Fresh food supply chain data fusion method and system based on privacy protection

Abstract

In recent years, a data security fusion technology serves as an emerging technology for data analysis and calculation on the premise of protecting data privacy, and at present, a mature scheme for applying the data security fusion technology to fresh food supply chain data circulation is still lacked. The invention aims to provide a fresh food supply chain data fusion method and system based on privacy protection, and aims to solve the security and privacy problems faced by data circulation and sharing in an existing fresh food supply chain, improve data circulation efficiency and promote collaborative development of the fresh food supply chain.

Description

Technical Field

[0001] The present invention relates to the technical field of fresh food supply chain, and in particular to a fresh food supply chain data fusion method and system based on privacy protection. Background Art

[0002] The fresh food supply chain covers the production, procurement, processing, transportation, and sales of fresh food products, involving many participants, including manufacturers, suppliers, wholesalers, retailers, purchasers, and logistics companies. In this complex ecosystem, the circulation and sharing of data are crucial to optimizing supply chain operations, improving efficiency, reducing costs, and meeting consumer needs. However, traditional data sharing methods often have problems such as data leakage, privacy infringement, and unclear data ownership and usage rights, which seriously restrict the development of the fresh food supply chain. Summary of the invention

[0003] The present invention relates to the technical field of fresh food supply chain, and in particular to a fresh food supply chain data fusion method and system based on privacy protection.

[0004] The system described in the present invention mainly includes the following parts:

[0005] 1. Data collection module: responsible for collecting relevant data from all links of the fresh food supply chain, including but not limited to production data, inventory data, logistics data, sales data, etc.

[0006] 2. Data preprocessing module: Perform preprocessing operations such as cleaning, conversion, and normalization on the collected data to ensure the quality and availability of the data.

[0007] 3. Privacy computing module: It uses data security fusion technologies such as multi-party secure computing, federated learning, and homomorphic encryption to encrypt and calculate pre-processed data to achieve "available but invisible" data.

[0008] 4. Data sharing and exchange module: Establish a secure data sharing mechanism to achieve data sharing and exchange based on preset rules and permissions while protecting data privacy.

[0009] 5. Data analysis and application module: Based on shared and calculated data, data analysis and mining are carried out to provide decision support for each link of the fresh food supply chain, such as demand forecasting, inventory optimization, logistics route planning, precision marketing, etc. Method steps

[0010] 1. Data Collection

[0011] - Use sensors, IoT devices, enterprise information systems and other channels to collect data from all links of the fresh food supply chain in real time or regularly.

[0012] - Conduct preliminary screening and classification of collected data to remove invalid and duplicate data.

[0013] 2. Data Preprocessing

[0014] - Clean the collected data to remove noise, missing values ​​and outliers.

[0015] - Perform data conversion and convert data in different formats and units into a standard format.

[0016] - Normalize the data to make them comparable.

[0017] 3. Privacy computing

[0018] - Use multi-party secure computing technology to conduct joint computing among multiple parties to ensure that the data of each party is not leaked during the computing process.

[0019] - Federated learning technology is used to enable each participant to train the model locally and achieve collaborative optimization of the model through encrypted parameter exchange.

[0020] - Use homomorphic encryption algorithms to encrypt data to ensure effective calculation and analysis in an encrypted state.

[0021] 4. Data sharing and exchange

[0022] - Establish a data sharing platform, and all participants register on the platform and apply for data sharing permissions.

[0023] - Review and authorize applications based on preset rules and permissions.

[0024] - During data transmission, encrypted channels are used to ensure secure data transmission.

[0025] 5. Data analysis and application

[0026] - Use data mining, machine learning and other technologies to analyze shared and calculated data.

[0027] - Provide decision support for each link of the fresh food supply chain based on the analysis results, such as predicting market demand, optimizing inventory levels, planning logistics routes, and formulating precise marketing strategies. Beneficial Effects

[0028] 1. Ensure data security and privacy

[0029] - Use advanced data security fusion technology to ensure that fresh food supply chain data is not leaked during circulation and sharing, effectively protecting corporate business secrets and personal privacy.

[0030] 2. Promote data circulation and sharing

[0031] - Break down data silos, enable data circulation and sharing among all participants in the fresh food supply chain, and improve the value of data utilization.

[0032] 3. Optimize supply chain decisions

[0033] - Through data analysis and mining, provide accurate decision support for each link of the supply chain and improve the overall efficiency and competitiveness of the supply chain.

[0034] 4. Enhance industry trust and cooperation

[0035] - Establish a safe and reliable data sharing environment, promote trust and cooperation among all participants in the fresh food supply chain, and promote the coordinated development of the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the overall structure of a fresh food supply chain data fusion method and system based on privacy protection in the present invention. DETAILED DESCRIPTION

[0037] The technical solution of the present invention is described in detail below through specific embodiments.

[0038] Embodiment 1:

[0039] A supply chain alliance consisting of a fresh food production enterprise, a logistics enterprise, and a sales enterprise hopes to achieve data sharing and collaborative optimization through the system of the present invention. First, each enterprise collects its own production plan, inventory status, logistics progress, and sales data through the data acquisition module. Then, the data preprocessing module cleans and preprocesses these data. Next, the privacy computing module uses multi-party secure computing technology to calculate the optimal logistics distribution plan and inventory allocation strategy without leaking the original data of each party. Finally, the calculation results are shared within the alliance through the data sharing and exchange module, and the data analysis and application module provides decision support for each enterprise, such as adjusting production plans and optimizing inventory management.

[0040] Embodiment 2:

[0041] A large fresh food e-commerce platform uses the system of the present invention to integrate the data of suppliers, warehouses and distribution stations. The data collection module obtains the product information of suppliers, the inventory level of warehouses and the distribution capacity of distribution stations. After preprocessing and privacy calculation, accurate procurement plans and distribution routes are formulated. At the same time, precision marketing is carried out according to the results of data analysis to improve customer satisfaction and sales.

Claims

1. A method for secure circulation of data elements in a fresh food supply chain based on data security fusion technology, characterized in that: The following steps are involved: - Data collection: collect relevant data from all links of the fresh food supply chain; - Data preprocessing: cleaning, conversion, normalization and other processing of the collected data; - Privacy computing: Use data security fusion technologies such as multi-party secure computing, federated learning, and homomorphic encryption to encrypt and calculate data; - Data sharing and exchange: Data sharing and exchange shall be carried out in accordance with preset rules and permissions while protecting data privacy; - Data analysis and application: Analyze shared and calculated data to provide decision support for the fresh food supply chain.

2. The method according to claim 1, characterized in that The data collection is carried out through multiple channels such as sensors, IoT devices and enterprise information systems.

3. The method according to claim 1, characterized in that: The data preprocessing includes removing noise, missing values ​​and outliers, as well as data conversion and normalization.

4. The method according to claim 1, characterized in that: The privacy computing adopts multi-party secure computing technology to realize joint computing among multiple participants, adopts federated learning technology to enable each participant to train the model locally and achieve collaborative optimization through encrypted parameter exchange, and adopts homomorphic encryption algorithm to encrypt data to ensure calculation and analysis in an encrypted state.

5. The method according to claim 1, characterized in that The data sharing and exchange is achieved by establishing a data sharing platform, where each participant registers on the platform and applies for data sharing permissions, which are reviewed and authorized based on rules and permissions, and encrypted channels are used for data transmission.

6. The method according to claim 1, characterized in that The data analysis and application include the use of data mining and machine learning techniques to provide decision support for production, inventory, logistics, sales and other aspects of the fresh food supply chain.

7. A fresh food supply chain data element security circulation system based on data security fusion technology, characterized in that: include: - Data collection module, used to collect relevant data from all links of the fresh food supply chain; - Data preprocessing module, used to clean, convert, normalize and process the collected data; - Privacy computing module, which is used to encrypt and calculate data using data security fusion technologies such as multi-party secure computing, federated learning, and homomorphic encryption; - Data sharing and exchange module, which is used to share and exchange data in accordance with preset rules and permissions while protecting data privacy; - Data analysis and application module, used to analyze shared and calculated data and provide decision support for the fresh food supply chain.

8. The system according to claim 7, characterized in that The data acquisition module collects data through multiple channels such as sensors, Internet of Things devices and enterprise information systems.

9. The system according to claim 7, characterized in that The data preprocessing module includes a unit for removing noise, missing values ​​and outliers, and a unit for data conversion and normalization.

10. The system according to claim 7, characterized in that The privacy computing module includes a unit that uses multi-party secure computing technology to achieve joint computing among multiple participants, a unit that uses federated learning technology to enable each participant to train the model locally and achieve collaborative optimization through encrypted parameter exchange, and a unit that uses a homomorphic encryption algorithm to encrypt data to ensure that calculation and analysis are performed in an encrypted state.

11. The system according to claim 7, characterized in that The data sharing and exchange module includes a unit for establishing a data sharing platform, a unit for each participant to register on the platform and apply for data sharing rights, a unit for reviewing and authorizing according to rules and rights, and a unit for transmitting data using an encrypted channel.

12. The system according to claim 7, characterized in that The data analysis and application module includes a unit that uses data mining and machine learning techniques for analysis, and a unit that provides decision support for production, inventory, logistics, sales and other links of the fresh food supply chain.

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