub-sequences, obtain the process sub-sequences to which each participant in the wolfberry information management platform belongs, and obtain the historical wolfberry data uploaded to the blockchain node by several participants corresponding to each process sub-sequence in the wolfberry information management platform, wherein the monitoring indicators of various types of wolfberry include origin indicators, processing indicators, logistics indicators, processed quality indicators and unprocessed quality indicators, and obtain the error input probability and data upload method of the monitoring indicators of various types of wolfberry corresponding to each process sub-sequence according to the historical wolfberry data, wherein the data upload method includes integrated input of Internet of Things devices and manual input; The error input probability and data upload method of each type of wolfberry monitoring indicator corresponding to each process subsequence are used as evaluation indicators, and the indicator weight matrix of the evaluation indicator is set. The membership matrix of each process subsequence to the preset error level is judged through fuzzy comprehensive evaluation. The error level of each process subsequence is obtained according to the membership matrix and the indicator weight matrix, and the process subsequences with an error level greater than the preset error level threshold are screened out, the participants corresponding to the process subsequences are obtained, and a feature matching detection mechanism is added to the blockchain nodes of the communication links of the participants.

4. According to claim 3, a blockchain-based wolfberry origin information traceability management system is characterized in that: The process of the full-process prediction module outputting the predicted wolfberry data uploaded by each participant corresponding to each process subsequence includes: Construct a full-process prediction model based on deep learning, obtain wolfberry data within several historical collection cycles of each blockchain node, use the wolfberry data as a training set and a test set, input the training set into the full-process prediction model for training until the loss function training is stable, save the model parameters, test the full-process prediction model with the test set until it meets the preset requirements, and output the full-process prediction model; According to the full-process prediction model, the predicted wolfberry data uploaded by each participant corresponding to each process subsequence in the current collection cycle is output.

5. According to claim 4, a blockchain-based wolfberry origin information traceability management system is characterized in that: The process of the feature matching detection module performing a feature matching detection operation on the wolfberry data received by the blockchain node to which the feature matching detection mechanism is added includes: Obtain the predicted wolfberry data uploaded by each participant corresponding to each process subsequence in the current collection cycle. When the blockchain node receives the wolfberry data uploaded by the participant, obtain the predicted wolfberry data of the participant, perform feature matching on the wolfberry data and the predicted wolfberry data, obtain the feature matching degree of the wolfberry data uploaded by the participant, compare the feature matching degree with a preset feature matching degree threshold, and if the feature matching degree is greater than or equal to the feature matching degree threshold, store the wolfberry data in the temporary storage space of the blockchain node; If the feature matching degree is less than the feature matching degree threshold, a data error warning signal of the participant is generated and fed back to the blockchain node. The blockchain node sends a data retransmission instruction to the participant according to the data error warning signal. The participant sends new wolfberry data to the blockchain node according to the data retransmission instruction. If the new wolfberry data is consistent with the wolfberry data, the participant's identity verification operation is performed; If the new wolfberry data is inconsistent with the wolfberry data, the new wolfberry data is feature matched with the predicted wolfberry data to obtain the feature matching degree of the new wolfberry data. If the feature matching degree of the new wolfberry data is less than the feature matching degree threshold, the participant identity authentication operation is performed. If the feature matching degree of the new wolfberry data is greater than or equal to the feature matching degree threshold, the new wolfberry data is stored in the temporary storage space of the blockchain node.

6. According to claim 5, a blockchain-based wolfberry origin information traceability management system is characterized in that: The batch clustering module obtains the wolfberry batch number in the wolfberry data, clusters the wolfberry data stored in the temporary storage space of several blockchain nodes according to the wolfberry batch number, and constructs a delayed detection flow program list of several wolfberry batch numbers, including: Acquire wolfberry data stored in the temporary storage space of several blockchain nodes in the wolfberry information management platform, extract the wolfberry batch number from the wolfberry data, screen out wolfberry data with the same wolfberry batch number from the wolfberry data stored in the temporary storage space of several blockchain nodes, acquire the process subsequence to which the wolfberry data belongs, acquire the sequential connection relationship of the process subsequences, sort the wolfberry data of different process subsequences according to the sequential connection relationship, and generate a delay detection flow program list; Pre-set the delay detection standard of the last process subsequence in the delay detection flow program list, obtain the processed quality index in the wolfberry data of each process subsequence except the last process subsequence in the delay detection flow program list, obtain the next process subsequence of each process subsequence except the last process subsequence in the delay detection flow program list, and use the processed quality index in the wolfberry data of each process subsequence as the delay detection standard for the next process subsequence of each process subsequence.

7. According to claim 6, a blockchain-based wolfberry origin information traceability management system is characterized in that: The process in which the delay detection module performs delay detection on the wolfberry data stored in the temporary storage space of each blockchain node according to the delay detection flow program list includes: Obtain a delay detection flow program list corresponding to the wolfberry data stored in the temporary storage space of the blockchain node, obtain the delay detection standard corresponding to the wolfberry data according to the delay detection flow program list, and simultaneously obtain the unprocessed quality index in the wolfberry data, compare the unprocessed quality index with the delay detection standard for consistency, if they are consistent, perform the wolfberry data chain operation, if not consistent, mark the participant who uploaded the wolfberry data as a malicious participant, and remove the wolfberry data stored in the temporary storage space.

8. According to claim 7, a blockchain-based wolfberry origin information traceability management system is characterized in that: The process of the identity verification module performing the participant identity verification operation according to the participant's data uploading method includes: If the data upload method of the participant is the integrated input of the IoT device, the fault warning information of the participant's IoT device is generated; If the participant's data upload method is manual input, a biometric database is pre-built, and the biometric database includes the biometric data of the participant whose data upload method is manual input. The blockchain node sends a biometric verification instruction to the participant, and the participant sends the biometric data to the blockchain according to the biometric verification instruction. The biometric data is matched with the biometric data in the biometric database for consistency. If the match is successful, the wolfberry data uploaded by the participant is stored in the temporary storage space of the blockchain node. If the match fails, the wolfberry data uploaded by the participant received by the blockchain node is removed, and the participant is marked as a malicious participant.

9. According to claim 8, a blockchain-based wolfberry origin information traceability management system is characterized in that: The process of the data uplink module performing a data uplink operation on the wolfberry data that has passed the delayed detection includes: The mining nodes, verification rules and consensus mechanism of the blockchain network are preset, a new block is created through the mining nodes, the wolfberry data stored in the temporary storage space of the blockchain node is transferred to the new block, and the new block is broadcast to the blockchain network. Other blockchain nodes in the blockchain network verify the new block based on the verification rules and consensus mechanism; After the new block is verified, the new block is linked to the blockchain node, and the wolfberry data in the new block is updated to the blockchain copy of all blockchain nodes.

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