A food supply chain traceability method and system based on blockchain

Abstract

The present invention discloses a food supply chain traceability method and system based on blockchain, wherein the method comprises: classifying first food collection information to obtain first category food information; inputting the first food collection information and the first category food information into a first supply chain feature analysis model to obtain first food supply chain features; performing distributed blockchain deployment according to the first food supply chain features to obtain N data traceability feature modules; obtaining first process production data information, performing quality analysis on the first process production data information to obtain first food data coordination information; and so on to obtain Nth food data coordination information, and storing the first food data coordination information to the Nth food data coordination information in the N data traceability feature modules respectively. The method solves the technical problem that the prior art does not trace the food supply chain in a timely and accurate manner, which affects food quality and safety.

Description

Technical Field

[0001] The present invention relates to the field of food safety, and in particular to a food supply chain tracing method and system based on blockchain. Background Art

[0002] Food quality traceability is an information management method that connects production, inspection, supervision and consumption, allowing consumers to understand the production and distribution process that complies with hygiene and safety, and improving consumer confidence. Once a food quality problem occurs, food supply chain traceability can confirm the food production process, identify the cause of the accident, and recall the problematic food in a timely manner, minimizing the company's economic and reputation losses.

[0003] However, existing technologies have technical problems such as insufficient timeliness and accuracy in tracing the food supply chain, which affects food quality and safety. Summary of the invention

[0004] This application solves the technical problem that the prior art does not provide timely and accurate food supply chain traceability, which affects food quality and safety, by providing a blockchain-based food supply chain traceability method and system. It achieves real-time collection of food production data information, and tracks the upstream and downstream transmission of food production data in the supply chain through blockchain technology, thereby achieving real-time dynamic traceability of the food supply chain, improving the timeliness and accuracy of food supply chain traceability, and thus ensuring the technical effect of food quality and safety.

[0005] In view of the above problems, the present invention provides a food supply chain traceability method and system based on blockchain.

[0006] In a first aspect, the present application provides a food supply chain traceability method based on blockchain, the method comprising: obtaining first food collection information, the first food collection information including various types of production data information of food; classifying the first food collection information to obtain first category food information; inputting the first food collection information and the first category food information into a first supply chain feature analysis model to obtain first food supply chain features; performing distributed blockchain deployment according to the first food supply chain features to obtain N data traceability feature modules; obtaining first process production data information, performing quality analysis on the first process production data information to obtain first food data collaborative information; and so on to obtain the Nth food data collaborative information, and storing the first food data collaborative information to the Nth food data collaborative information in the N data traceability feature modules respectively.

[0007] On the other hand, the present application also provides a food supply chain traceability system based on blockchain, the system comprising: a first acquisition unit, the first acquisition unit is used to obtain first food collection information, the first food collection information includes various types of production data information of food; a second acquisition unit, the second acquisition unit is used to classify the first food collection information to obtain first category food information; a third acquisition unit, the third acquisition unit is used to input the first food collection information and the first category food information into a first supply chain feature analysis model to obtain the first food supply chain feature; a fourth acquisition unit, the fourth acquisition unit is used to perform distributed blockchain deployment according to the first food supply chain feature to obtain N data traceability feature modules; a fifth acquisition unit, the fifth acquisition unit is used to obtain first process production data information, perform quality analysis on the first process production data information, and obtain first food data collaborative information; a first processing unit, the first processing unit is used to obtain the Nth food data collaborative information by analogy, and store the first food data collaborative information to the Nth food data collaborative information in the N data traceability feature modules respectively.

[0008] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0009] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Due to the adoption of the technical solution of classifying the first food collection information, inputting the first food collection information and the first category food information into the first supply chain feature analysis model, obtaining the output result of the model, i.e., the first food supply chain feature, performing distributed blockchain deployment according to the first food supply chain feature, obtaining the corresponding N data traceability feature modules, and then performing quality analysis on the first process production data information to obtain the first food data coordination information, and so on, obtaining N food data coordination information, and storing the first food data coordination information to the Nth food data coordination information in N data traceability feature modules respectively. Thus, the real-time collection of food production data information is achieved, and the upstream and downstream transmission and tracking of food production data in the supply chain are carried out through blockchain technology, so as to realize the real-time dynamic traceability of the food supply chain, improve the timeliness and accuracy of food supply chain traceability, and thus ensure the technical effect of food quality and safety.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of a food supply chain traceability method based on blockchain is provided for this application;

[0014] Figure 2 A schematic diagram of a process of calling a first supply chain feature analysis model in a food supply chain traceability method based on blockchain in this application;

[0015] Figure 3 A schematic diagram of the process of constructing a supply chain feature analysis model library in a food supply chain traceability method based on blockchain in this application;

[0016] Figure 4 A schematic diagram of the process of obtaining first food data collaborative information in a food supply chain traceability method based on blockchain in this application;

[0017] Figure 5 A schematic diagram of the structure of a food supply chain traceability system based on blockchain for this application;

[0018] Figure 6 This is a schematic diagram of the structure of an exemplary electronic device of the present application.

[0019] Explanation of the reference numerals: first obtaining unit 11, second obtaining unit 12, third obtaining unit 13, fourth obtaining unit 14, fifth obtaining unit 15, first processing unit 16, bus 1110, processor 1120, transceiver 1130, bus interface 1140, memory 1150, operating system 1151, application 1152 and user interface 1160. DETAILED DESCRIPTION

[0020] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0021] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0022] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0023] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0024] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0025] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0026] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0027] The present application is described below in conjunction with the drawings in the present application.

[0028] Embodiment 1

[0029] like Figure 1 As shown, the present application provides a food supply chain traceability method based on blockchain, the method comprising:

[0030] Step S100: obtaining first food collection information, wherein the first food collection information includes various production data information of food;

[0031] Specifically, food quality traceability is an information management method that connects production, inspection, supervision and consumption, allowing consumers to understand the production and circulation process that complies with hygiene and safety, and improving consumer confidence. Once a food quality problem occurs, food supply chain traceability can confirm the food production process, the specific cause of the accident, and promptly recall the problematic food, minimizing the company's economic losses and reputation losses. Collect all kinds of food production data information, including food type, name, specification, production date, shelf life, ingredient list, ingredient list, shelf life, wholesalers, manufacturers, distributors, food processing technology, nutrition labels, production materials and other detailed production data in each production process, comprehensively and accurately collect food production data, and ensure that food supply chain data is complete and accurate.

[0032] Step S200: classifying the first food collection information to obtain first category food information;

[0033] Specifically, different types of food have different supply chain characteristics. The first food collection information is classified to obtain first-category food information. The first-category food information is food types, such as milk and dairy products, fats, oils and emulsified fat products, frozen drinks, fruits, vegetables, beans, edible fungi, algae, nuts, cocoa products, candy, grains and grain products, baked goods, meat and meat products, aquatic products and their products, eggs and egg products, sweeteners, condiments, special nutritional foods, beverages, alcohol, etc.

[0034] Step S300: inputting the first food collection information and the first category food information into a first supply chain characteristic analysis model to obtain first food supply chain characteristics;

[0035] like Figure 2 As shown, further, step S300 of the present application also includes:

[0036] Step S310: obtaining a food information data set through big data;

[0037] Step S320: classifying the food information data set to obtain food information of each category;

[0038] Step S330: constructing a supply chain characteristic analysis model library according to the food information of each category;

[0039] Step S340: calling the first supply chain characteristic analysis model from the supply chain characteristic analysis model library according to the first category food information.

[0040] Specifically, in order to analyze the characteristics of the food supply chain more accurately and reasonably, firstly, a food information data set is obtained through big data. The acquisition of big data can increase the sample size of the model data, and the processing efficiency is high, making the data processing more comprehensive and accurate. The food information data set includes various types of food production data information. The food information data set is classified to obtain the corresponding food information of each category, and then the collected food information of each category is trained on the supply chain model according to the food type, so as to construct a supply chain characteristic analysis model library, and the supply chain characteristic analysis model library includes the supply chain model of each food type. When performing food supply chain characteristic analysis, the corresponding first supply chain characteristic analysis model is called from the supply chain characteristic analysis model library according to the first category of food information.

[0041] The first food collection information and the first category food information are input into the first supply chain feature analysis model, which is a recurrent neural network model for analyzing food supply chain features to obtain the training output of the model, i.e., the first food supply chain feature, which includes the process features of each supply chain node of the food and the upstream and downstream transmission features of the supply chain. The supply chain feature analysis model is called by individual food type, so that the obtained food supply chain features are more reasonable and accurate, thereby realizing real-time dynamic traceability of the food supply chain.

[0042] Step S400: Deploy a distributed blockchain according to the first food supply chain characteristics to obtain N data traceability feature modules;

[0043] Specifically, distributed blockchain deployment is performed according to the first food supply chain feature to obtain N data traceability feature modules, that is, data blocks are sequentially connected to form a chain data structure according to the order of upstream and downstream nodes in the supply chain, and a distributed ledger that cannot be tampered with or forged is cryptographically guaranteed to generate N unchangeable data traceability feature block units. By using the blockchain data structure to verify and store data, food supply chain data is distributedly stored to ensure the security of food data transmission and storage.

[0044] Step S500: obtaining first process production data information, performing quality analysis on the first process production data information, and obtaining first food data collaborative information;

[0045] Step S600: Similarly, the Nth food data collaborative information is obtained, and the first food data collaborative information to the Nth food data collaborative information are respectively stored in the N data traceability feature modules.

[0046] like Figure 4 As shown, further, the obtaining of the first food data collaborative information, step S500 of the present application further includes:

[0047] Step S510: normalizing the first process production data information to obtain first standard production data information;

[0048] Step S520: inputting the first production process standard and the first standard production data information into the food quality analysis model to obtain a first production quality result;

[0049] Step S530: generating the first food data coordination information according to the first standard production data information and the first production quality result;

[0050] Step S540: storing the first food data collaborative information in the first data traceability feature module, and so on, storing the Nth food data collaborative information in the Nth data traceability feature module.

[0051] Specifically, the first process production data information is the first production process link in the food supply chain. The first process production data information is normalized. Normalization is to convert dimensional data into dimensionless data, eliminate the dimensional influence between data indicators, make each indicator data at the same order of magnitude, and obtain the first standard production data information after data standardization, so that data analysis is more standardized and accurate. The first production process standard is the quality standard of the first production process in the food supply chain, such as the quality inspection standard of the food raw material supply process. The first production process standard and the first standard production data information are input into the food quality analysis model, which is a neural network model for evaluating food production quality, and obtaining the training output result of the model, i.e., the first production quality result.

[0052] The first standard production data information and the first production quality result are integrated to generate the first food data collaborative information, which is the shared supply chain production data information of the first production process link in the food supply chain. Similarly, N food data collaborative information corresponding to each link in the food supply chain production process is obtained, and then the first food data collaborative information is stored in the corresponding first data traceability feature module, the second food data collaborative information is stored in the corresponding second data traceability feature module, and so on, the Nth food data collaborative information is stored in the Nth data traceability feature module. Through blockchain technology, food production data is tracked in the upstream and downstream of the supply chain, and real-time dynamic traceability of the food supply chain is realized, and the timeliness and accuracy of food supply chain traceability are improved, thereby ensuring food quality and safety.

[0053] like Figure 3 As shown, further, the construction of the supply chain feature analysis model library, step S330 of this application also includes:

[0054] Step S331: inputting the food information of different categories into the recurrent neural network model respectively to obtain a supply chain feature analysis model set corresponding to the food information of each category;

[0055] Step S332: construct the supply chain characteristic analysis model library according to the supply chain characteristic analysis model set.

[0056] Specifically, in order to make the food supply chain feature model more comprehensive, the food information corresponding to different categories of food is input into the recurrent neural network model. The recurrent neural network is a type of recurrent neural network that takes sequence data as input, recursively in the direction of sequence evolution, and all nodes (recurrent units) are connected in a chain, including an input layer, a hidden layer, and an output layer. In the process of processing input information in the processing layer of the recurrent neural network, in addition to processing according to the current input information, it also saves the output information of the previous time series, and processes the output information as the input information of the current time series, and then obtains the output. As the time series progresses, the processing layer is constantly updated.

[0057] The recurrent neural network uses neurons with self-feedback so that the network output is not only related to the current input, but also to the output at the previous moment, so it has short-term memory when processing time series data of any length. A set of supply chain feature analysis models corresponding to the food information of each category is obtained through recurrent neural network data training, and the supply chain feature analysis model library is constructed based on each supply chain feature analysis model in the supply chain feature analysis model set. By constructing a supply chain feature analysis model library, the food supply chain feature model is made more comprehensive, thereby ensuring the personalization and accuracy of the call of the supply chain feature analysis model.

[0058] Furthermore, step S540 of the present application also includes:

[0059] Step S541: obtaining the upstream and downstream relationship of the supply chain according to the first food supply chain characteristics;

[0060] Step S542: Analyze the production correlation between each downstream production process and the upstream production process according to the upstream and downstream relationship of the supply chain to obtain corresponding production correlation coefficients;

[0061] Step S543: supplement and correct the food data coordination information of each downstream production process according to the corresponding production correlation coefficients.

[0062] Specifically, according to the first food supply chain characteristics, the upstream and downstream relationship of the supply chain is obtained, and the upstream and downstream relationship of the supply chain is the supply and demand relationship of the food production industry chain. According to the upstream and downstream relationship of the supply chain, the production correlation between each downstream production process and its upstream production process is analyzed, that is, the degree of correlation between the production processes, such as the correlation between the food processing process and the raw material supply process and the raw material wholesale process, and the corresponding upstream and downstream production correlation coefficients are obtained. The larger the correlation coefficient, the greater the correlation between the downstream process and its upstream process. The quality of the upstream production process will affect the production quality of the downstream process. According to the corresponding production correlation coefficients, the food data collaborative information of each downstream production process is supplemented and corrected for quality correlation. Through the correlation analysis of the upstream and downstream relationships of the food supply chain, the obtained food data collaborative information is more comprehensive and accurate, thereby improving the timeliness and accuracy of food supply chain traceability.

[0063] Furthermore, step S332 of the present application also includes:

[0064] Step S3321: Select a model encryption algorithm according to the food production level;

[0065] Step S3322: Encrypting the food information of each category based on the model encryption algorithm to obtain encrypted food information of each category;

[0066] Step S3323: Input the encrypted food information of different categories into the recurrent neural network model for data training.

[0067] Specifically, the food production level is the market production requirement level of the food, such as formula confidential food, organic food, green food, pollution-free food and ordinary food, etc. The higher the food level, the higher the encryption security required. According to the food production level, the model encryption algorithm is selected. Model encryption is to process the model training data that was originally in plain text according to a certain algorithm, so that it becomes an unreadable code as "ciphertext", so that it can only be displayed after the corresponding key is entered. In this way, the purpose of protecting data from being stolen and read by illegal persons is achieved. Based on the model encryption algorithm, the food information of each category is encrypted to obtain the encrypted food information of each category, and then the encrypted food information of each category of different categories is respectively input into the recurrent neural network model for data training. By encrypting the training data to train the model, data loss and theft are prevented, the confidentiality of model construction is enhanced, and the security of model construction is improved.

[0068] Furthermore, step S543 of the present application also includes:

[0069] Step S5431: Obtaining a first transportation sensitivity factor according to the first food transportation information;

[0070] Step S5432: Obtaining a first freshness influencing factor according to the first food collection information;

[0071] Step S5433: performing sensitivity weighted calculation on the first transport sensitivity factor and the first freshness sensitivity factor to obtain a first sensitivity factor;

[0072] Step S5434: Modify the food data collaborative information based on the first sensitive factor.

[0073] Specifically, the first food transportation information includes information such as transportation location, transportation method, transportation time, transportation route, and transportation conditions. The transportation information will affect the production quality of the food. According to the first food transportation information, a first transportation sensitive factor is obtained. The first transportation sensitive factor is the impact of transportation factors on food quality. According to the first food collection information, information such as the production season and shelf life of the food is obtained, and then a first freshness influencing factor is obtained. The first freshness influencing factor is the impact on food quality caused by changes in freshness over time.

[0074] According to the importance of transportation information and freshness on food quality, the corresponding weights are obtained, and then the first transportation sensitive factor and the first freshness sensitive factor are subjected to sensitivity weighted calculation to obtain the first sensitive factor after comprehensive influencing factors. Based on the first sensitive factor, the food data collaborative information is corrected, and the food quality is analyzed in combination with the transportation factors and the food freshness influencing factors, so that the obtained food data collaborative information is more accurate and reasonable, and the real-time dynamic traceability of the food supply chain is realized, thereby ensuring food quality and safety.

[0075] In summary, the food supply chain traceability method and system based on blockchain provided by this application has the following technical effects:

[0076] Due to the adoption of the technical solution of classifying the first food collection information, inputting the first food collection information and the first category food information into the first supply chain feature analysis model, obtaining the output result of the model, i.e., the first food supply chain feature, performing distributed blockchain deployment according to the first food supply chain feature, obtaining the corresponding N data traceability feature modules, and then performing quality analysis on the first process production data information to obtain the first food data coordination information, and so on, obtaining N food data coordination information, and storing the first food data coordination information to the Nth food data coordination information in N data traceability feature modules respectively. Thus, the real-time collection of food production data information is achieved, and the upstream and downstream transmission and tracking of food production data in the supply chain are carried out through blockchain technology, so as to realize the real-time dynamic traceability of the food supply chain, improve the timeliness and accuracy of food supply chain traceability, and thus ensure the technical effect of food quality and safety.

[0077] Embodiment 2

[0078] Based on the same inventive concept as the food supply chain traceability method based on blockchain in the aforementioned embodiment, the present invention also provides a food supply chain traceability system based on blockchain, such as Figure 5 As shown, the system comprises:

[0079] A first obtaining unit 11, the first obtaining unit 11 is used to obtain first food collection information, the first food collection information includes various production data information of food;

[0080] A second obtaining unit 12, the second obtaining unit 12 is used to classify the first food collection information to obtain first category food information;

[0081] A third obtaining unit 13, the third obtaining unit 13 is used to input the first food collection information and the first category food information into a first supply chain characteristic analysis model to obtain first food supply chain characteristics;

[0082] A fourth obtaining unit 14, wherein the fourth obtaining unit 14 is used to perform distributed blockchain deployment according to the first food supply chain characteristics to obtain N data traceability feature modules;

[0083] A fifth obtaining unit 15, the fifth obtaining unit 15 is used to obtain the first process production data information, perform quality analysis on the first process production data information, and obtain the first food data collaborative information;

[0084] The first processing unit 16 is used to obtain the Nth food data collaborative information by analogy, and store the first food data collaborative information to the Nth food data collaborative information in the N data traceability feature modules respectively.

[0085] Furthermore, the system also includes:

[0086] A sixth obtaining unit, the sixth obtaining unit being used to obtain a food information data set through big data;

[0087] A seventh obtaining unit, the seventh obtaining unit is used to classify the food information data set to obtain food information of each category;

[0088] A first construction unit, the first construction unit is used to construct a supply chain characteristic analysis model library according to the food information of each category;

[0089] A first calling unit is used to call the first supply chain characteristic analysis model from the supply chain characteristic analysis model library according to the first category of food information.

[0090] Furthermore, the system also includes:

[0091] An eighth obtaining unit, the eighth obtaining unit is used to input the food information of different categories into the recurrent neural network model respectively, and obtain a supply chain feature analysis model set corresponding to the food information of each category;

[0092] A second construction unit, wherein the second construction unit is used to construct the supply chain feature analysis model library according to the supply chain feature analysis model set.

[0093] Furthermore, the system also includes:

[0094] a ninth obtaining unit, the ninth obtaining unit being used to perform normalization processing on the first process production data information to obtain first standard production data information;

[0095] a tenth obtaining unit, the tenth obtaining unit being used to input the first production process standard and the first standard production data information into a food quality analysis model to obtain a first production quality result;

[0096] a first generating unit, the first generating unit being configured to generate the first food data collaborative information according to the first standard production data information and the first production quality result;

[0097] The first storage unit is used to store the first food data collaborative information in the first data traceability feature module, and so on, to store the Nth food data collaborative information in the Nth data traceability feature module.

[0098] Furthermore, the system also includes:

[0099] an eleventh obtaining unit, the eleventh obtaining unit being used to obtain an upstream and downstream relationship of a supply chain according to the first food supply chain characteristic;

[0100] A twelfth obtaining unit, the twelfth obtaining unit is used to analyze the production correlation between each downstream production process and the upstream production process according to the upstream and downstream relationship of the supply chain, and obtain corresponding production correlation coefficients;

[0101] The first correction unit is used to supplement and correct the food data coordination information of each downstream production process according to the corresponding production correlation coefficients.

[0102] Furthermore, the system also includes:

[0103] A first selection unit, the first selection unit is used to select a model encryption algorithm according to the food production level;

[0104] A thirteenth obtaining unit, the thirteenth obtaining unit is used to encrypt the food information of each category based on the model encryption algorithm to obtain the encrypted food information of each category;

[0105] The first training unit is used to input the encrypted food information of different categories into the recurrent neural network model for data training.

[0106] Furthermore, the system also includes:

[0107] a fourteenth obtaining unit, the fourteenth obtaining unit being used to obtain a first transportation sensitivity factor according to the first food transportation information;

[0108] a fifteenth obtaining unit, the fifteenth obtaining unit being configured to obtain a first freshness influencing factor according to the first food collection information;

[0109] a sixteenth obtaining unit, the sixteenth obtaining unit being configured to perform sensitivity weighted calculation on the first transport sensitivity factor and the first freshness sensitivity factor to obtain a first sensitivity factor;

[0110] A second correction unit, the second correction unit is used to correct the food data collaborative information based on the first sensitive factor.

[0111] The foregoing Figure 1The various variations and specific examples of a food supply chain traceability method based on blockchain in Example 1 are also applicable to a food supply chain traceability system based on blockchain in this embodiment. Through the above detailed description of a food supply chain traceability method based on blockchain, those skilled in the art can clearly know the implementation method of a food supply chain traceability system based on blockchain in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0112] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and run on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method embodiment for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0113] Exemplary Electronic Devices

[0114] For details, see Figure 6 As shown, the present application also provides an electronic device, which includes a bus 1110 , a processor 1120 , a transceiver 1130 , a bus interface 1140 , a memory 1150 and a user interface 1160 .

[0115] In the present application, the electronic device further includes: a computer program stored in the memory 1150 and executable on the processor 1120, and when the computer program is executed by the processor 1120, each process of the above-mentioned method embodiment for controlling output data is implemented.

[0116] The transceiver 1130 is configured to receive and send data under the control of the processor 1120 .

[0117] In the present application, a bus architecture (represented by bus 1110 ) may include any number of interconnected buses and bridges, and bus 1110 connects various circuits including one or more processors represented by processor 1120 and a memory represented by memory 1150 .

[0118] Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. Such architectures include, by way of example and not limitation, an Industry Standard Architecture bus, a Micro Channel Architecture bus, an expansion bus, a Video Electronics Standards Association, a Peripheral Component Interconnect bus.

[0119] The processor 1120 may be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the above method embodiment may be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processors include: a general-purpose processor, a central processing unit, a network processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, a complex programmable logic device, a programmable logic array, a microcontroller unit or other programmable logic device, a discrete gate, a transistor logic device, and a discrete hardware component. The various methods, steps, and logic block diagrams disclosed in this application may be implemented or executed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated into a single chip or located on multiple different chips.

[0120] Processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in the present application may be directly executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a readable storage medium known in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, a register, etc. The readable storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0121] The bus 1110 may also connect various other circuits such as peripheral devices, voltage regulators or power management circuits, and the bus interface 1140 provides an interface between the bus 1110 and the transceiver 1130, which are well known in the art. Therefore, this application will not further describe them.

[0122] The transceiver 1130 may be one element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on a transmission medium. For example, the transceiver 1130 receives external data from other devices, and the transceiver 1130 is used to send data processed by the processor 1120 to other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, such as a touch screen, a physical keyboard, a display, a mouse, a speaker, a microphone, a trackball, a joystick, or a stylus.

[0123] It should be understood that in the present application, the memory 1150 may further include a memory remotely arranged relative to the processor 1120, and these remotely arranged memories may be connected to the server through a network. One or more parts of the above network may be a self-organizing network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the Internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless network, a wireless fidelity network, and a combination of two or more of the above networks. For example, the cellular telephone network and the wireless network may be a global mobile communication device, a code division multiple access device, a global microwave interconnection access device, a general packet radio service device, a wideband code division multiple access device, a long term evolution device, a LTE frequency division duplex device, a LTE time division duplex device, an advanced long term evolution device, a universal mobile communication device, an enhanced mobile broadband device, a massive machine type communication device, an ultra-reliable low latency communication device, and the like.

[0124] It should be understood that the memory 1150 in the present application may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. Among them, the non-volatile memory includes: a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, or a flash memory.

[0125] Volatile memory includes random access memory, which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronously linked dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in the present application includes, but is not limited to, the above and any other suitable types of memory.

[0126] In the present application, the memory 1150 stores the following elements of the operating system 1151 and the application program 1152: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0127] Specifically, the operating system 1151 includes various device programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 1152 includes various application programs, such as a media player and a browser, which are used to implement various application services. The program implementing the method of the present application may be included in the application 1152. The application 1152 includes applets, objects, components, logic, data structures, and other computer device executable instructions that perform specific tasks or implement specific abstract data types.

[0128] In addition, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned method embodiment for controlling output data are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0129] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A food supply chain traceability method based on blockchain, characterized in that: The method comprises: Obtaining first food collection information, wherein the first food collection information includes various production data information of food; Classifying the first food collection information to obtain first category food information; Inputting the first food collection information and the first category food information into a first supply chain feature analysis model to obtain first food supply chain features; Deploy distributed blockchain according to the first food supply chain characteristics to obtain N data traceability feature modules; Obtaining first process production data information, performing quality analysis on the first process production data information, and obtaining first food data collaborative information; Similarly, the Nth food data collaborative information is obtained, and the first food data collaborative information to the Nth food data collaborative information are respectively stored in the N data traceability feature modules; The obtaining of the first food data collaborative information includes: Normalizing the first process production data information to obtain first standard production data information; Inputting the first production process standard and the first standard production data information into a food quality analysis model to obtain a first production quality result; generating the first food data coordination information according to the first standard production data information and the first production quality result; The first food data collaborative information is stored in the first data traceability feature module, and by analogy, the Nth food data collaborative information is stored in the Nth data traceability feature module; The method comprises: According to the first food supply chain characteristics, the upstream and downstream relationships of the supply chain are obtained; According to the upstream and downstream relationship of the supply chain, the production correlation between each downstream production process and the upstream production process is analyzed to obtain the corresponding production correlation coefficients; According to the corresponding production correlation coefficients, the food data coordination information of each downstream production process is supplemented and corrected.

2. The method according to claim 1, characterized in that The method comprises: Obtain food information datasets through big data; Classifying the food information data set to obtain food information of each category; According to the food information of each category, a supply chain characteristic analysis model library is constructed; The first supply chain characteristic analysis model is called from the supply chain characteristic analysis model library according to the first category of food information.

3. The method according to claim 2, characterized in that The construction of the supply chain characteristic analysis model library includes: Inputting the food information of different categories into the recurrent neural network model respectively to obtain a set of supply chain feature analysis models corresponding to the food information of different categories; The supply chain characteristic analysis model library is constructed based on the supply chain characteristic analysis model set.

4. The method according to claim 3, characterized in that The method comprises: Select the model encryption algorithm according to the food production level; Encrypting the food information of each category based on the model encryption algorithm to obtain encrypted food information of each category; The encrypted food information of different categories is input into the recurrent neural network model for data training.

5. The method according to claim 1, characterized in that The method comprises: Obtaining a first transportation sensitivity factor according to the first food transportation information; Obtaining a first freshness influencing factor according to the first food collection information; Performing sensitivity weighted calculation on the first transport sensitivity factor and the first freshness sensitivity factor to obtain a first sensitivity factor; The food data collaborative information is modified based on the first sensitive factor.

6. A food supply chain traceability system based on blockchain, characterized in that: The system comprises: A first obtaining unit, the first obtaining unit is used to obtain first food collection information, the first food collection information includes various production data information of food; a second obtaining unit, the second obtaining unit being used to classify the first food collection information to obtain first category food information; a third obtaining unit, the third obtaining unit being used to input the first food collection information and the first category food information into a first supply chain characteristic analysis model to obtain first food supply chain characteristics; A fourth obtaining unit, the fourth obtaining unit is used to perform distributed blockchain deployment according to the first food supply chain characteristics to obtain N data traceability feature modules; a fifth obtaining unit, the fifth obtaining unit being used to obtain the first process production data information, perform quality analysis on the first process production data information, and obtain the first food data collaborative information; a first processing unit, the first processing unit being used to obtain Nth food data collaborative information by analogy, and store the first food data collaborative information to the Nth food data collaborative information respectively in the N data traceability feature modules; The system further comprises: a ninth obtaining unit, the ninth obtaining unit being used to perform normalization processing on the first process production data information to obtain first standard production data information; a tenth obtaining unit, the tenth obtaining unit being used to input the first production process standard and the first standard production data information into a food quality analysis model to obtain a first production quality result; a first generating unit, the first generating unit being configured to generate the first food data collaborative information according to the first standard production data information and the first production quality result; A first storage unit, the first storage unit is used to store the first food data collaborative information in a first data traceability feature module, and so on, to store the Nth food data collaborative information in an Nth data traceability feature module; The system further comprises: an eleventh obtaining unit, the eleventh obtaining unit being used to obtain an upstream and downstream relationship of a supply chain according to the first food supply chain characteristic; A twelfth obtaining unit, the twelfth obtaining unit is used to analyze the production correlation between each downstream production process and the upstream production process according to the upstream and downstream relationship of the supply chain, and obtain corresponding production correlation coefficients; The first correction unit is used to supplement and correct the food data coordination information of each downstream production process according to the corresponding production correlation coefficients.

7. A food supply chain traceability electronic device based on blockchain, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 5 are implemented.

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