CN112052467A - Food safety big data sharing method - Google Patents
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Abstract
The invention belongs to the technical field of food safety, and particularly relates to a food safety big data sharing method. The method comprises the steps of multi-source information system integration, data encryption and desensitization, data standardization, data extraction and collection, data arrangement, coding analysis and information restoration, and hierarchical sharing. The invention provides a big data sharing method related to food safety, which is used for classifying, grading and sharing data related to each production and processing link, supervision link and consumer link of food production, processing, logistics and the like, and corresponding public data can be consulted according to different inquiry codes. The data of the invention has confidentiality and openness, and the data leakage caused by data sharing can not occur, thus protecting the safety of data providers. The data source is wide, and the method has important significance for food safety tracing.
Description
Technical Field
The invention belongs to the technical field of food safety, and particularly relates to a food safety big data sharing method.
Background
The food safety is directly related to the social stability and the healthy development of the food industry, and is related to the physical and mental health of each common people. Food potential safety hazards may exist in each link of food planting, processing, selling and storing, and food safety incidents may occur as long as a problem occurs in one link. In the food processing process, due to the fact that the processing links are multiple, the food nodes are smooth, the adding amount of food raw materials is large, and careless mistakes in each link can finally cause the food safety problem. Each link independently analyzes, which causes large workload, repeated detection and waste of manpower and material cost. If the food supervision departments can mutually share a part of data in the upstream and downstream industry chains in the food processing process, the detection workload of corresponding links can be reduced, and a large amount of detection and monitoring cost can be saved.
For ordinary consumers, the available food safety data are limited to related information disclosed by a network, the data volume is small, the reference value is low, and the food safety data have no guiding significance for the ordinary consumers. If the related data can be publicly shared by common consumers, the consumers can verify information and rationally consume according to own characteristics and nutritional requirements, and select products more suitable for the consumers, so that waste is avoided.
Disclosure of Invention
Based on the above purpose, the present invention aims to provide a food safety big data sharing method, by which multi-level data sharing among various main bodies, supervision departments and consumers in an industry chain can be realized.
In order to achieve the purpose, the invention is realized by the following technical scheme:
1. a food safety big data sharing method is characterized by comprising multi-source information system integration, data encryption and desensitization, data standardization, data extraction and collection, data sorting, coding analysis and information restoration, and hierarchical sharing;
the method comprises the following specific steps:
(1) multi-source information system integration: collecting information generated in the links of food production, circulation, supervision and management, government monitoring and Internet platform publishing to a big data sharing platform to form an original database;
(2) data encryption and desensitization: according to the requirement of a data owner, data are encrypted or desensitized, and when the data are desensitized, the data are deleted according to set sensitive words and are coded, so that an encrypted database is formed finally;
(3) data normalization: carrying out standardization processing on format, terms and units of data, and then uniformly storing the coded data in a coding database;
(4) data extraction and collection: extracting shared data required in the coding database according to data sharing requirements, and collecting data to be shared to form a shared database;
(5) data arrangement: the data in the shared database is sorted, and the data is cleaned and supplemented with information to form a new full information database;
(6) code analysis and information reduction: coding and analyzing the data in the full information database, and restoring the original information to form a restoration database;
(7) and (3) grading and sharing: the restoration database is divided into social personal level sharing, enterprise level sharing and supervision level sharing according to sharing levels, different user codes are set at each level, shared data of the level to which the user codes belong are obtained according to the user codes, and data grading sharing is achieved.
As a specific technical scheme, the multi-source information system integration refers to the collection of data generated by a data collection system, a management information system, a material information system, a process control system and an Internet platform information system used by different food production enterprises, supervision agencies and detection agencies in the data generation process, and the integration of the data into an original database.
As a specific technical scheme, the code in the data standardization process refers to a data code taking food as a carrier, the code consists of a characteristic field and a characteristic number, and the code and the time generated by the code jointly represent unique and specific food safety information.
As a specific technical scheme, the characteristic field comprises a category field, an industrial chain node field, an industry status field, a data type field and a region field; the characteristic numbers are specific numbers assigned to each different description object, and the different description objects comprise different categories, different industry chain nodes, different industries, different data types and different regions.
As a specific technical solution, the codes with the same (or) feature number in the feature field may form condition sharing information, where the condition sharing includes category sharing, industry chain sharing, node sharing, state sharing, region sharing, and management level sharing, and the sharing condition may be found by comparing different codes.
As a specific technical scheme, the data of each sharing level in the data sharing process has cross sharing.
As a specific technical scheme, the data cleaning in the data arrangement process is to eliminate errors, redundancies and data noises; the information completion refers to the completion of obvious missing information and unifies data units of the same type.
The invention provides a big data sharing method related to food safety, which is used for classifying, grading and sharing data related to each production and processing link, supervision link and consumer link of food production, processing, logistics and the like, and corresponding public data can be consulted according to different inquiry codes. The invention gives consideration to confidentiality and openness to data processing and sharing, avoids data leakage caused by data sharing, and protects the safety of data providers. The data source is wide, and the method has important significance for food safety tracing.
The food production full chain, circulation link, supervision and management link, government monitoring link and internet platform five-level data are integrated and collected, the range of collected data is wide, the data are collected respectively by using the independent data collection systems of all units, and the data are encrypted and desensitized by all data collection ends to form an encrypted database, so that the confidentiality and the accuracy of the data are ensured. And the encrypted data is subjected to standardized coding processing, so that the readability of the data is ensured. And finally, extracting data to be shared to form a shared database. And further sorting the data on the basis of the shared database, combining a series of operations such as completion, sorting, noise reduction and the like of the earlier-stage data and the shared requirement to finally reach the shared requirement, and analyzing and restoring the coded information to form a restored database. And forming a shared database of each level according to the sharing level rule to ensure the smooth realization of data sharing.
Drawings
FIG. 1 is a flow chart of data sharing according to the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be exemplary only, and any equivalent alterations made by those skilled in the art to the spirit of the present invention are intended to fall within the scope of the present invention.
Example 1
A food safety big data sharing method is shown in figure 1 and comprises multi-source information system integration, data encryption and desensitization, data standardization, data extraction and collection, data sorting, coding analysis and information restoration, and hierarchical sharing;
the method comprises the following specific steps:
(1) multi-source information system integration: collecting information generated in the links of food production, circulation, supervision and management, government monitoring and Internet platform publishing to a big data sharing platform to form an original database;
(2) data encryption and desensitization: according to the requirement of a data owner, data are encrypted or desensitized, and when the data are desensitized, the data are deleted according to set sensitive words and are coded, so that an encrypted database is formed finally;
(3) data normalization: carrying out standardization processing on format, terms and units of data, and then uniformly storing the coded data in a coding database;
(4) data extraction and collection: extracting shared data required in the coding database according to data sharing requirements, and collecting data to be shared to form a shared database;
(5) data arrangement: the data in the shared database is sorted, and the data is cleaned and supplemented with information to form a new full information database;
(6) code analysis and information reduction: coding and analyzing the data in the full information database, and restoring the original information to form a restoration database; the information of code analysis is a dynamic reversible process of code design and code assignment; according to the codes, restoring the specific contents of the coded fields and the numbers, including the contents of categories, industrial chain nodes, statuses, data types, areas and the like; the acquired information is real-time, and comprises real-time food, grease, aquatic products, livestock and poultry, egg and milk, fruits and vegetables and other types, industrial chain nodes for breeding, storing, processing and the like, production, circulation, catering and other industrial states, and information generation objects such as people, machines, materials, methods, rings and the like; (ii) a
(7) And (3) grading and sharing: the restoration database is divided into social personal level sharing, enterprise level sharing and supervision level sharing according to sharing levels, different user codes are set at each level, shared data of the level to which the user codes belong are obtained according to the user codes, and data grading sharing is achieved.
Example 2
A food safety big data sharing method comprises multi-source information system integration, data encryption and desensitization, data standardization, data extraction and collection, data arrangement, coding analysis and information restoration, and hierarchical sharing;
the method comprises the following specific steps:
(1) multi-source information system integration: collecting information generated in the links of food production, circulation, supervision and management, government monitoring and Internet platform publishing to a big data sharing platform to form an original database; the multi-source information system integration process refers to the process of collecting data generated by data acquisition systems, information management systems, material information systems, process control systems and internet platform information systems used by different food production enterprises, supervision agencies and detection agencies in the data generation process, and integrating the data into an original database;
(2) data encryption and desensitization: according to the requirement of a data owner, data are encrypted or desensitized, when the data are desensitized, sensitive words are deleted or formatted according to set sensitive words, then the data are coded, and finally an encrypted database is formed;
(3) data normalization: carrying out standardization processing on format, terms, units and codes on the data, and then uniformly storing the coded data in a coding database; the code in the data standardization process refers to a data code taking food as a carrier, and the code consists of a characteristic field and a characteristic number; the code and the time generated by the code jointly represent a unique specific food safety information; the characteristic fields comprise category fields, industry chain node fields, industry status fields, data type fields and area fields; the characteristic number is a specific number assigned to each different description object, and the different description objects comprise different types, different industry chain nodes, different industries, different data types and different regions.
(4) Data extraction and collection: extracting shared data required in the coding database according to data sharing requirements, and collecting data to be shared to form a shared database;
(5) data arrangement: the data in the shared database is sorted, and the data is cleaned and supplemented with information to form a new full information database; data cleaning in the data sorting process refers to elimination of errors, redundancies and data noises; the information completion refers to the completion of obvious missing information and unifies data units of the same type;
(6) code analysis and information reduction: coding and analyzing the data in the full information database, and restoring the original information to form a restoration database;
(7) and (3) grading and sharing: the restoration database is divided into social personal level sharing, enterprise level sharing and supervision level sharing according to sharing levels, different user codes are set at each level, shared data of the level to which the user codes belong are obtained according to the user codes, and data grading sharing is achieved. In the data sharing process, data of each sharing level are in cross sharing.
Example 3
A food safety big data sharing method can be used for safe big data sharing of agricultural products such as rice, fruits and the like. The sharing process comprises multi-source information system integration, data encryption and desensitization, data standardization, data extraction and collection, data arrangement, coding analysis and information restoration, and hierarchical sharing;
the method comprises the following specific steps:
(1) multi-source information system integration: for example, the rice is planted with the variety, the producing area climate, the soil characteristic, the water quality characteristic, the mature period, the output, the rice physical and chemical index and other related data acquisition; in the process of processing rice into rice, data information such as additive indexes, production process, operation rules, product quality, rice quality, heavy metal content and the like related to each production node is acquired; collecting data information such as rice storage environment indexes, stock, flow period, storage method and the like; the quality supervision department collects the sampling detection data information of the rice products; collecting data information such as rice varieties, nutrient compositions, public opinions and the like collected and obtained on an internet platform; the collected data are collected together to form an original database;
each data acquisition main body acquires data of the whole rice processing process by using various data acquisition systems, information management systems, material information systems, process control systems, internet platforms and the like used in the self-function execution process;
(2) data encryption and desensitization: each data acquisition main body encrypts and desensitizes acquired data according to self needs; replacing or hiding the hidden vocabulary which possibly exists, coding the data, dividing the data according to the importance of the data, and setting different security levels to form an encryption database;
(3) data normalization: each data acquisition main body carries out standardized processing of format, terminology, unit and coding on data in the encrypted database, and then the coded data are uniformly stored in the coded database; the code in the data standardization process refers to a data code taking food as a carrier, and the code consists of a characteristic field and a characteristic number; the characteristic fields comprise category fields, industry chain node fields, industry status fields, data type fields and area fields; the characteristic number is a specific number assigned to each category; data sources include dynamic data (10), knowledgebase stored data (20), industry segment point characterization numbers (including plant breeding (11xxx), harvest (12xxx), storage (13xxx), processing (14xxx), transportation (15xxx), trading (16xxx), health (17 xxx)); channel characteristic numbers (including prepackage (1x), fresh (2x), and catering (3 x)); the characteristic figures of the grades comprise grain (1xxx), grease (2xxx), domestic animals (3xxx), poultry (4xxx), aquatic products (5xxx), egg products (6xxx), dairy products (7xxx), fruits (8xx), vegetables (9xxx) and seasonings (10 xx); business attribute numbers (including production (1xX), circulation (2xX), consumer (3 xX)); data type characteristic numbers (including responsibility subject (1), equipment (2), material (3), regulation (4), environment (5)), regional characteristic numbers (including province (13-14 th on the left, 15-16 th city/county and 17-18 th on district), data after coding form a coding database, for example, data for representing cadmium content in apples is represented by coding rule 10, dynamic data is represented by coding rule 10, the node is a harvesting node, the number 120 is characterized, the channel is fresh, 20 is represented by the channel, the class is the primary processing fruit and is represented by the number 810, the state is the production data 100, the data type is Weihai silver-pollutant-physical-heavy metal, data 21111 is represented by the data 110, and the data for representing the material is finally coded as 101202081010021111110;
(4) data extraction and collection: extracting shared data required in the coding database according to data sharing requirements, and collecting data to be shared to form a shared database;
(5) data arrangement: the data in the shared database is sorted, and the data is cleaned and supplemented with information to form a new full information database; the completion process comprises the steps of supplementing obviously missing data, for example, certain data in the shared database only contains data information of a producer but does not contain data of a planting unit, the source information of the rice raw materials can be roughly estimated according to other data uploaded by the producer, and the data are completed; data cleaning in the data sorting process refers to elimination of errors, redundancies and data noises; the information completion refers to completing obvious missing information, and data units of the same type are unified, such as data of quantity units are lacked;
(6) code analysis and information reduction: coding and analyzing the data in the full information database, and restoring the original information to form a restoration database; the process analyzes the information of the standardized codes to obtain readable information, and can also compile the readable, understandable and accurate reduction database information aiming at different shared objects according to the sharing requirements;
(7) and (3) grading and sharing: dividing a reduction database into social personal level sharing, enterprise level sharing and supervision level sharing according to sharing levels, setting different user codes at each level, and acquiring sharing data of the level according to the user codes to realize data grading sharing; in the data sharing process, data of each sharing level are in cross sharing; the enterprise-level information monitoring system can be used for monitoring the information of the enterprise-level users, and can also be used for monitoring the information of the enterprise-level users.
Claims (7)
1. A food safety big data sharing method is characterized by comprising multi-source information system integration, data encryption and desensitization, data standardization, data extraction and collection, data sorting, coding analysis and information restoration, and hierarchical sharing;
the method comprises the following specific steps:
(1) multi-source information system integration: collecting information generated in the links of food production, circulation, supervision and management, government monitoring and Internet platform publishing to a big data sharing platform to form an original database;
(2) data encryption and desensitization: according to the requirement of a data owner, data are encrypted and desensitized, and when the data are desensitized, the data are deleted according to set sensitive words and are coded, so that an encrypted database is formed finally;
(3) data normalization: carrying out standardization processing on format, terms and units of data, and then uniformly storing the coded data in a coding database;
(4) data extraction and collection: extracting shared data required in the coding database according to data sharing requirements, and collecting data to be shared to form a shared database;
(5) data arrangement: the data in the shared database is sorted, and the data is cleaned and supplemented with information to form a new full information database;
(6) code analysis and information reduction: coding and analyzing the data in the full information database, and restoring the original information to form a restoration database;
(7) and (3) grading and sharing: the restoration database is divided into social personal level sharing, enterprise level sharing and supervision level sharing according to sharing levels, different user codes are set at each level, shared data of the level to which the user codes belong are obtained according to the user codes, and data grading sharing is achieved.
2. The food safety big data sharing method according to claim 1, wherein the multi-source information system integration is to collect data generated by data acquisition systems, information management systems, material information systems, process control systems and internet platform information systems used by different food production enterprises, supervision agencies and detection agencies in the data generation process and integrate the data into an original database.
3. The method for sharing big data of food safety according to claim 1, wherein the code in the data standardization process refers to a data code using food as a carrier, the code is composed of a characteristic field and a characteristic number, and the code and the time generated by the code jointly represent a unique specific food safety information.
4. The food safety big data sharing method according to claim 3, wherein the characteristic fields comprise a category field, an industry chain node field, an industry status field, a data type field, and a region field; the characteristic numbers are specific numbers assigned to each different description object, and the different description objects comprise different categories, different industry chain nodes, different industries, different data types and different regions.
5. The method for sharing big data in food safety according to claim 4, wherein the same code for the characteristic field and/or the characteristic number can form condition sharing information, and the condition sharing includes category sharing, industry chain sharing, node sharing, state sharing, region sharing and management hierarchy sharing.
6. The method for sharing big data of food safety according to claim 1, wherein the data of each sharing level is cross-shared in the data sharing process.
7. The food safety big data sharing method according to claim 1, wherein the data cleaning in the data sorting process is to eliminate errors, redundancies and data noises; the information completion refers to the completion of obvious missing information and unifies data units of the same type.
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